Modeling security evaluation framework for IoHT-driven systems using integrated decision-making methodology.

The intensification of the Internet of Health Things devices created security concerns due to the limitations of these devices and the nature of the healthcare data. While dealing with the security challenges, several authentication schemes, protocols, processes, and standards have been adopted. Consequently, making the right decision regarding the installation of a secure authentication solution or procedure becomes tricky and challenging due to the large number of security protocols, complexity, and lack of understanding. The major objective of this study is to propose an IoHT-based assessment framework for evaluating and prioritizing authentication schemes in the healthcare domain. Initially, in the proposed work, the security issues related to authentication are collected from the literature and consulting experts' groups. In the second step, features of various authentication schemes are collected under the supervision of an Internet of Things security expert using the Delphi approach. The collected features are used to design suitable criteria for assessment and then Graph Theory and Matrix approach applies for the evaluation of authentication alternatives. Finally, the proposed framework is tested and validated to ensure the results are consistent and accurate by using other multi-criteria decision-making methods. The framework produces promising results such as 93%, 94%, and 95% for precision, accuracy, and recall, respectively in comparison to the existing approaches in this area. The proposed framework can be picked as a guideline by healthcare security experts and stakeholders for the evaluation and decision-making related to authentication issues in IoHT systems.

IoT platforms assessment methodology for COVID-19 vaccine logistics and transportation: a multi-methods decision making model.

The supply chain management (SCM) of COVID-19 vaccine is the most daunting task for logistics and supply managers due to temperature sensitivity and complex logistics process. Therefore, several technologies have been applied but the complexity of COVID-19 vaccine makes the Internet of Things (IoT) a strong use case due to its multiple features support like excursion notification, data sharing, connectivity management, secure shipping, real-time tracking and monitoring etc. All these features can only feasible through choosing and deploying the right IoT platform. However, selection of right IoT platform is also a major concern due to lack of experience and technical knowledge of supply chain managers and diversified landscape of IoT platforms. Therefore, we introduce a decision making model for evaluation and decision making of IoT platforms that fits for logistics and transportation (L&T) process of COVID-19 vaccine. This study initially identifies the major challenges addressed during the SCM of COVID-19 vaccine and then provides reasonable solution by presenting the assessment model for selection of rational IoT platform. The proposed model applies hybrid Multi Criteria Decision Making (MCDM) approach for evaluation. It also adopts Estimation-Talk-Estimation (ETE) approach for response collection during the survey. As, this is first kind of model so the proposed model is validated and tested by conducting a survey with experts. The results of the proposed decision making model are also verified by Simple Additive Weighting (SAW) technique which indicates higher results accuracy and reliability of the proposed model. Similarly, the proposed model yields the best possible results and it can be judged by the precision, accuracy and recall values i.e. 93%, 93% and 94% respectively. The survey-based testing also suggests that this model can be adopted in practical scenarios to deal with complexities which may arise during the decision making of IoT platform for COVID-19 SCM process.

Toward explainable AI-empowered cognitive health assessment.

Explainable artificial intelligence (XAI) is of paramount importance to various domains, including healthcare, fitness, skill assessment, and personal assistants, to understand and explain the decision-making process of the artificial intelligence (AI) model. Smart homes embedded with smart devices and sensors enabled many context-aware applications to recognize physical activities. This study presents XAI-HAR, a novel XAI-empowered human activity recognition (HAR) approach based on key features identified from the data collected from sensors located at different places in a smart home. XAI-HAR identifies a set of new features (i.e., the total number of sensors used in a specific activity), as physical key features selection (PKFS) based on weighting criteria. Next, it presents statistical key features selection (SKFS) (i.e., mean, standard deviation) to handle the outliers and higher class variance. The proposed XAI-HAR is evaluated using machine learning models, namely, random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), naive Bayes (NB) and deep learning models such as deep neural network (DNN), convolution neural network (CNN), and CNN-based long short-term memory (CNN-LSTM). Experiments demonstrate the superior performance of XAI-HAR using RF classifier over all other machine learning and deep learning models. For explainability, XAI-HAR uses Local Interpretable Model Agnostic (LIME) with an RF classifier. XAI-HAR achieves 0.96% of F-score for health and dementia classification and 0.95 and 0.97% for activity recognition of dementia and healthy individuals, respectively.

