Exploring the knowledge and practices on road safety measures among motorbikers in Dhaka, Bangladesh: a cross-sectional study.

INTRODUCTION: Road traffic injuries (RTIs), particularly motorbike injuries, are one of the leading causes of death worldwide and have been a serious concern in low- and middle-income countries like Bangladesh. Therefore, this study aimed to assess the level of knowledge and practices on road safety measures among motorbikers in Dhaka, Bangladesh. METHODS: This cross-sectional study was conducted from January 2022 to December 2022 among 350 motorbikers of Dhaka city via a series of face-to-face interviews. Motorbikers who regularly ride a motorcycle were interviewed about their road safety knowledge and practices through a two-stage cluster sampling technique. Frequency distribution, independent sample t-test and one-way analysis of variance (ANOVA) were performed in data analysis. RESULTS: Out of the 350 motorbikers, only 54.6% had good knowledge and 16.9% had poor knowledge on the signs and safety regulations of roads. Moreover, only 50.6% of respondents followed good practices while 23.4% followed poor practices of road safety measures. One-way ANOVA analysis demonstrates that the average knowledge score was significantly (p<0.05) higher among higher-educated, unmarried and non-smokers. Additionally, higher education level, non-smoking status and being Muslim were significantly (p<0.05) associated with good road safety practices. CONCLUSIONS: The overall good knowledge level and practices of road safety measures among the motorbikers was not satisfactory although the majority of them knew individual signs and regulations. Therefore, this study suggests that education and strict enforcement of traffic rules may increase their knowledge and practice behaviour regarding road safety which in turn would minimise traffic injuries and fatalities.

Emerging Therapeutic Potential of Cannabidiol (CBD) in Neurological Disorders: A Comprehensive Review.

Cannabidiol (CBD), derived from Cannabis sativa, has gained remarkable attention for its potential therapeutic applications. This thorough analysis explores the increasing significance of CBD in treating neurological conditions including epilepsy, multiple sclerosis, Parkinson's disease, and Alzheimer's disease, which present major healthcare concerns on a worldwide scale. Despite the lack of available therapies, CBD has been shown to possess a variety of pharmacological effects in preclinical and clinical studies, making it an intriguing competitor. This review brings together the most recent findings on the endocannabinoid and neurotransmitter systems, as well as anti-inflammatory pathways, that underlie CBD's modes of action. Synthesized efficacy and safety assessments for a range of neurological illnesses are included, covering human trials, in vitro studies, and animal models. The investigation includes how CBD could protect neurons, control neuroinflammation, fend off oxidative stress, and manage neuronal excitability. This study emphasizes existing clinical studies and future possibilities in CBD research, addressing research issues such as regulatory complications and contradicting results, and advocates for further investigation of therapeutic efficacy and ideal dose methodologies. By emphasizing CBD's potential to improve patient well-being, this investigation presents a revised viewpoint on its suitability as a therapeutic intervention for neurological illnesses.

ScribbleDom: using scribble-annotated histology images to identify domains in spatial transcriptomics data.

MOTIVATION: Spatial domain identification is a very important problem in the field of spatial transcriptomics. The state-of-the-art solutions to this problem focus on unsupervised methods, as there is lack of data for a supervised learning formulation. The results obtained from these methods highlight significant opportunities for improvement. RESULTS: In this article, we propose a potential avenue for enhancement through the development of a semi-supervised convolutional neural network based approach. Named "ScribbleDom", our method leverages human expert's input as a form of semi-supervision, thereby seamlessly combines the cognitive abilities of human experts with the computational power of machines. ScribbleDom incorporates a loss function that integrates two crucial components: similarity in gene expression profiles and adherence to the valuable input of a human annotator through scribbles on histology images, providing prior knowledge about spot labels. The spatial continuity of the tissue domains is taken into account by extracting information on the spot microenvironment through convolution filters of varying sizes, in the form of "Inception" blocks. By leveraging this semi-supervised approach, ScribbleDom significantly improves the quality of spatial domains, yielding superior results both quantitatively and qualitatively. Our experiments on several benchmark datasets demonstrate the clear edge of ScribbleDom over state-of-the-art methods-between 1.82% to 169.38% improvements in adjusted Rand index for 9 of the 12 human dorsolateral prefrontal cortex samples, and 15.54% improvement in the melanoma cancer dataset. Notably, when the expert input is absent, ScribbleDom can still operate, in a fully unsupervised manner like the state-of-the-art methods, and produces results that remain competitive. AVAILABILITY AND IMPLEMENTATION: Source code is available at Github (https://github.com/1alnoman/ScribbleDom) and Zenodo (https://zenodo.org/badge/latestdoi/681572669).

