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1. A potential allosteric inhibitor of SARS-CoV-2 main protease (Mpro) identified through metastable state analysis

Author: Asma Fatima, Anupriya M. Geethakumari, Wesam S. Ahmed, and Kabir H. Biswas
Subjects: COVID-19, allosteric regulator, MD simulation, metastable states, Mpro, ZINC15, Biology (General), QH301-705.5
Abstract: Anti-COVID19 drugs, such as nirmatrelvir, have been developed targeting the SARS-CoV-2 main protease, Mpro, based on the critical requirement of its proteolytic processing of the viral polyproteins into functional proteins essential for viral replication. However, the emergence of SARS-CoV-2 variants with Mpro mutations has raised the possibility of developing resistance against these drugs, likely due to therapeutic targeting of the Mpro catalytic site. An alternative to these drugs is the development of drugs that target an allosteric site distant from the catalytic site in the protein that may reduce the chance of the emergence of resistant mutants. Here, we combine computational analysis with in vitro assay and report the discovery of a potential allosteric site and an allosteric inhibitor of SARS-CoV-2 Mpro. Specifically, we identified an Mpro metastable state with a deformed catalytic site harboring potential allosteric sites, raising the possibility that stabilization of this metastable state through ligand binding can lead to the inhibition of Mpro activity. We then performed a computational screening of a library (∼4.2 million) of drug-like compounds from the ZINC database and identified several candidate molecules with high predicted binding affinity. MD simulations showed stable binding of the three top-ranking compounds to the putative allosteric sites in the protein. Finally, we tested the three compounds in vitro using a BRET-based Mpro biosensor and found that one of the compounds (ZINC4497834) inhibited the Mpro activity. We envisage that the identification of a potential allosteric inhibitor of Mpro will aid in developing improved anti-COVID-19 therapy.
Published: 2024
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2. BRET-based biosensors for SARS-CoV-2 oligonucleotide detection

Author: Asfia Sultana, Anupriya M. Geethakumari, Zeyaul Islam, Prasanna R. Kolatkar, and Kabir H. Biswas
Subjects: biosensor, bioluminescence, bioluminescence resonance energy transfer, COVID-19, SARS-CoV-2, molecular beacon, Biotechnology, TP248.13-248.65
Abstract: The need for the early detection of emerging pathogenic viruses and their newer variants has driven the urgent demand for developing point-of-care diagnostic tools. Although nucleic acid-based methods such as reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and loop-mediated isothermal amplification (LAMP) have been developed, a more facile and robust platform is still required. To address this need, as a proof-of-principle study, we engineered a prototype—the versatile, sensitive, rapid, and cost-effective bioluminescence resonance energy transfer (BRET)-based biosensor for oligonucleotide detection (BioOD). Specifically, we designed BioODs against the SARS-CoV-2 parental (Wuhan strain) and B.1.617.2 Delta variant through the conjugation of specific, fluorescently modified molecular beacons (sensor module) through a complementary oligonucleotide handle DNA functionalized with the NanoLuc (NLuc) luciferase protein such that the dissolution of the molecular beacon loop upon the binding of the viral oligonucleotide will result in a decrease in BRET efficiency and, thus, a change in the bioluminescence spectra. Following the assembly of the BioODs, we determined their kinetics response, affinity for variant-specific oligonucleotides, and specificity, and found them to be rapid and highly specific. Furthermore, the decrease in BRET efficiency of the BioODs in the presence of viral oligonucleotides can be detected as a change in color in cell phone camera images. We envisage that the BioODs developed here will find application in detecting viral infections with variant specificity in a point-of-care-testing format, thus aiding in large-scale viral infection surveillance.
Published: 2024
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3. Exosome nanovesicles as potential biomarkers and immune checkpoint signaling modulators in lung cancer microenvironment: recent advances and emerging concepts

