Elliptic Curve Cryptography Based Data Transmission against Blackhole Attack in MANET

Mobile nodes roaming around in the hostile environment of mobile adhoc network (MANET) play the role of router as well as terminal. While acting as a router, a node needs to choose a reliable routing protocol. Besides, an encryption algorithm is needed to secure data to be conveyed through the unfriendly atmosphere while acting as a terminal. We have implemented Elliptic Curve Cryptography (ECC) along with Adhoc On Demand Multipath Distance Vector (AOMDV) routing protocol to secure data transmission against blackhole attack in a MANET. ECC, a public key cryptography that works on discrete logarithm problem with a much smaller key size, has been used to encrypt data packets at source node before transmission. We have used AOMDV, a reliable routing protocol compared to its parent protocol, Adhoc On Demand Distance Vector (AODV), with a multipath extension, for routing. The encrypted packets transferring between nodes via AOMDV, has been proved secured against blackhole attack. The performance of the secured protocol has been analyzed in terms of different performance metrics and in terms of varying number of blackhole attacker nodes

Animal models and natural products to investigate in vivo and in vitro antidiabetic activity

Diabetes mellitus is a chronic disease which has high prevalence. The deficiency in insulin production or impaired insulin function is the underlying cause of this disease. Utilization of plant sources as a cure of diabetes has rich evidence in the history. Recently, the traditional medicinal plants have been investigated scientifically to understand the underlying mechanism behind antidiabetic potential. In this regard, a substantial number of in vivo and in vitro models have been introduced for investigating the bottom-line mechanism of the antidiabetic effect. A good number of methods have been reported to be used successfully to determine antidiabetic effects of plant extracts or isolated compounds. This review encompasses all the possible methods with a list of medicinal plants which may contribute to discovering a novel drug to treat diabetes more efficaciously with the minimum or no side effects

Mimosa pudica L.: a comparative study via in vitro analysis and GC Q-TOF MS profiling on conventional and supercritical fluid extraction using food grade ethanol

The present study compared conventional (maceration) extraction (EtOHconv) and supercritical fluid extraction (SFE) methods as a mean of comparing conventional and green process for a weed namely Mimosa pudica L. to obtain a safe antidiabetic natural agent. In vitro analysis comprised of two different assays, antioxidant assay (determination of total phenolic content, total flavonoid content, and 2,2-diphenyl-1-picrylhydrazyl assay) and antidiabetic assay (inhibition of αamylase and α-glucosidase). GC Q-TOF MS profiling for both extracts was done after derivetisation to confirm the presence of bioactive compounds. SFE was performed at 40 MPa pressure, 60 °C temperature and 5 mL/min CO2 flow rate using 30 % ethanol (co-solvent) for 2 h. EtOHconv prepared using 95 % ethanol through conventional method (maceration) showed a good in vitro antioxidant potential and digestive enzymes inhibitory effect compared to supercritical fluid extract. α-amylase and α-glucosidase inhibitory activities for EtOHconv at 1 mg/mL were 30.08 % (±5.23) and 38.29 % (±2.52), whereas for standard acarbose it was 28.24 % (±13.66) and 36.93 % (±2.70), respectively. Supercritical fluid extract showed less potent in vitro antioxidant and digestive enzymes inhibitory effects (15.67±4.03- α-amylase, 28.36±2.01- α-glucosidase). GC Q-TOF MS analysis was done to confirm the presence of bioactive compounds in both the extracts. Although EtOHconv showed better results, SFE was found to contain more bioactive compounds associated with various pharmacological effects especially antioxidative as per GC Q-TOF MS results. SFE being a clean and green technology could be employed in future with more focus on method development and optimization to reproduce better and safe bioactive products from the neglected weed M. pudica

Rapid antigen testing by community health workers for detection of SARS-CoV-2 in Dhaka, Bangladesh : a cross-sectional study

Peer reviewe

Determining clinical biomarkers to predict long-term SARS-CoV-2 antibody response among COVID-19 patients in Bangladesh

