HIV and AIDS in Bangladesh

Bangladesh initiated an early response to the HIV epidemic starting in the mid-1980s. Since then, the res-ponse has been enhanced considerably, and many HIV-prevention interventions among the most at-risk populations and the general youth are being undertaken. Alongside prevention activities, gathering of data has been a key activity fostered by both the Government and individual development partners. This paper reviews available sources of data, including routine surveillance (HIV and behavioural among most at-risk populations), general population surveys, and various research studies with the aim to understand the dynamics of the HIV epidemic in Bangladesh. Available data show that the HIV epidemic is still at relatively low levels and is concentrated mainly among injecting drug users (IDUs) in Dhaka city. In addition, when the passively-reported cases were analyzed, another population group that appears to be especially vulnerable is migrant workers who leave their families and travel abroad for work. However, all sources of data confirm that risk behaviours that make individuals vulnerable to HIV are high—this is apparent within most at-risk populations and the general population (adult males and youth males and females). Based on the current activities and the sources of data, modelling exercises of the future of the HIV epidemic in Dhaka suggest that, if interventions are not enhanced further, Bangladesh is likely to start with an IDU-driven epidemic, similar to other neighbouring countries, which will then move to other population groups, including sex workers, males who have sex with males, clients of sex workers, and ultimately their families. This review reiterates the often repeated message that if Bangladesh wants to be an example of how to avert an HIV epidemic, it needs to act now using evidence-based programming

HIV and AIDS in Bangladesh

Bangladesh initiated an early response to the HIV epidemic starting in the mid-1980s. Since then, the res-ponse has been enhanced considerably, and many HIV-prevention interventions among the most at-risk populations and the general youth are being undertaken. Alongside prevention activities, gathering of data has been a key activity fostered by both the Government and individual development partners. This paper reviews available sources of data, including routine surveillance (HIV and behavioural among most at-risk populations), general population surveys, and various research studies with the aim to understand the dynamics of the HIV epidemic in Bangladesh. Available data show that the HIV epidemic is still at relatively low levels and is concentrated mainly among injecting drug users (IDUs) in Dhaka city. In addition, when the passively-reported cases were analyzed, another population group that appears to be especially vulnerable is migrant workers who leave their families and travel abroad for work. However, all sources of data confirm that risk behaviours that make individuals vulnerable to HIV are high-this is apparent within most at-risk populations and the general population (adult males and youth males and females). Based on the current activities and the sources of data, modelling exercises of the future of the HIV epidemic in Dhaka suggest that, if interventions are not enhanced further, Bangladesh is likely to start with an IDU-driven epidemic, similar to other neighbouring countries, which will then move to other population groups, including sex workers, males who have sex with males, clients of sex workers, and ultimately their families. This review reiterates the often repeated message that if Bangladesh wants to be an example of how to avert an HIV epidemic, it needs to act now using evidence-based programming

A Review of Machine Learning Approaches for the Personalization of Amplification in Hearing Aids

This paper provides a review of various machine learning approaches that have appeared in the literature aimed at individualizing or personalizing the amplification settings of hearing aids. After stating the limitations associated with the current one-size-fits-all settings of hearing aid prescriptions, a spectrum of studies in engineering and hearing science are discussed. These studies involve making adjustments to prescriptive values in order to enable preferred and individualized settings for a hearing aid user in an audio environment of interest to that user. This review gathers, in one place, a comprehensive collection of works that have been conducted thus far with respect to achieving the personalization or individualization of the amplification function of hearing aids. Furthermore, it underscores the impact that machine learning can have on enabling an improved and personalized hearing experience for hearing aid users. This paper concludes by stating the challenges and future research directions in this area

A novel butterfly-shaped core mode-based asymmetric slotted sensor for ultra-high sensitivity in sucrose concentration detection