Author: Naushad Ahmad Khan, Mohammad Asim, Kabir H. Biswas, Amani N Alansari, Harman Saman, Mohammad Zahid Sarwar, Kudaibergen Osmonaliev, and Shahab Uddin
Subjects: Tumor-derived exosomes, Lung cancer, Biomarkers, Immune checkpoint signaling inhibitors, Tumor micro-environment, Immunotherapy, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract: Abstract Lung cancer remains the leading cause of cancer-related deaths globally, and the survival rate remains low despite advances in diagnosis and treatment. The progression of lung cancer is a multifaceted and dynamic phenomenon that encompasses interplays among cancerous cells and their microenvironment, which incorporates immune cells. Exosomes, which are small membrane-bound vesicles, are released by numerous cell types in normal and stressful situations to allow communication between cells. Tumor-derived exosomes (TEXs) possess diverse neo-antigens and cargoes such as proteins, RNA, and DNA and have a unique molecular makeup reflecting tumor genetic complexity. TEXs contain both immunosuppressive and immunostimulatory factors and may play a role in immunomodulation by influencing innate and adaptive immune components. Moreover, they transmit signals that contribute to the progression of lung cancer by promoting metastasis, epithelial-mesenchymal transition (EMT), angiogenesis, and immunosuppression. This makes them a valuable resource for investigating the immune environment of tumors, which could pave the way for the development of non-invasive biomarkers that could aid in the prognosis, diagnosis, and immunotherapy of lung cancer. While immune checkpoint inhibitor (ICI) immunotherapy has shown promising results in treating initial-stage cancers, most patients eventually develop adaptive resistance over time. Emerging evidence demonstrates that TEXs could serve as a prognostic biomarker for immunotherapeutic response and have a significant impact on both systemic immune suppression and tumor advancement. Therefore, understanding TEXs and their role in lung cancer tumorigenesis and their response to immunotherapies is an exciting research area and needs further investigation. This review highlights the role of TEXs as key contributors to the advancement of lung cancer and their clinical significance in lung immune-oncology, including their possible use as biomarkers for monitoring disease progression and prognosis, as well as emerging shreds of evidence regarding the possibility of using exosomes as targets to improve lung cancer therapy.
Published: 2023
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4. Reversal of the unique Q493R mutation increases the affinity of Omicron S1-RBD for ACE2

Author: Angelin M. Philip, Wesam S. Ahmed, and Kabir H. Biswas
Subjects: ACE2, COVID-19, Omicron, Molecular Dynamics Simulation, Infectious Disease, SARS-CoV-2, Biotechnology, TP248.13-248.65
Abstract: The SARS-CoV-2 Omicron variant containing 15 mutations, including the unique Q493R, in the spike protein receptor binding domain (S1-RBD) is highly infectious. While comparison with previously reported mutations provide some insights, the mechanism underlying the increased infections and the impact of the reversal of the unique Q493R mutation seen in BA.4, BA.5, BA.2.75, BQ.1 and XBB lineages is not yet completely understood. Here, using structural modelling and molecular dynamics (MD) simulations, we show that the Omicron mutations increases the affinity of S1-RBD for ACE2, and a reversal of the unique Q493R mutation further increases the ACE2-S1-RBD affinity. Specifically, we performed all atom, explicit solvent MD simulations using a modelled structure of the Omicron S1-RBD-ACE2 and compared the trajectories with the WT complex revealing a substantial reduction in the Cα-atom fluctuation in the Omicron S1-RBD and increased hydrogen bond and other interactions. Residue level analysis revealed an alteration in the interaction between several residues including a switch in the interaction of ACE2 D38 from S1-RBD Y449 in the WT complex to the mutated R residue (Q493R) in Omicron complex. Importantly, simulations with Revertant (Omicron without the Q493R mutation) complex revealed further enhancement of the interaction between S1-RBD and ACE2. Thus, results presented here not only provide insights into the increased infectious potential of the Omicron variant but also a mechanistic basis for the reversal of the Q493R mutation seen in some Omicron lineages and will aid in understanding the impact of mutations in SARS-CoV-2 evolution.
Published: 2023
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5. Protegrin-2, a potential inhibitor for targeting SARS-CoV-2 main protease Mpro

