Investigating the Working Environment and the Hindrance Faced by Street Hawkers in Bangladesh: An Empirical investigation in Dhaka City

The working condition of street hawkers in Dhaka city is not a pleasant scenario. This study is conducted to investigate their working condition highlighting corruption, the threat of e-commerce, lack of education, exposure to noise and people's perception of them. Several field surveys on Dhaka city have been conducted based on a well prepared questionnaire covering six places and we collected data on the noise level (Levels of sound were taken through a mobile app from 5 busy places of the city where street hawking prevails), amount of bribe, level of education and price variation with the online shop. We also conducted an online survey to determine people's perception of street hawkers. We find that 59.8% people are not aware of the condition the hawkers work in, where 57.4% prefer to shop from online shops over street hawkers and 42.6% were in favor of shopping from the street hawkers, 86.1% opined that they are in favor of the eviction of the hawkers and only 13.9% were in against the eviction. We also found that on average the hawkers pay about 100-150 taka per day as extortion money, the average level (Db) of sound in Mirpur, Newmarket, Motijheel, Gulistan, Savar are respectively 70.4, 71.2, 77.2, 71, and 70.6 (Db). Keywords: Street hawkers, working conditions, exposure to noise, Dhaka city, Bangladesh DOI: 10.7176/JESD/10-2-0

A comprehensive in vitro biological investigation of metal complexes of tolfenamic acid

Objective: The inquisitive objective of the study was to observe the antimicrobial, cytotoxicity, and antioxidant activities of some newly synthesized metal complexes of tolfenamic acid.Methods: While antimicrobial activity was studied by disk diffusion method, cytotoxicity was studied by performing brine shrimp lethality bioassay. Moreover, DPPH radical scavenging potential was observed to determine the antioxidant property of the complexes.Results: From the disk diffusion antimicrobial screening of tolfenamic acid and its metal complexes, it was found out that considerable antimicrobial activity in terms of zone of inhibition against the tested organisms had been demonstrated by Cu and Zn complex of tolfenamic acid. In addition, the brine shrimp lethality bioassay corroborated that tolfenamic acid and Cu, Co, Zn complexes of the parent NSAID exhibited cytotoxicity with LC50 values 1.23 ± 0.91 lg/ml, 1.12 ± 0.12 lg/ml, 1.17 ± 0.56 lg/ml, 1.35 ± 0.24 lg/ ml respectively, compared to the vincristine sulfate had LC50 value of 0.82 ± 0.09 lg/ml. Furthermore, 1,1- diphenyl-2-picrylhydrazyl assay revealed that in comparison with standard BHT had IC50 of 11.84 ± 0.65, Cu and Co complex of tolfenamic acid exhibited significant antioxidant or radical-scavenging properties with IC50 values 13.61 ± 0.58 lg/ml and 15.38 ± 0.09 lg/ml, respectively.Conclusion: It can be postulated that metal complexes of tolfenamic acid have auspicious pharmacological effects: antimicrobial, cytotoxicity, and antioxidant potency. Hence, these complexes might have better therapeutic responses in future; notwithstanding, it needs further detailed analysis in other pharmacological perspectives.Keywords: Tolfenamic acid, Metal complex, Antimicrobial screening, Cytotoxicity, Antioxidant activit

Prevalence of Neuroradiological Abnormalities in First-Episode Psychosis: A Systematic Review and Meta-analysis

IMPORTANCE: Individuals presenting with first-episode psychosis (FEP) may have a secondary ("organic") etiology to their symptoms that can be identified using neuroimaging. Because failure to detect such cases at an early stage can have serious clinical consequences, it has been suggested that brain magnetic resonance imaging (MRI) should be mandatory for all patients presenting with FEP. However, this remains a controversial issue, partly because the prevalence of clinically relevant MRI abnormalities in this group is unclear. OBJECTIVE: To derive a meta-analytic estimate of the prevalence of clinically relevant neuroradiological abnormalities in FEP. DATA SOURCES: Electronic databases Ovid, MEDLINE, PubMed, Embase, PsychINFO, and Global Health were searched up to July 2021. References and citations of included articles and review articles were also searched. STUDY SELECTION: Magnetic resonance imaging studies of patients with FEP were included if they reported the frequency of intracranial radiological abnormalities. DATA EXTRACTION AND SYNTHESIS: Independent extraction was undertaken by 3 researchers and a random-effects meta-analysis of pooled proportions was calculated. Moderators were tested using subgroup and meta-regression analyses. Heterogeneity was evaluated using the I2 index. The robustness of results was evaluated using sensitivity analyses. Publication bias was assessed using funnel plots and Egger tests. MAIN OUTCOMES AND MEASURES: Proportion of patients with a clinically relevant radiological abnormality (defined as a change in clinical management or diagnosis); number of patients needed to scan to detect 1 such abnormality (number needed to assess [NNA]). RESULTS: Twelve independent studies (13 samples) comprising 1613 patients with FEP were included. Of these patients, 26.4% (95% CI, 16.3%-37.9%; NNA of 4) had an intracranial radiological abnormality, and 5.9% (95% CI, 3.2%-9.0%) had a clinically relevant abnormality, yielding an NNA of 18. There were high degrees of heterogeneity among the studies for these outcomes, 95% to 73%, respectively. The most common type of clinically relevant finding was white matter abnormalities, with a prevalence of 0.9% (95% CI, 0%-2.8%), followed by cysts, with a prevalence of 0.5% (95% CI, 0%-1.4%). CONCLUSIONS AND RELEVANCE: This systematic review and meta-analysis found that 5.9% of patients presenting with a first episode of psychosis had a clinically relevant finding on MRI. Because the consequences of not detecting these abnormalities can be serious, these findings support the use of MRI as part of the initial clinical assessment of all patients with FEP