In this research, we proposed an asymmetric rectangular slotted biosensor using photonic crystal fiber (PCF), which is subject to surface plasmon resonance (SPR). The proposed sensor utilizes only seven air holes, ensuring better fabrication feasibility and their properties are determined through finite element method (FEM)-based numerical analysis. To simplify the manufacturing process and ensure chemical stability, chemically impermeable gold (Au) strips are employed around the perimeter of the structure as the plasmonic material. With effective structural parameters, the maximum amplitude sensitivity for the proposed sensor is 663.13 RIU−1, the maximum wavelength sensitivity is 204,000 nm/RIU, and the maximum wavelength resolution is 4.90 × 10−7 RIU for the y-polarized mode. It also shows a high linearity of 0.9849. For the detection of sucrose content, it has a maximum wavelength sensitivity of 207070.71 nm/RIU and a maximum amplitude sensitivity of 662.80 RIU−1. The newly introduced sensor can detect the refractive index (RI) of various analytes across a broad spectrum, ranging from 1.28 to 1.41. This extensive detection range allows the sensor to precisely measure sucrose concentrations up to 40%. This broad range enables the analysis of various analytes like sucrose, viruses, cancer cells, proteins, and numerous others

Highly sensitive photonic crystal fiber based surface plasmon resonance biosensor for detection of wide range of organic solutions

This study introduces a dual-core photonic crystal fiber incorporating a highly responsive plasmonic refractive index (RI) sensor. The performance of the RI sensor is evaluated based on amplitude sensitivity, wavelength resolution, wavelength sensitivity, and the linearity of the resonance wavelength. Employing the finite element technique (FEM), a numerical analysis of the proposed design is conducted. Results indicate that employing the amplitude interrogation method yields a peak amplitude sensitivity of 605.82 RIU−1 for y-polarization. Furthermore, the wavelength interrogation approach for y-polarized modes demonstrates a maximum wavelength sensitivity of approximately 17,000 nm/RIU and a maximum wavelength resolution of 5.88 × 10−6 RIU. The proposed sensor exhibits a figure of merit of approximately 298 and effectively responds to refractive index variations within the range of 1.28 to 1.40. These promising outcomes, coupled with the broad sensing range, establish the suggested sensor as a promising candidate for the detection of organic chemical solutions

Monkeypox Skin Lesion Detection Using Deep Learning Models: A Feasibility Study

The recent monkeypox outbreak has become a public health concern due to its rapid spread in more than 40 countries outside Africa. Clinical diagnosis of monkeypox in an early stage is challenging due to its similarity with chickenpox and measles. In cases where the confirmatory Polymerase Chain Reaction (PCR) tests are not readily available, computer-assisted detection of monkeypox lesions could be beneficial for surveillance and rapid identification of suspected cases. Deep learning methods have been found effective in the automated detection of skin lesions, provided that sufficient training examples are available. However, as of now, such datasets are not available for the monkeypox disease. In the current study, we first develop the ``Monkeypox Skin Lesion Dataset (MSLD)" consisting skin lesion images of monkeypox, chickenpox, and measles. The images are mainly collected from websites, news portals, and publicly accessible case reports. Data augmentation is used to increase the sample size, and a 3-fold cross-validation experiment is set up. In the next step, several pre-trained deep learning models, namely, VGG-16, ResNet50, and InceptionV3 are employed to classify monkeypox and other diseases. An ensemble of the three models is also developed. ResNet50 achieves the best overall accuracy of $82.96(\pm4.57\%)$, while VGG16 and the ensemble system achieved accuracies of $81.48(\pm6.87\%)$ and $79.26(\pm1.05\%)$, respectively. A prototype web-application is also developed as an online monkeypox screening tool. While the initial results on this limited dataset are promising, a larger demographically diverse dataset is required to further enhance the generalizability of these models.Comment: 4 pages, 6 figures, conferenc

Molecular and Serological Characterization of the SARS-CoV-2 Delta Variant in Bangladesh in 2021

Novel SARS-CoV-2 variants are emerging at an alarming rate. The delta variant and other variants of concern (VoC) carry spike (S)-protein mutations, which have the potential to evade protective immunity, to trigger break-through infections after COVID-19 vaccination, and to propagate future waves of COVID-19 pandemic. To identify SARS CoV-2 variants in Bangladesh, patients who are RT-PCR-positive for COVID-19 infections in Dhaka were screened by a RT-PCR melting curve analysis for spike protein mutations. To assess the anti-SARS CoV-2 antibody responses, the levels of the anti-S -proteins IgA and IgG and the anti-N-protein IgG were measured by ELISA. Of a total of 36 RT-PCR positive samples (75%), 27 were identified as delta variants, with one carrying an additional Q677H mutation and two with single nucleotide substitutions at position 23029 (compared to Wuhan-Hu-1 reference NC 045512) in the genome sequence. Three (8.3%) were identified as beta variants, two (5.5%) were identified as alpha variants, three (8.3%) were identified as having a B.1.1.318 lineage, and one sample was identified as an eta variant (B.1.525) carrying an additional V687L mutation. The trend of higher viral load (lower Cp values) among delta variants than in the alpha and beta variants was of borderline statistical significance (p = 0.045). Prospective studies with larger Bangladeshi cohorts are warranted to confirm the emergence of S-protein mutations and their association with antibody response in natural infection and potential breakthrough in vaccinated subjects