Author: Zainab Jan, Anupriya M. Geethakumari, Kabir H. Biswas, and Puthen Veettil Jithesh
Subjects: Mpro, COVID-19, SARS-CoV-2, antimicrobial peptides, BRET assay, Protegrin-2, Biotechnology, TP248.13-248.65
Abstract: Background: SARS-CoV-2 variants continue to spread throughout the world and cause waves of COVID-19 infections. It is important to find effective antiviral drugs to combat SARS-CoV-2 and its variants. The main protease (Mpro) of SARS-CoV-2 is a promising therapeutic target due to its crucial role in viral replication and its conservation in all the variants. Therefore, the aim of this work was to identify an effective inhibitor of Mpro. Methods: We studied around 200 antimicrobial peptides using in silico methods including molecular docking and allergenicity and toxicity prediction. One selected antiviral peptide was studied experimentally using a Bioluminescence Resonance Energy Transfer (BRET)-based Mpro biosensor, which reports Mpro activity through a decrease in energy transfer. Results: Molecular docking identified one natural antimicrobial peptide, Protegrin-2, with high binding affinity and stable interactions with Mpro allosteric residues. Furthermore, free energy calculations and molecular dynamics simulation illustrated a high affinity interaction between the two. We also determined the impact of the binding of Protegrin-2 to Mpro using a BRET-based assay, showing that it inhibits the proteolytic cleavage activity of Mpro. Conclusions: Our in silico and experimental studies identified Protegrin-2 as a potent inhibitor of Mpro that could be pursued further towards drug development against COVID-19 infection.
Published: 2023
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6. A genetically encoded BRET-based SARS-CoV-2 Mpro protease activity sensor

Author: Anupriya M. Geethakumari, Wesam S. Ahmed, Saad Rasool, Asma Fatima, S. M. Nasir Uddin, Mustapha Aouida, and Kabir H. Biswas
Subjects: Chemistry, QD1-999
Abstract: The main protease (Mpro) of SARS-CoV-2 is an important target for COVID-19 therapy. Here, a pair of genetically encoded BRET-based sensors for detecting Mpro activity are generated by sandwiching N-terminal autocleavage sites in between the mNeonGreen and NanoLuc proteins.
Published: 2022
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7. Prevalence and Determinants of Antibiotic Self-Administration Among Adult Antibiotic Users: A Cross-Sectional Study

Author: Kabir H, Hasan MK, Tutul AH, Islam MS, Jamil S, Das BC, Islam MF, Jannat H, Ara R, Akter O, Biswas L, Miah ME, and Mitra DK
Subjects: antibiotic, self-administration, antibiotic resistance, bangladesh, Medicine (General), R5-920
Abstract: Humayun Kabir,1,&ast; Md Kamrul Hasan,1,2,&ast; Ariful Haque Tutul,3,&ast; Mohammad Shahidul Islam,3,&ast; Safayet Jamil,4 Bikash Chandra Das,1 Md Fakrul Islam,1 Hasina Jannat,1 Rawshan Ara,5 Ojifa Akter,6 Lila Biswas,7 Md Ebrahim Miah,6,8 Dipak Kumar Mitra1 1Department of Public Health, North South University, Dhaka, 1229, Bangladesh; 2Department of Biochemistry and Molecular Biology, Tejgaon College, Dhaka, 1215, Bangladesh; 3Dhaka Medical College and Hospital, Dhaka, 1000, Bangladesh; 4Department of Pharmacy, Khwaja Yunus Ali University, Sirajganj, 6751, Bangladesh; 5Prime College of Nursing, Dhaka, 1229, Bangladesh; 6School of Medical Sciences, Shahjalal University of Science & Technology, Sylhet, 3114, Bangladesh; 7Department of Nursing, CRP Nursing College, Savar, 1343, Bangladesh; 8Institute of Social Welfare and Research, University of Dhaka, Dhaka, 1000, Bangladesh&ast;These authors contributed equally to this workCorrespondence: Humayun Kabir; Md Kamrul Hasan, Department of Public Health, North South University, Dhaka, 1229, Bangladesh, Tel +880 1785811449; +880 1684867565, Email humayun.kabir03@northsouth.edu; kamrul.hasan11@northsouth.eduIntroduction: Each year, antibiotics save hundreds of thousands of lives; nonetheless, antibiotic self-administration is a major concern all over the world. This study aimed to investigate the prevalence of antibiotic self-administration among two-month adult antibiotic users as well as the factors contributing to this prevalence.Method and Participants: This cross-sectional study was conducted among 295 Bangladeshi adults between May 22nd and June 15th, 2021, during the COVID-19 pandemic. Descriptive statistics included frequency distribution, while inferential statistics included the Pearson chi-square test. For data analysis, the statistical software STATA-16 was used.Results: In this study, the prevalence of antibiotic self-administration was 17.97%. Antibiotic self-administration was found to be significantly more prevalent among those who were unable to take antibiotic on time, incomplete doses, did not know over prescriptions may cause antibiotic resistance, and could not correctly recognize amoxicillin and azithromycin are antibiotics.Conclusion: Due to the increased rate of antibiotic self-administration among adults in Bangladesh, the responsible authority should give more attention towards the factors responsible for antibiotic self-administration and revise their current policy to ensure the safe and effective use of antibiotics.Keywords: antibiotic, self-administration, antibiotic resistance, Bangladesh
Published: 2022

