An interactive national digital surveillance system to fight against COVID-19 in Bangladesh.

Background: COVID-19 has affected many people globally, including in Bangladesh. Due to a lack of preparedness and resources, Bangladesh has experienced a catastrophic health crisis, and the devastation caused by this deadly virus has not yet been halted. Hence, precise and rapid diagnostics and infection tracing are essential for managing the condition and limiting its spread. The conventional screening procedure, such as reverse transcription polymerase chain reaction (RT-PCR), is not available in most rural areas and is time-consuming. Therefore, a data-driven intelligent surveillance system can be advantageous for rapid COVID-19 screening and risk estimation. Objectives: This study describes the design, development, implementation, and characteristics of a nationwide web-based surveillance system for educating, screening, and tracking COVID-19 at the community level in Bangladesh. Methods: The system consists of a mobile phone application and a cloud server. The data is collected by community health professionals via home visits or telephone calls and analyzed using rule-based artificial intelligence (AI). Depending on the results of the screening procedure, a further decision is made regarding the patient. This digital surveillance system in Bangladesh provides a platform to support government and non-government organizations, including health workers and healthcare facilities, in identifying patients at risk of COVID-19. It refers people to the nearest government healthcare facility, collecting and testing samples, tracking and tracing positive cases, following up with patients, and documenting patient outcomes. Results: This study began in April 2020, and the results are provided in this paper till December 2022. The system has successfully completed 1,980,323 screenings. Our rule-based AI model categorized them into five separate risk groups based on the acquired patient information. According to the data, around 51% of the overall screened populations are safe, 35% are low risk, 9% are high risk, 4% are mid risk, and the remaining 1% is very high risk. The dashboard integrates all collected data from around the nation onto a single platform. Conclusion: This screening can help the symptomatic patient take immediate action, such as isolation or hospitalization, depending on the severity. This surveillance system can also be utilized for risk mapping, planning, and allocating health resources to more vulnerable areas to reduce the virus's severity.

Mechanosensitive expression of the mesenchymal subtype marker connective tissue growth factor in glioblastoma.

Mechanical forces created by the extracellular environment regulate biochemical signals that modulate the inter-related cellular phenotypes of morphology, proliferation, and migration. A stiff microenvironment induces glioblastoma (GBM) cells to develop prominent actin stress fibres, take on a spread morphology and adopt trapezoid shapes, when cultured in 2D, which are phenotypes characteristic of a mesenchymal cell program. The mesenchymal subtype is the most aggressive among the molecular GBM subtypes. Recurrent GBM have been reported to transition to mesenchymal. We therefore sought to test the hypothesis that stiffer microenvironments-such as those found in different brain anatomical structures and induced following treatment-contribute to the expression of markers characterising the mesenchymal subtype. We cultured primary patient-derived cell lines that reflect the three common GBM subtypes (mesenchymal, proneural and classical) on polyacrylamide (PA) hydrogels with controlled stiffnesses spanning the healthy and pathological tissue range. We then assessed the canonical mesenchymal markers Connective Tissue Growth Factor (CTGF) and yes-associated protein (YAP)/transcriptional co-activator with PDZ-binding motif (TAZ) expression, via immunofluorescence. Replating techniques and drug-mediated manipulation of the actin cytoskeleton were utilised to ascertain the response of the cells to differing mechanical environments. We demonstrate that CTGF is induced rapidly following adhesion to a rigid substrate and is independent of actin filament formation. Collectively, our data suggest that microenvironmental rigidity can stimulate expression of mesenchymal-associated molecules in GBM.

BCI-Based Consumers' Choice Prediction From EEG Signals: An Intelligent Neuromarketing Framework.

