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Arsenic is a potent environmental toxicant and human carcinogen. Skin lesions are the most common manifestations of chronic exposure to arsenic. Advanced-stage skin lesions, particularly hyperkeratosis have been recognized as precancerous diseases. However, the underlying mechanism of arsenic-induced skin lesions remains unknown. Periostin, a matricellular protein, is implicated in the pathogenesis of many forms of skin lesions. The objective of this study was to examine whether periostin is associated with arsenic-induced skin lesions. A total of 442 individuals from low- (n = 123) and high-arsenic exposure areas (n = 319) in rural Bangladesh were evaluated for the presence of arsenic-induced skin lesions (Yes/No). Participants with skin lesions were further categorized into two groups: early-stage skin lesions (melanosis and keratosis) and advanced-stage skin lesions (hyperkeratosis). Drinking water, hair, and nail arsenic concentrations were considered as the participants’ exposure levels. The higher levels of arsenic and serum periostin were significantly associated with skin lesions. Causal mediation analysis revealed the significant effect of arsenic on skin lesions through the mediator, periostin, suggesting that periostin contributes to the development of skin lesions. When skin lesion was used as a three-category outcome (none, early-stage, and advanced-stage skin lesions), higher serum periostin levels were significantly associated with both early-stage and advanced-stage skin lesions. Median (IQR) periostin levels were progressively increased with the increasing severity of skin lesions. Furthermore, there were general trends in increasing serum type 2 cytokines (IL-4, IL-5, IL-13, and eotaxin) and immunoglobulin E (IgE) levels with the progression of the disease. The median (IQR) of IL-4, IL-5, IL-13, eotaxin, and IgE levels were significantly higher in the early-and advanced-stage skin lesions compared to the group of participants without skin lesions. The results of this study suggest that periostin is implicated in the pathogenesis and progression of arsenic-induced skin lesions through the dysregulation of type 2 immune response.

Arsenic (As) toxicity has caused an environmental tragedy affecting millions of people in the world. Little is known about the toxic effects of As on neurobehavioral and biochemical changes in vivo. Along this line of metal toxicity, co-exposure of lead (Pb) could aggravate the situation in the host. The present study was designed to explore the combined effects of As and Pb on behavioral changes like anxiety, spatial memory and learning impairment, and blood indices related to organ dysfunction. Exposure of mice to As (10 mg/kg body weight), Pb (10 mg/kg body weight), and As + Pb via drinking water significantly decreased the time spent exploring the open arms while it increased the time spent in the closed arms compared to control mice in the elevated plus maze. The mean latency time of the control group to find the platform decreased significantly during the learning for 7 days compared to all three treated groups in the Morris water maze test, and the As-exposed group spent significantly less time in the desired quadrant as compared to the control group in the probe trial. Both metals posed an anxiety-like behavior and deficits in spatial memory and learning, and also altered blood indices related to liver and kidney dysfunction, and a combined exposure of these metals inhibited the individual accumulation of As and Pb. Taken together, these data suggest that As has more toxic effects on neurobehavioral and biochemical changes than Pb, and there may be antagonism in the effects and accumulation between these two toxicants.

In this study, a systematic analysis of fresh and sea water fishes, poultry and their feeds and sediments was conducted using research reactor based neutron activation analysis (NAA) technique with the aim to determine elemental status with special emphasis on arsenic and chromium contamination. Sixty nine samples in different categories (fresh water fishes from three ponds, and corresponding sediments, sea water fishes from Bay of Bengal, chicken from five farms, their feeds) were analyzed through a series of NAA experiments to determine the translocations of toxic elements from feed to fish, sediments and birds and their excretion through litters. The analytical results revealed that some of the locally produced feeds for poultry and fish are highly contaminated with chromium. The flesh of both the fresh water fish and poultry are free from arsenic contamination. The sea fish contains high arsenic concentration. However, the major form of seafood arsenic as arsenobetaine that is completely harmless and its occurrence in seafood presented no human health concerns. The present study will certainly help to the Food Controlling Authority in Bangladesh to produce fish and poultry in a controlled sustainable manner and will also create public awareness.

