Factors affecting injury severity in motorcycle crashes: Different age groups analysis using Catboost and SHAP techniques.

OBJECTIVE: Motorcycle crashes often result in severe injuries on roads that affect people's lives physically, financially, and psychologically. These injuries could be notably harmful to drivers of all age groups. The main objective of this study is to investigate the risk factors contributing to the severity of crash injuries in different age groups. METHODS: This Objective is achieved by developing accurate machine learning (ML) based prediction models. This research examines the relationship between potential risk factors of motorcycle-associated crashes using (ML) and Shapley Additive explanations (SHAP) technique. The SHAP technique further helped interpreting ML methods for traffic injury severity prediction. It indicates the significant non-linear interactions between dependent and independent variables. The data for this study was collected from the Provincial Emergency Response Service RESCUE 1122 for the Rawalpindi region (Pakistan) over three years (from 2017 to 2020). The Synthetic Minority Oversampling Technique (SMOTE) is employed to balance injury severity classes in the pre-processing phase. RESULTS: The results demonstrate that age, gender, posted speed limit, the number of lanes, and month of the year are positively associated with severe and fatal injuries. This research also assesses how the modeling framework varies between the ML and classical statistical methods. The predictive performance of proposed ML models was assessed using several evaluation metrics, and it is found that Catboost outperformed the XGBoost, Random Forest (RF) and Multinomial Logit (MNL) model. CONCLUSION: The findings of this study will assist road users, road safety authorities, stakeholders, policymakers, and decision-makers in obtaining substantial and essential guidance for reducing the severity of crash injuries in Pakistan and other countries with prevailing conditions.

Safety climate in Pakistani universities' laboratories: an assessment of individual factors.

Objectives. Current research aims to identify factors that affect the occupational safety climate in university laboratories despite their perception as low-risk areas compared to industrial environments. Methods. A safety climate survey was conducted in science laboratories across various engineering universities in Pakistan. The survey questionnaire was administered to 406 personnel, and a quantitative method for analysis was selected to examine the socio-demographic variables. A 5-point Likert scale (1 = strongly disagree to 5 = strongly agree) was used to perceive responses from participants. Additionally, a scale reliability test was conducted, and multivariate analysis of variance was performed to determine the relationship between selected dependent and independent variables. Results. The study found an overall safety climate score of 3.16 ± 0.55, indicating a moderate to high perception of safety on a scale of 1-5. Parameters such as role in the laboratory, departments/disciplines, accident experience and safety training significantly affected the safety climate score, while gender, age group, duration in university and accident witnessing did not. Conclusion. Upper management involvement, safety communication and direct supervision are crucial for improving the safety climate of university laboratories. The study recommends the consideration of the identified significant safety climate dimensions in laboratory safety policy-making at academic institutes.

Presence of fimH and iss type 1, 2 and 3 genes in uropathogenic Escherichia coli isolates recovered from an apex medical institute in North India.

PURPOSE: To detect the presence of fimH and iss type 1, 2 and 3 genes in uropathogenic Escherichia coli (UPEC) isolates recovered from patients coming to the out patient department (OPD) of our hospital. METHODS: E. coli isolates recovered from patients who had symptoms of urinary tract infection (UTI) were processed for the presence of fimH and iss genes. DNA was extracted using an in house method after which conventional PCR using forward and reverse primers targeting the four genes was carried out. The amplified products were electrophoresed and visualized in a gel documentation imager. Relevant demographic details of the patients were recorded on a pre-designed pro-forma and antimicrobial susceptibility testing of the isolates was done by disc diffusion method. RESULTS: fimH was present in 87.5% of UPEC isolates whereas iss type 1 was seen in 7.3%, type 2 in 4.2% and iss type 3 in 71.9% isolates. Age of the patients ranged from 3 months to 82 â€‹yrs (mean 43.5 SD â€‹± â€‹18.20). UTI was more common in females (60.2%) as compared to males patients (39.8%). Dysuria (66.7%) was the most common symptom in the studied subjects and diabetes mellitus (42.6%) the most common co-morbidity. A total of 56.5% patients gave a history of prior antibiotic intake. The UPEC isolates were resistant to most of the antibiotics tested. However all the isolates were sensitive to polymyxin B and colistin. Fosfomycin resistance was seen in 9.5% of the UPEC isolates harbouring fimH gene. CONCLUSION: This is the first study that highlights the presence of iss type 3 gene in UPEC isolates along with the fimH and iss type 1 and 2 genes. The results of this study can serve as a stepping stone for future in depth research into the significance of the iss genes in causing UTI.

Identification and pattern of antifungal susceptibility of Candida species isolated from cases of vaginitis in a tertiary care hospital in India.

Background and Objectives: Vulvovaginal candidiasis (VVC) is one of the most frequent reasons for gynecological consultations. Candida albicans is responsible in the majority of cases. Lately, VVC caused by non-albicans Candida spp. (NAC), which are resistant to routinely used antifungals, is on the rise. This study was designed to determine the prevalence of Candida in patients suffering from vaginitis and to assess the predisposing factors along with identification of Candida species and evaluation of their susceptibility profile. Materials and Methods: High vaginal swabs were collected from 225 women. Sample processing consisted of Gram stain and culture onto Sabouraud's dextrose agar and HiChrom Candida Differential agar. Isolates were identified and speciated using VITEK2 Compact System. Susceptibility testing was done using VITEK2 AST-Y S08 cards and disc diffusion. Results: Candida spp. were isolated from 94 (41.8%) of the cases. C. albicans was the predominant species (71.6%) followed by other NAC spp. (28.4%). Pregnancy and diabetes were the most frequently implicated risk factors (67.1% and 44.4%). High resistance was observed in NAC spp. as opposed to C. albicans to all antifungal agents tested. Conclusion: Empirical therapy with routinely used antifungals can be initiated for C. albicans. In the case of NAC spp., identification should be followed by susceptibility testing.