Spike protein mutations and structural insights of pangolin lineage B.1.1.25 with implications for viral pathogenicity and ACE2 binding affinity

Abstract Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the causative agent of COVID -19, is constantly evolving, requiring continuous genomic surveillance. In this study, we used whole-genome sequencing to investigate the genetic epidemiology of SARS-CoV-2 in Bangladesh, with particular emphasis on identifying dominant variants and associated mutations. We used high-throughput next-generation sequencing (NGS) to obtain DNA sequences from COVID-19 patient samples and compared these sequences to the Wuhan SARS-CoV-2 reference genome using the Global Initiative for Sharing All Influenza Data (GISAID). Our phylogenetic and mutational analyzes revealed that the majority (88%) of the samples belonged to the pangolin lineage B.1.1.25, whereas the remaining 11% were assigned to the parental lineage B.1.1. Two main mutations, D614G and P681R, were identified in the spike protein sequences of the samples. The D614G mutation, which is the most common, decreases S1 domain flexibility, whereas the P681R mutation may increase the severity of viral infections by increasing the binding affinity between the spike protein and the ACE2 receptor. We employed molecular modeling techniques, including protein modeling, molecular docking, and quantum mechanics/molecular mechanics (QM/MM) geometry optimization, to build and validate three-dimensional models of the S_D614G-ACE2 and S_P681R-ACE2 complexes from the predominant strains. The description of the binding mode and intermolecular contacts of the referenced systems suggests that the P681R mutation may be associated with increased viral pathogenicity in Bangladeshi patients due to enhanced electrostatic interactions between the mutant spike protein and the human ACE2 receptor, underscoring the importance of continuous genomic surveillance in the fight against COVID -19. Finally, the binding profile of the S_D614G-ACE2 and S_P681R-ACE2 complexes offer valuable insights to deeply understand the binding site characteristics that could help to develop antiviral therapeutics that inhibit protein–protein interactions between SARS-CoV-2 spike protein and human ACE2 receptor

Detection of Anti-Nucleocapsid Antibody in COVID-19 Patients in Bangladesh Is not Correlated with Previous Dengue Infection

Background: The assessment of antibody responses to severe acute respiratory syndrome coronavirus-2 is potentially confounded by exposures to flaviviruses. The aims of the present research were to determine whether anti-dengue antibodies affect the viral load and the detection of anti-coronavirus nucleocapsid (N)-protein antibodies in coronavirus infectious disease 2019 (COVID-19) in Bangladesh. Methods: Viral RNA was evaluated in swab specimens from 115 COVID-19 patients by real-time reverse transcription polymerase chain reaction (rT-PCR). The anti-N-protein antibodies, anti-dengue virus E-protein antibodies and the dengue non-structural protein-1 were determined in serum from 115 COVID-19 patients, 30 acute dengue fever pre-COVID-19 pandemic and nine normal controls by ELISA. Results: The concentrations of viral RNA in the nasopharyngeal; Ct median (95% CI); 22 (21.9–23.3) was significantly higher than viral RNA concentrations in oropharyngeal swabs; and 29 (27–30.5) p < 0.0001. Viral RNA concentrations were not correlated with-dengue IgG levels. The anti-nucleocapsid antibodies were IgA 27% positive and IgG 35% positive at days 1 to 8 post-onset of COVID-19 symptoms versus IgA 0% and IgG 0% in dengue patients, p < 0.0001. The levels of anti- nucleocapsid IgA or IgG versus the levels of anti-dengue IgM or IgG revealed no significant correlations. Conclusions: Viral RNA and anti-nucleocapsid antibodies were detected in COVID-19 patients from dengue-endemic regions of Bangladesh, independently of the dengue IgG levels