Own price elasticities of the demand for sugar-sweetened beverages in Bangladesh.

BACKGROUND: Consumption of sugar-sweetened beverages (SSB) is a major global public health problem. Increasing the price of SSBs through taxation is an effective tool to reduce SSB consumption. Price-elasticity estimates are useful in measuring the effect of taxation on consumption. We estimated the own price elasticities of demand for SSBs in Bangladesh, which will inform how SSB taxes could affect behaviour. METHODS: We used Household Income and Expenditure Survey (HIES) 2016 data, which is a nationally representative dataset at the household level across the country and is conducted using stratified random sampling method. Deaton's method was used to estimate the price elasticities for SSBs in Bangladesh. RESULTS: We found that the own price elasticity for SSBs varied between - 0.53% to -1.17% by types of SSBs in Bangladesh. The price elasticity for soft drinks was - 1.17, indicating that if the price of soft drinks increases by 10% via taxes, the quantity consumed of these beverages would reduce by 11.7%. CONCLUSION: This is the first study that estimates the own price elasticities of demand for SSBs in Bangladesh. Our results suggest to raise SSB prices through increased taxation in order to reduce SSB consumption and ensure public health gains in Bangladesh.

Electronic aggregated data collection on cervical cancer screening in Bangladesh since 2014: what the data tells us?

INTRODUCTION: To reduce the high prevalence of cervical cancers among the Bangladeshi women, the Government of Bangladesh established a national cervical cancer screening programme in 2005 for women aged 30 to 60 years. The District Health Information System Version 2 (DHIS2) based electronic aggregated data collection system is used since the year 2013. This study summarises data from the year 2014 to 2022 to assess the effectiveness of the electronic data collection system in understanding the outcome of the screening programme. METHODS: This is a descriptive study based on secondary data extracted in MS Excel from the DHIS2-based electronic repository of the national cervical cancer screening programme of Bangladesh. The respondents were women aged 30-60 years, screened for cervical cancer using VIA (Visual Inspection of cervix with Acetic acid) method in 465 government health facilities. The data were collected on the participants' residential location, month and year of screening, name and type of health facilities performing VIA, and VIA screening results. RESULTS: The national screening programme reported a total 3.36 million VIA tests from 465 government hospitals in 8 years (2014 to 2022). The national average VIA-positivity rate was 3.6%, which varied from 1.4 to 9.5% among the districts. This national screening programme witnessed an exponential growth, year after year, with 83.3% increase in VIA test from 2014 to 2022. The primary and the secondary care hospitals were the highest collective contributors of VIA tests (86.2%) and positive cases (77.8%). The VIA-positivity rates in different hospital types varied widely, 7.0% in the medical university hospital, 5.7% in the medical college hospitals, 3.9% in the district/general hospitals, and 3.0% in the upazila health complexes. CONCLUSIONS: A national cervical cancer screening programme using VIA method and a DHIS2-based electronic data collection backbone, is effective, sustainable, and useful to understand the screening coverage, VIA positivity rate and geographic distribution of the participants and case load to initiate policy recommendations and actions. Decentralization of the screening programme and more efforts at the primary and secondary care level is required to increase screening performances.

Assessment of the botanical origin of Saudi Arabian honey samples to identify pollen with chromatographic tools and packing and storage.

The increasing demand for honey purification and authentication necessitates the global utilization of advanced processing tools. Common honey processing techniques, such as chromatography, are commonly used to assess the quality and quantity of valuable honey. In this study, 15 honey samples were authenticated using HPLC and GC-MS chromatographic methods to analyze their pollen spectrum. Various monofloral honey samples were collected, including Acacia, Hypoestes, Lavandula, Tamarix, Trifolium, and Ziziphus species, based on accurate identification by apiarists in 2023 from the Kingdom of Saudi Arabia. Honey analysis revealed the extraction of pollen from 20 different honeybee floral species. Pollen identified from honey samples using advanced chromatographic tools revealed dominant vegetation resources: Ziziphus species (23%), Acacia species (25%), Tamarix species (34%), Lavandula species (26%), Hypoestes species (34%), and Trifolium species (31%). This study uses HPLC to extract phenolic compounds, revealing dominant protocatechuic acid (4.71 mg g-1), and GC-MS to analyze organic compounds in honey pollen. Specifically, 2-dodecanone was detected with a retention time of 7.34 min. The utilization of chromatographic tools in assessing honey samples for pollen identification provides a reliable and efficient method for determining their botanical origins, thereby contributing to the quality control and authentication of honey products.

