Effects of Commercial Probiotics on the Growth Performance, Intestinal Microbiota and Intestinal Histomorphology of Nile Tilapia (Oreochromis niloticus) Reared in Biofloc Technology (BFT).

Though different types of commercial probiotics are supplemented in biofloc technology (BFT), very little information is available on their effects on the farmed fish. Therefore, this study focused on evaluating the effects of three most commonly used commercial probiotics on the growth performance, intestinal histomorphology, and intestinal microbiota of Nile tilapia (Oreochromis niloticus) reared in BFT. Tilapia fry, with an average weight of 3.02 ± 0.50 g, were stocked at a density of 60 fry/0.2 m3, and cultured for 90 days. Three commercial probiotics were administered, with three replications for each: a single-genus multi-species probiotic (Bacillus spp.) (T1), a multi-genus multi-species probiotic (Bacillus sp., Lactobacillus sp., Nitrosomonas sp., Nitrobacter sp.) (T2), and a multi-species probiotic (Bacillus spp.) combined with enzymes including amylase, protease, cellulase, and xylanase (T3). The results showed significant variations in growth and feed utilization, with T3 outperforming other treatments in terms of weight gain, liver weight, and intestine weight. Adding Bacillus spp. with enzymes (T3) to water significantly increased the histomorphological parameters (villi length, villi depth, crypt depth, muscle thickness, intestinal thickness) as well as microbes (total viable count and total lactic acid bacteria) of intestine of fish compared to T1 and T2, leading to improved digestion and absorption responses. It is concluded that the supplementation of commercial probiotics has potential benefits on farmed fish species in BFT.

Multi trait stability indexing and trait correlation from a dataset of sweet potato (Ipomoea batatas L.).

A study was conducted in five regions of Bangladesh, specifically Gazipur, Bogura, Jamalpur, Jashore, and Chattogram, each characterized by suitable agro-ecologies for sweet potato cultivation. The purpose of this data article was to demonstrate the correlations between traits and the selection of stable varieties based on the multi-trait stability index (MTSI). The data indicated a direct link between multiple characteristics and both the yield and factors contributing to yield. This implies that enhancing these traits might result in a higher overall production of sweet potato storage roots. Furthermore, the factor analysis for MTSI demonstrated that the desired goal for selection was achieved for all traits, except for mean vine length (VL) and storage root dry weight (DW). The broad sense heritability ranged from 0 to 0.97, and the selection gain percentage ranged from 0 to 42.8. The MTSI analysis identified the sweet potato variety BARI Mistialu-15 as the most stable among the other studied varieties.

Diagnostic criteria for Buerger's disease: International Consensus of VAS - European Independent Foundation in Angiology/Vascular Medicine.

Buerger's disease (BD) remains a debilitating condition and early diagnosis is paramount for its effective management. Despite many published diagnostic criteria for BD, selective criteria have been utilized in different vascular centers to manage patients with BD worldwide. A recent international Delphi Consensus Study on the diagnostic criteria of BD showed that none of these published diagnostic criteria have been universally accepted as a gold standard. Apart from the presence of smoking, these published diagnostic criteria have distinct differences between them, rendering the direct comparison of patient outcomes difficult. Hence, the expert committees from the Working Group of the VAS-European Independent Foundation in Angiology/Vascular Medicine critically reviewed the findings from the Delphi study and provided practical recommendations on the diagnostic criteria for BD, facilitating its universal use. We recommend that the 'definitive' diagnosis of BD must require the presence of three features (history of smoking, typical angiographic features and typical histopathological features) and the use of a combination of major and minor criteria for the 'suspected' diagnosis of BD. The major criterion is the history of active tobacco smoking. The five minor criteria are disease onset at age less than 45 years, ischemic involvement of the lower limbs, ischemic involvement of one or both of the upper limbs, thrombophlebitis migrans and red-blue shade of purple discoloration on edematous toes or fingers. We recommend that a 'suspected' diagnosis of BD is confirmed in the presence of a major criterion plus four or more minor criteria. In the absence of the major criterion or in cases of fewer than four minor criteria, imaging and laboratory data could facilitate the diagnosis. Validation studies on the use of these major and minor criteria are underway.

Knee Osteoarthritis Detection and Severity Classification Using Residual Neural Networks on Preprocessed X-ray Images.

