Evaluation of 12-Week Standardized Beetroot Extract Supplementation in Older Participants: A Preliminary Study of Human Health Safety.

In recent years, there has been a notable surge in the popularity of beetroot-based dietary supplements, driven by their rich nitrate composition. Several types of beetroot-based dietary supplements can be found in markets worldwide; however, ensuring the safety of dietary supplements is a crucial consideration, as there is limited evidence on their safety, especially for older populations. Therefore, the purpose of the current study was to evaluate the safety and tolerability of a nitrate-rich beetroot extract in older participants taking supplements over 12 weeks. The participants were randomly assigned to receive 20 g daily of beetroot extract or a matching placebo. The safety and tolerability of the supplementation were evaluated as the occurrence of adverse events and anthropometric, biochemical, and hemodynamic parameters were measured. No serious adverse events were reported in any group. Anthropometric, biochemical, and hemodynamic parameter changes between the baseline and the end of the study were not statistically significant in either group. However, interestingly, the group receiving beetroot extract supplementation exhibited a notable increase in plasma nitrate levels (p = 0.076, f = 0.50) and showed a decrease in insulin levels (p = 0.026, f = 0.59). In conclusion, we found that 20 g of beetroot extract supplementation for 12 weeks was safe and well tolerated in older participants.

The Neuroprotective Effects of Oroxylum indicum Extract in SHSY-5Y Neuronal Cells by Upregulating BDNF Gene Expression under LPS Induced Inflammation.

The brain-derived neurotrophic factor (BDNF) plays a crucial role during neuronal development as well as during differentiation and synaptogenesis. They are important proteins present in the brain that support neuronal health and protect the neurons from detrimental signals. The results from the present study suggest BDNF expression can be increase up to ~8-fold by treating the neuroblastoma cells SHSY-5Y with an herbal extract of Oroxylum indicum (50 µg/mL) and ~5.5-fold under lipopolysaccharides (LPS)-induced inflammation conditions. The Oroxylum indicum extract (Sabroxy) was standardized to 10% oroxylin A, 6% chrysin, and 15% baicalein. In addition, Sabroxy has shown to possess antioxidant activity that could decrease the damage caused by the exacerbation of radicals during neurodegeneration. A mode of action of over expression of BDNF with and without inflammation is proposed for the Oroxylum indicum extract, where the three major hydroxyflavones exert their effects through additive or synergistic effects via five possible targets including GABA, Adenoside A2A and estrogen receptor bindings, anti-inflammatory effects, and reduced mitochondrial ROS production.

Application of Bacillus coagulans in Animal Husbandry and Its Underlying Mechanisms.

In recent decades, probiotics have attracted widespread attention and their application in healthcare and animal husbandry has been promising. Among many probiotics, Bacillus coagulans (B. coagulans) has become a key player in the field of probiotics in recent years. It has been demonstrated to be involved in regulating the balance of the intestinal microbiota, promoting metabolism and utilization of nutrients, improving immunity, and more importantly, it also has good industrial properties such as high temperature resistance, acid resistance, bile resistance, and the like. This review highlights the effects of B. coagulans in animal husbandry and its underlying mechanisms.

Evaluation of the Safety and Efficacy of a Multienzyme Complex in Patients with Functional Dyspepsia: A Randomized, Double-Blind, Placebo-Controlled Study.

Functional dyspepsia (FD) is a highly prevalent disorder having nonspecific symptoms and varied pathophysiology. Its treatment remains a challenge as therapeutic options are limited, unsatisfactory, and elusive. Thus, safety and efficacy of DigeZyme®, a proprietary multienzyme complex (MEC), was evaluated as a dietary supplement in FD patients. In this randomized, double-blind, placebo-controlled, parallel-group study, 40 patients were randomly assigned (1:1 ratio) to receive either MEC (50 mg, TID; n = 20) or placebo (n = 20) for 60 days. Reports of adverse or serious adverse events (AEs), abnormal results of vital signs, abnormal findings during physical examination, and abnormal laboratory investigations were monitored closely. Efficacy measures were change in Short-Form Leeds Dyspepsia Questionnaire (SF-LDQ), Nepean Dyspepsia Index-Short Form (NDI-SF), Visual Analog Scale (VAS), Clinical Global Impression Severity Rating Scale (CGI-S), and Glasgow Dyspepsia Severity Score (GDSS) at baseline and follow-up visits on day 15, 30, and 60. Supplementation with MEC was associated with statistically significant differences (P value ranging from .0401 to .0033) in all efficacy parameters compared with placebo. The between-group comparison also revealed that MEC supplement had a significantly greater effect (P < .001) versus placebo. No investigation product-related AEs were reported. There were no clinically significant abnormalities in physical findings and no statistically significant changes in biochemical and hematological parameters, vital signs, body weight, and body mass index observed between the two groups at baseline and follow-up visits. MEC supplementation represents an effective and safe alternative to manage dyspepsia symptoms in FD patients.

