Faculty Publications
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Item Miners’ return to work following injuries in coal mines; Powrót do pracy górników poszkodowanych w wypadkach w kopalni w?gla(Nofer Institute of Occupational Medicine ul. sw. Teresy od Dzieciatka Jezus 8 Lodz 91-348, 2016) Bhattacherjee, A.; Kunar, B.M.Background: The occupational injuries in mines are common and result in severe socio-economical consequences. Earlier studies have revealed the role of multiple factors such as demographic factors, behavioral factors, health-related factors, working environment, and working conditions for mine injuries. However, there is a dearth of information about the role of some of these factors in delayed return to work (RTW) following a miner’s injury. These factors may likely include personal characteristics of injured persons and his or her family, the injured person’s social and economic status, and job characteristics. This study was conducted to assess the role of some of these factors for the return to work following coal miners’ injuries. Material and Methods: A study was conducted for 109 injured workers from an underground coal mine in the years 2000-2009. A questionnaire, which was completed by the personnel interviews, included among others age, height, weight, seniority, alcohol consumption, sleeping duration, presence of diseases, job stress, job satisfaction, and injury type. The data was analyzed using the Kaplan-Meier estimates and the Cox proportional hazard model. Results: According to Kaplan-Meier estimate it was revealed that a lower number of dependents, longer sleep duration, no job stress, no disease, no alcohol addiction, and higher monthly income have a great impact on early return to work after injury. The Cox regression analysis revealed that the significant risk factors which influenced miners’ return to work included presence of disease, job satisfaction and injury type. Conclusions: The mine management should pay attention to significant risk factors for injuries in order to develop effective preventive measures. © 2016, Nofer Institute of Occupational Medicine. All rights reserved.Item Carotid inter-adventitial diameter is more strongly related to plaque score than lumen diameter: An automated tool for stroke analysis(John Wiley and Sons Inc. P.O.Box 18667 Newark NJ 07191-8667, 2016) Saba, L.; Araki, T.; Krishna Kumar, P.; Rajan, J.; Lavra, F.; Ikeda, N.; Sharma, A.M.; Shafique, S.; Nicolaïdes, A.; Laird, J.R.; Gupta, A.; Suri, J.S.Purpose: To compare the strength of correlation between automatically measured carotid lumen diameter (LD) and interadventitial diameter (IAD) with plaque score (PS). Methods: Retrospective study on a database of 404 common carotid artery B-mode sonographic images from 202 diabetic patients. LD and IAD were computed automatically using an advanced computerized edge detection method and compared with two distinct manual measurements. PS was computed by adding the maximal thickness in millimeters of plaques in segments taken from the internal carotid artery, bulb, and common carotid artery on both sides. Results: The coefficient of correlation was 0.19 (p < 0.007) between LD and PS, and 0.25 (p < 0.0006) between IAD and PS. After excluding 10 outliers, coefficient of correlation was 0.25 (p < 0.0001) between LD and PS, and 0.38 (p < 0.0001) between IAD and PS. The precision of merit of automated versus the two manual measurements was 96.6% and 97.2% for LD, and 97.7% and 98.1%, for IAD, respectively. Conclusions: Our automated measurement system gave satisfying results in comparison with manual measurements. Carotid IAD was more strongly correlated to PS than carotid LD in this population sample of Japanese diabetic patients. © 2016 Wiley Periodicals, Inc.Item Early diagnosis of osteoporosis using radiogrammetry and texture analysis from hand and wrist radiographs in Indian population(Springer London, 2018) Areeckal, A.S.; Jayasheelan, N.; Kamath, J.; Zawadynski, S.; Kocher, M.; Sumam David, S.Summary: We propose an automated low cost tool for early diagnosis of onset of osteoporosis using cortical radiogrammetry and cancellous texture analysis from hand and wrist radiographs. The trained classifier model gives a good performance accuracy in classifying between healthy and low bone mass subjects. Introduction: We propose a low cost automated diagnostic tool for early diagnosis of reduction in bone mass using cortical radiogrammetry and cancellous texture analysis of hand and wrist radiographs. Reduction in bone mass could lead to osteoporosis, a disease observed to be increasingly occurring at a younger age in recent times. Dual X-ray absorptiometry (DXA), currently used in clinical practice, is expensive and available only in urban areas in India. Therefore, there is a need to develop a low cost diagnostic tool in order to facilitate large-scale screening of people for early diagnosis of osteoporosis at primary health centers. Methods: Cortical radiogrammetry from third metacarpal bone shaft and cancellous texture analysis from distal radius are used to detect low bone mass. Cortical bone indices and cancellous features using Gray Level Run Length Matrices and Laws’ masks are extracted. A neural network classifier is trained using these features to classify healthy subjects and subjects having low bone mass. Results: In our pilot study, the proposed segmentation method shows 89.9 and 93.5% accuracy in detecting third metacarpal bone shaft and distal radius ROI, respectively. The trained classifier shows training accuracy of 94.3% and test accuracy of 88.5%. Conclusion: An automated diagnostic technique for early diagnosis of onset of osteoporosis is developed using cortical radiogrammetric measurements and cancellous texture analysis of hand and wrist radiographs. The work shows that a combination of cortical and cancellous features improves the diagnostic ability and is a promising low cost tool for early diagnosis of increased risk of osteoporosis. © 2017, International Osteoporosis Foundation and National Osteoporosis Foundation.Item Particulate matter (PM10) enhances RNA virus infection through modulation of innate immune responses(Elsevier Ltd, 