BackgroundInformation on antibody responses following SARS-CoV-2 infection, including the magnitude and duration of responses, is limited. In this analysis, we aimed to identify clinical biomarkers that can predict long-term antibody responses following natural SARS-CoV-2 infection.MethodologyIn this prospective study, we enrolled 100 COVID-19 patients between November 2020 and February 2021 and followed them for 6 months. The association of clinical laboratory parameters on enrollment, including lactate dehydrogenase (LDH), neutrophil–lymphocyte ratio (NLR), C-reactive protein (CRP), ferritin, procalcitonin (PCT), and D-dimer, with predicting the geometric mean (GM) concentration of SARS-CoV-2 receptor-binding domain (RBD)-specific IgG antibody at 3 and 6 months post-infection was assessed in multivariable linear regression models.ResultThe mean ± SD age of patients in the cohort was 46.8 ± 14 years, and 58.8% were male. Data from 68 patients at 3 months follow-up and 55 patients at 6 months follow-up were analyzed. Over 90% of patients were seropositive against RBD-specific IgG till 6 months post-infection. At 3 months, for any 10% increase in absolute lymphocyte count and NLR, there was a 6.28% (95% CI: 9.68, −2.77) decrease and 4.93% (95% CI: 2.43, 7.50) increase, respectively, in GM of IgG concentration, while any 10% increase for LDH, CRP, ferritin, and procalcitonin was associated with a 10.63, 2.87, 2.54, and 3.11% increase in the GM of IgG concentration, respectively. Any 10% increase in LDH, CRP, and ferritin was similarly associated with an 11.28, 2.48, and 3.0% increase in GM of IgG concentration at 6 months post-infection.ConclusionSeveral clinical biomarkers in the acute phase of SARS-CoV-2 infection are associated with enhanced IgG antibody response detected after 6 months of disease onset. The measurement of SARS-CoV-2 specific antibody responses requires improved techniques and is not feasible in all settings. Baseline clinical biomarkers can be a useful alternative as they can predict antibody response during the convalescence period. Individuals with an increased level of NLR, CRP, LDH, ferritin, and procalcitonin may benefit from the boosting effect of vaccines. Further analyses will determine whether biochemical parameters can predict RBD-specific IgG antibody responses at later time points and the association of neutralizing antibody responses

In vivo and in vitro antidiabetic studies of Pereskia bleo leaves

Background review Since ancient times, plants have been used as natural agents to treat diseases particularly diabetes whose prevalence is increasing worldwide. Leaves of Pereskia bleo (Jarum Tujuh Bilah) are traditionally used to treat diabetes in many countries including Malaysia, however, no scientific claim exists in literature. Objective To investigate in vivo and in vitro antidiabetic activity of P. bleo with respect to understand its role in the management of diabetes. Methods Freeze dried aqueous (AQ) and ethanol (ETOH) extracts of the leaves were examined for in vivo antidiabetic activity (alloxan induced diabetic adult albino male rats of Sprague Dawley strain) and in vitro activity (inhibition of alpha-glucosidase and alpha-amylase enzymes). Two doses (250 and 500 mg/kg body weight) of both extracts were administered orally to the normal and diabetic rats. The blood glucose level of the rats was measured by using glucometer at 0, 2, 4, 6, 8 and 24 h after administering both extracts. For in vitro method, the inhibitory activities of both extracts against α- amylase and α-glucosidase were evaluated at 5 different concentrations (i.e. 50, 100, 250, 500, and 1000 µg/ml). Toxicological study was also performed to know the safe nature of both extracts. Results and Conclusion The acute toxicity study revealed LD50 for the both AQ and ETOH extracts above 2500 mg/kg b.w. Both extracts exhibited a significant antihyperglycemic effect in diabetic rats after 24 h treatment of the extracts without showing hypoglycemic effect in normal rats. The highest blood glucose reduction (from 28.3 to 9.0 mmol/l) in diabetic rats was seen in ETOH extract at 250 mg/kg b.w. after 24 h. For in vitro antidiabetic study, both extracts showed high inhibitory activity against α-amylase. The highest inhibition (99.23%) was seen at 1000 µg/mL by AQ extract. On the other hand, AQ extract did not show inhibitory activity against α-glucosidase and ETOH showed a moderate inhibition (15.46%) against α-glucosidase at 1000 µg/mL. The results from this study further justify the traditional claims of P. bleo in the management of diabetes in Malaysia

