Search

Background: To compare the survival rate of paclitaxel plus cisplatin (PC arm), paclitaxel plus gemcitabine (PG arm) and gemcitabine plus cisplatin (GC arm) in chemotherapy patients with non resectable lung cancer. Methods: This was a retrospective observational study to evaluate chemotherapy response among non resectable lung cancer patients with their survival at cancer center CMH, Dhaka since 01 July 2013 to 31 March 2015. One hundred fifty-four (154) non resectable lung cancer patients were randomly divided into three groups, 50 patients in PC arm, 51 patients in PG arm and 53 patients in GC arm. In PC arm paclitaxel 175 mg/m2 (day 1) with cisplatin 75mg/m2 (day 1), in PG arm Paclitaxel 175 mg/m2 (day 1) with gemcitabine 1000 mg/m2 (days 1 and 8) and in GC arm gemcitabine 1000 mg/m2 (days 1 and 8) with cisplatin 100mg/m2 (day 1). Results: Patients characteristics were similar between the three groups. The overall response rate was 40% in the PC arm,43.1% in the PG arm, 43.4% in the GC arm. The median survival time in PC arm was 8.5 months, in PG arm was 8.8 months, in GC arm was 9.2 months. The major side effect was myelosuppression which accounts 71% patients. The average treatment costs were 57% and 30% lower in PC arm as compared with GC and PG arm respectively. Conclusion: The median survival time, disease free survival time and 1-year survival rate in PC, PG, GC arms without significant difference. Treatment were well tolerable; quality of life parameter was mostly similar but paclitaxel with cisplatin was most cost effective than others chemotherapy regimen. [ABSTRACT FROM AUTHOR]

Climatic condition is conducive to cultivate mustard in Bangladesh and there are ample opportunity to improve the present scenario of production, since the demand of oilseeds is high. But economic analysis of production of this crop remains unnoticed most of the time. For assessment of the profitability and resource use efficiency, a field level study was conducted with 100 mustard growers, who were selected purposively and interviewed with pre tested questionnaire from Manikganj district during the period May to August 2019. Applying the Cobb-Douglas production model the results suggested that farmers earned 18577.91 Tk ha-1 by producing 1683.75 kg ha-1 Mustard with the Benefit Cost Ratio (BCR) 1.28. Land preparation cost, seed, human labour, irrigation and fertilizer had a significant positive effect on the yield of Mustard while insecticides had negative insignificant effect. Farmers were inefficient in case of resource use. Mustard cultivation is profitable in Bangladesh and has the potentiality to minimize import cost of oilseeds. Mustard production can be increased further by ensuring adequate supply of labor at peak period with reasonable wage rate, incentive price of produce for farmers, sufficient drainage system after flood, collateral free and easy access to credit, crop insurance to mustard growers. [ABSTRACT FROM AUTHOR]

Natural plant based fibres are being increasingly used in sustainable fibre reinforced composite applications in order to meet the demand of using environmentally friendly materials for composites. Fibre metal laminates (FMLs) are used in aerospace, automobile, marine and civil engineering applications, due to their excellent mechanical behaviors compared to traditional metals and their alloys. This study describes a novel fabrication of jute fibre reinforced aluminum metal laminates, using different jute fibre architectures (plain and twill fabric structures), wherein jute fibres were used in the skins and aluminum in the core layers. Jute fibres and aluminum sheets were chemically treated to enhance the compatibility and interfacial bonding at fibre-metals-matrix interfaces. FMLs were manufactured by hot pressing technique, after the application of wet lay-up process for the resin impregnation and they were further tested under tensile, flexural and impact loading conditions. While comparing results, the twill architecture showed improved tensile and flexural properties compared to plain fabric based FMLs. Chemical treatments on twill jute fibres and metal sheets further exceptionally enhanced the flexural properties (151 MPa flexural strength and 21.3 GPa modulus and they were increased by 186.5 % and 722.7 % respectively compared to the untreated jute fibre counterparts) of the laminates due to a significant improvement in the adhesion between the jute fibre and aluminum sheet after alkali treatment applied. Therefore, with these enhanced properties, jute based FML laminates can be used as sustainable composite materials in many structural applications., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors. Published by Elsevier Ltd.)