One of the most common and challenging medical conditions to deal with in old-aged people is the occurrence of knee osteoarthritis (KOA). Manual diagnosis of this disease involves observing X-ray images of the knee area and classifying it under five grades using the Kellgren-Lawrence (KL) system. This requires the physician's expertise, suitable experience, and a lot of time, and even after that the diagnosis can be prone to errors. Therefore, researchers in the ML/DL domain have employed the capabilities of deep neural network (DNN) models to identify and classify KOA images in an automated, faster, and accurate manner. To this end, we propose the application of six pretrained DNN models, namely, VGG16, VGG19, ResNet101, MobileNetV2, InceptionResNetV2, and DenseNet121 for KOA diagnosis using images obtained from the Osteoarthritis Initiative (OAI) dataset. More specifically, we perform two types of classification, namely, a binary classification, which detects the presence or absence of KOA and secondly, classifying the severity of KOA in a three-class classification. For a comparative analysis, we experiment on three datasets (Dataset I, Dataset II, and Dataset III) with five, two, and three classes of KOA images, respectively. We achieved maximum classification accuracies of 69%, 83%, and 89%, respectively, with the ResNet101 DNN model. Our results show an improved performance from the existing work in the literature.

Effect of coated cysteamine hydrochloride and probiotics supplemented alone or in combination on feed intake, nutrients digestibility, ruminal fermentation, and blood metabolites of Kamphaeng Saen beef heifers.

This study aimed to determine the effects of coated cysteamine hydrochloride (CSH) and probiotics (PB) supplemented alone or in combination on feed intake, digestibility, ruminal fermentation, and blood metabolites of heifer beef cattle. Sixteen heifers (body weight = 210 ± 41 kg; age = 9 ± 2 months) were assigned according to a randomized complete block design in a 2 × 2 factorial arrangement. All animals were fed the basal diet, which contained an 82:17 concentrate-to-forage ratio, and the forage source was rice straw. The treatments were as follows: (1) 0% PB + 0 g/d CSH, (2) 0.1% PB + 0 g/d CSH, (3) 0% PB + 20 g/d CSH, and (4) 0.1% PB + 20 g/d CSH. The main effect of CSH supplementation has been found to improve feed intake (P < 0.05). There were no treatment interactions with nutrient digestibility or rumen fermentation parameters. Supplementation of CSH did not affect any of the variables evaluated, while probiotics supplementation increased DM digestibility due to the increases in CP and fiber fraction digestibility. Compared to controls and CSH, at 16 h post-feeding, heifers receiving probiotics tended (P = 0.07) to show 17% greater ruminal NH3-N concentration, but this effect was not evident at 2 h post-feeding. However, the main effects of probiotic supplementation showed a tendency to increase the number of total bacteria and fungal zoospores in the rumen at 2 h post-feeding. The blood triglyceride (BTG) concentration of heifers fed a diet supplemented with 20 g/d CSH and 0.1% probiotics was found to be greater than those fed CSH alone (P < 0.1) at 16 h post-feeding, and then, there were greater BTG concentrations than other treatments (P < 0.05) at 2 h post-feeding. In conclusion, the combination of CSH and PB did not potentiate the effects of probiotics on digestibility and rumen fermentation and had minimal effects on blood parameters.

Multiclass tumor identification using combined texture and statistical features.

Early detection and diagnosis of brain tumors are essential for early intervention and eventually successful treatment plans leading to either a full recovery or an increase in the patient lifespan. However, diagnosis of brain tumors is not an easy task since it requires highly skilled professionals, making this procedure both costly and time-consuming. The diagnosis process relying on MR images gets even harder in the presence of similar objects in terms of their density, size, and shape. No matter how skilled professionals are, their task is still prone to human error. The main aim of this work is to propose a system that can automatically classify and diagnose glioma brain tumors into one of the four tumor types: (1) necrosis, (2) edema, (3) enhancing, and (4) non-enhancing. In this paper, we propose a combined texture discrete wavelet transform (DWT) and statistical features based on the first- and second-order features for the accurate classification and diagnosis of multiclass glioma tumors. Four well-known classifiers, namely, support vector machines (SVM), random forest (RF), multilayer perceptron (MLP), and naïve Bayes (NB), are used for classification. The BraTS 2018 dataset is used for the experiments, and with the combined DWT and statistical features, the RF classifier achieved the highest average accuracy whether for separated modalities or combined modalities. The highest average accuracy of 89.59% and 90.28% for HGG and LGG, respectively, was reported in this paper. It has also been observed that the proposed method outperforms similar existing methods reported in the extant literature.