α-glucosidase inhibitors isolated from Mimosa pudica L.

The aim of the study was to isolate digestive enzymes inhibitors from Mimosa pudica through a bioassay-guided fractionation approach. Repeated silica gel and sephadex LH 20 column chromatographies of bioactive fractions afforded stigmasterol, quercetin and avicularin as digestive enzymes inhibitors whose IC50 values as compared to acarbose (351.02 ± 1.46 μg mL−1) were found to be as 91.08 ± 1.54, 75.16 ± 0.92 and 481.7 ± 0.703 μg mL−1, respectively. In conclusion, M. pudica could be a good and safe source of digestive enzymes inhibitors for the management of diabetes in future

Water level forecasting using spatiotemporal attention-based long short-term memory network

Bangladesh is in the floodplains of the Ganges, Brahmaputra, and Meghna River delta, crisscrossed by an intricate web of rivers. Although the country is highly prone to flooding, the use of state-of-the-art deep learning models in predicting river water levels to aid flood forecasting is underexplored. Deep learning and attention-based models have shown high potential for accurately forecasting floods over space and time. The present study aims to develop a long short-term memory (LSTM) network and its attention-based architectures to predict flood water levels in the rivers of Bangladesh. The models developed in this study incorporated gauge-based water level data over 7 days for flood prediction at Dhaka and Sylhet stations. This study developed five models: artificial neural network (ANN), LSTM, spatial attention LSTM (SALSTM), temporal attention LSTM (TALSTM), and spatiotemporal attention LSTM (STALSTM). The multiple imputation by chained equations (MICE) method was applied to address missing data in the time series analysis. The results showed that the use of both spatial and temporal attention together increases the predictive performance of the LSTM model, which outperforms other attention-based LSTM models. The STALSTM-based flood forecasting system, developed in this study, could inform flood management plans to accurately predict floods in Bangladesh and elsewhere

A Food Frequency Questionnaire for Hemodialysis Patients in Bangladesh (BDHD-FFQ): Development and Validation

Diet is a recognized risk factor and cornerstone for chronic kidney disease (CKD) management; however, a tool to assess dietary intake among Bangladeshi dialysis patients is scarce. This study aims to validate a prototype Bangladeshi Hemodialysis Food Frequency Questionnaire (BDHD-FFQ) against 3-day dietary recall (3DDR) and corresponding serum biomarkers. Nutrients of interest were energy, macronutrients, potassium, phosphate, iron, sodium and calcium. The BDHD-FFQ, comprising 132 food items, was developed from 606 24-h recalls and had undergone face and content validation. Comprehensive facets of relative validity were ascertained using six statistical tests (correlation coefficient, percent difference, paired t-test, cross-quartiles classification, weighted kappa, and Bland-Altman analysis). Overall, the BDHD-FFQ showed acceptable to good correlations (p 0.05). Cross-quartile classification indicated that <10% of patients were incorrectly classified. Weighted kappa statistics showed agreement with all but iron. Bland-Altman analysis showed positive mean differences were observed for all nutrients when compared to 3DDR, whilst energy, carbohydrates, fat, iron, sodium, and potassium had percentage data points within the limit of agreement (mean ± 1.96 SD), above 95%. In summary, the BDHD-FFQ demonstrated an acceptable relative validity for most of the nutrients as four out of the six statistical tests fulfilled the cut-off standard in assessing dietary intake of CKD patients in Bangladesh