Neuromarketing relies on Brain Computer Interface (BCI) technology to gain insight into how customers react to marketing stimuli. Marketers spend about $750 billion annually on traditional marketing camping. They use traditional marketing research procedures such as Personal Depth Interviews, Surveys, Focused Group Discussions, and so on, which are frequently criticized for failing to extract true consumer preferences. On the other hand, Neuromarketing promises to overcome such constraints. This work proposes a machine learning framework for predicting consumers' purchase intention (PI) and affective attitude (AA) from analyzing EEG signals. In this work, EEG signals are collected from 20 healthy participants while administering three advertising stimuli settings: product, endorsement, and promotion. After preprocessing, features are extracted in three domains (time, frequency, and time-frequency). Then, after selecting features using wrapper-based methods Recursive Feature Elimination, Support Vector Machine is used for categorizing positive and negative (AA and PI). The experimental results show that proposed framework achieves an accuracy of 84 and 87.00% for PI and AA ensuring the simulation of real-life results. In addition, AA and PI signals show N200 and N400 components when people tend to take decision after visualizing static advertisement. Moreover, negative AA signals shows more dispersion than positive AA signals. Furthermore, this work paves the way for implementing such a neuromarketing framework using consumer-grade EEG devices in a real-life setting. Therefore, it is evident that BCI-based neuromarketing technology can help brands and businesses effectively predict future consumer preferences. Hence, EEG-based neuromarketing technologies can assist brands and enterprizes in accurately forecasting future consumer preferences.

An intelligent neuromarketing system for predicting consumers' future choice from electroencephalography signals.

Neuromarketing utilizes Brain-Computer Interface (BCI) technologies to provide insight into consumers responses on marketing stimuli. In order to achieve insight information, marketers spend about $400 billion annually on marketing, promotion, and advertisement using traditional marketing research tools. In addition, these tools like personal depth interviews, surveys, focus group discussions, etc. are expensive and frequently criticized for failing to extract actual consumer preferences. Neuromarketing, on the other hand, promises to overcome such constraints. In this work, an EEG-based neuromarketing framework is employed for predicting consumer future choice (affective attitude) while they view E-commerce products. After preprocessing, three types of features, namely, time, frequency, and time-frequency domain features are extracted. Then, wrapper-based Support Vector Machine-Recursive Feature Elimination (SVM-RFE) along with correlation bias reduction is used for feature selection. Lastly, we use SVM for categorizing positive affective attitude and negative affective attitude. Experiments show that the frontal cortex achieves the best accuracy of 98.67±2.98, 98±3.22, and 98.67±3.52 for 5-fold, 10-fold, and leave-one-subject-out (LOSO) respectively. In addition, among all the channels, Fz achieves best accuracy 90±7.81, 90.67±9.53, and 92.67±7.03 for 5-fold, 10-fold, and LOSO respectively. Subsequently, this work opens the door for implementing such a neuromarketing framework using consumer-grade devices in a real-life setting for marketers. As a result, it is evident that EEG-based neuromarketing technologies can assist brands and enterprises in forecasting future consumer preferences accurately. Hence, it will pave the way for the creation of an intelligent marketing assistive system for neuromarketing applications in future.

MarketBrain: An EEG based intelligent consumer preference prediction system.

The traditional marketing research tools (Personal Depth Interview, Surveys, FGD, etc.) are cost-prohibitive and often criticized for not extracting true consumer preferences. Neuromarketing tools promise to overcome such limitations. In this study, we proposed a framework, MarketBrain, to predict consumer preferences. In our experiment, we administered marketing stimuli (five products with endorsements), collected EEG signals by EMOTIV EPOC+, and used signal processing and classification algorithms to develop the prediction system. Wavelet Packet Transform was used to extract frequency bands (δ, θ, α, ß1, ß2, γ) and then statistical features were extracted for classification. Among the classifiers, Support Vector Machine (SVM) achieved the best accuracy (96.01±0.71) using 5-fold cross-validation. Results also suggested that specific target consumers and endorser appearance affect the prediction of the preference. So, it is evident that EEG-based neuromarketing tools can help brands and businesses effectively predict future consumer preferences. Hence, it will lead to the development of an intelligent market driving system for neuromarketing applications.

Educational and Training Interventions Aimed at Healthcare Workers in the Detection and Management of People With Mental Health Conditions in South and South-East Asia: A Systematic Review.