Impacts of climate change on fish hatchery productivity in Bangladesh: A critical review.

Bangladesh is among the countries most vulnerable to climate change due to its geographical location. Climate change issues have become major concerns in aquaculture industry, particularly for fish hatchery productivity. Fish production in Bangladesh is mainly steered by the aquaculture sector, which is dependent on private hatchery-based fish seed production to a great extent. This review aimed to present the impacts of climate change on fish hatcheries, particularly during different stages of hatchery production, and the economic loss from the onset of disease and other impairments due to environmental causes. Geographically, most hatcheries in Bangladesh are operated within a narrow range of temperature (22.8-23.1 °C, equivalent to 73-73.5 °F) and rainfall (1750-2000 mm). Thus, slightest fluctuations in these parameters affect seed production in fish hatcheries. The broodstock, produced in natural and captive conditions, is severely affected by flash flooding, water quality deterioration, river siltation, erratic rainfall, and temperature fluctuations. Based on our review, temperature fluctuation is the main factor hampering maturation and breeding performances of broodstock. Temperature has also been reported to affect embryonic development and cause stunted growth of larvae and juvenile. In shrimp and prawn hatcheries, fluctuations in temperature, pH, and salinity are responsible for post-larval disease outbreaks. In some instances, storms and heavy rainfall wash away reared broodfish and fish seed from the hatcheries, causing massive socioeconomic losses. This review presents indisputable negative impacts of climate change on hatchery production. As of now, no cost-effective proven strategies have been developed to minimize the effects of climate change on Bangladesh's fish hatchery production, on which the aquaculture industry is inextricably dependent. For sustainable fish hatchery production, basic research on climate impacts on hatcheries is inevitable, as well as improving capacity of hatchery owners are needed for resilient hatchery operations in Bangladesh and similar environments worldwide.

COVID-19 impacts on the Bangladesh shrimp industry: A sequential survey-based case study from southwestern Bangladesh.

Shrimp farming is fundamental to the national economy of Bangladesh, particularly through earning foreign currency. The nationwide lockdown and international cargo restriction jeopardized the sector and breaking its marketing chain. Assessing the degree of farming socio-economic peril from COVID-19 and suggesting early coping strategies and long-term mitigation measures are pressing to build resilience for this food production sector. To collect survey data, two key-informant face-to-face surveys with 51 shrimp farmers and 62 consumers in southwest Bangladesh were accomplished. As national lockdowns restricted access to export markets and movements within the country, farm incomes decreased against rising production costs. To compensate, farmers reduced their workforce (29.4%), but even with the sale of co-cultured finfish still suffered from large drops in revenue (42.8% average profit reduction). Furthermore, we present evidence that shrimp farmers should consider diversification of aquaculture product type as co-culture of additional shrimp species was a poor mitigation strategy against large market price fluctuations. Product price reductions were passed on to the consumer, who enjoyed falling product prices including more expensive shrimp products, but the markup for nearly all aquaculture products increased. The current jeopardy and consequences of shrimp farming future are discussed, including coping strategies to help policymakers in building resilience against future uncertainties. Supplementary Information: The online version contains supplementary material available at 10.1007/s12562-022-01630-0.

Antimicrobial Resistance in Aquaculture Environments: Unravelling the Complexity and Connectivity of the Underlying Societal Drivers.

Food production environments in low- and middle-income countries (LMICs) are recognized as posing significant and increasing risks to antimicrobial resistance (AMR), one of the greatest threats to global public health and food security systems. In order to maximize and expedite action in mitigating AMR, the World Bank and AMR Global Leaders Group have recommended that AMR is integrated into wider sustainable development strategies. Thus, there is an urgent need for tools to support decision makers in unravelling the complex social and environmental factors driving AMR in LMIC food-producing environments and in demonstrating meaningful connectivity with other sustainable development issues. Here, we applied the Driver-Pressure-State-Impact-Response (DPSIR) conceptual framework to an aquaculture case study site in rural Bangladesh, through the analysis of distinct social, microbiological, and metagenomic data sets. We show how the DPSIR framework supports the integration of these diverse data sets, first to systematically characterize the complex network of societal drivers of AMR in these environments and second to delineate the connectivity between AMR and wider sustainable development issues. Our study illustrates the complexity and challenges of addressing AMR in rural aquaculture environments and supports efforts to implement global policy aimed at mitigating AMR in aquaculture and other rural LMIC food-producing environments.