Factors contributing to antibiotic misuse among parents of school-going children in Dhaka City, Bangladesh.

Antimicrobial resistance (AMR) is a pressing global health concern, especially in resource-constrained countries, such as Bangladesh. This study aimed to identify the factors contributing to antibiotic misuse by assessing knowledge, attitude, and practice (KAP). A cross-sectional study was conducted from August 20 to August 30, 2022, among 704 parents of school-going children in Dhaka South City. Descriptive statistics were used to analyze the KAP, and multivariate models, including linear and ordinal logistic regression, were used to explore the associations between these factors. The findings revealed that approximately 22% of the participants were male and 78% were female. Most parents (58%) had completed higher secondary education. Approximately 45% of the respondents demonstrated moderate knowledge, 53% had uncertain attitudes, and 64% exhibited antibiotic misuse. Factors such as parental age, education level, employment status, income, child's age, and family type significantly influenced KAP. These findings emphasize the importance of targeted education and awareness initiatives to enhance knowledge and responsible antibiotic use among parents, contributing to global efforts against antibiotic resistance. The government should enforce laws and regulations regarding the misuse of antibiotics.

Navigating Sustainability through Greenhouse Gas Emission Inventory: ESG Practices and Energy Shift in Bangladesh's Textile and Readymade Garment Industries.

As the world's demand for textiles and clothing rapidly increases, this industry's greenhouse gas (GHG) emissions are becoming a major environmental concern. Bangladesh, a key player in the global textile supply chain and one of the top producers, contributes significantly to these emissions. However, accessible data on activity and GHG emissions, crucial for researchers, the private sector, and policymakers in decision-making, is scarce. To address this gap, this study combines a detailed field survey with expert interviews to establish a comprehensive emission inventory. This inventory aims to identify hotspots and facilitate the adoption of effective mitigation strategies. Focusing on a prominent industrial zone's textile and readymade garments (RMG) industries, the research employs a mix of top-down and bottom-up approaches and follows the IPCC guidelines to develop a GHG emission inventory for 2022. The study evaluates various emission sources, including scope 1 (onsite fuel combustions), scope 2 (grid electricity usage), and scope 3 (waste and wastewater treatment). In the total emissions (6043.5 Gg CO2eq.), textile and RMG industries contribute 67.8% and 32.2%, respectively, with scope 1 emissions dominating at 85%. Notably, scope 2 emissions exhibit significant uncertainty (-10.4% to +11.9%), largely due to variations in national grid emission factors. This study forecasts GHG emissions until 2030, considering current trends (26 thousand Gg CO2 eq.). It also explores various energy mix scenarios, factoring in the depletion of existing natural gas reserves (ranging from 8 thousand to 33 thousand Gg CO2 eq.). This study delves into the impact of the Environmental, Social, and Governance (ESG) system on industries' GHG emissions. Besides improving worldwide emission databases and identifying hotspots, this research aims to promote a sustainable transition in both Bangladesh and other developing textile manufacturing nations across the globe.

Alginate-Based Encapsulation Fabrication Technique for Drug Delivery: An Updated Review of Particle Type, Formulation Technique, Pharmaceutical Ingredient, and Targeted Delivery System.