A Survey on harnessing the Applications of Mobile Computing in Healthcare during the COVID-19 Pandemic: Challenges and Solutions.

The COVID-19 pandemic ravaged almost every walk of life but it triggered many challenges for the healthcare system, globally. Different cutting-edge technologies such as Internet of things (IoT), machine learning, Virtual Reality (VR), Big data, Blockchain etc. have been adopted to cope with this menace. In this regard, various surveys have been conducted to highlight the importance of these technologies. However, among these technologies, the role of mobile computing is of paramount importance which is not found in the existing literature. Hence, this survey in mainly targeted to highlight the significant role of mobile computing in alleviating the impacts of COVID-19 in healthcare sector. The major applications of mobile computing such as software-based solutions, hardware-based solutions and wireless communication-based support for diagnosis, prevention, self-symptom reporting, contact tracing, social distancing, telemedicine and treatment related to coronavirus are discussed in detailed and comprehensive fashion. A state-of-the-art work is presented to identify the challenges along with possible solutions in adoption of mobile computing with respect to COVID-19 pandemic. Hopefully, this research will help the researchers, policymakers and healthcare professionals to understand the current research gaps and future research directions in this domain. To the best level of our knowledge, this is the first survey of its type to address the COVID-19 pandemic by exploring the holistic contribution of mobile computing technologies in healthcare area.

Assessing the Role of AI-Based Smart Sensors in Smart Cities Using AHP and MOORA.

We know that in today's advanced world, artificial intelligence (AI) and machine learning (ML)-grounded methodologies are playing a very optimistic role in performing difficult and time-consuming activities very conveniently and quickly. However, for the training and testing of these procedures, the main factor is the availability of a huge amount of data, called big data. With the emerging techniques of the Internet of Everything (IoE) and the Internet of Things (IoT), it is very feasible to collect a large volume of data with the help of smart and intelligent sensors. Based on these smart sensing devices, very innovative and intelligent hardware components can be made for prediction and recognition purposes. A detailed discussion was carried out on the development and employment of various detectors for providing people with effective services, especially in the case of smart cities. With these devices, a very healthy and intelligent environment can be created for people to live in safely and happily. With the use of modern technologies in integration with smart sensors, it is possible to use energy resources very productively. Smart vehicles can be developed to sense any emergency, to avoid injuries and fatal accidents. These sensors can be very helpful in management and monitoring activities for the enhancement of productivity. Several significant aspects are obtained from the available literature, and significant articles are selected from the literature to properly examine the uses of sensor technology for the development of smart infrastructure. The analytical hierarchy process (AHP) is used to give these attributes weights. Finally, the weights are used with the multi-objective optimization on the basis of ratio analysis (MOORA) technique to provide the different options in their order of importance.

Systematic analysis of healthcare big data analytics for efficient care and disease diagnosing.

Big data has revolutionized the world by providing tremendous opportunities for a variety of applications. It contains a gigantic amount of data, especially a plethora of data types that has been significantly useful in diverse research domains. In healthcare domain, the researchers use computational devices to extract enriched relevant information from this data and develop smart applications to solve real-life problems in a timely fashion. Electronic health (eHealth) and mobile health (mHealth) facilities alongwith the availability of new computational models have enabled the doctors and researchers to extract relevant information and visualize the healthcare big data in a new spectrum. Digital transformation of healthcare systems by using of information system, medical technology, handheld and smart wearable devices has posed many challenges to researchers and caretakers in the form of storage, minimizing treatment cost, and processing time (to extract enriched information, and minimize error rates to make optimum decisions). In this research work, the existing literature is analysed and assessed, to identify gaps that result in affecting the overall performance of the available healthcare applications. Also, it aims to suggest enhanced solutions to address these gaps. In this comprehensive systematic research work, the existing literature reported during 2011 to 2021, is thoroughly analysed for identifying the efforts made to facilitate the doctors and practitioners for diagnosing diseases using healthcare big data analytics. A set of rresearch questions are formulated to analyse the relevant articles for identifying the key features and optimum management solutions, and laterally use these analyses to achieve effective outcomes. The results of this systematic mapping conclude that despite of hard efforts made in the domains of healthcare big data analytics, the newer hybrid machine learning based systems and cloud computing-based models should be adapted to reduce treatment cost, simulation time and achieve improved quality of care. This systematic mapping will also result in enhancing the capabilities of doctors, practitioners, researchers, and policymakers to use this study as evidence for future research.