Natural-based lignocellulose fibrous materials can be used as a sustainable alternative to conventional fossil-based fibers such as glass fibers, in lightweight fiber-reinforced thermoplastic composites for marine, automotive, aerospace, or other advanced applications. However, one of the main challenges in using natural fiber-based thermoplastic composites is the low mechanical performance of composite structures. This can be improved significantly through the development of an optimized novel fiber architecture with enhanced fiber packing properties, following a low-cost production process. In this context, this study demonstrates a less energy-consuming and cheaper manufacturing process, for developing highly individualized short jute fiber-based dry fiber preform architecture, with an improved fiber packing property. Short jute fibers were chemically treated with alkali and PVA sizing treatments in the processing of new fiber preform architectures, and they were used in manufacturing of ultimate short jute fiber/polypropylene (PP) thermoplastic composites. The newly developed short fiber thermoplastic composites showed a significant improvement in mechanical properties (tensile, flexural, and impact) compared to any other natural fiber architecture-based (woven, knitted, nonwoven, unidirectional, etc.) composites found in the literature. Due to the use of new fiber architecture, the developed composites' fiber content was observed to increase. In addition, the compatibility of jute fibers with the polypropylene matrix was strengthened with the application of chemical treatments on highly individualized jute fibers. These reasons were responsible for the enhancement of mechanical properties of developed composites. Micromechanics of the fibers in composites were evaluated using the modified rule of the mixture and Halpin-Tsai equations for stiffness prediction of the composites in order to develop a theoretical understanding of newly developed composites' mechanics. It is thought that the improved mechanical performance of short jute fiber/PP thermoplastic composites can extend the use of these composites in many load-demanding applications, wherein normally synthetic fiber composites are used., Competing Interests: The authors declare no competing financial interest., (© 2023 The Authors. Published by American Chemical Society.)

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., Competing Interests: FS, MC, KM were employed by CMED Health Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2023 Sarker, Chowdhury, Ratul, Islam and Mamun.)

In the rotational moulding industry, non-used, scrap, and waste purge materials have tremendous potential to be reprocessed and applied in skin-foam-skin sandwich structures to replace and reduce the use of virgin polymers. This approach not only encourages the re-use of these waste materials but also significantly contributes to reduce environmental impacts associated with the use of virgin polymers in this sector. The demand of rotationally moulded sandwich structures is rapidly increasing in automotive, marine, and storage tanks, where investigating their impact and after-impact responses are crucial. Hence, this study investigated the low-velocity impact (LVI) and flexure-after-impact (FAI) responses of rotationally moulded sandwich structures manufactured using reprocessed materials. Results obtained from LVI induced damage at two different incident energy levels (15 J, 30 J), and the residual flexural strength of impacted structures evaluated by three-points bending tests were compared with non-reprocessed sandwich structures (virgin materials). The impact damage progression mechanism was characterized using the X-ray micro-computer-tomography technique. Reprocessed sandwiches demonstrated 91% and 66% post-impact residual strength at 15 J and 30 J respectively, while for non-reprocessed sandwiches, these values were calculated as 93% and 88%. Although reprocessed sandwich structures showed a lower performance over non-reprocessed sandwiches, they have a strong potential to be used in sandwich structures for various applications.

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, F z 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., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Gestational diabetes mellitus (GDM) is a heterogeneous group of metabolic disorder, which result in varying degrees of maternal hyperglycemia and pregnancy associated risk. Glucose intolerance usually returns to normal range within 6 weeks after delivery. This study was undertaken to determine the glycemic status who attended for antenatal care. This study enrolled pregnant women, with their gestational age between 24th - 28th weeks. This analytical cross sectional study was carried out in the department of Biochemistry, BIRDEM General Hospital, Dhaka, Bangladesh from July 2014 to June 2015. Total 135 subjects were selected to evaluate the glycemic status among Bangladeshi pregnant women attending BIRDEM General Hospital. Among them 41 were GDM subjects and 94 were non GDM subjects. The mean fasting plasma glucose values (mmol/L) and 2 hours after 75gm glucose values of GDM were 6.06±1.26 and 9.78±2.74 respectively compared to non GDM patients were 4.82±0.38 and 7.26±0.41 respectively and the mean of HbA₁C (%) of GDM and non GDM were 5.87±0.73 and 5.43±0.31 respectively.

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Mashrur, Rahman, Miya, Vaidyanathan, Anwar, Sarker and Mamun.)

The fiber architecture can significantly influence the rate of impregnation of a resin in making composites and the load-bearing ability of individual fibers on testing of the loading directions. Moreover, achieving the maximum mechanical performance of a natural fiber composite selection of yarn liner density and optimization of fabric structure and further modification of the composites remains a great challenge for the composite research community. In this study, a number of jute-based woven derivatives (plain, 2/1 twill, 3/1 twill, zigzag based on a 2/2 twill, and diamond based on a 2/2 twill) have been constructed from similar linear densities of yarn. The effect of the fabric architecture and further modification of optimized composites by applying γ-radiation is also explained in this study. The experimental results show a 54% increase in tensile strength, a 75% increase in tensile modulus, a 69% increase in flexural strength, a 124% increase in flexural modulus, and 64% increase in impact strength of twill (3/1) structured jute fiber polyester composites in comparison to other plain and twill structured composites. A further mechanical improvement of around 20-30% is possible for the optimized twill structured composites by applying γ-radiation on the composites. An FTIR, TGA, and SEM study confirms the chemical, thermal, and fractographic changes after applying the modification of composites., Competing Interests: The authors declare no competing financial interest., (© 2022 The Authors. Published by American Chemical Society.)

