Experience in establishing a high-risk biocontainment facility in response to COVID-19 pandemic under resource constrain settings

The health care systems in resource limited countries are facing major challenges in dealing with Coronavirus disease (COVID-19). In Bangladesh, a steady increase in the number of COVID-19 cases since its first report on March 8, 2020, has led to an increased demand for COVID-19 detection facilities throughout the country. The detection of severe acute respiratory syndrome (SARS-CoV-2), the causative organism of COVID-19 and a highly infectious group 3(three) organism, requires a high biocontainment laboratory with a certain standard prerequisite infrastructure. This study describes the necessary steps for establishing and running a COVID-19 laboratory under resource constraint settings. Our experience indicates that, with collaborative efforts, funding, and technical support from locally available expertise, it is feasible to set up an optimally functional biocontainment facility with an acceptable quality performance despite several short comings. BSMMU J 2021; 14 (COVID -19 Supplement): 45-5

Penalty-free feasibility boundary convergent multi-objective evolutionary algorithm for the optimization of water distribution systems

This paper presents a new penalty-free multi-objective evolutionary approach (PFMOEA) for the optimization of water distribution systems (WDSs). The proposed approach utilizes pressure dependent analysis (PDA) to develop a multi-objective evolutionary search. PDA is able to simulate both normal and pressure deficient networks and provides the means to accurately and rapidly identify the feasible region of the solution space, effectively locating global or near global optimal solutions along its active constraint boundary. The significant advantage of this method over previous methods is that it eliminates the need for ad-hoc penalty functions, additional “boundary search” parameters, or special constraint handling procedures. Conceptually, the approach is downright straightforward and probably the simplest hitherto. The PFMOEA has been applied to several WDS benchmarks and its performance examined. It is demonstrated that the approach is highly robust and efficient in locating optimal solutions. Superior results in terms of the initial network construction cost and number of hydraulic simulations required were obtained. The improvements are demonstrated through comparisons with previously published solutions from the literature

Surfactant protein D modulates HIV infection of both T-cells and dendritic cells

Surfactant Protein D (SP-D) is an oligomerized C-type lectin molecule with immunomodulatory properties and involvement in lung surfactant homeostasis in the respiratory tract. SP-D binds to the enveloped viruses, influenza A virus and respiratory syncytial virus and inhibits their replication in vitro and in vivo. SP-D has been shown to bind to HIV via the HIV envelope protein gp120 and inhibit infectivity in vitro. Here we show that SP-D binds to different strains of HIV (BaL and IIIB) and the binding occurs at both pH 7.4 and 5.0 resembling physiological relevant pH values found in the body and the female urogenital tract, respectively. The binding of SP-D to HIV particles and gp120 was inhibited by the presence of several hexoses with mannose found to be the strongest inhibitor. Competition studies showed that soluble CD4 and CVN did not interfere with the interaction between SP-D and gp120. However, soluble recombinant DC-SIGN was shown to inhibit the binding between SP-D and gp120. SP-D agglutinated HIV and gp120 in a calcium dependent manner. SP-D inhibited the infectivity of HIV strains at both pH values of 7.4 and 5.0 in a concentration dependent manner. The inhibition of the infectivity was abolished by the presence of mannose. SP-D enhanced the binding of HIV to immature monocyte derived dendritic cells (iMDDCs) and was also found to enhance HIV capture and transfer to the T-cell like line PM1. These results suggest that SP-D can bind to and inhibit direct infection of T-cells by HIV but also enhance the transfer of infectious HIV particles from DCs to T-cells in vivo

MultiMiTar: A Novel Multi Objective Optimization based miRNA-Target Prediction Method

BACKGROUND: Machine learning based miRNA-target prediction algorithms often fail to obtain a balanced prediction accuracy in terms of both sensitivity and specificity due to lack of the gold standard of negative examples, miRNA-targeting site context specific relevant features and efficient feature selection process. Moreover, all the sequence, structure and machine learning based algorithms are unable to distribute the true positive predictions preferentially at the top of the ranked list; hence the algorithms become unreliable to the biologists. In addition, these algorithms fail to obtain considerable combination of precision and recall for the target transcripts that are translationally repressed at protein level. METHODOLOGY/PRINCIPAL FINDING: In the proposed article, we introduce an efficient miRNA-target prediction system MultiMiTar, a Support Vector Machine (SVM) based classifier integrated with a multiobjective metaheuristic based feature selection technique. The robust performance of the proposed method is mainly the result of using high quality negative examples and selection of biologically relevant miRNA-targeting site context specific features. The features are selected by using a novel feature selection technique AMOSA-SVM, that integrates the multi objective optimization technique Archived Multi-Objective Simulated Annealing (AMOSA) and SVM. CONCLUSIONS/SIGNIFICANCE: MultiMiTar is found to achieve much higher Matthew's correlation coefficient (MCC) of 0.583 and average class-wise accuracy (ACA) of 0.8 compared to the others target prediction methods for a completely independent test data set. The obtained MCC and ACA values of these algorithms range from -0.269 to 0.155 and 0.321 to 0.582, respectively. Moreover, it shows a more balanced result in terms of precision and sensitivity (recall) for the translationally repressed data set as compared to all the other existing methods. An important aspect is that the true positive predictions are distributed preferentially at the top of the ranked list that makes MultiMiTar reliable for the biologists. MultiMiTar is now available as an online tool at www.isical.ac.in/~bioinfo_miu/multimitar.htm. MultiMiTar software can be downloaded from www.isical.ac.in/~bioinfo_miu/multimitar-download.htm