Alginate is a natural biopolymer widely studied for pharmaceutical applications due to its biocompatibility, low toxicity, and mild gelation abilities. This review summarizes recent advances in alginate-based encapsulation systems for targeted drug delivery. Alginate formulations like microparticles, nanoparticles, microgels, and composites fabricated by methods including ionic gelation, emulsification, spray drying, and freeze drying enable tailored drug loading, enhanced stability, and sustained release kinetics. Alginate microspheres prepared by spray drying or ionic gelation provide gastric protection and colon-targeted release of orally delivered drugs. Alginate nanoparticles exhibit enhanced cellular uptake and tumor-targeting capabilities through the enhanced permeation and retention effect. Crosslinked alginate microgels allow high drug loading and controlled release profiles. Composite alginate gels with cellulose, chitosan, or inorganic nanomaterials display improved mechanical properties, mucoadhesion, and tunable release kinetics. Alginate-based wound dressings containing antimicrobial nanoparticles promote healing of burns and chronic wounds through sustained topical delivery. Although alginate is well-established as a pharmaceutical excipient, more extensive in vivo testing is needed to assess clinical safety and efficacy of emerging formulations prior to human trials. Future opportunities include engineered systems combining stimuli-responsiveness, active targeting, and diagnostic capabilities. In summary, this review discusses recent advances in alginate encapsulation techniques for oral, transdermal, and intravenous delivery, with an emphasis on approaches enabling targeted and sustained drug release for enhanced therapeutic outcomes.

Prevalence, antibiotic resistance pattern for bacteriuria from patients with urinary tract infections.

Background and Aims: Antibiotic resistance presents a significant global public health challenge, particularly for urinary tract infections (UTIs), and is notably severe in developing countries. Surveillance of the antimicrobial susceptibility patterns of UTI-causing bacteria is crucial for effective treatment selection. This study aimed to analyze these patterns in bacteria isolated from the urine samples of patients at Mughda Medical College Hospital, Dhaka, Bangladesh. Methods: A retrospective study (January 2019 to December 2020) at Mugdha Medical College and Hospital, Dhaka, examined clinical and laboratory data from patients with positive urine cultures (≥105 CFU/mL). The study classified patients into four age groups: children (1-<18 years), young adults (18-<33 years), middle-aged adults (33-50 years), and old adults (>50 years). The standard Kirby-Bauer method was used to assess antibiotic sensitivity to 28 common antibiotics. Results: Among 243 positive urine cultures in both community- and hospital-acquired UTIs, Escherichia coli was the most common uropathogen (65.84%), followed by Klebsiella spp. (12.34%), Enterococcus spp. (8.23%), and other types of bacteria. Conclusion: Old adults are particularly vulnerable to UTIs, with E. coli being the predominant causative agent in the study region. The observed antimicrobial resistance patterns underscore the necessity of judicious antibiotic selection to effectively treat UTIs across different age groups.

Current strategies to address data scarcity in artificial intelligence-based drug discovery: A comprehensive review.

Artificial intelligence (AI) has played a vital role in computer-aided drug design (CADD). This development has been further accelerated with the increasing use of machine learning (ML), mainly deep learning (DL), and computing hardware and software advancements. As a result, initial doubts about the application of AI in drug discovery have been dispelled, leading to significant benefits in medicinal chemistry. At the same time, it is crucial to recognize that AI is still in its infancy and faces a few limitations that need to be addressed to harness its full potential in drug discovery. Some notable limitations are insufficient, unlabeled, and non-uniform data, the resemblance of some AI-generated molecules with existing molecules, unavailability of inadequate benchmarks, intellectual property rights (IPRs) related hurdles in data sharing, poor understanding of biology, focus on proxy data and ligands, lack of holistic methods to represent input (molecular structures) to prevent pre-processing of input molecules (feature engineering), etc. The major component in AI infrastructure is input data, as most of the successes of AI-driven efforts to improve drug discovery depend on the quality and quantity of data, used to train and test AI algorithms, besides a few other factors. Additionally, data-gulping DL approaches, without sufficient data, may collapse to live up to their promise. Current literature suggests a few methods, to certain extent, effectively handle low data for better output from the AI models in the context of drug discovery. These are transferring learning (TL), active learning (AL), single or one-shot learning (OSL), multi-task learning (MTL), data augmentation (DA), data synthesis (DS), etc. One different method, which enables sharing of proprietary data on a common platform (without compromising data privacy) to train ML model, is federated learning (FL). In this review, we compare and discuss these methods, their recent applications, and limitations while modeling small molecule data to get the improved output of AI methods in drug discovery. Article also sums up some other novel methods to handle inadequate data.