Ocular manifestations of dengue fever in Bangladesh during its out break

Dengue fever, borne by Aedes aegypti mosquito, is one of the most common and most prevalent forms of flavivirus infections in humans, endemic in tropics and warm temperate regions of the world. We report a spectrum of ocular manifestations of dengue fever along with its associated laboratory findings. To study the ocular manifestations associated with dengue fever. This study was conducted in 600 patients hospitalized with diagnosis of dengue fever over a period of 3 months from March 2019 to May 2019. All patients underwent complete evaluation with respect to systemic and ophthalmic examination in different institutions of Bangladesh. A total of 600 patients were diagnosed with dengue fever; of which, 375 (62.5%) were men and 225 (37.5%) were women. Mean age was 32 years (20–60 years). Only 195 patients (32.7%) had complaints of retrobulbar pain in the eyes. 10 patients (1.67%) had blurring of vision. Ocular findings were present in 340 patients (56.7%). Most common anterior segment findings were subconjunctival haemorrhage in 275 patients (45.8%). Posterior segment findings were present in 80 patients (13.3%); of which, 70 (87.5%) had retinal haemorrhages. Ocular changes had resolved in all the cases, which came for follow-up in 8–10 weeks. It was mostly attributed to the improving platelet count. The incidence of ocular complications in dengue fever is increasing, hence all patients with dengue should be referred to an ophthalmologist to prevent any sight-threatening BSMMU J 2022; 15(1): 16-1

Automated triaging of head MRI examinations using convolutional neural networks

The growing demand for head magnetic resonance imaging (MRI) examinations, along with a global shortage of radiologists, has led to an increase in the time taken to report head MRI scans around the world. For many neurological conditions, this delay can result in increased morbidity and mortality. An automated triaging tool could reduce reporting times for abnormal examinations by identifying abnormalities at the time of imaging and prioritizing the reporting of these scans. In this work, we present a convolutional neural network for detecting clinically-relevant abnormalities in $\text{T}_2$-weighted head MRI scans. Using a validated neuroradiology report classifier, we generated a labelled dataset of 43,754 scans from two large UK hospitals for model training, and demonstrate accurate classification (area under the receiver operating curve (AUC) = 0.943) on a test set of 800 scans labelled by a team of neuroradiologists. Importantly, when trained on scans from only a single hospital the model generalized to scans from the other hospital ($\Delta$AUC $\leq$ 0.02). A simulation study demonstrated that our model would reduce the mean reporting time for abnormal examinations from 28 days to 14 days and from 9 days to 5 days at the two hospitals, demonstrating feasibility for use in a clinical triage environment.Comment: Accepted as an oral presentation at Medical Imaging with Deep Learning (MIDL) 202