8. COVID-19Base v3: Update of the knowledgebase for drugs and biomedical entities linked to COVID-19

Author: Syed Abdullah Basit, Rizwan Qureshi, Saleh Musleh, Reto Guler, M. Sohel Rahman, Kabir H. Biswas, and Tanvir Alam
Subjects: SARS-CoV-2, CORD-19, deep learning, machine learning, COVID-19, Public aspects of medicine, RA1-1270
Abstract: COVID-19 has taken a huge toll on our lives over the last 3 years. Global initiatives put forward by all stakeholders are still in place to combat this pandemic and help us learn lessons for future ones. While the vaccine rollout was not able to curb the spread of the disease for all strains, the research community is still trying to develop effective therapeutics for COVID-19. Although Paxlovid and remdesivir have been approved by the FDA against COVID-19, they are not free of side effects. Therefore, the search for a therapeutic solution with high efficacy continues in the research community. To support this effort, in this latest version (v3) of COVID-19Base, we have summarized the biomedical entities linked to COVID-19 that have been highlighted in the scientific literature after the vaccine rollout. Eight different topic-specific dictionaries, i.e., gene, miRNA, lncRNA, PDB entries, disease, alternative medicines registered under clinical trials, drugs, and the side effects of drugs, were used to build this knowledgebase. We have introduced a BLSTM-based deep-learning model to predict the drug-disease associations that outperforms the existing model for the same purpose proposed in the earlier version of COVID-19Base. For the very first time, we have incorporated disease-gene, disease-miRNA, disease-lncRNA, and drug-PDB associations covering the largest number of biomedical entities related to COVID-19. We have provided examples of and insights into different biomedical entities covered in COVID-19Base to support the research community by incorporating all of these entities under a single platform to provide evidence-based support from the literature. COVID-19Base v3 can be accessed from: https://covidbase-v3.vercel.app/. The GitHub repository for the source code and data dictionaries is available to the community from: https://github.com/91Abdullah/covidbasev3.0.
Published: 2023
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9. Suicidality in Bangladeshi Young Adults During the COVID-19 Pandemic: The Role of Behavioral Factors, COVID-19 Risk and Fear, and Mental Health Problems