Arsenic is a potent environmental toxicant and human carcinogen. Skin lesions are the most common manifestations of chronic exposure to arsenic. Advanced-stage skin lesions, particularly hyperkeratosis have been recognized as precancerous diseases. However, the underlying mechanism of arsenic-induced skin lesions remains unknown. Periostin, a matricellular protein, is implicated in the pathogenesis of many forms of skin lesions. The objective of this study was to examine whether periostin is associated with arsenic-induced skin lesions. A total of 442 individuals from low- (n = 123) and high-arsenic exposure areas (n = 319) in rural Bangladesh were evaluated for the presence of arsenic-induced skin lesions (Yes/No). Participants with skin lesions were further categorized into two groups: early-stage skin lesions (melanosis and keratosis) and advanced-stage skin lesions (hyperkeratosis). Drinking water, hair, and nail arsenic concentrations were considered as the participants’ exposure levels. The higher levels of arsenic and serum periostin were significantly associated with skin lesions. Causal mediation analysis revealed the significant effect of arsenic on skin lesions through the mediator, periostin, suggesting that periostin contributes to the development of skin lesions. When skin lesion was used as a three-category outcome (none, early-stage, and advanced-stage skin lesions), higher serum periostin levels were significantly associated with both early-stage and advanced-stage skin lesions. Median (IQR) periostin levels were progressively increased with the increasing severity of skin lesions. Furthermore, there were general trends in increasing serum type 2 cytokines (IL-4, IL-5, IL-13, and eotaxin) and immunoglobulin E (IgE) levels with the progression of the disease. The median (IQR) of IL-4, IL-5, IL-13, eotaxin, and IgE levels were significantly higher in the early-and advanced-stage skin lesions compared to the group of participants without skin lesions. The results of this study suggest that periostin is implicated in the pathogenesis and progression of arsenic-induced skin lesions through the dysregulation of type 2 immune response.

The Recency, Frequency, and Monetary model, also known as the RFM model, is a popular and widely used business model for determining beneficial client segments and analyzing profit. It is also recommended and frequently used in superstores to identify customer segments and increase profit margins. Later, the Length, Recency, Frequency, and Monetary model, also known as the LRFM model, was introduced as an improved version of the RFM model to identify more relevant and exact consumer groups for profit maximization. Superstores have a varying number of different products. In RFM and LRFM models, the relationship between profit and purchased quantity has never been investigated. Therefore, this paper proposed an efficient customer segmentation model, namely LRFMV (Length, Recency, Frequency, Monetary and Volume) and studied the profit-quantity relationship. A new dimension V (volume) has been added to the existing LRFM model to show a direct profit-quantity relationship in customer segmentation. The V stands for volume, which is derived by calculating the average number of products purchased by a frequent superstore client in a single day. The data obtained from feature extraction of the LRMFV model is then clustered by using conventional K-means, K-Medoids, and Mini Batch K-means methods. The results obtained from the three algorithms are compared, and the K-means algorithm is chosen for the superstore dataset of the proposed LRFMV model. All clusters created using these three algorithms are evaluated in the LRFMV model, and a close relationship between profit and volume is observed. A clear profit-quantity relationship of items has yet not been seen in any prior study on the RFM and LRFM models. Grouping customers aiming at profit maximization existed previously, but there was no clear and direct depiction of profit and quantity of sold items. This study applied unsupervised machine learning to investigate the patterns, trends, and correlations between volume and profit. The traits of all the clusters are analyzed by the Customer-Classification Matrix. The LRFMV values, larger or less than the overall average for each cluster, are identified as their traits. The performance of the proposed LRFMV model is compared with the legacy RFM and LRFM customer segmentation models. The outcome shows that the LRFMV model creates precise customer segments with the same number of customers while maintaining a greater profit.

Cerebrovascular remodeling driven by subtle molecular changes starts early in the asymptomatic stage of Alzheimer's disease (AD). Despite progress in human vascular imaging and tissue analysis, there is limited data on the early features of small vessel reorganization, particularly in the context of cell-specific molecular drivers. This is largely because of the invasive nature of the tools for direct cellular observation and analysis. Since early detection is key, histopathology falls short with end-point data from people that died in late stages of the disease. This is a critical knowledge gap, because the early vascular processes are thought to be strongly correlated with health outcomes, tipping the scales from mild cognitive impairment to AD. To meet these translational challenges, we performed near life-span two-photon imaging and MRI of the cerebrovascular tree in a mouse model of amyloidosis. We identified precisely when subtle abnormalities in vessel tortuosity and red blood cell velocity first emerge in the context of differential amyloid accumulation in vessels walls and tissues. We then isolated the brain vessels for transcriptional analysis at this flagship timepoint and performed cross-species analysis linking changes in vascular cells to genes and pathways common to both mice and humans. Importantly, using 7T MRI of aging humans, we directly associated vascular remodeling trajectories of mice and humans and identified a remarkably analogous tortuosity course in the smallest brain vessels. Our integrated framework across scales and species advances neuroimaging biomarker understanding and uncovers early mechanistic routs of dysfunctional angiogenesis and actin-mediated contractility.