Accurate brain-age models for routine clinical MRI examinations

Convolutional neural networks (CNN) can accurately predict chronological age in healthy individuals from structural MRI brain scans. Potentially, these models could be applied during routine clinical examinations to detect deviations from healthy ageing, including early-stage neurodegeneration. This could have important implications for patient care, drug development, and optimising MRI data collection. However, existing brain-age models are typically optimised for scans which are not part of routine examinations (e.g., volumetric T1-weighted scans), generalise poorly (e.g., to data from different scanner vendors and hospitals etc.), or rely on computationally expensive pre-processing steps which limit real-time clinical utility. Here, we sought to develop a brain-age framework suitable for use during routine clinical head MRI examinations. Using a deep learning-based neuroradiology report classifier, we generated a dataset of 23,302 'radiologically normal for age' head MRI examinations from two large UK hospitals for model training and testing (age range = 18-95 years), and demonstrate fast (&lt; 5 seconds), accurate (mean absolute error [MAE] &lt; 4 years) age prediction from clinical-grade, minimally processed axial T2-weighted and axial diffusion-weighted scans, with generalisability between hospitals and scanner vendors (Δ MAE &lt; 1 year). The clinical relevance of these brain-age predictions was tested using 228 patients whose MRIs were reported independently by neuroradiologists as showing atrophy 'excessive for age'. These patients had systematically higher brain-predicted age than chronological age (mean predicted age difference = +5.89 years, 'radiologically normal for age' mean predicted age difference = +0.05 years, p &lt; 0.0001). Our brain-age framework demonstrates feasibility for use as a screening tool during routine hospital examinations to automatically detect older-appearing brains in real-time, with relevance for clinical decision-making and optimising patient pathways.</p

Optimising brain age estimation through transfer learning:A suite of pre-trained foundation models for improved performance and generalisability in a clinical setting

Estimated age from brain MRI data has emerged as a promising biomarker of neurological health. However, the absence of large, diverse, and clinically representative training datasets, along with the complexity of managing heterogeneous MRI data, presents significant barriers to the development of accurate and generalisable models appropriate for clinical use. Here, we present a deep learning framework trained on routine clinical data (N up to 18,890, age range 18–96 years). We trained five separate models for accurate brain age prediction (all with mean absolute error ≤4.0 years, R2 ≥.86) across five different MRI sequences (T2-weighted, T2-FLAIR, T1-weighted, diffusion-weighted, and gradient-recalled echo T2*-weighted). Our trained models offer dual functionality. First, they have the potential to be directly employed on clinical data. Second, they can be used as foundation models for further refinement to accommodate a range of other MRI sequences (and therefore a range of clinical scenarios which employ such sequences). This adaptation process, enabled by transfer learning, proved effective in our study across a range of MRI sequences and scan orientations, including those which differed considerably from the original training datasets. Crucially, our findings suggest that this approach remains viable even with limited data availability (as low as N = 25 for fine-tuning), thus broadening the application of brain age estimation to more diverse clinical contexts and patient populations. By making these models publicly available, we aim to provide the scientific community with a versatile toolkit, promoting further research in brain age prediction and related areas.</p

Optimizing tea waste as a sustainable substrate for oyster mushroom (Pleurotus ostreatus) cultivation: a comprehensive study on biological efficiency and nutritional aspect

IntroductionIn Bangladesh, rice straw (RS) and sawdust (SD) substrates have traditionally been used in the production of oyster mushrooms (Pleurotus ostreatus). However, the rising costs of these substrates have led many to look for alternatives.ObjectivesThe present study thus focuses on the potential of waste tea leaves (WTL) for mushroom farming.MethodsWe prepared various substrate mixtures by combining WTL with SD and RS, subsequently evaluating mushroom yield and various quality parameters such as amino acid concentration, mineral content, and biological efficiency.Results and discussionOur investigation revealed that WTL alone is not a suitable substrate for mushroom (Pleurotus ostreatus) growth. However, when combined with SD at a 50% ratio, it significantly boosts mushroom yield and biological efficiency (BE). Conversely, a reduction in yield was noted when WTL was mixed with RS in all tested treatments, although BE surpassed 50%. In summary, incorporating WTL into both substrates proves economically viable from the BE standpoint. According to PCA analysis, the minerals and amino acid content varied based on the different substrate formulations involving WTL blending with both SD and RS at different ratios. Remarkably, mushroom fruiting bodies exhibited lower levels of Na and Fe despite these elements being present in higher concentrations in the growing substrates, suggesting the inability of P. ostreatus to bioaccumulate Na and Fe. Conversely, we observed higher bioaccumulation of Zn and P, even exceeding substrate levels. Importantly, our findings showed that mushrooms cultivated on WTL-based formulations consistently contained elevated Zn levels irrespective of substrate types, indicating that WTL enriched Zn in mushrooms. Additionally, the Fe level increased specifically in RS + WTL-based formulations. All essential and non-essential amino acids were detected, with the highest concentration of histidine, isoleucine, and methionine found in the WTL + SD formulation. Non-essential amino acids (NEAA) like alanine and glutamic acid were more prominent in formulations combining WTL with RS. This study represents the first documented exploration of the impact of WTL on the accumulation of intracellular metabolites including minerals and amino acids, in P. ostreatus

Evolutionarily Conserved Protein Sequences of Influenza A Viruses, Avian and Human, as Vaccine Targets

10.1371/journal.pone.0001190PLoS ONE21