A preliminary investigation of the relationships between historical crash and naturalistic driving.

This paper describes a project that was undertaken using naturalistic driving data collected via Global Positioning System (GPS) devices to demonstrate a proof-of-concept for proactive safety assessments of crash-prone locations. The main hypothesis for the study is that the segments where drivers have to apply hard braking (higher jerks) more frequently might be the "unsafe" segments with more crashes over a long-term. The linear referencing methodology in ArcMap was used to link the GPS data with roadway characteristic data of US Highway 101 northbound (NB) and southbound (SB) in San Luis Obispo, California. The process used to merge GPS data with quarter-mile freeway segments for traditional crash frequency analysis is also discussed in the paper. A negative binomial regression analyses showed that proportion of high magnitude jerks while decelerating on freeway segments (from the driving data) was significantly related with the long-term crash frequency of those segments. A random parameter negative binomial model with uniformly distributed parameter for ADT and a fixed parameter for jerk provided a statistically significant estimate for quarter-mile segments. The results also indicated that roadway curvature and the presence of auxiliary lane are not significantly related with crash frequency for the highway segments under consideration. The results from this exploration are promising since the data used to derive the explanatory variable(s) can be collected using most off-the-shelf GPS devices, including many smartphones.

Bacillus coagulans MTCC 5856 supplementation in the management of diarrhea predominant Irritable Bowel Syndrome: a double blind randomized placebo controlled pilot clinical study.

BACKGROUND: Bacillus coagulans MTCC 5856 has been marketed as a dietary ingredient, but its efficacy in diarrhea predominant irritable bowel syndrome (IBS) condition has not been clinically elucidated till date. Thus, a double blind placebo controlled multi-centered trial was planned to evaluate the safety and efficacy of B. coagulans MTCC 5856 in diarrhea predominant IBS patients. METHODS: Thirty six newly diagnosed diarrhea predominant IBS patients were enrolled in three clinical centres. Along with standard care of treatment, 18 patients in group one received placebo while in group two 18 patients received B. coagulans MTCC 5856 tablet containing 2 × 10(9) cfu/day as active for 90 days. Clinical symptoms of IBS were considered as primary end point measures and were evaluated through questionnaires. The visual analog scale (VAS) was used for abdominal pain. Physician's global assessment and IBS quality of life were considered as secondary efficacy measures and were monitored through questionnaires. RESULTS: Laboratory parameters, anthropometric and vital signs were within the normal clinical range during the 90 days of supplementation in placebo and B. coagulans MTCC 5856 group. There was a significant decrease in the clinical symptoms like bloating, vomiting, diarrhea, abdominal pain and stool frequency in a patient group receiving B. coagulans MTCC 5856 when compared to placebo group (p < 0.01). Similarly, disease severity also decreased and the quality of life increased in the patient group receiving B. coagulans MTCC 5856 when compared to placebo group. CONCLUSIONS: The study concluded that the B. coagulans MTCC 5856 at a dose of 2 × 10(9) cfu/day along with standard care of treatment was found to be safe and effective in diarrhea predominant IBS patients for 90 days of supplementation. Hence, B. coagulans MTCC 5856 could be a potential agent in the management of diarrhea predominant IBS patients.

Transferability and robustness of real-time freeway crash risk assessment.

