Real-time geospatial surveillance of localized emotional stress responses to COVID-19: A proof of concept analysis.
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ABSTRACT:
SUBMITTER: Edry T
PROVIDER: S-EPMC8166515 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The emergence of pandemic H1N1 influenza in 2009 has prompted public health responses, including production and licensure of new influenza A (H1N1) 2009 monovalent vaccines. Safety monitoring is a critical component of vaccination programs. As proof-of-concept, the authors mimicked near real-time prospective surveillance for prespecified neurologic and allergic adverse events among enrollees in 8 medical care organizations (the Vaccine Safety Datalink Project) who received seasonal trivalent inactivated influenza vaccine during the 2005/06-2007/08 influenza seasons. In self-controlled case series analysis, the risk of adverse events in a prespecified exposure period following vaccination was compared with the risk in 1 control period for the same individual either before or after vaccination. In difference-in-difference analysis, the relative risk in exposed versus control periods each season was compared with the relative risk in previous seasons since 2000/01. The authors used Poisson-based analysis to compare the risk of Guillain-Barré syndrome following vaccination in each season with that in previous seasons. Maximized sequential probability ratio tests were used to adjust for repeated analyses on weekly data. With administration of 1,195,552 doses to children under age 18 years and 4,773,956 doses to adults, no elevated risk of adverse events was identified. Near real-time surveillance for selected adverse events can be implemented prospectively to rapidly assess seasonal and pandemic influenza vaccine safety.
| S-EPMC2878099 | biostudies-literature
Project description:The Portable In Vitro Exposure Cassette (PIVEC) was developed for on-site air quality testing using lung cells. Here, we describe the incorporation of a sensor within the PIVEC for real time monitoring of cellular oxidative stress during exposure to contaminated air. An electrochemical, enzymatic biosensor based on cytochrome c (cyt c) was selected to measure reactive oxygen species (ROS), including hydrogen peroxide and super oxides, due to the stability of signal over time. Human A549 lung cells were grown at the air-liquid interface and exposed within the PIVEC to dry 40 nm copper nanoparticle aerosols for 10 minutes. The generation of ROS compounds was measured during exposure and post-exposure for one hour using the biosensor and compared to intracellular ROS determined using the 2',7'-dichlorodihydrofluoroscein diacetate (DCFH-DA) assay. A similar increase in oxidative stress upon aerosol exposure was measured using both the cyt c biosensor and DCFH-DA assay. The incorporation of a biosensor within the PIVEC is a unique, first-of-its-kind system designed to monitor the real-time effect of aerosols.
| S-EPMC7736142 | biostudies-literature
Project description:The amygdala is a central target of emotion regulation. It is overactive and dysregulated in affective and anxiety disorders and amygdala activity normalizes with successful therapy of the symptoms. However, a considerable percentage of patients do not reach remission within acceptable duration of treatment. The amygdala could therefore represent a promising target for real-time functional magnetic resonance imaging (rtfMRI) neurofeedback. rtfMRI neurofeedback directly improves the voluntary regulation of localized brain activity. At present, most rtfMRI neurofeedback studies have trained participants to increase activity of a target, i.e. up-regulation. However, in the case of the amygdala, down-regulation is supposedly more clinically relevant. Therefore, we developed a task that trained participants to down-regulate activity of the right amygdala while being confronted with amygdala stimulation, i.e. negative emotional faces. The activity in the functionally-defined region was used as online visual feedback in six healthy subjects instructed to minimize this signal using reality checking as emotion regulation strategy. Over a period of four training sessions, participants significantly increased down-regulation of the right amygdala compared to a passive viewing condition to control for habilitation effects. This result supports the concept of using rtfMRI neurofeedback training to control brain activity during relevant stimulation, specifically in the case of emotion, and has implications towards clinical treatment of emotional disorders.
| S-EPMC4072124 | biostudies-literature
Project description:Rationale & objectiveAcute kidney injury (AKI) is diagnosed based on changes in serum creatinine concentration, a late marker of this syndrome. Algorithms that predict elevated risk for AKI are of great interest, but no studies have incorporated such an algorithm into the electronic health record to assist with clinical care. We describe the experience of implementing such an algorithm.Study designProspective observational cohort study.Setting & participants2,856 hospitalized adults in a single urban tertiary-care hospital with an algorithm-predicted risk for AKI in the next 24 hours>15%. Alerts were also used to target a convenience sample of 100 patients for measurement of 16 urine and 6 blood biomarkers.ExposureClinical characteristics at the time of pre-AKI alert.OutcomeAKI within 24 hours of pre-AKI alert (AKI24).Analytical approachDescriptive statistics and univariable associations.ResultsAt enrollment, mean predicted probability of AKI24 was 19.1%; 18.9% of patients went on to develop AKI24. Outcomes were generally poor among this population, with 29% inpatient mortality among those who developed AKI24 and 14% among those who did not (P<0.001). Systolic blood pressure<100mm Hg (28% of patients with AKI24 vs 18% without), heart rate>100 beats/min (32% of patients with AKI24 vs 24% without), and oxygen saturation<92% (15% of patients with AKI24 vs 6% without) were all more common among those who developed AKI24. Of all biomarkers measured, only hyaline casts on urine microscopy (72% of patients with AKI24 vs 25% without) and fractional excretion of urea nitrogen (20% [IQR, 12%-36%] among patients with AKI24 vs 34% [IQR, 25%-44%] without) differed between those who did and did not develop AKI24.LimitationsSingle-center study, reliance on serum creatinine level for AKI diagnosis, small number of patients undergoing biomarker evaluation.ConclusionsA real-time AKI risk model was successfully integrated into the EHR.