Diallel Cross Application and Histomolecular Characterization: An Attempt to Develop Reference Stock of Labeo ariza.

The objective of the present study was to evaluate the growth performance and genetic variation in diallel crosses of Ariza labeo (Labeo ariza) originating from three geographically separated rivers (Atrai, Jamuna and Kangsha) in Bangladesh. Intra (G1K♀K♂, G2J♀J♂, and G3A♀A♂) and inter (G4K♀A♂, G5K♀J♂, G6A♀K♂, G7A♀J♂, G8J♀K♂, and G9J♀A♂) stocks were produced following diallel cross (sex ratio-1:1 and n = 48; 16 from each river). Reproductive and growth performance, muscle cellularity and genetic variation following genotyping of eight microsatellite markers (Lr1, Lr2, Lr3, Lr22, Lr24, Lr27, Lr28 and Lr29) and analysis of all crossbreeds was performed. The fertilization (95% ± 2.11%), hatching (88% ± 1.03%), and survival rates (82% ± 1.88%) of G4K♀A♂ were higher compared to other groups. With respect to length and weight gains (2.67 ± 0.4 cm and 3.39 ± 0.2 g), SGR (3.23% ± 0.20%), and heterosis (8.87% and 24.74%) G4K♀A♂ was the superior group. A higher number of hyperplastic muscle fibers, mean number of alleles (2.75) and mean observed heterozygosity (0.417) from G4K♀A♂ could be interpreted to mean that G4K♀A♂ comprise better performance efficiency compared to others and are considered for continuing the L. ariza stock improvement program.

Writer verification of partially damaged handwritten Arabic documents based on individual character shapes.

Author verification of handwritten text is required in several application domains and has drawn a lot of attention within the research community due to its importance. Though, several approaches have been proposed for the text-independent writer verification of handwritten text, none of these have addressed the problem domain where author verification is sought based on partially-damaged handwritten documents (e.g., during forensic analysis). In this paper, we propose an approach for offline text-independent writer verification of handwritten Arabic text based on individual character shapes (within the Arabic alphabet). The proposed approach enables writer verification for partially damaged documents where certain handwritten characters can still be extracted from the damaged document. We also provide a mechanism to identify which Arabic characters are more effective during the writer verification process. We have collected a new dataset, Arabic Handwritten Alphabet, Words and Paragraphs Per User (AHAWP), for this purpose in a classroom setting with 82 different users. The dataset consists of 53,199 user-written isolated Arabic characters, 8,144 Arabic words, 10,780 characters extracted from these words. Convolutional neural network (CNN) based models are developed for verification of writers based on individual characters with an accuracy of 94% for isolated character shapes and 90% for extracted character shapes. Our proposed approach provided up to 95% writer verification accuracy for partially damaged documents.

Glioma Tumors' Classification Using Deep-Neural-Network-Based Features with SVM Classifier.

The complexity of brain tissue requires skillful technicians and expert medical doctors to manually analyze and diagnose Glioma brain tumors using multiple Magnetic Resonance (MR) images with multiple modalities. Unfortunately, manual diagnosis suffers from its lengthy process, as well as elevated cost. With this type of cancerous disease, early detection will increase the chances of suitable medical procedures leading to either a full recovery or the prolongation of the patient's life. This has increased the efforts to automate the detection and diagnosis process without human intervention, allowing the detection of multiple types of tumors from MR images. This research paper proposes a multi-class Glioma tumor classification technique using the proposed deep-learning-based features with the Support Vector Machine (SVM) classifier. A deep convolution neural network is used to extract features of the MR images, which are then fed to an SVM classifier. With the proposed technique, a 96.19% accuracy was achieved for the HGG Glioma type while considering the FLAIR modality and a 95.46% for the LGG Glioma tumor type while considering the T2 modality for the classification of four Glioma classes (Edema, Necrosis, Enhancing, and Non-enhancing). The accuracies achieved using the proposed method were higher than those reported by similar methods in the extant literature using the same BraTS dataset. In addition, the accuracy results obtained in this work are better than those achieved by the GoogleNet and LeNet pre-trained models on the same dataset.