This work developed novel jute-yarn, non-crimp, unidirectional (UD) preforms and their composites, with three different types of warp jute yarns of varying linear densities and twists in the dry UD preforms, in order to present a possible solution to the detrimental effects of higher yarn twists and crimp at the warp-weft yarn interlacements of traditional, woven, preform-based composites on their mechanical properties. In the developed UD preforms, warp jute yarns were placed in parallel by using a wooden picture-frame pin board, with the minimal number of glass weft yarns to avoid crimp at the warp-weft yarns interlacements, which can significantly enhance the load-bearing ability of UD composites compared to traditional, woven, preform composites. It was found that an optimal combination of jute warp yarn linear densities and twists in the UD preforms is important to achieve the best possible mechanical properties of newly developed UD composites, because it encourages a proper polymer-matrix impregnation on jute fibres, leading to excellent fibre-matrix interface bonding. Composites made from the 25 lb/spindle jute warp yarn linear density (UD25) exhibited higher tensile and flexural properties than other UD composites (UD20, UD30). All the UD composites showed a much better performance compared to the traditional woven preform composites (W20), which were obviously related to the higher crimp and yarn interlacements, less load-carrying capacity, and poor fiber-matrix interfaces of W20 composites. UD25 composites exhibited a significant enhancement in tensile modulus by ~232% and strength by ~146%; flexural modulus by 138.5% and strength by 145% compared to W20 composites. This reveals that newly developed, non-crimp, UD preform composites can effectively replace the traditional woven composites in lightweight, load-bearing, complex-shaped composite applications, and hence, this warrants further investigations of the developed composites, especially on long-term and dynamic-loading mechanical characterizations.

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, β 1 , β 2 , 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.

This study presents the mechanical performance enhancements of jute fibre composites, manufactured from two newly developed novel jute fibre unidirectional (UD) preforms, namely, stitching-based and sizing-based examples. To increase the use of jute fibres, which are naturally abundant and inexpensive, and to provide research into the use of mechanically advantageous continuous unidirectional (UD) preforms in composites (which are still limited in use), this study employed polyvinyl alcohol (PVA) sizing and stitching techniques, thus increasing the abilities of jute fibres to withstand higher loads and enabling them to be used for lightweight structural applications. Alkali treatment was used on jute fibres in stitched and sized preforms, and bamboo slices were introduced to the jute preforms to further optimize the mechanical properties. The jute composites exhibited significant mechanical property enhancements, with maximum improvement observed in the case of the PVA-sized alkali-treated specimen, thanks to the excellent compatibility between the sized and alkali-treated jute fibres., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)

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.

Breast cancer among women is gradually increasing in Bangladesh day by day. A number of breast cancer related etiological factors identified as age, reproductive factors, menarche, menopausal status, life style, hormone replacement therapy, genetics and alcohol intake. Obesity is an important factor for developing breast cancer in different countries. Obesity is one of the modifiable factors. The aim of the study was to find out the factors which might be associated with obesity among female breast cancer patients in Bangladesh. It was a case-control study conducted at the Department of Medical Oncology, National Institute of Cancer Research & Hospital (NICRH), Dhaka, Bangladesh from August 2014 to July 2015. Ninety one case and equal numbers of age matched controls were included in the study. The mean age of the case was 42.99 (±9.24) years and that of the control was 44.11±8.97 years. Majority of patients i.e. 59.3% (n=54) in case group was in pre-menopausal state where as 52.7% (n=48) of respondents in control group were in menopausal state. Increased waist to hip ratio (>0.85) was associated with increased risk of breast cancer (OR: 8.1). This was also true for increased BMI of ≥25kg/m² (OR: 4.57), increased waist circumference (OR: 3.52) and ever OCP use (OR: 2.11). However, para >3, education and moderate to heavy work were found to be protective against breast cancer (OR: <1). In clinical setting waist-to-hip ratio, body mass index (BMI) and waist circumference (WC) can be used effectively to identify women with an increased risk of breast cancer.

Natural fibers composites are considered as a sustainable alternative to synthetic composites due to their environmental and economic benefits. However, they suffer from poor mechanical and interfacial properties due to a random fiber orientation and weak fiber-matrix interface. Here we report nanoengineered graphene-based natural jute fiber preforms with a new fiber architecture (NFA) which significantly improves their mechanical properties and performances. Our graphene-based NFA of jute fiber preform enhances the Young modulus of jute-epoxy composites by ∼324% and tensile strength by ∼110% more than untreated jute fiber composites, by arranging fibers in a parallel direction through individualization and nanosurface engineering with graphene derivatives. This could potentially lead to manufacturing of high-performance natural alternatives to synthetic composites in various stiffness-driven applications.

Natural fiber composites are attracting significant interest due to their potential for replacing synthetic composites at lower cost with improved environmental sustainability. However, natural fiber composites suffer from poor mechanical and interfacial properties. Here, we report coating of graphene oxide (GO) and graphene flakes (G) onto natural jute fibers to improve mechanical and interfacial properties. The coating of graphene materials onto jute fibers enhanced interfacial shear strength by ∼236% and tensile strength by ∼96% more than untreated fibers by forming either bonding (GO) or mechanical interlocking (G) between fibers and graphene-based flakes. This could lead to manufacturing of high-performance and environmental friendly natural fiber composites that can potentially replace synthetic composites in numerous applications, such as the automotive industry, naval vessels, household products, and even in the aerospace industry.