Ensemble deep learning for brain tumor detection.

With the quick evolution of medical technology, the era of big data in medicine is quickly approaching. The analysis and mining of these data significantly influence the prediction, monitoring, diagnosis, and treatment of tumor disorders. Since it has a wide range of traits, a low survival rate, and an aggressive nature, brain tumor is regarded as the deadliest and most devastating disease. Misdiagnosed brain tumors lead to inadequate medical treatment, reducing the patient's life chances. Brain tumor detection is highly challenging due to the capacity to distinguish between aberrant and normal tissues. Effective therapy and long-term survival are made possible for the patient by a correct diagnosis. Despite extensive research, there are still certain limitations in detecting brain tumors because of the unusual distribution pattern of the lesions. Finding a region with a small number of lesions can be difficult because small areas tend to look healthy. It directly reduces the classification accuracy, and extracting and choosing informative features is challenging. A significant role is played by automatically classifying early-stage brain tumors utilizing deep and machine learning approaches. This paper proposes a hybrid deep learning model Convolutional Neural Network-Long Short Term Memory (CNN-LSTM) for classifying and predicting brain tumors through Magnetic Resonance Images (MRI). We experiment on an MRI brain image dataset. First, the data is preprocessed efficiently, and then, the Convolutional Neural Network (CNN) is applied to extract the significant features from images. The proposed model predicts the brain tumor with a significant classification accuracy of 99.1%, a precision of 98.8%, recall of 98.9%, and F1-measure of 99.0%.

Designing evaluation framework for the empirical assessment of COVID-19 mobile apps in Pakistan.

The significant proliferation in the mobile health applications (Apps) amidst Coronaviruses disease 2019 (COVID-19) resulted in decision making problems for healthcare professionals, decision makers and mobile users in Pakistan. This decision making process is also hampered by mobile app trade-offs, multiple features support, evolving healthcare needs and varying vendors. In this regard, evaluation model for mobile apps is presented which completes in three different phases. In first phase, features-based criteria is designed by leveraging Delphi method, and twenty (20) mobile apps are selected from app stores. In second stage, empirical evaluation is performed by using hybrid multi criteria decision approaches like CRiteria Importance Through Inter-criteria Correlation (CRITIC) method has been used for assigning weights to criteria features; and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method has been used for assessment of mobile app alternatives. In last step, decision making is performed to select the best mobile app for COVID-19 situations. The results suggest that proposed model can be adopted as a guideline by mobile app subscribers, patients and healthcare professionals.

A Novel Deep Learning and Ensemble Learning Mechanism for Delta-Type COVID-19 Detection.

Recently, the novel coronavirus disease 2019 (COVID-19) has posed many challenges to the research community by presenting grievous severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that results in a huge number of mortalities and high morbidities worldwide. Furthermore, the symptoms-based variations in virus type add new challenges for the research and practitioners to combat. COVID-19-infected patients comprise trenchant radiographic visual features, including dry cough, fever, dyspnea, fatigue, etc. Chest X-ray is considered a simple and non-invasive clinical adjutant that performs a key role in the identification of these ocular responses related to COVID-19 infection. Nevertheless, the defined availability of proficient radiologists to understand the X-ray images and the elusive aspects of disease radiographic replies to remnant the biggest bottlenecks in manual diagnosis. To address these issues, the proposed research study presents a hybrid deep learning model for the accurate diagnosing of Delta-type COVID-19 infection using X-ray images. This hybrid model comprises visual geometry group 16 (VGG16) and a support vector machine (SVM), where the VGG16 is accustomed to the identification process, while the SVM is used for the severity-based analysis of the infected people. An overall accuracy rate of 97.37% is recorded for the assumed model. Other performance metrics such as the area under the curve (AUC), precision, F-score, misclassification rate, and confusion matrix are used for validation and analysis purposes. Finally, the applicability of the presumed model is assimilated with other relevant techniques. The high identification rates shine the applicability of the formulated hybrid model in the targeted research domain.