INTRODUCTION: This study examines the data from single loop detectors on northbound (NB) US-101 in San Jose, California to estimate real-time crash risk assessment models. METHOD: The classification tree and neural network based crash risk assessment models developed with data from NB US-101 are applied to data from the same freeway, as well as to the data from nearby segments of the SB US-101, NB I-880, and SB I-880 corridors. The performance of crash risk assessment models on these nearby segments is the focus of this research. RESULTS: The model applications show that it is in fact possible to use the same model for multiple freeways, as the underlying relationships between traffic data and crash risk remain similar. IMPACT ON INDUSTRY: The framework provided here may be helpful to authorities for freeway segments with newly installed traffic surveillance apparatuses, since the real-time crash risk assessment models from nearby freeways with existing infrastructure would be able to provide a reasonable estimate of crash risk. The robustness of the model output is also assessed by location, time of day, and day of week. The analysis shows that on some locations the models may require further learning due to higher than expected false positive (e.g., the I-680/I-280 interchange on US-101 NB) or false negative rates. The approach for post-processing the results from the model provides ideas to refine the model prior to or during the implementation.

Quality of traffic flow on urban arterial streets and its relationship with safety.

The two-fluid model for vehicular traffic flow explains the traffic on arterials as a mix of stopped and running vehicles. It describes the relationship between the vehicles' running speed and the fraction of running vehicles. The two parameters of the model essentially represent 'free flow' travel time and level of interaction among vehicles, and may be used to evaluate urban roadway networks and urban corridors with partially limited access. These parameters are influenced by not only the roadway characteristics but also by behavioral aspects of driver population, e.g., aggressiveness. Two-fluid models are estimated for eight arterial corridors in Orlando, FL for this study. The parameters of the two-fluid model were used to evaluate corridor level operations and the correlations of these parameters' with rates of crashes having different types/severity. Significant correlations were found between two-fluid parameters and rear-end and angle crash rates. Rate of severe crashes was also found to be significantly correlated with the model parameter signifying inter-vehicle interactions. While there is need for further analysis, the findings suggest that the two-fluid model parameters may have potential as surrogate measures for traffic safety on urban arterial streets.

A classification tree based modeling approach for segment related crashes on multilane highways.

INTRODUCTION: This study presents a classification tree based alternative to crash frequency analysis for analyzing crashes on mid-block segments of multilane arterials. METHOD: The traditional approach of modeling counts of crashes that occur over a period of time works well for intersection crashes where each intersection itself provides a well-defined unit over which to aggregate the crash data. However, in the case of mid-block segments the crash frequency based approach requires segmentation of the arterial corridor into segments of arbitrary lengths. In this study we have used random samples of time, day of week, and location (i.e., milepost) combinations and compared them with the sample of crashes from the same arterial corridor. For crash and non-crash cases, geometric design/roadside and traffic characteristics were derived based on their milepost locations. The variables used in the analysis are non-event specific and therefore more relevant for roadway safety feature improvement programs. First classification tree model is a model comparing all crashes with the non-crash data and then four groups of crashes (rear-end, lane-change related, pedestrian, and single-vehicle/off-road crashes) are separately compared to the non-crash cases. The classification tree models provide a list of significant variables as well as a measure to classify crash from non-crash cases. ADT along with time of day/day of week are significantly related to all crash types with different groups of crashes being more likely to occur at different times. CONCLUSIONS: From the classification performance of different models it was apparent that using non-event specific information may not be suitable for single vehicle/off-road crashes. IMPACT ON INDUSTRY: The study provides the safety analysis community an additional tool to assess safety without having to aggregate the corridor crash data over arbitrary segment lengths.

Using conditional inference forests to identify the factors affecting crash severity on arterial corridors.