Rapid and sensitive detection of aqueous ammonia harnessing nanocomposite functionalized tilted fiber Bragg grating.

A high sensitive aqueous ammonia sensor based on tilted fiber Bragg grating (TFBG) had been reported. The sensors were fabricated by a 10 ° TFBG coated by a membrane receptor named as Polyaniline/Graphene oxide on the surface of the fiber. The correlative concentrations of aqueous ammonia were demodulated by global monitoring of the envelope area of cladding modes in the transmitted spectrum of the TFBG. Tests have shown that the proposed sensor can provide a linear and rapid response of aqueous ammonia within 22 seconds, in a concentration range from 1-12 ppm. Moreover, the limit of detection can even reach 0.08 ppm, through the theoretical analysis of our experimental results. The proposed sensor has good performance, is easy to manufacture and of small size, making it a good choice for real-time, in-situ, label-free detection of aqueous ammonia in the future.

Label-Free DNA Detection Using Etched Tilted Bragg Fiber Grating-Based Biosensor.

A label-free-based fiber optic biosensor based on etched tilted Bragg fiber grating (TFBG) is proposed and practically demonstrated. Conventional phase mask technic has been utilized to inscribe tilted fiber Bragg grating with a tilt angle of 10°, while the etching has been accomplished with hydrofluoric acid. A composite of polyethylenimine (PEI)/poly(acrylic acid) (PAA) has been thermally deposited on the etched TFBG, followed by immobilization of probe DNA (pDNA) on this deposited layer. The hybridization of pDNA with the complementary DNA (cDNA) has been monitored using wavelength-dependent interrogation. The reproducibility of the probes has been demonstrated by fabricating three identical probes and their response has been investigated for cDNA concentration ranging from 0 µM to 3 µM. The maximum sensitivity has been found to be 320 pm/µM, with the detection limit being 0.65 µM. Furthermore, the response of the probes towards non-cDNA has also been investigated in order to establish its specificity.

Twenty-two years of dengue outbreaks in Bangladesh: epidemiology, clinical spectrum, serotypes, and future disease risks.

Dengue is the most rapidly spreading mosquito-borne disease and has become a major public health threat, particularly for tropical and subtropical countries including Bangladesh. This comprehensive review aims to summarize the overall scenario of dengue, including disease burden, clinical spectrum, seroprevalence, circulating serotypes/genotypes, and spatial distribution since the first recorded outbreak in Bangladesh. Since the first recorded outbreak in 2000, dengue epidemiology has shown the typical epidemic pattern with more frequent and bigger outbreaks and gradual geographic expansion to non-endemic regions in Bangladesh. For instance, highly confined Rohingya refugee camps that provide shelters to nearly 1.2 million forcibly displaced vulnerable Myanmar nationals in Cox's Bazar district confronted a massive outbreak in 2022. Recent major outbreaks are found to be associated with the emergence of serotype DENV-3, which was undetected for a long time. Consequently, changes in serotypes might be attributed to increased severity in clinical manifestation in recent years. The existing weak surveillance and risk management systems are inadequate to deal with impending dengue risks. The healthcare system, particularly at the district level, is not prepared to manage impending large-scale dengue outbreaks in Bangladesh. Our findings would contribute to the development of strategies for dengue control and management in Bangladesh as well as other similar settings elsewhere in the world.

Mach-Zehnder interferometer based fiber-optic nitrate sensor.

A biocompatible, reliable and quick responsive fiber-optic sensor based on Mach-Zehnder interferometer (MZI) is demonstrated for nitrate analytes tracing. The sensor was constructed by collapsing the air holes of a short length photonic crystal fiber (PCF) with the single-mode fibers (SMFs) on both ways. The proposed sensor has been coated with a graphene-PVA (polyvinyl alcohol) membrane using the thermal coating technique to make the sensor attractive to the nitrate ions in the aqueous solution. The maximum response is found to be 0.15 pm/ppm on the nitrate measurement scale of 0 ppm to 100 ppm with an average reaction time of ∼10 s. Also, a short length of FBG (fiber Bragg grating) is implanted with SMF to improve the sensing accuracy of the presented sensor.

Detection of SARS-CoV-2 RNA by Reverse Transcription-Polymerase Chain Reaction (RT-PCR) on Self-Collected Nasal Swab Compared With Professionally Collected Nasopharyngeal Swab.

