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BACKGROUND: Smoking and smokeless tobacco use during the postpartum period is well studied in high-income countries, whereas low-income and middle-income countries (LMICs) lack evidence. METHODS: In this cross-sectional study we used data from the Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS) conducted in 78 LMICs between January 2010 and December 2019 to study tobacco use among 0.32 million sample lactating women. Age-standardized prevalence of smoking and smokeless tobacco use was estimated and presented with a 95% Confidence Interval (CI) for 78 LMICs. Pooled estimates overall and by WHO regions were obtained using random-effects meta-analyses. Country-level and community-level variance to understand contextual factors was also quantified using multilevel modelling. FINDINGS: Pooled prevalence of any tobacco use among breastfeeding women in LMICs was 3.61% (95% CI 3.53-3.70); with the lowest prevalence in regions of the Americas (1.44%, 1.26-1.63) and the highest in the Southeast Asia region (6.13%, 6.0-6.27). The pooled prevalence of tobacco smoking was reported to be 1.16% (1.11-1.21), with the highest prevalence in the Eastern Mediterranean region (4.27%, 3.88-4.67) and the lowest in the African region (0.81%, 0.76-0.86). The pooled prevalence of smokeless tobacco use was reported to be 2.56% (2.49-2.63), with the highest prevalence in the Southeast Asia region (4.92%, 4.80-5.04). Illiterate and poor women in LMICs bore the enormous burden of tobacco use. INTERPRETATION: The prevalence of smoking and smokeless tobacco use among lactating women in LMICs varied considerably across different WHO regions. Considering the cross-sectional design of the study, caution is required while interpreting the results. To improve mothers' and children's health and nutrition outcomes and reduce health inequalities in LMICs, reducing tobacco use through evidence-based interventions is critical. FUNDING: None.

The human body is designed to experience stress and react to it, and experiencing challenges causes our body to produce physical and mental responses and also helps our body to adjust to new situations. However, stress becomes a problem when it continues to remain without a period of relaxation or relief. When a person has long-term stress, continued activation of the stress response causes wear and tear on the body. Chronic stress results in cancer, cardiovascular disease, depression, and diabetes, and thus is deeply detrimental to our health. Previous researchers have performed a lot of work regarding mental stress, using mainly machine-learning-based approaches. However, most of the methods have used raw, unprocessed data, which cause more errors and thereby affect the overall model performance. Moreover, corrupt data values are very common, especially for wearable sensor datasets, which may also lead to poor performance in this regard. This paper introduces a deep-learning-based method for mental stress detection by encoding time series raw data into Gramian Angular Field images, which results in promising accuracy while detecting the stress levels of an individual. The experiment has been conducted on two standard benchmark datasets, namely WESAD (wearable stress and affect detection) and SWELL. During the studies, testing accuracies of 94.8% and 99.39% are achieved for the WESAD and SWELL datasets, respectively. For the WESAD dataset, chest data are taken for the experiment, including the data of sensor modalities such as three-axis acceleration (ACC), electrocardiogram (ECG), body temperature (TEMP), respiration (RESP), etc.

Human Action Recognition (HAR) is a popular area of research in computer vision due to its wide range of applications such as surveillance, health care, and gaming, etc. Action recognition based on 3D skeleton data allows simplistic, cost-efficient models to be formed making it a widely used method. In this work, we propose DSwarm-Net, a framework that employs deep learning and swarm intelligence-based metaheuristic for HAR that uses 3D skeleton data for action classification. We extract four different types of features from the skeletal data namely: Distance, Distance Velocity, Angle, and Angle Velocity, which capture complementary information from the skeleton joints for encoding them into images. Encoding the skeleton data features into images is an alternative to the traditional video-processing approach and it helps in making the classification task less complex. The Distance and Distance Velocity encoded images have been stacked depth-wise and fed into a Convolutional Neural Network model which is a modified version of Inception-ResNet. Similarly, the Angle and Angle Velocity encoded images have been stacked depth-wise and fed into the same network. After training these models, deep features have been extracted from the pre-final layer of the networks, and the obtained feature representation is optimized by a nature-inspired metaheuristic, called Ant Lion Optimizer, to eliminate the non-informative or misleading features and to reduce the dimensionality of the feature set. DSwarm-Net has been evaluated on three publicly available HAR datasets, namely UTD-MHAD, HDM05, and NTU RGB+D 60 achieving competitive results, thus confirming the superiority of the proposed model compared to state-of-the-art models.