INTRODUCTION: The study aims at identifying traffic/highway design/driver-vehicle information significantly related with fatal/severe crashes on urban arterials for different crash types. Since the data used in this study are observational (i.e., collected outside the purview of a designed experiment), an information discovery approach is adopted for this study. METHOD: Random Forests, which are ensembles of individual trees grown by CART (Classification and Regression Tree) algorithm, are applied in numerous applications for this purpose. Specifically, conditional inference forests have been implemented. In each tree of the conditional inference forest, splits are based on how good the association is. Chi-square test statistics are used to measure the association. Apart from identifying the variables that improve classification accuracy, the methodology also clearly identifies the variables that are neutral to accuracy, and also those that decrease it. RESULTS: The methodology is quite insightful in identifying the variables of interest in the database (e.g., alcohol/ drug use and higher posted speed limits contribute to severe crashes). Failure to use safety equipment by all passengers and presence of driver/passenger in the vulnerable age group (more than 55 years or less than 3 years) increased the severity of injuries given a crash had occurred. A new variable, 'element' has been used in this study, which assigns crashes to segments, intersections, or access points based on the information from site location, traffic control, and presence of signals. IMPACT: The authors were able to identify roadway locations where severe crashes tend to occur. For example, segments and access points were found to be riskier for single vehicle crashes. Higher skid resistance and k-factor also contributed toward increased severity of injuries in crashes.

A novel approach for analyzing severe crash patterns on multilane highways.

This study presents a novel approach for analysis of patterns in severe crashes that occur on mid-block segments of multilane highways with partially limited access. A within stratum matched crash vs. non-crash classification approach is adopted towards that end. Under this approach crashes serve as units of analysis and it does not require aggregation of crash data over arterial segments of arbitrary lengths. Also, the proposed approach does not use information on non-severe crashes and hence is not affected by under-reporting of the minor crashes. Random samples of time, day of week, and location (i.e., milepost) combinations were collected for multilane arterials in the state of Florida and matched with severe crashes from the corresponding corridor to form matched strata consisting of severe crash and non-crash cases. For these cases, geometric design/roadside and traffic characteristics were derived based on the corresponding milepost locations. Four groups of crashes, severe rear-end, lane-change related, pedestrian, and single-vehicle/off-road crashes, on multilane arterials segments were compared separately to the non-crash cases. Severe lane-change related crashes may primarily be attributed to exposure while single-vehicle crashes and pedestrian crashes have no significant relationship with the ADT (Average Daily Traffic). For severe rear-end crashes speed limit, ADT, K-factor, time of day/day of week, median type, pavement condition, and presence of horizontal curvature were significant factors. The proposed approach uses general roadway characteristics as independent variables rather than event-specific information (i.e., crash characteristics such as driver/vehicle details); it has the potential to fit within a safety evaluation framework for arterial segments.

Safety evaluation of multilane arterials in Florida.

Resurfacing is one of the more common construction activities on highways. While its effect on riding quality on any type of roadway is obviously positive; its impact on safety as measured in terms of crashes is far from obvious. This study examines the safety effects of the resurfacing projects on multilane arterials with partially limited access. Empirical Bayes method, which is one of the most accepted approaches for conducting before-after evaluations, has been used to assess the safety effects of the resurfacing projects. Safety effects are estimated not only in terms of all crashes but also rear-end as well as severe crashes (crashes involving incapacitating and fatal injuries). The safety performance functions (SPFs) used in this study are negative binomial crash frequency estimation models that use the information on ADT, length of the segments, speed limit and number of lanes. These SPFs are segregated by crash groups (all, rear-end, and severe), length of the segments being evaluated, and land use (urban, suburban, and rural). The results of the analysis show that the resulting changes in safety following resurfacing projects vary widely. Evaluating additional improvements carried out with resurfacing activities showed that all (other than sidewalk improvements for total crashes) of them consistently led to improvements in safety of multilane arterial sections. It leads to the inference that it may be a good idea to take up additional improvements if it is cost effective to do them along with resurfacing. It was also found that the addition of turning lanes (left and/or right) and paving shoulders were two improvements associated with a project's relative performance in terms of reduction in rear-end crashes.

Crash data analysis: collective vs. individual crash level approach.

