Safety and immunogenicity of an inactivated SARS-CoV-2 vaccine (FAKHRAVAC®) in healthy adults aged 18-55 years: Randomized, double-blind, placebo-controlled, phase I clinical trial.

Background: The FAKHRAVAC®, an inactivated SARS-CoV-2 vaccine, was assessed for safety and immunogenicity. Methods and findings: In this double-blind, placebo-controlled, phase I trial, we randomly assigned 135 healthy adults between 18 and 55 to receive vaccine strengths of 5 or 10 µg/dose or placebo (adjuvant only) in 0-14 or 0-21 schedules. This trial was conducted in a single center in a community setting. The safety outcomes in this study were reactogenicity, local and systemic adverse reactions, abnormal laboratory findings, and Medically Attended Adverse Events (MAAE). Immunogenicity outcomes include serum neutralizing antibody activity and specific IgG antibody levels.The most frequent local adverse reaction was tenderness (28.9%), and the most frequent systemic adverse reaction was headache (9.6%). All adverse reactions were mild, occurred at a similar incidence in all six groups, and were resolved within a few days. In the 10-µg/dose vaccine group, the geometric mean ratio for neutralizing antibody titers at two weeks after the second injection compared to the placebo group was 9.03 (95% CI: 3.89-20.95) in the 0-14 schedule and 11.77 (95% CI: 2.77-49.94) in the 0-21 schedule. The corresponding figures for the 5-µg/dose group were 2.74 (1.2-6.28) and 5.2 (1.63-16.55). The highest seroconversion rate (four-fold increase) was related to the 10-µg/dose group (71% and 67% in the 0-14 and 0-21 schedules, respectively). Conclusions: FAKHRAVAC® is safe and induces a strong humoral immune response to the SARS-CoV-2 virus at 10-µg/dose vaccine strength in adults aged 18-55. This vaccine strength was used for further assessment in the phase II trial.Trial registrationThis study is registered with https://www.irct.ir; IRCT20210206050259N1.

Phase II randomized, double blind, placebo controlled, clinical trial of safety and immunogenicity of an inactivated SARS-CoV-2 vaccine FAKHRAVAC in adults aged 18-70 years.

BACKGROUND: The FAKHRAVAC®, an inactivated SARS-CoV-2 vaccine, was assessed for safety and immunogenicity in a phase II trial. METHODS: We did a phase II, single-centered, randomized, double-blind, placebo-controlled clinical trial of the FAKHRAVAC inactivated SARS-CoV-2 vaccine on adults aged 18 to 70. The two parallel groups received two intramuscular injections of either a 10-µg vaccine or a placebo at 2-week intervals. The participants' immunogenicity responses and the occurrence of solicited and unsolicited adverse events were compared over the study period of up to 6 months. Immunogenicity outcomes include serum neutralizing antibody activity and specific IgG antibody levels. RESULTS: Five hundred eligible participants were randomly (1:1) assigned to vaccine or placebo groups. The median age of the participants was 36 years, and 75% were male. The most frequent local adverse reaction was tenderness (21.29% after the first dose and 8.52% after the second dose), and the most frequent systemic adverse reaction was headache (11.24% after the first dose and 8.94% after the second dose). Neutralizing antibody titers two and four weeks after the second injection in the vaccine group showed about 3 and 6 times increase compared to the placebo group (GMR = 2.69, 95% CI 2.32-3.12, N:309) and (GMR = 5.51, 95% CI 3.94-8.35, N:285). A four-fold increase in the neutralizing antibody titer was seen in 69.6% and 73.4% of the participants in the vaccine group two and four weeks after the second dose, respectively. Specific ELIZA antibody response against a combination of S1 and RBD antigens 4 weeks after the second injection increased more than three times in the vaccine compared to the placebo group (GMR = 3.34, 95% CI 2.5-4.47, N:142). CONCLUSIONS: FAKHRAVAC® is safe and induces a significant humoral immune response to the SARS-CoV-2 virus at 10-µg antigen dose in adults aged 18-70. A phase III trial is needed to assess the clinical efficacy. TRIAL REGISTRATION: Trial Registry Number: Ref., IRCT20210206050259N2 ( http://irct.ir ; registered on 08/06/2021).

Impact of an exclusive narrow automated vehicle lane on adjacent lane driver behavior.

This study contributes to understanding the behavioral impacts of infrastructure adaptation to Automated Vehicles (AVs) on non-AV drivers. It attempts to answer the question of how a narrow (9 ft) lane dedicated to AVs would affect the behavior of drivers in terms of safety measures who are driving in the adjacent lane to the right. To this end, a custom designed driving simulator world was designed mimicking the Interstate 15 smart corridor in San Diego. A group of participants were assigned to drive next to the simulated 9 ft narrow lane while a control group were assigned to drive next to a regular 12 ft AV lane. Behavior of drivers was analyzed by measuring the mean lane position, mean speed, and the mental effort. In addition to AV lane width, AV headway, gender, and right lane traffic were taken into consideration in the experimental design to investigate interaction effects. The results showed no significant differences in speed and mental effort of drivers while indicating significant differences in lane positioning. Although the overall effect of AV lane width was not significant, there were some significant interaction effects between lane width and other factors (i.e., driver gender and presence of traffic on the next regular lane to the right). In all the significant interactions, there was no case in which those factors stayed constant while AV lane width changed between the groups indicating that the significant difference might be stemmed from the other factors rather than the lane width. However, the trend observed was that drivers driving next to the 12 ft lane had better lane centering compared to the 9 ft lane. The analysis also showed that while in general female drivers tended to drive further away from the 9 ft lane and performed worse in terms of lane centering, they performed better than male drivers when right lane traffic was present.