An International Delphi Consensus on Diagnostic Criteria for Buerger's Disease.

BACKGROUND: Buerger's disease (BD) remains a debilitating condition. Despite multiple published diagnostic criteria for BD, none is universally accepted as a gold standard. METHODS: We conducted a 2-round modified Delphi consensus study to establish a consensus on the diagnostic. The questionnaire included statements from several commonly used diagnostic criteria for BD. Qualitative and quantitative analysis methods were performed. An agreement level of 70% was applied. RESULTS: Twenty nine experts from 18 countries participated in this study. Overall, 75 statements were circulated in Round 1. Of these, 28% of statements were accepted. Following comments, 21 statements were recirculated in Round 2 and 90% were accepted. Although more than 90% of the experts did not agree that the diagnosis of BD can be based only on clinical manifestation, none of the nonclinical manifestations of BD were agreed as a part of the diagnostic criteria. There was an agreement that a history of tobacco consumption in any form, not necessarily confined to the current use, should be a part of the diagnostic criteria of BD. The history of thrombophlebitis migrans, even if not present at presentation, was accepted as a clue for BD diagnosis. It was also agreed that discoloration of the toes or fingers could be included in the diagnostic criteria of BD. Experts agreed that histology results could differentiate BD from atherosclerosis obliterans and other types of vasculitis. The presence of corkscrew collaterals on imaging and burning pain reached the agreement at the first round but not at the second. There was no consensus regarding age cut-off, the requirement of normal lipid profile, and normal blood glucose for BD diagnosis. CONCLUSIONS: The present study demonstrated discrepancies in the various published diagnostic criteria for BD and their selective utilization in routine clinical practice worldwide. We propose that all published diagnostic criteria for BD be re-evaluated for harmonization and universal use.

Alterations in hematological parameters and the structure of peripheral erythrocytes in Nile tilapia (Oreochromis niloticus) exposed to profenofos.

The burden of pesticide use from agricultural intensification lies in the fact that pesticides may end up in aquatic ecosystems and have pernicious effects on non-target organisms, including fish. Different blood biomarkers, including hemato-biochemical indices, erythrocytic nuclear abnormalities (ENA), and erythrocytic cellular abnormalities (ECA), were observed in Nile tilapia (Oreochromis niloticus) after exposure to varying sub-lethal concentrations (0%, 5%, 10%, 20%, and 40% of 96-h LC50) of profenofos at different time intervals (7, 14, 21, and 28 days). The results revealed that glucose and white blood cell (WBC) levels significantly increased, while hemoglobin, red blood cell (RBC), and packed cell volume (PCV) significantly decreased in a time- and concentration-dependent manner. Aberrant erythrocytic morphology-derived ENA, such as nuclear degeneration, micronuclear formation, binuclear development, nuclear budding, and karyopyknosis, significantly increased with time in profenofos-exposed groups compared to controls. Between the treatment and control groups, a significant execution was discerned for teardrop and fusion type ECA. For other cellular aberrations of erythrocytes, including elongated, twin, and spindle, a significant difference appeared only at the beginning of the experiment (day 7). This study concludes that the presence of widely used profenofos in aquatic systems has a pernicious effect on Nile tilapia.

Recent Advances in Classification of Brain Tumor from MR Images - State of the Art Review from 2017 to 2021.