| S-EPMC8667815 | biostudies-literature
Project description:Novel methods that stimulate neuroplasticity are increasingly being studied to treat neurological and psychiatric conditions. We sought to determine whether real-time fMRI neurofeedback training is feasible in Huntington's disease (HD), and assess any factors that contribute to its effectiveness. In this proof-of-concept study, we used this technique to train 10 patients with HD to volitionally regulate the activity of their supplementary motor area (SMA). We collected detailed behavioral and neuroimaging data before and after training to examine changes of brain function and structure, and cognitive and motor performance. We found that patients overall learned to increase activity of the target region during training with variable effects on cognitive and motor behavior. Improved cognitive and motor performance after training predicted increases in pre-SMA grey matter volume, fMRI activity in the left putamen, and increased SMA-left putamen functional connectivity. Although we did not directly target the putamen and corticostriatal connectivity during neurofeedback training, our results suggest that training the SMA can lead to regulation of associated networks with beneficial effects in behavior. We conclude that neurofeedback training can induce plasticity in patients with Huntington's disease despite the presence of neurodegeneration, and the effects of training a single region may engage other regions and circuits implicated in disease pathology.
| S-EPMC5838530 | biostudies-literature
Project description:BACKGROUND:The successful introduction of homomorphic encryption (HE) in clinical research holds promise for improving acceptance of data-sharing protocols, increasing sample sizes, and accelerating learning from real-world data (RWD). A well-scoped use case for HE would pave the way for more widespread adoption in healthcare applications. Determining the efficacy of targeted cancer treatments used off-label for a variety of genetically defined conditions is an excellent candidate for introduction of HE-based learning systems because of a significant unmet need to share and combine confidential data, the use of relatively simple algorithms, and an opportunity to reach large numbers of willing study participants. METHODS:We used published literature to estimate the numbers of patients who might be eligible to receive treatments approved for other indications based on molecular profiles. We then estimated the sample size and number of variables that would be required for a successful system to detect exceptional responses with sufficient power. We generated an appropriately sized, simulated dataset (n = 5000) and used an established HE algorithm to detect exceptional responses and calculate total drug exposure, while the data remained encrypted. RESULTS:Our results demonstrated the feasibility of using an HE-based system to identify exceptional responders and perform calculations on patient data during a hypothetical 3-year study. Although homomorphically encrypted computations are time consuming, the required basic computations (i.e., addition) do not pose a critical bottleneck to the analysis. CONCLUSION:In this proof-of-concept study, based on simulated data, we demonstrate that identifying exceptional responders to targeted cancer treatments represents a valuable and feasible use case. Past solutions to either completely anonymize data or restrict access through stringent data use agreements have limited the utility of abundant and valuable data. Because of its privacy protections, we believe that an HE-based learning system for real-world cancer treatment would entice thousands more patients to voluntarily contribute data through participation in research studies beyond the currently available secondary data populated from hospital electronic health records and administrative claims. Forming collaborations between technical experts, physicians, patient advocates, payers, and researchers, and testing the system on existing RWD are critical next steps to making HE-based learning a reality in healthcare.
| S-EPMC6894133 | biostudies-literature
Project description:Rapid diagnosis is key to curtailing the Covid-19 pandemic. One path to such rapid diagnosis may rely on identifying volatile organic compounds (VOCs) emitted by the infected body, or in other words, identifying the smell of the infection. Consistent with this rationale, dogs can use their nose to identify Covid-19 patients. Given the scale of the pandemic, however, animal deployment is a challenging solution. In contrast, electronic noses (eNoses) are machines aimed at mimicking animal olfaction, and these can be deployed at scale. To test the hypothesis that SARS CoV-2 infection is associated with a body-odor detectable by an eNose, we placed a generic eNose in-line at a drive-through testing station. We applied a deep learning classifier to the eNose measurements, and achieved real-time detection of SARS CoV-2 infection at a level significantly better than chance, for both symptomatic and non-symptomatic participants. This proof of concept with a generic eNose implies that an optimized eNose may allow effective real-time diagnosis, which would provide for extensive relief in the Covid-19 pandemic.