Extracellular vesicles (EVs) are critical mediators of intercellular communication, carrying molecular cargos such as small noncoding RNAs (ncRNAs) that reflect the physiological and pathological state of their cells of origin. However, studying brain-derived EVs has been challenging due to the blood-brain barrier. Here, we optimized and validated an open-flow microdialysis (OFM) protocol for sampling EVs directly from brain interstitial fluid (ISF) in wild-type and APP/PS1 transgenic mice. validation using plasma EVs demonstrated that OFM effectively captures the full EV population. cerebral OFM (cOFM) enabled successful collection of brain ISF EVs, which were characterized by nanoparticle tracking analysis (NTA), electron microscopy, and western blotting, confirming their similarity to EVs isolated directly from brain tissue and plasma. Identification of small ncRNA cargos revealed that EVs sampled from brain ISF by cOFM were enriched in brain-specific signatures, many of which are associated with neuronal cell populations and biological functions. Furthermore, we observed a unique small ncRNA signature from the brain ISF EVs in the Alzheimer's disease preclinical model compared to wild-type mice. These small ncRNAs were associated with genes considered important in biological functions associated with neurodegeneration. Our findings demonstrate that cOFM is a powerful tool for sampling of brain EVs and highlight the unique molecular landscape of ISF EV small ncRNA cargos. This study offers new opportunities for biomarker discovery and mechanistic insights into neurodegenerative diseases, such as Alzheimer's disease.

Over the past two years, e-banking services became very popular and safe transaction processes in the context of COVID-19 in Bangladesh. The purpose of this study is to analyze how the pandemic has affected Bangladesh's e-banking system. Using stratified random sampling in a randomized block design, a questionnaire was developed that registered participants' responses on a five-point Likert scale to examine the current state of e-banking during the COVID-19 pandemic (January-February 2022). Survey response data from 200 respondents in the commercial port city of Chattogram, Bangladesh, were delivered and returned via e-mail and hand-to-hand delivery, to enable the researcher to learn users' opinions and e-banking satisfaction levels. To test the hypotheses, the study applied the Kolmogorov-Smirnov test, the Shapiro-Wilk test, Spearman's rho correlation coefficient, the Mann-Whitney U test, and the Kruskal-Wallis H test. The study found that e-banking infrastructure facility, customer e-banking awareness, and the e-banking security service facility were important determinants in increasing bank e-service quality. The e-banking infrastructure and security services facility impressed younger users more than older customers (mean performance: 3.21 and 2.85 vs. 2.48 and 2.16, respectively). Educational qualifications did not affect perceptions of bank e-service quality, the e-banking infrastructure facility, customer e-banking professional knowledge, customer e-banking awareness, and the e-banking security service facility. Customers reported more fascination with private banks than with government-owned banks regarding bank e-service quality, e-banking infrastructure facilities, and customer e-banking awareness (mean performance: 3.51, 3.17, and 4.19 vs. 2.97, 2.29, and 3.65, respectively). Moreover, income level affected customers' e-banking professional knowledge.