BACKGROUND: The task of identifying a tumor in the brain is a complex problem that requires sophisticated skills and inference mechanisms to accurately locate the tumor region. The complex nature of the brain tissue makes the problem of locating, segmenting, and ultimately classifying Magnetic Resonance (MR) images a complex problem. The aim of this review paper is to consolidate the details of the most relevant and recent approaches proposed in this domain for the binary and multi-class classification of brain tumors using brain MR images. OBJECTIVE: In this review paper, a detailed summary of the latest techniques used for brain MR image feature extraction and classification is presented. A lot of research papers have been published recently with various techniques proposed for identifying an efficient method for the correct recognition and diagnosis of brain MR images. The review paper allows researchers in the field to familiarize themselves with the latest developments and be able to propose novel techniques that have not yet been explored in this research domain. In addition, the review paper will facilitate researchers who are new to machine learning algorithms for brain tumor recognition to understand the basics of the field and pave the way for them to be able to contribute to this vital field of medical research. RESULTS: In this paper, the review is performed for all recently proposed methods for both feature extraction and classification. It also identifies the combination of feature extraction methods and classification methods that, when combined, would be the most efficient technique for the recognition and diagnosis of brain tumor from MR images. In addition, the paper presents the performance metrics, particularly the recognition accuracy, of selected research published between 2017-2021.

COVID-19 Pneumonia Detection Using Optimized Deep Learning Techniques.

It became apparent that mankind has to learn to live with and adapt to COVID-19, especially because the developed vaccines thus far do not prevent the infection but rather just reduce the severity of the symptoms. The manual classification and diagnosis of COVID-19 pneumonia requires specialized personnel and is time consuming and very costly. On the other hand, automatic diagnosis would allow for real-time diagnosis without human intervention resulting in reduced costs. Therefore, the objective of this research is to propose a novel optimized Deep Learning (DL) approach for the automatic classification and diagnosis of COVID-19 pneumonia using X-ray images. For this purpose, a publicly available dataset of chest X-rays on Kaggle was used in this study. The dataset was developed over three stages in a quest to have a unified COVID-19 entities dataset available for researchers. The dataset consists of 21,165 anterior-to-posterior and posterior-to-anterior chest X-ray images classified as: Normal (48%), COVID-19 (17%), Lung Opacity (28%) and Viral Pneumonia (6%). Data Augmentation was also applied to increase the dataset size to enhance the reliability of results by preventing overfitting. An optimized DL approach is implemented in which chest X-ray images go through a three-stage process. Image Enhancement is performed in the first stage, followed by Data Augmentation stage and in the final stage the results are fed to the Transfer Learning algorithms (AlexNet, GoogleNet, VGG16, VGG19, and DenseNet) where the images are classified and diagnosed. Extensive experiments were performed under various scenarios, which led to achieving the highest classification accuracy of 95.63% through the application of VGG16 transfer learning algorithm on the augmented enhanced dataset with freeze weights. This accuracy was found to be better as compared to the results reported by other methods in the recent literature. Thus, the proposed approach proved superior in performance as compared with that of other similar approaches in the extant literature, and it made a valuable contribution to the body of knowledge. Although the results achieved so far are promising, further work is planned to correlate the results of the proposed approach with clinical observations to further enhance the efficiency and accuracy of COVID-19 diagnosis.

Milestones in thromboangiitis obliterans: a position paper of the VAS-European independent foundation in angiology/vascular medicine.

Even today thromboangiitis obliterans has disease features that remain misunderstood or underappreciated. The epidemiology, etiology and pathophysiology of the disease are still unclear. Biomarkers and disease activity markers are lacking, thus clinical assessment is difficult. We are still struggling to establish unique diagnostic, staging and treatment criteria. This is an academic-collaborative effort to describe the pathophysiology, the clinical manifestations, the diagnostic approach, and the challenges of management of patients with TAO. A systematic search for relevant studies dating from 1900 to the end of 2020 was performed on the PubMed, SCOPUS, and Science Direct databases. Given the intriguing nature of presentation of TAO, its management, to some extent is not only different in different regions of the world but also varies within the same region. Following this project, we discovered ambiguity, overlap and lack of clear-cut criteria for management of TAO. An international group of experts however came to one conclusion. They all agree that management of TAO needs a call for action for a renewed global look with multi-center studies, to update the geographical distribution of the disease and to establish a unique set of diagnostic criteria and a consensus-based guideline for best treatment based on current evidence.

Assessment of heavy metals in farmed shrimp, Penaeus monodon sampled from Khulna, Bangladesh: An inimical to food safety aspects.