BACKGROUND: Self-collection of nasal swabs for the detection of SARS-CoV-2 RNA by reverse transcription-polymerase chain reaction (RT-PCR) would considerably increase the testing capability and decrease the risk of transmission among healthcare workers (HCW) and the use of personal protective equipment (PPE). OBJECTIVES: This study aimed to evaluate the performance of self-collected nasal swabs compared with professionally collected nasopharyngeal (NP) swabs for detection of SARS-CoV-2 RNA by RT-PCR. MATERIALS AND METHODS: We performed a cross-sectional study where the suspected cases of coronavirus disease 2019 (COVID-19) were instructed about the self-collection of nasal swabs from their mid-turbinate. The results were compared to a nasopharyngeal swab collected by a trained healthcare worker in the same patient at the same sitting. RESULTS: We enrolled 100 participants, of which, 69 (69%) were male and 31 (31%) were female. The median age of the study participant was 36 years. Of the participants, 58 (58%) were symptomatic, and the commonest clinical presentation was cough, which was present in 42 (42%) participants. Out of 100 samples, 31 (31%) professionally collected nasopharyngeal swabs and 28 (28%) self-collected nasal swabs were positive for SARS-CoV-2 by RT-PCR. Out of 31 professionally collected positive samples, three samples were negative in self-collection. Out of 28 self-collected positive samples, no sample was negative in the professional collection. The sensitivity and specificity of self-collected nasal swabs compared to professionally collected nasopharyngeal swabs were 90.32% and 100.00%, respectively. The sensitivity of self-collected nasal was 100% when the cycle threshold (Ct) value of the professionally collected NP swab was less than 30. CONCLUSION: Our study showed that self-collected nasal swabs' sensitivities were similar to professionally collected NP swabs with a high viral load (low Ct value). Hence, this method could be used when the patient is symptomatic and come to the health providers in the early stage of COVID-19 illness.

InP high power monolithically integrated widely tunable laser and SOA array for hybrid integration.

We present a monolithic InP-based photonic integrated circuit (PIC) consisting of a widely tunable laser master oscillator feeding an array of integrated semiconductor optical amplifiers that are interferometrically combined on-chip in a single-mode waveguide. We demonstrate a stable and efficient on-chip coherent beam combination and obtain up to 240 mW average power from the monolithic PIC, with 30-50 kHz Schawlow-Townes linewidths and >180 mW average power across the extended C-band. We also explored hybrid integration of the InP-based laser and amplifier array PIC with a high quality factor silicon nitride microring resonator. We observe lasing based on gain from the interferometrically combined amplifier array in an external cavity formed via feedback from the silicon nitride microresonator chip; this configuration results in narrowing of the Schawlow-Townes linewidth to ∼3 kHz with 37.9 mW average power at the SiN output facet. This work demonstrates a new approach toward high power, narrow linewidth sources that can be integrated with on-chip single-mode waveguide platforms for potential applications in nonlinear integrated photonics.

Deep Learning-Driven Automated Detection of COVID-19 from Radiography Images: a Comparative Analysis.

The COVID-19 pandemic has wreaked havoc on the whole world, taking over half a million lives and capsizing the world economy in unprecedented magnitudes. With the world scampering for a possible vaccine, early detection and containment are the only redress. Existing diagnostic technologies with high accuracy like RT-PCRs are expensive and sophisticated, requiring skilled individuals for specimen collection and screening, resulting in lower outreach. So, methods excluding direct human intervention are much sought after, and artificial intelligence-driven automated diagnosis, especially with radiography images, captured the researchers' interest. This survey marks a detailed inspection of the deep learning-based automated detection of COVID-19 works done to date, a comparison of the available datasets, methodical challenges like imbalanced datasets and others, along with probable solutions with different preprocessing methods, and scopes of future exploration in this arena. We also benchmarked the performance of 315 deep models in diagnosing COVID-19, normal, and pneumonia from X-ray images of a custom dataset created from four others. The dataset is publicly available at https://github.com/rgbnihal2/COVID-19-X-ray-Dataset. Our results show that DenseNet201 model with Quadratic SVM classifier performs the best (accuracy: 98.16%, sensitivity: 98.93%, specificity: 98.77%) and maintains high accuracies in other similar architectures as well. This proves that even though radiography images might not be conclusive for radiologists, but it is so for deep learning algorithms for detecting COVID-19. We hope this extensive review will provide a comprehensive guideline for researchers in this field.

