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Distribution of adeB and NDM-1 genes in multidrug resistant Acinetobacter baumannii isolated from infected wound of patients admitted in a tertiary care hospital in Bangladesh

Hasan MJ, Shamsuzzaman SM

Malays J Pathol, 2017 Dec;39(3):277-283.
PMID: 29279590

BACKGROUND: The adeB gene in Acinetobacter baumannii regulates the bacterial internal drug efflux pump that plays a significant role in drug resistance. The aim of our study was to determine the occurrence of adeB gene in multidrug resistant and New Delhi metallo-beta-lactamase-1 (NDM- 1) gene in imipenem resistant Acinetobacter baumannii isolated from wound swab samples in a tertiary care hospital of Bangladesh.
METHODS: A total of 345 wound swab samples were tested for bacterial pathogens. Acinetobacter baumannii was identified by culture and biochemical tests. Antimicrobial susceptibility pattern was determined by the disc diffusion method according to CLSI standards. Extended spectrum beta-lactamases were screened using the double disc synergy technique. Gene encoding AdeB efflux pump and NDM-1 were detected by Polymerase Chain Reaction (PCR).
RESULTS: A total 22 (6.37%) Acinetobacter baumannii were identified from 345 wound swab samples and 20 (91%) of them were multidrug resistant. High resistance rates to some antibiotics were seen namely, cefotaxime (95%), amoxyclavulanic acid (90%) and ceftriaxone (82%). All the identified Acinetobacter baumannii were sensitive to colistin and 82% to imipenem. Two (9%) ESBL producing Acinetobacter baumannii strains were detected. adeB gene was detected in 16 (80%) out of 20 multidrug resistant Acinetobacter baumannii. 4 (18%) of 22 Acinetobacter baumannii were imipenem resistant. NDM-1 gene was detected in 2 (50%) of the imipenem resistant strains of Acinetobacter baumannii.
CONCLUSION: The results of this study provide insight into the role of adeB gene as a potential regulator of drug resistance in Acinetobacter baumanni in Bangladesh. NDM-1 gene also contributes in developing such resistance for Acinetobacter baumannii.
Fulltext The classification of motor imagery response: an accuracy enhancement through the ensemble of random subspace k-NN

Rashid M, Bari BS, Hasan MJ, Razman MAM, Musa RM, Ab Nasir AF, et al.

PeerJ Comput Sci, 2021;7:e374.
PMID: 33817022 DOI: 10.7717/peerj-cs.374

Brain-computer interface (BCI) is a viable alternative communication strategy for patients of neurological disorders as it facilitates the translation of human intent into device commands. The performance of BCIs primarily depends on the efficacy of the feature extraction and feature selection techniques, as well as the classification algorithms employed. More often than not, high dimensional feature set contains redundant features that may degrade a given classifier's performance. In the present investigation, an ensemble learning-based classification algorithm, namely random subspace k-nearest neighbour (k-NN) has been proposed to classify the motor imagery (MI) data. The common spatial pattern (CSP) has been applied to extract the features from the MI response, and the effectiveness of random forest (RF)-based feature selection algorithm has also been investigated. In order to evaluate the efficacy of the proposed method, an experimental study has been implemented using four publicly available MI dataset (BCI Competition III dataset 1 (data-1), dataset IIIA (data-2), dataset IVA (data-3) and BCI Competition IV dataset II (data-4)). It was shown that the ensemble-based random subspace k-NN approach achieved the superior classification accuracy (CA) of 99.21%, 93.19%, 93.57% and 90.32% for data-1, data-2, data-3 and data-4, respectively against other models evaluated, namely linear discriminant analysis, support vector machine, random forest, Naïve Bayes and the conventional k-NN. In comparison with other classification approaches reported in the recent studies, the proposed method enhanced the accuracy by 2.09% for data-1, 1.29% for data-2, 4.95% for data-3 and 5.71% for data-4, respectively. Moreover, it is worth highlighting that the RF feature selection technique employed in the present study was able to significantly reduce the feature dimension without compromising the overall CA. The outcome from the present study implies that the proposed method may significantly enhance the accuracy of MI data classification.
A real-time approach of diagnosing rice leaf disease using deep learning-based faster R-CNN framework

Bari BS, Islam MN, Rashid M, Hasan MJ, Razman MAM, Musa RM, et al.