INTRODUCTION: Traffic safety literature has traditionally focused on identification of location profiles where "more crashes are likely to occur" over a period of time. The analysis involves estimation of crash frequency and/or rate (i.e., frequency normalized based on some measure of exposure) with geometric design features (e.g., number of lanes) and traffic characteristics (e.g., Average Annual Daily Traffic [AADT]) of the roadway location. In the recent past, a new category of traffic safety studies has emerged, which attempts to identify locations where a "crash is more likely to occur." The distinction between the two groups of studies is that the latter group of locations would change based on the varying traffic patterns over the course of the day or even within the hour. METHOD: Hence, instead of estimation of crash frequency over a period of time, the objective becomes real-time estimation of crash likelihood. The estimation of real-time crash likelihood has a traffic management component as well. It is a proactive extension to the traditional approach of incident detection, which involves analysis of traffic data recorded immediately after the incident. The units of analysis used in these studies are individual crashes rather than counts of crashes. RESULTS: In this paper, crash data analysis based on the two approaches, collective and at individual crash level, is discussed along with the advantages and shortcomings of the two approaches.

Assessment of freeway traffic parameters leading to lane-change related collisions.

This study aims at 'predicting' the occurrence of lane-change related freeway crashes using the traffic surveillance data collected from a pair of dual loop detectors. The approach adopted here involves developing classification models using the historical crash data and corresponding information on real-time traffic parameters obtained from loop detectors. The historical crash and loop detector data to calibrate the neural network models (corresponding to crash and non-crash cases to set up a binary classification problem) were collected from the Interstate-4 corridor in Orlando (FL) metropolitan area. Through a careful examination of crash data, it was concluded that all sideswipe collisions and the angle crashes that occur on the inner lanes (left most and center lanes) of the freeway may be attributed to lane-changing maneuvers. These crashes are referred to as lane-change related crashes in this study. The factors explored as independent variables include the parameters formulated to capture the overall measure of lane-changing and between-lane variations of speed, volume and occupancy at the station located upstream of crash locations. Classification tree based variable selection procedure showed that average speeds upstream and downstream of crash location, difference in occupancy on adjacent lanes and standard deviation of volume and speed downstream of the crash location were found to be significantly associated with the binary variable (crash versus non-crash). The classification models based on data mining approach achieved satisfactory classification accuracy over the validation dataset. The results indicate that these models may be applied for identifying real-time traffic conditions prone to lane-change related crashes.

Phenolic contents and antioxidant activity of some food and medicinal plants.

To identify promising sources of antioxidants, some food and medicinal plants were studied for total phenolic contents and antioxidant activity. The leaves, bark and fruits of Terminalia arjuna, Terminalia bellerica, Terminalia chebula and Terminalia muelleri, the leaves and fruits of Phyllanthus emblica, and the seeds of Syzygium cumini were found to have high total phenolic contents (72.0-167.2 mg/g) and high antioxidant activity (69.6-90.6%). Leaves of Eucalyptusglobulus were a rich source of rutin, Moringa oleifera for kaempferol, aerial parts of Centella asiatica for quercetin, fruits of T. bellerica and T. chebula for gallic acid, and bark of T. arjuna, leaves and fruits of T. bellerica and bark, leaves and fruits of T. muelleri for ellagic acid.

Identifying crash propensity using specific traffic speed conditions.

INTRODUCTION: In spite of recent advances in traffic surveillance technology and ever-growing concern over traffic safety, there have been very few research efforts establishing links between real-time traffic flow parameters and crash occurrence. This study aims at identifying patterns in the freeway loop detector data that potentially precede traffic crashes. METHOD: The proposed solution essentially involves classification of traffic speed patterns emerging from the loop detector data. Historical crash and loop detector data from the Interstate-4 corridor in the Orlando metropolitan area were used for this study. Traffic speed data from sensors embedded in the pavement (i.e., loop detector stations) to measure characteristics of the traffic flow were collected for both crash and non-crash conditions. Bayesian classifier based methodology, probabilistic neural network (PNN), was then used to classify these data as belonging to either crashes or non-crashes. PNN is a neural network implementation of well-known Bayesian-Parzen classifier. With its superb mathematical credentials, the PNN trains much faster than multilayer feed forward networks. The inputs to final classification model, selected from various candidate models, were logarithms of the coefficient of variation in speed obtained from three stations, namely, station of the crash (i.e., station nearest to the crash location) and two stations immediately preceding it in the upstream direction (measured in 5 minute time slices of 10-15 minutes prior to the crash time). RESULTS: The results showed that at least 70% of the crashes on the evaluation dataset could be identified using the classifiers developed in this paper.