| S-EPMC8172018 | biostudies-literature
Project description:Real-time fMRI (rt-fMRI) neurofeedback can be used to non-invasively modulate brain activity and has shown initial effectiveness in symptom reduction for psychiatric disorders. Neurofeedback paradigms often target the neurocircuitry underlying emotion regulation, as difficulties with emotion regulation are common across many psychiatric conditions. Adolescence is a key period for the development of emotion regulation, with the parent-adolescent relationship providing an important context for learning how to modulate one's emotions. Here, we present evidence for a novel extension of rt-fMRI neurofeedback wherein a second person (the parent) views neurofeedback from the focal participant (adolescent) and attempts to regulate the other person's brain activity. In this proof-of-concept study, mother-adolescent dyads (n = 6; all female) participated in a dyadic neurofeedback protocol, during which they communicated via active noise-canceling microphones and headphones. During the scan, adolescents described current emotionally upsetting situations in their lives, and their mothers responded while viewing neurofeedback from the adolescent's right anterior insular cortex (aIC)-a key hub for emotion-related processing. The mother was instructed to supportively respond to her daughter's negative emotions and attempt to downregulate the aIC activity. Mean right aIC activation during each run was calculated for each adolescent participant, and results revealed a downward trend across the session (β = -0.17, SE β = 0.19, Cohen's f 2 = 0.03). Results of this proof-of-concept study support further research using dyadic neurofeedback to target emotion-related processing. Future applications may include therapist-client dyads and continued research with parents and children.Clinical trial registration[www.ClinicalTrials.gov], identifier [NCT03929263].
| S-EPMC9204632 | biostudies-literature
Project description:PurposeTo develop a novel four-dimensional (4D) intensity modulated radiation therapy (IMRT) treatment planning methodology based on dynamic virtual patient models.MethodsThe 4D model-based planning (4DMP) is a predictive tracking method which consists of two main steps: (1) predicting the 3D deformable motion of the target and critical structures as a function of time during treatment delivery; (2) adjusting the delivery beam apertures formed by the dynamic multi-leaf collimators (DMLC) to account for the motion. The key feature of 4DMP is the application of a dynamic virtual patient model in motion prediction, treatment beam adjustment, and dose calculation. A lung case was chosen to demonstrate the feasibility of the 4DMP. For the lung case, a dynamic virtual patient model (4D model) was first developed based on the patient's 4DCT images. The 4D model was capable of simulating respiratory motion of different patterns. A model-based registration method was then applied to convert the 4D model into a set of deformation maps and 4DCT images for dosimetric purposes. Based on the 4D model, 4DMP treatment plans with different respiratory motion scenarios were developed. The quality of 4DMP plans was then compared with two other commonly used 4D planning methods: maximum intensity projection (MIP) and planning on individual phases (IP).ResultsUnder regular periodic motion, 4DMP offered similar target coverage as MIP with much better normal tissue sparing. At breathing amplitude of 2 cm, the lung V20 was 23.9% for a MIP plan and 16.7% for a 4DMP plan. The plan quality was comparable between 4DMP and IP: PTV V97 was 93.8% for the IP plan and 93.6% for the 4DMP plan. Lung V20 of the 4DMP plan was 2.1% lower than that of the IP plan and Dmax to cord was 2.2 Gy higher. Under a real time irregular breathing pattern, 4DMP had the best plan quality. PTV V97 was 90.4% for a MIP plan, 88.6% for an IP plan and 94.1% for a 4DMP plan. Lung V20 was 20.1% for the MIP plan, 17.8% for the IP plan and 17.5% for the 4DMP plan. The deliverability of the real time 4DMP plan was proved by calculating the maximum leaf speed of the DMLC.ConclusionsThe 4D model-based planning, which applies dynamic virtual patient models in IMRT treatment planning, can account for the real time deformable motion of the tumor under different breathing conditions. Under regular motion, the quality of 4DMP plans was comparable with IP and superior to MIP. Under realistic motion in which breathing amplitude and period change, 4DMP gave the best plan quality of the three 4D treatment planning techniques.
| S-EPMC3107830 | biostudies-literature
Project description:Emotion recognition through computational modeling and analysis of physiological signals has been widely investigated in the last decade. Most of the proposed emotion recognition systems require relatively long-time series of multivariate records and do not provide accurate real-time characterizations using short-time series. To overcome these limitations, we propose a novel personalized probabilistic framework able to characterize the emotional state of a subject through the analysis of heartbeat dynamics exclusively. The study includes thirty subjects presented with a set of standardized images gathered from the international affective picture system, alternating levels of arousal and valence. Due to the intrinsic nonlinearity and nonstationarity of the RR interval series, a specific point-process model was devised for instantaneous identification considering autoregressive nonlinearities up to the third-order according to the Wiener-Volterra representation, thus tracking very fast stimulus-response changes. Features from the instantaneous spectrum and bispectrum, as well as the dominant Lyapunov exponent, were extracted and considered as input features to a support vector machine for classification. Results, estimating emotions each 10 seconds, achieve an overall accuracy in recognizing four emotional states based on the circumplex model of affect of 79.29%, with 79.15% on the valence axis, and 83.55% on the arousal axis.
| S-EPMC4028901 | biostudies-literature