Challenges and practices identification in complex outsourcing relationships: A systematic literature review.

Complex IT outsourcing relationships aptitude several benefits such as increased cost likelihood and lowered costs, higher scalability and flexibility upon demand. However, by virtue of its complexity, the complex outsourcing typically necessitates the interactions among various stakeholders from diverse regions and cultures, making it significantly more challenging to manage than traditional outsourcing. Furthermore, when compared to other types of outsourcing, complex outsourcing is extremely difficult because it necessitates a variety of control and coordination mechanisms for project management, which proportionally increases the risk of project failure. In order to overcome the failure of projects in complex outsourcing relationships, there is a need of robust systematic research to identify the key challenges and practices in this area. Therefore, this research implements systematic literature review as a research method and works as a pioneer attempt to accomplish the aforementioned objectives. Upon furnishing the SLR results, the authors identified 11 major challenges with 67 practices in hand from a total of 85 papers. Based on these findings, the authors intend to construct a comprehensive framework in the future by incorporating robust methodologies such as AHP and fuzzy logic, among others.

Automated Detection of Brain Tumor through Magnetic Resonance Images Using Convolutional Neural Network.

Brain tumor is a fatal disease, caused by the growth of abnormal cells in the brain tissues. Therefore, early and accurate detection of this disease can save patient's life. This paper proposes a novel framework for the detection of brain tumor using magnetic resonance (MR) images. The framework is based on the fully convolutional neural network (FCNN) and transfer learning techniques. The proposed framework has five stages which are preprocessing, skull stripping, CNN-based tumor segmentation, postprocessing, and transfer learning-based brain tumor binary classification. In preprocessing, the MR images are filtered to eliminate the noise and are improve the contrast. For segmentation of brain tumor images, the proposed CNN architecture is used, and for postprocessing, the global threshold technique is utilized to eliminate small nontumor regions that enhanced segmentation results. In classification, GoogleNet model is employed on three publicly available datasets. The experimental results depict that the proposed method is achieved average accuracies of 96.50%, 97.50%, and 98% for segmentation and 96.49%, 97.31%, and 98.79% for classification of brain tumor on BRATS2018, BRATS2019, and BRATS2020 datasets, respectively. The outcomes demonstrate that the proposed framework is effective and efficient that attained high performance on BRATS2020 dataset than the other two datasets. According to the experimentation results, the proposed framework outperforms other recent studies in the literature. In addition, this research will uphold doctors and clinicians for automatic diagnosis of brain tumor disease.

Augmented Reality Interface for Complex Anatomy Learning in the Central Nervous System: A Systematic Review.

The medical system is facing the transformations with augmentation in the use of medical information systems, electronic records, smart, wearable devices, and handheld. The central nervous system function is to control the activities of the mind and the human body. Modern speedy development in medical and computational growth in the field of the central nervous system enables practitioners and researchers to extract and visualize insight from these systems. The function of augmented reality is to incorporate virtual and real objects, interactively running in a real-time and real environment. The role of augmented reality in the central nervous system becomes a thought-provoking task. Gesture interaction approach-based augmented reality in the central nervous system has enormous impending for reducing the care cost, quality refining of care, and waste and error reducing. To make this process smooth, it would be effective to present a comprehensive study report of the available state-of-the-art-work for enabling doctors and practitioners to easily use it in the decision making process. This comprehensive study will finally summarise the outputs of the published materials associate to gesture interaction-based augmented reality approach in the central nervous system. This research uses the protocol of systematic literature which systematically collects, analyses, and derives facts from the collected papers. The data collected range from the published materials for 10 years. 78 papers were selected and included papers based on the predefined inclusion, exclusion, and quality criteria. The study supports to identify the studies related to augmented reality in the nervous system, application of augmented reality in the nervous system, technique of augmented reality in the nervous system, and the gesture interaction approaches in the nervous system. The derivations from the studies show that there is certain amount of rise-up in yearly wise articles, and numerous studies exist, related to augmented reality and gestures interaction approaches to different systems of the human body, specifically to the nervous system. This research organises and summarises the existing associated work, which is in the form of published materials, and are related to augmented reality. This research will help the practitioners and researchers to sight most of the existing studies subjected to augmented reality-based gestures interaction approaches for the nervous system and then can eventually be followed as support in future for complex anatomy learning.