Biomedical images contain a huge number of sensor measurements that can provide disease characteristics. Computer-assisted analysis of such parameters aids in the early detection of disease, and as a result aids medical professionals in quickly selecting appropriate medications. Human Activity Recognition, abbreviated as 'HAR', is the prediction of common human measurements, which consist of movements such as walking, running, drinking, cooking, etc. It is extremely advantageous for services in the sphere of medical care, such as fitness trackers, senior care, and archiving patient information for future use. The two types of data that can be fed to the HAR system as input are, first, video sequences or images of human activities, and second, time-series data of physical movements during different activities recorded through sensors such as accelerometers, gyroscopes, etc., that are present in smart gadgets. In this paper, we have decided to work with time-series kind of data as the input. Here, we propose an ensemble of four deep learning-based classification models, namely, 'CNN-net', 'CNNLSTM-net', 'ConvLSTM-net', and 'StackedLSTM-net', which is termed as 'Ensem-HAR'. Each of the classification models used in the ensemble is based on a typical 1D Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network; however, they differ in terms of their architectural variations. Prediction through the proposed Ensem-HAR is carried out by stacking predictions from each of the four mentioned classification models, then training a Blender or Meta-learner on the stacked prediction, which provides the final prediction on test data. Our proposed model was evaluated over three benchmark datasets, WISDM, PAMAP2, and UCI-HAR; the proposed Ensem-HAR model for biomedical measurement achieved 98.70%, 97.45%, and 95.05% accuracy, respectively, on the mentioned datasets. The results from the experiments reveal that the suggested model performs better than the other mul

The topical route is the most common route for delivery of the drug to the eyes because of its high patient compliance and noninvasive technique. Mostly, the drug is not able to cross the barriers and is unable to reach the posterior eye segment. Success of any treatment of ocular disease depends on the targeting of the drug to the posterior segment of eye, i.e., retina by crossing or bypassing ocular barriers for the management of ocular diseases. In particular, ocular diseases have been treated by two primary modalities, i.e., topical drops and intravitreal injections. But they can cause serious complications of eyes like impaired wound healing, retinal detachment, endophthalmitis, hypertension, proteinuria, and increased risk of cardiovascular diseases. Ocular conventional systems include ophthalmic drops, eye suspensions, eye emulsions, and eye ointments are not considered most advantageous for treatment of ocular diseases as they primarily target the anterior section of eye but systemic doses boost the treatment of posterior section of eye. Mostly topically applied drug entities are removed and washed off from the eyeball by mechanism of lacrimation, tear dilution, and tear turnover ultimately causing little bioavailability of drugs. The challenges of the aforementioned existing conventional dosage forms suggest to rethink on the need for noninvasive, effective, and economical delivery systems, i.e., novel drug delivery systems. They can deliver the drug effectively to retina overcoming the challenges associated with conventional approaches.

Eosinophils are bi-lobed, multi-functional innate immune cells with diverse cell surface receptors that regulate local immune and inflammatory responses. Several inflammatory and infectious diseases are triggered with their build up in the blood and tissues. The mobilization of eosinophils into the lungs is regulated by a cascade of processes guided by Th2 cytokine generating T-cells. Recruitment of eosinophils essentially leads to a characteristic immune response followed by airway hyperresponsiveness and remodeling, which are hallmarks of chronic respiratory diseases. By analysing the dynamic interactions of eosinophils with their extracellular environment, which also involve signaling molecules and tissues, various therapies have been invented and developed to target respiratory diseases. Having entered clinical testing, several eosinophil targeting therapeutic agents have shown much promise and have further bridged the gap between theory and practice. Moreover, researchers now have a clearer understanding of the roles and mechanisms of eosinophils. These factors have successfully assisted molecular biologists to block specific pathways in the growth, migration and activation of eosinophils. The primary purpose of this review is to provide an overview of the eosinophil biology with a special emphasis on potential pharmacotherapeutic targets. The review also summarizes promising eosinophil-targeting agents, along with their mechanisms and rationale for use, including those in developmental pipeline, in clinical trials, or approved for other respiratory disorders.