Computational model for behavior shaping as an adaptive health intervention strategy.

Adaptive behavioral interventions that automatically adjust in real-time to participants' changing behavior, environmental contexts, and individual history are becoming more feasible as the use of real-time sensing technology expands. This development is expected to improve shortcomings associated with traditional behavioral interventions, such as the reliance on imprecise intervention procedures and limited/short-lived effects. JITAI adaptation strategies often lack a theoretical foundation. Increasing the theoretical fidelity of a trial has been shown to increase effectiveness. This research explores the use of shaping, a well-known process from behavioral theory for engendering or maintaining a target behavior, as a JITAI adaptation strategy. A computational model of behavior dynamics and operant conditioning was modified to incorporate the construct of behavior shaping by adding the ability to vary, over time, the range of behaviors that were reinforced when emitted. Digital experiments were performed with this updated model for a range of parameters in order to identify the behavior shaping features that optimally generated target behavior. Narrowing the range of reinforced behaviors continuously in time led to better outcomes compared with a discrete narrowing of the reinforcement window. Rapid narrowing followed by more moderate decreases in window size was more effective in generating target behavior than the inverse scenario. The computational shaping model represents an effective tool for investigating JITAI adaptation strategies. Model parameters must now be translated from the digital domain to real-world experiments so that model findings can be validated.

Red-light running violation prediction using observational and simulator data.

In the United States, 683 people were killed and an estimated 133,000 were injured in crashes due to running red lights in 2012. To help prevent/mitigate crashes caused by running red lights, these violations need to be identified before they occur, so both the road users (i.e., drivers, pedestrians, etc.) in potential danger and the infrastructure can be notified and actions can be taken accordingly. Two different data sets were used to assess the feasibility of developing red-light running (RLR) violation prediction models: (1) observational data and (2) driver simulator data. Both data sets included common factors, such as time to intersection (TTI), distance to intersection (DTI), and velocity at the onset of the yellow indication. However, the observational data set provided additional factors that the simulator data set did not, and vice versa. The observational data included vehicle information (e.g., speed, acceleration, etc.) for several different time frames. For each vehicle approaching an intersection in the observational data set, required data were extracted from several time frames as the vehicle drew closer to the intersection. However, since the observational data were inherently anonymous, driver factors such as age and gender were unavailable in the observational data set. Conversely, the simulator data set contained age and gender. In addition, the simulator data included a secondary (non-driving) task factor and a treatment factor (i.e., incoming/outgoing calls while driving). The simulator data only included vehicle information for certain time frames (e.g., yellow onset); the data did not provide vehicle information for several different time frames while vehicles were approaching an intersection. In this study, the random forest (RF) machine-learning technique was adopted to develop RLR violation prediction models. Factor importance was obtained for different models and different data sets to show how differently the factors influence the performance of each model. A sensitivity analysis showed that the factor importance to identify RLR violations changed when data from different time frames were used to develop the prediction models. TTI, DTI, the required deceleration parameter (RDP), and velocity at the onset of a yellow indication were among the most important factors identified by both models constructed using observational data and simulator data. Furthermore, in addition to the factors obtained from a point in time (i.e., yellow onset), valuable information suitable for RLR violation prediction was obtained from defined monitoring periods. It was found that period lengths of 2-6m contributed to the best model performance.

Modeling driver stop/run behavior at the onset of a yellow indication considering driver run tendency and roadway surface conditions.

The ability to model driver stop/run behavior at signalized intersections considering the roadway surface condition is critical in the design of advanced driver assistance systems. Such systems can reduce intersection crashes and fatalities by predicting driver stop/run behavior. The research presented in this paper uses data collected from two controlled field experiments on the Smart Road at the Virginia Tech Transportation Institute (VTTI) to model driver stop/run behavior at the onset of a yellow indication for different roadway surface conditions. The paper offers two contributions. First, it introduces a new predictor related to driver aggressiveness and demonstrates that this measure enhances the modeling of driver stop/run behavior. Second, it applies well-known artificial intelligence techniques including: adaptive boosting (AdaBoost), random forest, and support vector machine (SVM) algorithms as well as traditional logistic regression techniques on the data in order to develop a model that can be used by traffic signal controllers to predict driver stop/run decisions in a connected vehicle environment. The research demonstrates that by adding the proposed driver aggressiveness predictor to the model, there is a statistically significant increase in the model accuracy. Moreover the false alarm rate is significantly reduced but this reduction is not statistically significant. The study demonstrates that, for the subject data, the SVM machine learning algorithm performs the best in terms of optimum classification accuracy and false positive rates. However, the SVM model produces the best performance in terms of the classification accuracy only.