Unani Formulation Habb-e-Suranjan: A Treasure of Biological Activities.

Habb-e-Suranjan (HES), an Unani formulation, has been studied for its anti-inflammatory properties in both in vitro and in vivo experiments. HES is recommended for arthritis, gout, and joint pain. The current endeavor is an attempt to put it to the test and verify its efficacy scientifically. It was tested for DPPH, hydroxyl, and nitric oxide scavenging activities. It was shown that HES had the greatest TAC and FRAC values when compared to catechin and ascorbic acid. HES exhibited DPPH and hydroxyl radical scavenging activity that was dose-dependent. Incubation of sodium nitroprusside solutions in PBS at 25°C for 150 min resulted in the production of nitric oxide. Nitric oxide production was effectively decreased by HES. Anti-inflammatory medications boosted the migration of PMN cells toward the chemoattractant FMLP in an agarose experiment of PMN chemotaxis. In carrageenan-induced rat paw edema, in the HES-treated group, paw thickness was 3.021 ± 0.084 at t = 0, but it showed an increase in paw inflammation after one hour, i.e., 3.195 ± 0.082 cm which again showed a decrease in paw thickness up to 4th hour, i.e., 3.018 ± 0.078, 2.98 ± 0.032, and 2.684 ± 0.061 at t = 2, 3, and 4, respectively. It showed again getting back to the normal thickness of paw at t = 24 hrs, i.e., 3.029 ± 0.118 cm. It is concluded that the formulation is potent enough and can be used effectively for the treatment of inflammation and associated health issues. Moreover, there is much scope to evaluate its effectiveness using different in vitro and in vivo models.

A New Pedestrian Crossing Level of Service (PCLOS) Method for Promoting Safe Pedestrian Crossing in Urban Areas.

Crosswalks are critical locations in the urban transport network that need to be designed carefully as pedestrians are directly exposed to vehicular traffic. Although various methods are available to evaluate the level of service (LOS) at pedestrian crossings, pedestrian crossing facilities are frequently ignored in assessing crosswalk conditions. This study attempts to provide a comprehensive framework for evaluating crosswalks based on several essential indicators adopted from different guidelines. A new pedestrian crossing level of service (PCLOS) method is introduced in this research, with an aimto promote safe and sustainable operations at such locations. The new PCLOS employs an analytical point system to compare existing street crossing conditions to the guidelines' standards, taking into account the scores and coefficients of the indicators. The quantitative scores and coefficients of indicators are assigned based on field observations and respondent opinions. The method was tested to evaluate four pedestrian crosswalks in the city of Putrajaya, Malaysia. A total of 17 indicators were selected for the study after a comprehensive literature review. Survey results show that the provision of a zebra crossing was the most critical indicator at the pedestrian crossings, while drainage near crosswalks was regarded as the least important. Four indicators had a coefficient value above 4, indicating that these are very critical pedestrian crossing facilities and significantly impact the calculation of LOS for pedestrian crossings. Four crosswalks were evaluated using the proposed method in Putrajaya, Malaysia. The crosswalk at the Ministry of Domestic Trade Putrajaya got the "PCLOS A". In contrast, the midblock crossing in front of the Putrajaya Corporation was graded "PCLOS C". While the remaining two crosswalks were graded as "PCLOS B" crosswalks. Based on the assigned PCLOS grade, the proposed method could also assist in identifying current design and operation issues in existing pedestrian crossings and providing sound policy recommendations for improvements to ensure pedestrian safety.

Predicting Risky and Aggressive Driving Behavior among Taxi Drivers: Do Spatio-Temporal Attributes Matter?

Risky and aggressive driving maneuvers are considered a significant indicator for traffic accident occurrence as well as they aggravate their severity. Traffic violations caused by such uncivilized driving behavior is a global issue. Studies in existing literature have used statistical analysis methods to explore key contributing factors toward aggressive driving and traffic violations. However, such methods are unable to capture latent correlations among predictor variables, and they also suffer from low prediction accuracies. This study aimed to comprehensively investigate different traffic violations using spatial analysis and machine learning methods in the city of Luzhou, China. Violations committed by taxi drivers are the focus of the current study since they constitute a significant proportion of total violations reported in the city. Georeferenced violation data for the year 2016 was obtained from the traffic police department. Detailed descriptive analysis is presented to summarize key statistics about various violation types. Results revealed that over-speeding was the most prevalent violation type observed in the study area. Frequency-based nearest neighborhood cluster methods in Arc map Geographic Information System (GIS) were used to develop hotspot maps for different violation types that are vital for prioritizing and conducting treatment alternatives efficiently. Finally, different machine learning (ML) methods, including decision tree, AdaBoost with a base estimator decision tree, and stack model, were employed to predict and classify each violation type. The proposed methods were compared based on different evaluation metrics like accuracy, F-1 measure, specificity, and log loss. Prediction results demonstrated the adequacy and robustness of proposed machine learning (ML) methods. However, a detailed comparative analysis showed that the stack model outperformed other models in terms of proposed evaluation metrics.