Corporate governance has been widely debated for over a decade with the collapse of the financial and capital market under the prejudicial roles of regulatory bodies. Therefore, the study examined the impact of corporate governance on firm value in Bangladesh. A total of 63 DSE-listed companies from 2005 to 2019 consisting of 8,505 observations on an average of 15 years were chosen. The subsequent tests for the given data were conducted to identify the appropriate panel data analysis method for adjusted diagnostic problems. In the specific panel data, the Panel Corrected Standard Error (PCSE) was utilised following the application of the random effects method to control econometric limitations. It was revealed that corporate governance lowered firm value when the board structure was familially and politically affiliated and led by CEO-duality. Moreover, the inclusion of dynamic professionals and independent members in the board structure increased the firm value. The use of the corporate governance code was proven to be highly challenging due to the participation of political and family leaders in corporate firms. Additionally, proper law enforcement was required to ensure transparency and accountability, thus reflecting firm value. As previous studies on corporate governance were conducted on a small scale and partial to the context of developing countries, this paper contributes a novel value in identifying and resolving the corporate governance crisis by reforming the board structure with diverse and professional directors. The regulatory bodies require improvement by including autonomous professional and independent members to exercise the corporate governance code.

The Recency, Frequency, and Monetary model, also known as the RFM model, is a popular and widely used business model for determining beneficial client segments and analyzing profit. It is also recommended and frequently used in superstores to identify customer segments and increase profit margins. Later, the Length, Recency, Frequency, and Monetary model, also known as the LRFM model, was introduced as an improved version of the RFM model to identify more relevant and exact consumer groups for profit maximization. Superstores have a varying number of different products. In RFM and LRFM models, the relationship between profit and purchased quantity has never been investigated. Therefore, this paper proposed an efficient customer segmentation model, namely LRFMV (Length, Recency, Frequency, Monetary and Volume) and studied the profit-quantity relationship. A new dimension V (volume) has been added to the existing LRFM model to show a direct profit-quantity relationship in customer segmentation. The V stands for volume, which is derived by calculating the average number of products purchased by a frequent superstore client in a single day. The data obtained from feature extraction of the LRMFV model is then clustered by using conventional K-means, K-Medoids, and Mini Batch K-means methods. The results obtained from the three algorithms are compared, and the K-means algorithm is chosen for the superstore dataset of the proposed LRFMV model. All clusters created using these three algorithms are evaluated in the LRFMV model, and a close relationship between profit and volume is observed. A clear profit-quantity relationship of items has yet not been seen in any prior study on the RFM and LRFM models. Grouping customers aiming at profit maximization existed previously, but there was no clear and direct depiction of profit and quantity of sold items. This study applied unsupervised machine learning to investigate the patterns, trends, and correlations between volume and profit. The traits of all the clusters are analyzed by the Customer-Classification Matrix. The LRFMV values, larger or less than the overall average for each cluster, are identified as their traits. The performance of the proposed LRFMV model is compared with the legacy RFM and LRFM customer segmentation models. The outcome shows that the LRFMV model creates precise customer segments with the same number of customers while maintaining a greater profit. Author summary Why was this study done? Superstore business has been booming in the last decades. In the FY-2017, retail revenue of the top 250 superstores was 4,530,059 million USD which achieved 5.7% economic growth [1]. The Length, Recency, Frequency, and Monetary model, also known as the LRFM model, was introduced as an improved version of the RFM model to identify more relevant and exact consumer groups for profit maximization. However, there exists a substantial association between the purchase quantity and revenue generation that had been overlooked in earlier models. In this research, we introduced the LRFMV model, an improved version of existing business models for superstores, to further assess how much revenue boost and marketing strategy can be developed for the superstore industry and contribute to both technical sectors and the business world. In this research, we searched for a new way to utilize the segmentation model based on the scoring procedure and encountered how a business based matrix can employ them to have a substantial influence on the existing collaborative business and technology sector. What did the researchers do and find? We proposed an efficient customer segmentation model, LRFMV and tried to observe the profit-quantity relationship. A new dimension V (volume) has been added to the existing LRFM model in order to show a direct profit-quantity relationship. Here, the V stands for volume, which was derived by calculating the average number of products purchased by a frequent superstore client in a single day. To get the final average as volume in a specified time frame, the previously found average amount was divided by total days in a limited period of time of visitation of that customer. Quantity of purchased goods refers to the average amount of procured product by repeatedly going customers. Superstores have a varying number of different products with the record of being bought in different quantities multiple times on the same day by a specific customer. In RFM and LRFM models, the relationship between profit and purchased quantity and how they can contribute to an effective customer behavioral analysis was not investigated and evaluated. What do these findings mean? It is visible that a large volume of purchased products positively influences the profit maximization of a superstore. The establishment of the proposed model will assist superstores in generating more profit and performing comprehensive business analysis by helping to find the most profitable group of customers.