Generative artificial intelligence in drug discovery: basic framework, recent advances, challenges, and opportunities.

There are two main ways to discover or design small drug molecules. The first involves fine-tuning existing molecules or commercially successful drugs through quantitative structure-activity relationships and virtual screening. The second approach involves generating new molecules through de novo drug design or inverse quantitative structure-activity relationship. Both methods aim to get a drug molecule with the best pharmacokinetic and pharmacodynamic profiles. However, bringing a new drug to market is an expensive and time-consuming endeavor, with the average cost being estimated at around $2.5 billion. One of the biggest challenges is screening the vast number of potential drug candidates to find one that is both safe and effective. The development of artificial intelligence in recent years has been phenomenal, ushering in a revolution in many fields. The field of pharmaceutical sciences has also significantly benefited from multiple applications of artificial intelligence, especially drug discovery projects. Artificial intelligence models are finding use in molecular property prediction, molecule generation, virtual screening, synthesis planning, repurposing, among others. Lately, generative artificial intelligence has gained popularity across domains for its ability to generate entirely new data, such as images, sentences, audios, videos, novel chemical molecules, etc. Generative artificial intelligence has also delivered promising results in drug discovery and development. This review article delves into the fundamentals and framework of various generative artificial intelligence models in the context of drug discovery via de novo drug design approach. Various basic and advanced models have been discussed, along with their recent applications. The review also explores recent examples and advances in the generative artificial intelligence approach, as well as the challenges and ongoing efforts to fully harness the potential of generative artificial intelligence in generating novel drug molecules in a faster and more affordable manner. Some clinical-level assets generated form generative artificial intelligence have also been discussed in this review to show the ever-increasing application of artificial intelligence in drug discovery through commercial partnerships.

A dataset of microstructure features of electro-hydrodynamic assisted 5-fluorouracil-grafted alginate microbeads and physicochemical properties for effective colon targeted carriers drug delivery.

5-Fluorouracil (5-FU) has been the primary drug used in chemotherapy for colorectal carcinoma, and localizing the drug would be effective in avoiding its side effects and improving therapeutic outcomes. One approach to achieve this is by encapsulating the drug in microbeads. Alginate microbeads, in particular, exhibit promising pH-sensitive properties, making them an attractive option for colon targeting. Thus, the main aim of this study is to formulate and characterize 5-FU-encapsulated alginate microbeads as a pH-sensitive drug delivery system for controlled release in the gastrointestinal tract. In this study, the alginate microbeads encapsulating 5-FU was manufactured using electrospray methods. This method offers the advantages of promoting the formulation of uniformly small-sized microbeads with improved performance in terms of swelling and diffusion rates. The size and shape of the 5-FU microbeads are 394.23 ± 3.077 µm and have a spherical factor of 0.026 ± 0.022, respectively, which are considered acceptable and indicative of a spherical shape. The microbeads' encapsulation efficiency was found to be 69.65 ± 0.18%, which is considered high in comparison to other literature. The attenuated total reflectance - Fourier transform infrared spectroscopy (ATR-FTIR) data confirmed the complexation of sodium alginate with calcium ions, along with the encapsulation of 5-FU in the microbeads matrix. The 5-FU microbeads displayed pH-dependent swelling, exhibiting less swelling in simulated gastric fluid (SGF) than in simulated intestinal fluid (SIF). Additionally, the release of 5-FU from the microbeads is pH-dependent, with the cumulative percentage drug release being higher in simulated intestinal fluid than in SGF. The data indicate that the 5-FU microbeads can be utilized for the delivery of 5-FU in colon-targeted therapy, potentially leading to improved tumor treatment.