Follicle Stimulating Hormone and Anti-Müllerian Hormone per Oocyte in Predicting in vitro Fertilization Pregnancy in High Responders: A Cohort Study

Background: Follicle stimulating hormone (FSH) and Anti-Müllerian hormone (AMH) are utilized to differentiate between good and poor response to controlled ovarian hyperstimulation. Their respective roles in defining functional ovarian reserve remain, however, to be elucidated. To better understand those we investigated AMH and FSH per oocyte retrieved (AMHo and FSHo). Methodology/Principal Findings: Three-hundred and ninety-six women, undergoing first in vitro fertilization cycles, were retrospectively evaluated. Women with oocyte yields.75 th percentile for their age group were identified as high responders. In a series of logistic regression analyses, AMHo and FSHo levels were then evaluated as predictive factors for pregnancy potential in high responders. Patients presented with a mean age of 38.065.0 years, mean baseline FSH of 11.868.7 mIU/mL and mean AMH of 1.662.1 ng/mL. Those 88 women, who qualified as high responders, showed mean FSH of 9.766.5 mIU/mL, AMH of 3.163.1 ng/mL and oocyte yields of 15.867.1. Baseline FSH and AMH did not predict pregnancy in high responders. However, a statistically significant association between FSHo and pregnancy was observed in high responders, both after univariate regression (p = 0.02) and when adjusted for age, percentage of usable embryos, and number of embryos transferred (p = 0.03). Rate of useable embryos also significantly affected pregnancy outcome independently of FSHo (p = 0.01). AMHo was also associated with clinical pregnancy chances in high responders (p = 0.03

DMSO and Betaine Greatly Improve Amplification of GC-Rich Constructs in De Novo Synthesis

In Synthetic Biology, de novo synthesis of GC-rich constructs poses a major challenge because of secondary structure formation and mispriming. While there are many web-based tools for codon optimizing difficult regions, no method currently exists that allows for potentially phenotypically important sequence conservation. Therefore, to overcome these limitations in researching GC-rich genes and their non-coding elements, we explored the use of DMSO and betaine in two conventional methods of assembly and amplification. For this study, we compared the polymerase (PCA) and ligase-based (LCR) methods for construction of two GC-rich gene fragments implicated in tumorigenesis, IGF2R and BRAF. Though we found no benefit in employing either DMSO or betaine during the assembly steps, both additives greatly improved target product specificity and yield during PCR amplification. Of the methods tested, LCR assembly proved far superior to PCA, generating a much more stable template to amplify from. We further report that DMSO and betaine are highly compatible with all other reaction components of gene synthesis and do not require any additional protocol modifications. Furthermore, we believe either additive will allow for the production of a wide variety of GC-rich gene constructs without the need for expensive and time-consuming sample extraction and purification prior to downstream application

Readthrough of Premature Termination Codons in the Adenomatous Polyposis Coli Gene Restores Its Biological Activity in Human Cancer Cells

The APC tumor suppressor gene is frequently mutated in human colorectal cancer, with nonsense mutations accounting for 30% of all mutations in this gene. Reintroduction of the WT APC gene into cancer cells generally reduces tumorigenicity or induces apoptosis. In this study, we explored the possibility of using drugs to induce premature termination codon (PTC) readthrough (aminoglycosides, negamycin), as a means of reactivating endogenous APC. By quantifying the readthrough of 11 nonsense mutations in APC, we were able to identify those giving the highest levels of readthrough after treatment. For these mutations, we demonstrated that aminoglycoside or negamycin treatment led to a recovery of the biological activity of APC in cancer cell lines, and showed that the level of APC activity was proportional to the level of induced readthrough. These findings show that treatment with readthrough inducers should be considered as a potential strategy for treating cancers caused by nonsense mutations APC gene. They also provide a rational basis for identifying mutations responsive to readthrough inducers