A Systematic Review on Cloud Storage Mechanisms Concerning e-Healthcare Systems.

As the expenses of medical care administrations rise and medical services experts are becoming rare, it is up to medical services organizations and institutes to consider the implementation of medical Health Information Technology (HIT) innovation frameworks. HIT permits health associations to smooth out their considerable cycles and offer types of assistance in a more productive and financially savvy way. With the rise of Cloud Storage Computing (CSC), an enormous number of associations and undertakings have moved their healthcare data sources to distributed storage. As the information can be mentioned whenever universally, the accessibility of information becomes an urgent need. Nonetheless, outages in cloud storage essentially influence the accessibility level. Like the other basic variables of cloud storage (e.g., reliability quality, performance, security, and protection), availability also directly impacts the data in cloud storage for e-Healthcare systems. In this paper, we systematically review cloud storage mechanisms concerning the healthcare environment. Additionally, in this paper, the state-of-the-art cloud storage mechanisms are critically reviewed for e-Healthcare systems based on their characteristics. In short, this paper summarizes existing literature based on cloud storage and its impact on healthcare, and it likewise helps researchers, medical specialists, and organizations with a solid foundation for future studies in the healthcare environment.

Single versus double burr-hole drainage for chronic subdural hematoma: A study of relevant prognostic factors conducted in Pakistan.

OBJECTIVE: To compare the efficacy of single versus double burr-hole for drainage of chronic subdural hematoma, keeping in consideration pertinent demographic, pre and postoperative associations. METHODS: A prospective cohort study carried out in Combined Military Hospital, Multan, (December 2016-August 2018), on adults with diagnosed chronic subdural hematoma (CSDH); being segregated by randomized control trial, non-probability purposive sampling into Group-A and Group-B (who underwent single and double burr-holes for CSDH-drainage respectively). Utilizing SPSS-21, data expressed as frequencies/percentages and mean± standard deviation (SD) and cross-tabulated; p-value <0.05 was taken as significant. RESULTS: Age and GCS scores were 62±13.694 (range 38-94) and 11.00±3.350 (range 3-15) respectively, males being 40(66.7). Post-operative fatality was Nil, while 8(13.3%) and 14(23.3%) had post-operative seizures and recurrence of hematoma respectively. There was no significant association between type of burr-hole and hospital stay (p 0-884), seizures (p 0.448) or recurrence (p 0.542). Hospital stay (p<0.000) and seizures (p-0.005) were inversely proportional to GCS scores on presentation. Recurrence rates were not affected by age (p-0 .175) or gender (p-0 .281). CONCLUSION: There was no significant difference between outcomes of single and double burr-hole surgeries; the former must be preferred because of lesser iatrogenic trauma. GCS-score on presentation was validated as a negative association to anticipate post-operative outcomes.

Prevalence of anxiety and depression among doctors; the unscreened and undiagnosed clientele in Lahore, Pakistan.