PeerJ Comput Sci, 2021;7:e432.
PMID: 33954231 DOI: 10.7717/peerj-cs.432

The rice leaves related diseases often pose threats to the sustainable production of rice affecting many farmers around the world. Early diagnosis and appropriate remedy of the rice leaf infection is crucial in facilitating healthy growth of the rice plants to ensure adequate supply and food security to the rapidly increasing population. Therefore, machine-driven disease diagnosis systems could mitigate the limitations of the conventional methods for leaf disease diagnosis techniques that is often time-consuming, inaccurate, and expensive. Nowadays, computer-assisted rice leaf disease diagnosis systems are becoming very popular. However, several limitations ranging from strong image backgrounds, vague symptoms' edge, dissimilarity in the image capturing weather, lack of real field rice leaf image data, variation in symptoms from the same infection, multiple infections producing similar symptoms, and lack of efficient real-time system mar the efficacy of the system and its usage. To mitigate the aforesaid problems, a faster region-based convolutional neural network (Faster R-CNN) was employed for the real-time detection of rice leaf diseases in the present research. The Faster R-CNN algorithm introduces advanced RPN architecture that addresses the object location very precisely to generate candidate regions. The robustness of the Faster R-CNN model is enhanced by training the model with publicly available online and own real-field rice leaf datasets. The proposed deep-learning-based approach was observed to be effective in the automatic diagnosis of three discriminative rice leaf diseases including rice blast, brown spot, and hispa with an accuracy of 98.09%, 98.85%, and 99.17% respectively. Moreover, the model was able to identify a healthy rice leaf with an accuracy of 99.25%. The results obtained herein demonstrated that the Faster R-CNN model offers a high-performing rice leaf infection identification system that could diagnose the most common rice diseases more precisely in real-time.
Fulltext Risk factors of nonalcoholic fatty liver disease in lean body mass population: A systematic review and meta-analysis

Alam S, Eslam M, Skm Hasan N, Anam K, Chowdhury MAB, Khan MAS, et al.

JGH Open, 2021 Nov;5(11):1236-1249.
PMID: 34816009 DOI: 10.1002/jgh3.12658

The pathophysiology and risk factors of nonalcoholic fatty liver disease (NAFLD) among lean patients is poorly understood and therefore investigated. We performed a meta-analysis of observational studies. Of 1175 articles found through searching from Medline/PubMed, Banglajol, and Google Scholar by two independent investigators, 22 were selected. Data from lean (n = 6768) and obese (n = 9253) patients with NAFLD were analyzed; lean (n = 43 398) and obese (n = 9619) subjects without NAFLD served as controls. Age, body mass index, waist circumference, systolic blood pressure, and diastolic blood pressure (DBP) had significantly higher estimates in lean NAFLD patients than in lean non-NAFLD controls. Fasting blood sugar [MD(mean difference) 5.17 mg/dl, 95% CI(confidence interval) 4.14-6.16], HbA1c [MD 0.29%, 95% CI 0.11-0.48], and insulin resistance [HOMA-IR] [MD 0.49 U, 95% CI 0.29-0.68]) were higher in lean NAFLD patients than in lean non-NAFLD controls. All components of the lipid profile were raised significantly in the former group except high-density lipoprotein. An increased uric acid (UA) level was found to be associated with the presence of NAFLD among lean. Cardio-metabolic profiles of nonlean NAFLD patients significantly differs from the counter group. However, the magnitude of the difference of lipid and glycemic profile barely reached statistical significance when subjects were grouped according to lean and nonlean NAFLD. But DBP (slope: 0.19, P