The Fabrication of Polymer-Based Curcumin-Loaded Formulation as a Drug Delivery System: An Updated Review from 2017 to the Present.

Turmeric contains curcumin, a naturally occurring compound with noted anti-inflammatory and antioxidant properties that may help fight cancer. Curcumin is readily available, nontoxic, and inexpensive. At high doses, it has minimal side effects, suggesting it is safe for human use. However, curcumin has extremely poor bioavailability and biodistribution, which further hamper its clinical applications. It is commonly administered through oral and transdermal routes in different forms, where the particle size is one of the most common barriers that decreases its absorption through biological membranes on the targeted sites and limits its clinical effectiveness. There are many studies ongoing to overcome this problem. All of this motivated us to conduct this review that discusses the fabrication of polymer-based curcumin-loaded formulation as an advanced drug delivery system and addresses different approaches to overcoming the existing barriers and improving its bioavailability and biodistribution to enhance the therapeutic effects against cancer and other diseases.

Correction: Shibly et al. Analysis of Cerebral Small Vessel Changes in AD Model Mice. Biomedicines 2023, 11, 50.

In the original publication [...].

Lamiales epidermal dynamics: Unveiling antimicrobial resilience in a structural symphony.

This research on Lamiales epidermal anatomy not only provides in-depth understanding of their structural traits but also highlights the significance of uncovering the inherent antimicrobial resilience embedded within these plants. Such insights hold promise for advancing natural product-based approaches in medicine, potentially contributing to the development of novel antimicrobial agents inspired by Lamiales unique biological defense mechanisms. Scanning microscopic tools were utilized to conduct foliar epidermal anatomy of nine species that belong to seven genera and four families within the Lamiales order, Plantaginaceae, Scrophulariaceae, Verbenaceae, and Lamiaceae. This approach aimed to gather both qualitative and quantitative data, facilitating the assessment of taxonomic microanatomical significance. The shape of epidermal cells and their anticlinal walls; number of epidermal cells, stomata, and trichomes; type of stomata and trichomes; length and width of epidermal cells, trichomes, stomatal pore, guard cells, and subsidiary cells; and stomatal index were determined statistically. Most of the species examined were amphistomatous and showed extensive array of trichomes diversity. The exploration of Lamiales epidermal micromorphology and their antimicrobial potential were significant for their implications in multidisciplinary fields. The pharmacological research to utilize sustainable agricultural practices prompts avenues to strengths of Lamiales order for the development of novel antimicrobial solutions and ecological benefits. RESEARCH HIGHLIGHTS: Diverse trichome morphometry reveals a wide array of trichome structures across Lamiales species. Epidermal microscopic architecture variability of epidermal cell shapes and sizes signifies the interspecies variability. Secondary metabolite localization within microanatomical structures elucidates potential hotspots for antimicrobial compound production.

Numerical analysis and device modelling of a lead-free Sr3PI3/Sr3SbI3 double absorber solar cell for enhanced efficiency.

Halide perovskites are the most promising options for extremely efficient solar absorbers in the field of photovoltaic (PV) technology because of their remarkable optical qualities, increased efficiency, lightweight design, and affordability. This work examines the analysis of a dual-absorber solar device that uses Sr3SbI3 as the bottom absorber layer and Sr3PI3 as the top absorber layer of an inorganic perovskite through the SCAPS-1D platform. The device architecture includes ZnSe as the electron transport layer (ETL), while the active layer consists of Sr3PI3 and Sr3SbI3 with precise bandgap values. The bandgap value of Sr3SbI3 is 1.307 eV and Sr3PI3 is 1.258 eV. By employing double-graded materials of Sr3PI3/Sr3SbI3, the study achieves an optimized efficiency of up to 34.13% with a V OC of 1.09 V, FF of 87.29%, and J SC of 35.61 mA cm-2. The simulation explores the influence of absorber layer thickness, doping level, and defect density on electrical properties like efficiency, short-circuit current, open-circuit voltage, and fill factor. It also examines variations in temperature and assesses series and shunt resistances in addition to electrical factors. The simulation's output offers valuable insights and suggestions for designing and developing double-absorber solar cells.

