Deriving neighborhood-level diet and physical activity measurements from anonymized mobile phone location data for enhancing obesity estimation.

BACKGROUND: Obesity is a serious public health problem. Existing research has shown a strong association between obesity and an individual's diet and physical activity. If we extend such an association to the neighborhood level, information about the diet and physical activity of the residents of a neighborhood may improve the estimate of neighborhood-level obesity prevalence and help identify the neighborhoods that are more likely to suffer from obesity. However, it is challenging to measure neighborhood-level diet and physical activity through surveys and interviews, especially for a large geographic area. METHODS: We propose a method for deriving neighborhood-level diet and physical activity measurements from anonymized mobile phone location data, and examine the extent to which the derived measurements can enhance obesity estimation, in addition to the socioeconomic and demographic variables typically used in the literature. We conduct case studies in three different U.S. cities, which are New York City, Los Angeles, and Buffalo, using anonymized mobile phone location data from the company SafeGraph. We employ five different statistical and machine learning models to test the potential enhancement brought by the derived measurements for obesity estimation. RESULTS: We find that it is feasible to derive neighborhood-level diet and physical activity measurements from anonymized mobile phone location data. The derived measurements provide only a small enhancement for obesity estimation, compared with using a comprehensive set of socioeconomic and demographic variables. However, using these derived measurements alone can achieve a moderate accuracy for obesity estimation, and they may provide a stronger enhancement when comprehensive socioeconomic and demographic data are not available (e.g., in some developing countries). From a methodological perspective, spatially explicit models overall perform better than non-spatial models for neighborhood-level obesity estimation. CONCLUSIONS: Our proposed method can be used for deriving neighborhood-level diet and physical activity measurements from anonymized mobile phone data. The derived measurements can enhance obesity estimation, and can be especially useful when comprehensive socioeconomic and demographic data are not available. In addition, these derived measurements can be used to study obesity-related health behaviors, such as visit frequency of neighborhood residents to fast-food restaurants, and to identify primary places contributing to obesity-related issues.

Human mobility data demonstrates increase in park visitation since start of COVID-19 pandemic in Buffalo, New York.

Background: The COVID-19 pandemic highlighted the importance of urban parks to provide safe places to visit and recreate. Recent research has suggested that park visitation over this time may not have occurred equitably, which may exacerbate existing health disparities. However, usual methods of estimating park visitation are labor intensive, requiring better solutions.. The objective of this study was to assess how park visitation changed in Buffalo, New York after the start of the COVID-19 pandemic utilizing human mobility data. Methods: Monthly mobile phone location data from January 2018 through October of 2021 from residents of Buffalo were analyzed to estimate total park visits for each census tract. A generalized linear mixed effect model was utilized to examine if selected factors affected park visitation. Factors examined included demographic, health, park, and crime data at the census tract level. Results: Across 587,487 park visits that were captured in the 79 census tracts, park visitation increased by 25% since March 2020. In our regression model, having cancer and currently smoking had negative effects on park visitation. The start of the COVID-19 pandemic positively affected park visitation. Season (of the year), was the other statistically significant variable that affected park visitation. Conclusions: Anonymous mobile phone data demonstrated that park visitation has increased by 25% since the beginning of the pandemic when we looked at census tract level data. While some data limitations must be addressed, mobile phone data is a novel method that can be used to understand behavioral and public health trends.

MAGI-MS: multiple seed-centric module discovery.

Summary: Complex disorders manifest by the interaction of multiple genetic and environmental factors. Through the construction of genetic modules that consist of highly coexpressed genes, it is possible to identify genes that participate in common biological pathways relevant to specific phenotypes. We have previously developed tools MAGI and MAGI-S for genetic module discovery by incorporating coexpression and protein interaction networks. Here, we introduce an extension to MAGI-S, denoted as Merging Affected Genes into Integrated Networks-Multiple Seeds (MAGI-MS), which permits the user to further specify a disease pathway of interest by selecting multiple seed genes likely to function in the same molecular mechanism. By providing MAGI-MS with seed genes involved in processes underlying certain classes of neurodevelopmental disorders, such as epilepsy, we demonstrate that MAGI-MS can reveal modules enriched in genes relevant to chemical synaptic transmission, glutamatergic synapse and other functions associated with the provided seed genes. Availability and implementation: MAGI-MS is free and available at https://github.com/jchow32/MAGI-MS. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Cancer vaccines: past, present and future; a review article.

Immunotherapy and vaccines have revolutionized disease treatment and prevention. Vaccines against infectious diseases have been in use for several decades. In contrast, only few cancer vaccines have been approved for human use. These include preventative vaccines against infectious agents associated with cancers, and therapeutic vaccines used as immunotherapy agents to treat cancers. Challenges in developing cancer vaccines include heterogeneity within and between cancer types, screening and identification of appropriate tumour-specific antigens, and the choice of vaccine delivery platforms. Recent advances in all of these areas and the lessons learnt from COVID-19 vaccines have significantly boosted interest in cancer vaccines. Further advances in these areas are expected to facilitate development of effective novel cancer vaccines. In this review, we aim to discuss the past, the present, and the future of cancer vaccines.