OBJECTIVES: To analyze prevalence of anxiety and depression among doctors serving in a tertiary care hospital in Lahore, with a study of impact of relevant demographic features. METHODS: A cross sectional study was conducted at Combined Military Hospital, Lahore, from February 2014 to Jan 2015. Participants were doctors serving in subject hospital for at least six months duration. Standardized Hospital Anxiety Depression Score (HADS) inventory was selected as inventory. Formal approval from hospital ethical committee and written informed consent from participants were obtained. Demographic characteristics of participants were recorded as independent variables; anxiety and depression scores being outcome variables. Data analysis was done via descriptive statistics (SPSS-20), qualitative data expressed as frequencies, percentages; quantitative as mean ± standard deviation(SD). Cross tabulation was done via chi-square, p-value < 0.05 considered as significant. RESULTS: Out of 203 volunteers, 97(47.78%) responded. Score of anxiety was 7.04±4.470, maximum being 19, scores of depression was 4.94±3.605, maximum score being 15. Mild to moderate anxiety and depression were revealed in 33(34%) and 24(24.8%) respectively, while 7(7.2%) and 1(1.0%) had severe anxiety and depression respectively. There was strong positive relation between anxiety and depression (p<0.001). There was significant impact of service years on depression (p-0.011) and gender on anxiety (p-0.002), 9(17.31%) males and 24(53.33%) females had mild to moderate anxiety while 4(7.69%) males and 3(6.66%) females revealed severe anxiety and other variables did not reveal significant impact on HADS scores. CONCLUSION: Doctors showed high grades of anxiety and depression. They must be promptly screened and managed at all medical institutions.

Do doctors have hidden distress; a study conducted at tertiary care hospital at Lahore.

OBJECTIVE: Toanalyse the prevalence of distress in doctors serving in a tertiary care hospital and studying the factors having significant impact on the subject. METHODS: The cross-sectional study was conducted at the Combined Military Hospital, Lahore, from February to December 2014, and comprised doctors serving for at least six months who volunteered to fill out the standardised General Health Questionnaire-12Demographic features and level of job satisfaction were taken as independent variables. Outcome variable was the questionnaire score. SPSS 20 was used for data analysis. RESULTS: The mean questionnaire score of the 97 respondents in the study was 12.27±6.397. Of them, 19(19.6%) and 11(11.3%) had distress and severe distress respectively. Marital status (p=0.006), age (p=0.029), income per month (p=0.010) and levels of job satisfaction (p=0.001) had significant impact on the scores. Variables having insignificant impact were gender (p=0.529), number of children (p=0.220), education (p=0.816), service years (p=0.155), current employment (p=0.504), nature of job (p=0.531), working hours (p=0.632), additional duties (p=0.663), and socioeconomic class (p=0.935). CONCLUSIONS: Almost one-third of the doctors had distress under the significant impact of multiple factors.

Job satisfaction among doctors, a multi-faceted subject studied at a tertiary care hospital in Lahore.

OBJECTIVE: To study the level of job satisfaction among doctors serving in a tertiary care hospital in Lahore and ascertain its co-relation with multiple demographic variables which had a profound impact. METHODS: This cross sectional study with non-probability purposive sampling was conducted at Combined Military Hospital, Lahore, from February 2014 to November 2014. Subjects were doctors serving in that hospital for minimum six months duration. Pre-formed questionnaires were distributed to volunteers (average filling time was 3 ½ to 7 minutes). Multiple demographic features were independent variables. Outcome variable was job satisfaction. Statistical analysis was done via descriptive statistics (SPSS 20), data expressed as mean ± standard deviation (SD). RESULTS: Out of 263 doctors serving in hospital, 203 (77.91%) volunteered to participate; response rate by depositing the filled forms was 47.78% (97 doctors). Amongst the respondents, 10 (10.3%) doctors had below average job satisfaction, 32(33.0%), 21(21.6%), 21(21.6%) and 13(13.3%) had average, above average, well above average and outstanding job satisfaction respectively. There was significant relation between job satisfaction and age group of the doctors (p 0.025), education (p 0.015), service years (p 0.013) income per month (p<0.001). There was no significant impact of gender (p 0.540), marital status (p 0.087), number of children (p 0.153), current employment (p 0.71), nature of job (p 0.204), working hours (p 0.089), additional duties p 0.421) and socioeconomic class (p 0.104) on outcome variable. CONCLUSION: A significant number of doctors was found discontented with their job, which may consequently impact their yield/performance. The job satisfaction can be substantially improved if these contributory factors are aptly addressed at all tiers.

Antitussive and toxicological evaluation of Vitex negundo.