Scanning electron microscopic investigation on Chicory tribe (Compositae) botanical sources and their antimicrobial potential.

This study discusses the micro-level structural details of Cichorieae pollen sources elucidated by scanning electron microscopy (SEM) and explains their symmetry and morphometry. The in-depth knowledge from the electron ultrastructure of Asteraceae pollen has provided insights into enhanced pollen morphology, and the antimicrobial significance of species under study presents novel avenues for their natural defense mechanisms in the development of antimicrobial agents. In this research, both quantitative and qualitative features of pollen were examined. The pollen grains are prolate-spheroidal and oblate-spheroidal in shape, characterized by a maximum polar diameter of 55.6-61.0 µm and a maximum equatorial distance of 68.3-74.4 µm. SEM reveals various configurations such as echinate perforate-tectate, psilate, and echino-lophate perforate. The Cichorieae species have significant antimicrobial efficacy and are promising sources for the development of novel antimicrobial drugs with potential implications in pharmaceutical and healthcare industries. SEM analysis of Cichorieae pollens has provided remarkable insights into their unique structures, revealing diverse shapes and surface ornamentations, which can be used for accurate Asteraceae species identification. RESEARCH HIGHLIGHTS: SEM provides unique pollen surface structures and patterns of Chicory pollen grains. Chemical composition of Chicory botanical sources provides valuable information on their potential as antimicrobial agents. SEM imaging reveals specialized fenestrate grain structures of taxonomic importance.

Did the COVID-19 pandemic affect levels of burnout, anxiety and depression among doctors and nurses in Bangladesh? A cross-sectional survey study.

INTRODUCTION: COVID-19 has caused severe disruption to clinical services in Bangladesh but the extent of this, and the impact on healthcare professionals is unclear. We aimed to assess the perceived levels of anxiety, depression and burnout among doctors and nurses during COVID-19 pandemic. METHODS: We undertook an online survey using RedCap, directed at doctors and nurses across four institutions in Bangladesh (The Sheikh Russel Gastro Liver Institute & Hospital (SRNGIH), Dhaka Medical College Hospital (DMCH), Mugda Medical College Hospital (MMCH) and M Abdur Rahim Medical College (MARMC) Hospital). We collected information on demographics, awareness of well-being services, COVID-19-related workload, as well as anxiety, depression and burnout using two validated questionnaires: the Hospital Anxiety and Depression Scale (HADS) and the Maslach Burnout Inventory (MBI). RESULTS: Of the 3000 participants approached, we received responses from 2705 (90.2%). There was a statistically significant difference in anxiety, depression and burnout scores across institutions (p<0.01). Anxiety, depression and burnout scores were statistically worse in COVID-19 active staff compared with those not working on COVID-19 activities (p<0.01 for HADS anxiety and depression and MBI emotional exhaustion (EE), depersonalisation (DP) and personal accomplishment (PA)). Over half of the participants exhibited some level of anxiety (SRNGIH: 52.2%; DMCH: 53.9%; MMCH: 61.3%; MARMC: 68%) with a high proportion experiencing depression (SRNGIH: 39.5%; DMCH: 38.7%; MMCH: 53.7%; MARMC: 41.1%). Although mean burnout scores were within the normal range for each institution, a high proportion of staff (almost 20% in some instances) were shown to be classified as experiencing burnout by their EE, DP and PA scores. CONCLUSION: We identified a high prevalence of perceived anxiety, depression and burnout among doctors and nurses during the COVID-19 pandemic. This was worse in staff engaged in COVID-19-related activities. These findings could help healthcare organisations to plan for future similar events.