Association of Periodic Limb Movements With Medication Classes: A Retrospective Cohort Study.

BACKGROUND AND OBJECTIVES: To investigate the association between various medication classes and the periodic limb movement index (PLMI) in a clinical cohort of adults who completed in-laboratory polysomnography. METHODS: A single, diagnostic, overnight, in-laboratory polysomnogram was completed for 3,488 patients consecutively referred from 2010 to 2015 to determine PLMI. Medication use and medical comorbidities were collected through patient questionnaires. Associations between medication classes and PLMI were ascertained using multivariable ordinal logistic regression models. RESULTS: The median age of the cohort was 56.0 years (48.2% male). After adjusting for age, sex, body mass index, relevant comorbidities, and sleep measures, the use of selective serotonin reuptake inhibitors (SSRIs) (odds ratio [OR] 1.52) and serotonin-norepinephrine reuptake inhibitors (SNRIs) (OR 1.99) was associated with increased PLMI. Conversely, gabapentinoids (OR 0.71), stimulants (OR 0.52), benzodiazepines (OR 0.79), and dopamine agonists (OR 0.38) were associated with decreased PLMI. A non-statistically significant trend for decreased PLMI with neuroleptic use was observed. No significant associations were found between PLMI and the use of antihypertensives, statins, tricyclic antidepressants, bupropion, anticoagulants, antiplatelets, modafinil, and antihistamines. DISCUSSION: The use of SSRIs and SNRIs was associated with elevated PLMI while the use of gabapentinoids, stimulants, benzodiazepines, and dopamine agonists was associated with decreased PLMI. These results can assist physicians in managing periodic limb movements in sleep (PLMS) and invite further research into the relationship between PLMS and medications with the modulating effects of dose, formulation type, and time of administration. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that SSRIs and SNRIs are associated with elevated PLMI while gabapentinoids, stimulants, benzodiazepines, and dopamine agonists are associated with decreased PLMI.

Cardiovascular biomarkers of response to accelerated low frequency repetitive transcranial magnetic stimulation in major depression.

BACKGROUND AND OBJECTIVE: Repetitive transcranial magnetic stimulation (rTMS) is an effective and safe treatment for major depressive disorder (MDD). rTMS is in need of a reliable biomarker of treatment response. High frequency (HF) dorsolateral prefrontal cortex (DLPFC) rTMS has been reported to induce significant changes in the cardiac activity of MDD patients. Low frequency DLPFC rTMS has many advantages over HF-DLPFC rTMS and thus this study aims to further investigate the effect of low frequency 1 Hz right hemisphere (R)-DLPFC rTMS on the cardiac activity of MDD patients, as well as the potential of using electrocardiogram (ECG) parameters as biomarkers of treatment outcome. METHODS: Baseline ECG sessions were performed for 19 MDD patients. All patients then underwent 40 sessions of accelerated 1 Hz R-DLPFC rTMS one week after the baseline session. RESULTS: Heart rate (HR) significantly decreased from the resting period to the first and third minute of the 1 Hz R-DLPFC rTMS period. Resting HR was found to have a significant negative association with treatment outcome. Prior to Bonferroni correction, HR during stimulation and the degree of rTMS-induced HR reduction were significantly negatively associated with treatment outcome. No significant changes were observed for the heart rate variability (HRV) parameters. LIMITATIONS: Sample size (n = 19); the use of electroencephalography equipment for ECG; lack of respiration monitoring; relatively short recording duration for HRV parameters. CONCLUSION: This novel study provides further preliminary evidence that ECG may be utilized as a biomarker of rTMS treatment response in MDD. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04376697.

Optimized repetitive transcranial magnetic stimulation techniques for the treatment of major depression: A proof of concept study.

Although effective in major depressive disorder (MDD), repetitive transcranial magnetic stimulation (rTMS) is costly and complex, limiting accessibility. To address this, we tested the feasibility of novel rTMS techniques with cost-saving opportunities, such as an open-room setting, large non-focal parabolic coils, and custom-built coil arms. We employed a low-frequency (LF) 1 Hz stimulation protocol (360 pulses per session), delivered on the most affordable FDA-approved device. MDD participants received an initial accelerated rTMS course (arTMS) of 6 sessions/day over 5 days (30 total), followed by a tapering course of daily sessions (up to 25) to decrease the odds of relapse. The self-reported Beck Depression Inventory II (BDI-II) was used to measure severity of depression. Forty-eight (48) patients completed the arTMS course. No serious adverse events occurred, and all patients reported manageable pain levels. Response and remission rates were 35.4% and 27.1% on the BDI-II, respectively, at the end of the tapering course. Repeated measures ANOVA showed significant changes of BDI-II scores over time. Even though our protocol will require further improvements, some of the concepts we introduced here could help guide the design of future trials aiming at increasing accessibility to rTMS.