Background: To bridge significant mental health treatment gaps, it is essential that the healthcare workforce is able to detect and manage mental health conditions. We aim to synthesise evidence of effective educational and training interventions aimed at healthcare workers to increase their ability to detect and manage mental health conditions in South and South-East Asia. Methods: Systematic review of six electronic academic databases from January 2000 to August 2020 was performed. All primary research studies were eligible if conducted among healthcare workers in South and South-East Asia and reported education and training interventions to improve detection and management of mental health conditions. Quality of studies were assessed using Modified Cochrane Collaboration, ROBINS-I, and Mixed Methods Appraisal Tools and data synthesised by narrative synthesis. Results are reported according to Preferred Reporting Items for Systematic Reviews and Meta-analysis guidelines. A review protocol was registered with the PROSPERO database (CRD42020203955). Findings: We included 48 of 3,654 screened articles. Thirty-six reported improvements in knowledge and skills in the detection and management of mental health conditions. Training was predominantly delivered to community and primary care health workers to identify and manage common mental health disorders. Commonly used training included the World Health Organization's mhGAP guidelines (n = 9) and Cognitive Behavioural Therapy (n = 8) and were successfully tailored and delivered to healthcare workers. Digitally delivered training was found to be acceptable and effective. Only one study analysed cost effectiveness. Few targeted severe mental illnesses and upskilling mental health specialists or offered long-term follow-up or supervision. We found 21 studies were appraised as low/moderate and 19 as high/critical risk of bias. Interpretation: In low resource country settings, upskilling and capacity building of primary care and community healthcare workers can lead to better detection and management of people with mental health disorders and help reduce the treatment gap. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42020203955.

Forcing a growth factor response - tissue-stiffness modulation of integrin signaling and crosstalk with growth factor receptors.

Research throughout the 90s established that integrin crosstalk with growth factor receptors stimulates robust growth factor signaling. These insights were derived chiefly from comparing adherent versus suspension cell cultures. Considering the new understanding that mechanosensory inputs tune adhesion signaling, it is now timely to revisit this crosstalk in different mechanical environments. Here, we present a brief historical perspective on integrin signaling against the backdrop of the mechanically diverse extracellular microenvironment, then review the evidence supporting the mechanical regulation of integrin crosstalk with growth factor signaling. We discuss early studies revealing distinct signaling consequences for integrin occupancy (binding to matrix) and aggregation (binding to immobile ligand). We consider how the mechanical environments encountered in vivo intersect with this diverse signaling, focusing on receptor endocytosis. We discuss the implications of mechanically tuned integrin signaling for growth factor signaling, using the epidermal growth factor receptor (EGFR) as an illustrative example. We discuss how the use of rigid tissue culture plastic for cancer drug screening may select agents that lack efficacy in the soft in vivo tissue environment. Tuning of integrin signaling via external mechanical forces in vivo and subsequent effects on growth factor signaling thus has implications for normal cellular physiology and anti-cancer therapies.

Technological advancements and opportunities in Neuromarketing: a systematic review.

Neuromarketing has become an academic and commercial area of interest, as the advancements in neural recording techniques and interpreting algorithms have made it an effective tool for recognizing the unspoken response of consumers to the marketing stimuli. This article presents the very first systematic review of the technological advancements in Neuromarketing field over the last 5 years. For this purpose, authors have selected and reviewed a total of 57 relevant literatures from valid databases which directly contribute to the Neuromarketing field with basic or empirical research findings. This review finds consumer goods as the prevalent marketing stimuli used in both product and promotion forms in these selected literatures. A trend of analyzing frontal and prefrontal alpha band signals is observed among the consumer emotion recognition-based experiments, which corresponds to frontal alpha asymmetry theory. The use of electroencephalogram (EEG) is found favorable by many researchers over functional magnetic resonance imaging (fMRI) in video advertisement-based Neuromarketing experiments, apparently due to its low cost and high time resolution advantages. Physiological response measuring techniques such as eye tracking, skin conductance recording, heart rate monitoring, and facial mapping have also been found in these empirical studies exclusively or in parallel with brain recordings. Alongside traditional filtering methods, independent component analysis (ICA) was found most commonly in artifact removal from neural signal. In consumer response prediction and classification, Artificial Neural Network (ANN), Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) have performed with the highest average accuracy among other machine learning algorithms used in these literatures. The authors hope, this review will assist the future researchers with vital information in the field of Neuromarketing for making novel contributions.