Prediction of Epidemics Trend of COVID-19 in Bangladesh.

Background: Amid a critical and emergent situation like the coronavirus disease (COVID-19) pandemic related to extreme health and economic repercussions, we used and presented the mathematical modeling like susceptible-infectious-recovered (SIR) to have a numerical demonstration that can shed light to decide the fate of the scourge in Bangladesh. To describe the idea about the factors influencing the outbreak data, we presented the current situation of the COVID-19 outbreak with graphical trends. Methods: Primary data were collected and analyzed by using a pre-created Google Survey form having a pre-set questionnaire on the social distancing status of different districts. Secondary data on the total and the daily number of laboratory tests, confirmed positive cases, and death cases were extracted from the publicly available sources to make predictions. We estimated the basic reproduction number (R◦) based on the SIR mathematical model and predicted the probable fate of this pandemic in Bangladesh. Results: Quarantine situations in different regions of Bangladesh were evaluated and presented. We also provided tentative forecasts until 31 May 2020 and found that the predicted curve followed the actual curve approximately. Estimated R◦-values (6.924) indicated that infection rate would be greater than the recovery rate. Furthermore, by calibrating the parameters of the SIR model to fit the reported data, we assume the ultimate ending of the pandemic in Bangladesh by December 2022. Conclusion: We hope that the results of our analysis could contribute to the elucidation of critical aspects of this outbreak and help the concerned authority toward decision making.

Hydrogel based Fabry-Pérot cavity for a pH sensor.

A simple, reliable, and quick reactive Fabry-Pérot (FP) structure-based fiber optic pH sensor is presented. The pH-sensitive hydrogel and single-mode fiber (SMF) are placed inside a fused silica capillary to form the FP cavity. The gel thickness is characterized by the spin coating method with respect to different spin speeds. The proposed sensor shows a pH sensitivity of 0.30 nm/pH along with a fast response time of 15 s to 20 s for different pH solvents in the acidic range. Also, the temperature sensitivity of the FPI sensor is found to be -0.56 nm/°C.

Sensitivity Enhancement of Modified D-Shaped Microchannel PCF-Based Surface Plasmon Resonance Sensor.

In this work, a highly sensitive dual-core configured microchannel-based plasmonic refractive index (RI) sensor was investigated, which can be used for low RI detection. Both the sensing layer and the plasmonic material layer were built outside of the fiber design to detect the surrounding medium's RI changes. Additionally, the effects of different plasmonic materials gold (Au), silver (Ag), and copper (Cu) toward sensitivity were investigated for the same structure. An adhesive agent was used in this work, titanium dioxide (TiO2), and was coated on top of the plasmonic material to prevent the oxidation of Ag and Cu. The coupling strength between the fundamental mode and the surface plasmon polariton (SPP) mode was observed to be very strong due to the TiO2 adhesive agent. With a resolution of 7.41 × 10-7 RIU, maximum wavelength sensitivity (WS) of 135,000 nm/RIU and amplitude sensitivity (AS) of 3239 RIU-1 were achieved using the proposed sensor while using Au as a plasmonic material for an analyte RI range of 1.29-1.39. A detailed study of relevant literature revealed that the achieved wavelength sensitivity for plasmonic material gold (Au) is the highest among reported photonic crystal fiber (PCF)-surface plasmon resonance (SPR) sensors to date.

Controlling evanescent waves using silicon photonic all-dielectric metamaterials for dense integration.

Ultra-compact, densely integrated optical components manufactured on a CMOS-foundry platform are highly desirable for optical information processing and electronic-photonic co-integration. However, the large spatial extent of evanescent waves arising from nanoscale confinement, ubiquitous in silicon photonic devices, causes significant cross-talk and scattering loss. Here, we demonstrate that anisotropic all-dielectric metamaterials open a new degree of freedom in total internal reflection to shorten the decay length of evanescent waves. We experimentally show the reduction of cross-talk by greater than 30 times and the bending loss by greater than 3 times in densely integrated, ultra-compact photonic circuit blocks. Our prototype all-dielectric metamaterial-waveguide achieves a low propagation loss of approximately 3.7±1.0 dB/cm, comparable to those of silicon strip waveguides. Our approach marks a departure from interference-based confinement as in photonic crystals or slot waveguides, which utilize nanoscale field enhancement. Its ability to suppress evanescent waves without substantially increasing the propagation loss shall pave the way for all-dielectric metamaterial-based dense integration.