The analytical experiment was executed to present detailed reports on the concentration of heavy metals (nickel, iron, zinc, manganese, chromium, lead, and cadmium) in farmed shrimp, Penaeus monodon and its concomitant human health risks upon consumption. A total of 147 farms from six sub-districts of Khulna were selected for sample collection and concentration of heavy metals were determined by Atomic Absorption Spectrometry (AAS) method, following electro-thermal heater digestion. Ni and Cr were found considerably below the detectable limit (BDL) in four sub-districts, while Cr found in shrimps from Rupsa and Paikgacha were far higher than the maximum recommended limit defined by FAO and WHO. The average concentrations of Fe and Mn in all sub-districts crossed the recommendations, whereas average concentrations of Zn, Ni, Cd, and Pb were within the recommendations. Regardless of sampling site, target hazard quotients (THQ) of more than 1 contributed by Fe confirmed higher level of hazard index (HI), indicating potential human health risk. Fortunately, no heavy metal or their additive effect found to offer lifetime potency of carcinogenesis upon consumption of these shrimps. Therefore, probabilistic non-carcinogenic human health risk from Fe contamination necessitates stringent monitoring and controlling of this metal from different sources to farms.

Vascular Surgery Practice Guidelines during COVID-19 Pandemic in a Setting of High Work Volume Against Limited Resources: Perspective of a Developing Country.

BACKGROUND: The situation of coronavirus disease 2019 (COVID-19) pandemic in the Indian subcontinent is worsening. In Bangladesh, rate of new infection has been on the rise despite limited testing facility. Constraint of resources in the health care sector makes the fight against COVID-19 more challenging for a developing country like Bangladesh. Vascular surgeons find themselves in a precarious situation while delivering professional services during this crisis. With the limited number of dedicated vascular surgeons in Bangladesh, it is important to safeguard these professionals without compromising emergency vascular care services in the long term. To this end, we at the National Institute of Cardiovascular Diseases and Hospital, Dhaka, have developed a working guideline for our vascular surgeons to follow during the COVID-19 pandemic. The guideline takes into account high vascular work volume against limited resources in the country. METHODS: A total of 307 emergency vascular patients were dealt with in the first 4 COVID-19 months (March through June 2020) according to the working guideline, and the results were compared with the 4 pre-COVID-19 months. Vascular trauma, dialysis access complications, and chronic limb-threatening ischemia formed the main bulk of the patient population. Vascular health care workers were regularly screened for COVID-19 infection. RESULTS: There was a 38% decrease in the number of patients in the COVID-19 period. Treatment outcome in COVID-19 months were comparable with that in the pre-COVID-19 months except that limb loss in the chronic limb-threatening ischemia patients was higher. COVID-19 infection among the vascular health care professionals was low. CONCLUSIONS: Vascular surgery practice guidelines customized for the high work volume and limited resources of the National Institute of Cardiovascular Diseases and Hospital, Dhaka were effective in delivering emergency care during COVID-19 pandemic, ensuring safety of the caregivers. Despite the fact that similar guidelines exist in different parts of the world, we believe that the present one is still relevant on the premises of a deepening COVID-19 crisis in a developing country like Bangladesh.

Face masks: protecting the wearer but neglecting the aquatic environment? - A perspective from Bangladesh.

In Bangladesh, as with many countries, the spread of COVID-19 made the wearing of single-use face masks, a non-pharmaceutical intervention to reduce viral transmission, surge in popularity amongst the general population. Consequently, irresponsible discarding of used masks into the environment, and mismanagement of the waste they produce, is potentially placing a large pollution burden on aquatic ecosystems in the country. Slow degradation of mask-derived polypropylene and polyethylene fibres creates large reservoirs of microplastic pollutants and these have acute and chronic effects on aquatic organism physiology. Using literature reviews, extrapolation of published data, and field observations, we present an emerging issue of pollution from COVID-19 personal protective equipment such as face masks in Bangladesh. We have estimated the volume of waste generated and document the potential consequences of its improper disposal, and subsequent degradation, in aquaculture ponds within country. In a field survey of 30 ponds in the Muktagacha upazilla, 76.7% were found to have plastics in contact with the water, or within 1m of the pond, and there was an average of 63 pieces of macro-plastic pollution per 5m2. This included floating discarded face masks. Bangladesh has a rich freshwater and marine resource which it depends upon for export trade, nutrition of the population, and jobs. To mitigate potential acute and chronic impacts on aquaculture and the environment, recommendations are made that, if adopted, would reduce entry of microplastics into the aquatic environments via face mask waste mismanagement.