2020) Mishra, R.; Krishnamoorthy, P.; Gangamma, S.; Raut, A.A.; Kumar, H.Particulate matter (PM10) enhances severity of influenza virus infection through skewing innate immunity via modulation of metabolic pathways-related genes.; Sensing of pathogens by specialized receptors is the hallmark of the innate immunity. Innate immune response also mounts a defense response against various allergens and pollutants including particulate matter present in the atmosphere. Air pollution has been included as the top threat to global health declared by WHO which aims to cover more than three billion people against health emergencies from 2019 to 2023. Particulate matter (PM), one of the major components of air pollution, is a significant risk factor for many human diseases and its adverse effects include morbidity and premature deaths throughout the world. Several clinical and epidemiological studies have identified a key link between the PM existence and the prevalence of respiratory and inflammatory disorders. However, the underlying molecular mechanism is not well understood. Here, we investigated the influence of air pollutant, PM10 (particles with aerodynamic diameter less than 10 ?m) during RNA virus infections using Highly Pathogenic Avian Influenza (HPAI) – H5N1 virus. We thus characterized the transcriptomic profile of lung epithelial cell line, A549 treated with PM10 prior to H5N1infection, which is known to cause severe lung damage and respiratory disease. We found that PM10 enhances vulnerability (by cellular damage) and regulates virus infectivity to enhance overall pathogenic burden in the lung cells. Additionally, the transcriptomic profile highlights the connection of host factors related to various metabolic pathways and immune responses which were dysregulated during virus infection. Collectively, our findings suggest a strong link between the prevalence of respiratory illness and its association with the air quality. © 2020 Elsevier Ltd; © 2020 Elsevier LtdItem Risk factors associated with work-related musculoskeletal disorders among dumper operators: A machine learning approach(Elsevier B.V., 2023) Kar, M.B.; Mangalpady, M.; Kunar, B.M.Aims: This study aimed to determine the risk factors associated with work-related musculoskeletal disorders (WRMSDs) among dumper operators working in Indian iron ore mines. Methods: A total of 246 dumper truck operators meeting inclusion and exclusion criteria were chosen for data collection. A self-report custom and the standard Nordic questionnaire were used for collecting data about risk factors and WRMSDs. The data were pre-processed and analyzed using machine learning (ML) algorithms (such as logistic regression ( LR), support vector machines (SVM), decision trees (DT), gradient boosting machine (GBM) and random forest (RF)). Results: RF model was found to outperform the other algorithms with high accuracy (0.71), precision (0.75), recall (0.78), F1 score (0.76), and area under the receiver operating characteristic curve (0.82). The mean rank of the risk factors showed that age is the most critical parameter, followed by awkward posture, experience in mines, job demand, alcohol consumption, smoking cigarettes, work design, and marriage status. Conclusion: Overall, the study provides valuable insights into the risk factors associated with WRMSDs among dumper operators and suggests that measures should be taken to address these risk factors to prevent WRMSDs in the dumper operator population. © 2023 The Author(s)Item Predictive Analysis of Whole Body Vibration Exposure of the Hydraulic Rock Breaker Operators working in Indian Quarries(World Researchers Associations, 2025) Vikram, P.; Mangalpady, M.This study aims to identify the various factors affecting Whole Body Vibration (WBV) exposure among operators of track-mounted hydraulic rock breakers in mechanized quarries. The objective is to develop a statistical model that predicts WBV exposure by considering rock properties, operator’s personal characteristics and equipment specifications. Data on personal factors, equipment specifications and WBV were collected from 84 hydraulic rock breaker operators. A univariate analysis was conducted to determine the association between independent factors and the dependent variable Vibration Dose Value (VDVz). Significant parameters including experience in mines, hydraulic working oil pressure, hydraulic oil flow, hammer blow rate and instantaneous cutting ratio (ICR) were identified. These parameters were then used to develop a multivariate linear regression model. The findings of the multivariate linear regression analysis revealed that among the risk factors, both ICR and hammer blow rate had a negative impact on VDVz. However, experience in mines and hydraulic working oil pressure positively influenced the outcome. The most impactful parameters were ICR (-0.67), experience in mines (0.13), hydraulic working oil pressure (0.097) and hammer blow rate (-0.006). This study recommends implementing vibration dampers to reduce VDVz while ensuring that ICR is maintained at a level that does not compromise productivity. © 2025, World Researchers Associations. All rights reserved.Item Analysis of Accidents Data of Contractual Workers in Open Cast Metal Mines(World Researchers Associations, 2025) Manohar, M.; Bijay, K.M.This research delves into the intricate dynamics of risk factors contributing to injuries in open cast metal mines where a multitude of personal and impersonal elements converge to shape the safety landscape. Drawing insights from a comprehensive literature review, risk factors considered in the study are skill level of workers, the role of mine officials, attitudes toward safety and the involvement of contractors. The one-year contractual workers’ accident data which includes offsite and onsite injuries was considered for the analysis. The analysis of the provided data on on-site and off-site injuries reveals distinct patterns and trends throughout the month. The comparison of one year data set indicates that overall off-site injuries are more prevalent. Ultimately, this analysis contributes valuable insights for enhancing overall safety measures and mitigating the incidence of injuries. © 2025, World Researchers Associations. All rights reserved.