Vitex negundo Linn. (Verbenaceae) is used in traditional medical system for respiratory disorders. This study was carried out to investigate its cough-relieving potential. The antitussive effect of the butanolic extract of V. negundo (Vn) on sulphur dioxide (SO(2))-induced cough was examined in mice. Safety profile of Vn was carried out by observing acute neurotoxicity, median lethal dose (LD(50)) and behavioural signs. Vn dose-dependently (250-1000 mg kg(-1)) inhibited the cough provoked by SO(2) gas in mice and exhibited maximum protection after 60 min of administration. At 1000 mg kg(-1), Vn caused maximum cough-suppressive effects i.e. cough inhibition at 60 min was 67.4%, as compared to codeine (10 mg kg(-1)), dextromethorphan (10 mg kg(-1)) and saline having cough-inhibitory potential 75.7%, 74.7% and 0%, respectively. LD(50) value of V. negundo was found to be greater than 5000 mg kg(-1). In toxicity tests, no signs of neural impairment and acute behavioural toxicity were observed at antitussive doses and extract has been well tolerated at higher doses. These results indicate that V. negundo exhibits antitussive effect and it was found devoid of toxicity.

Effect of vitamin D, calcium and multiple micronutrient supplementation on vitamin D and bone status in Bangladeshi premenopausal garment factory workers with hypovitaminosis D: a double-blinded, randomised, placebo-controlled 1-year intervention.

Due to little outdoor activity and low dietary intake of vitamin D (VD), Bangladeshi low-income women are at risk for osteoporosis at an early age. The present study assessed the effect of VD, Ca and multiple micronutrient supplementation on VD and bone status in Bangladeshi young female garment factory workers. This placebo-controlled 1-year intervention randomly assigned 200 apparently healthy subjects (aged 16-36 years) to four groups: VD group, daily 10 microg VD; VD and Ca (VD-Ca) group, daily 10 microg VD+600 mg Ca; multiple micronutrient and Ca (MMN-Ca) group, 10 microg VD and other micronutrients+600 mg Ca; a placebo group. Serum 25-hydroxyvitamin D (S-25OHD), intact parathyroid hormone (S-iPTH), Ca, phosphate and alkaline phosphatase were measured. Bone mineral density and bone mineral content were measured by dual-energy X-ray absorptiometry. All measurements were made at baseline and at 12 months. Significantly (P < 0.001) higher S-25OHD concentrations were observed in the supplemented groups than in the placebo group after the intervention. Supplementation had an effect (P < 0.001) on S-iPTH in the VD-Ca and MMN-Ca groups compared with the placebo group. Bone mineral augmentation increased at the femur in the supplemented groups. Supplementation with VD-Ca should be recommended as a strategic option to reduce the risk of osteomalacia and osteoporosis in these subjects. MMN-Ca may have analogous positive health implications with additional non-skeletal benefits.

Vitamin D deficiency and low bone status in adult female garment factory workers in Bangladesh.

The manufacture of garments is the main industry in Bangladesh and employs 1.6 million female workers. Due to the indoor lifestyle and low dietary intake of calcium, we hypothesised that they are at risk of low vitamin D and bone mineral status. Two hundred female garment workers (aged 18-36 years) were randomly selected. Serum 25-hydroxyvitamin D (S-25OHD), serum intact parathyroid hormone (S-iPTH), serum calcium (S-Ca), serum phosphate (S-P) concentration and serum alkaline phosphatase activity (S-ALP) were measured from fasting samples. Bone indexes of hip and spine were measured by dual-energy X-ray absorptiometry. The mean S-25OHD (36.7 nmol/l) was low compared to that recommended for vitamin D sufficiency. About 16% of the subjects were found to be vitamin D-deficient (S-25OHD 21 ng/l) was associated with progressive reduction in bone mineral density at the femoral neck and lumbar spine. According to the WHO criteria, the mean T-score of the femoral neck and lumbar spine of the subjects were within osteopenic range. We observed that subjects with a bone mineral density T-score < -2.5 had a trend of lower values of BMI, waist-hip circumference, mid-upper-arm circumference, S-25OHD and higher S-iPTH and S-ALP. The high prevalence of hypovitaminosis D and low bone mineral density among these subjects are indicative of higher risk for osteomalacia or osteoporosis and fracture.