Author: Mamun MA, Mamun FA, Hosen I, Hasan M, Rahman A, Jubayar AM, Maliha Z, Abdullah AH, Sarker MA, Kabir H, Jyoti AS, Kaggwa MM, and Sikder MT
Subjects: covid-19 suicide, psychological impact, suicide, self-harm, depression, fear of covid-19, bangladeshi youths, Public aspects of medicine, RA1-1270
Abstract: Mohammed A Mamun,1,2 Firoj al Mamun,1,2 Ismail Hosen,1,2 Mahmudul Hasan,1,3 Abidur Rahman,1,3 Ahsanul Mahbub Jubayar,1,3 Zeba Maliha,1,4 Abu Hasnat Abdullah,1,2 Md Abedin Sarker,1,2 Humayun Kabir,1,5 Avijit Sarker Jyoti,6 Mark Mohan Kaggwa,7,8 Md Tajuddin Sikder2 1CHINTA Research Bangladesh, Dhaka, Bangladesh; 2Department of Public Health and Informatics, Jahangirnagar University, Dhaka, Bangladesh; 3Department of Biochemistry and Molecular Biology, University of Rajshahi, Rajshahi, Bangladesh; 4Bangabandhu Sheikh Mujib Medical College, Faridpur, Bangladesh; 5Department of Public Health, North South University, Dhaka, Bangladesh; 6Mugda Medical College, Dhaka, Bangladesh; 7Department of Psychiatry, Mbarara University of Science & Technology, Mbarara, Uganda; 8African Centre for Suicide Prevention and Research, Mbarara, UgandaCorrespondence: Mohammed A Mamun Tel +880 1738592653Email mamunphi46@gmail.comBackground: It is said that psychological stressors have risen during the COVID-19 pandemic, which may contribute to suicidality. A few studies were conducted investigating suicidality amid the first wave of the pandemic in Bangladesh, but none of these studies explored the predictive role of the suicidality-related factors (eg, behavior and health-related variables, COVID-19 risk, fear of COVID-19). Thus, this study aimed to investigate the prevalence of suicidality and the predictive models explaining suicidality variance among Bangladeshi young adults during the second wave of the pandemic.Methods: An online-based cross-sectional survey was conducted during the second wave of the pandemic (between 1 and 13 April 2021; a year after the pandemic’s inception in the country). The survey questionnaire collected information on socio-demographics, behavior and health-related variables, COVID-19 risk, fear of COVID-19, depression, anxiety, and suicidality. A total of 756 data were analyzed (male 59%; mean age 22.24 ± 4.39 years), and hierarchical regression was performed considering suicidality as the dependent variable. A p-value < 0.05 was set to be statistically significant with a 95% of confidence interval.Results: About 8.2% reported having at least suicidal thoughts within the pandemic inception to survey time (one-year suicidal ideation). More specifically, 7.40% had only suicidal ideation, whereas 0.10% planned for suicide, 0.70% attempted suicide. None of the socio-demographic variables was significantly associated with suicidality. Taking drugs, performing less physical activity, poor self-reporting health condition, being comorbid, being at higher COVID-19 risk, having fear of COVID-19 infection, and suffering from depression and anxiety were significantly associated with a higher risk of suicidality. However, the final model including all of the studied variables explained a 17.1% (F=8.245, p< 0.001) variance in terms of suicidality.Conclusion: Although this study reported a lower severity of suicidality than prior Bangladeshi studies conducted during the first wave of the pandemic, a portion of people are still at risk of suicide and they should be given attention.Keywords: COVID-19 suicide, psychological impact, suicide, self-harm, depression, fear of COVID-19, Bangladeshi youths
Published: 2021

10. Corrigendum: Decreased interfacial dynamics caused by the N501Y mutation in the SARS-CoV-2 S1 spike:ACE2 complex

Author: Wesam S. Ahmed, Angelin M. Philip, and Kabir H. Biswas
Subjects: ACE2, COVID-19, molecular dynamics simulation, SARS-CoV-2, S1 spike protein, N501Y mutant, Biology (General), QH301-705.5
Published: 2022
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11. The evolving role of extracellular vesicles (exosomes) as biomarkers in traumatic brain injury: Clinical perspectives and therapeutic implications

Author: Naushad Ahmad Khan, Mohammad Asim, Ayman El-Menyar, Kabir H. Biswas, Sandro Rizoli, and Hassan Al-Thani
Subjects: extracellular vesicles, exosomes, traumatic brain injury, neurodegenerative diseases, blood–brain barrier, biomarkers, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract: Developing effective disease-modifying therapies for neurodegenerative diseases (NDs) requires reliable diagnostic, disease activity, and progression indicators. While desirable, identifying biomarkers for NDs can be difficult because of the complex cytoarchitecture of the brain and the distinct cell subsets seen in different parts of the central nervous system (CNS). Extracellular vesicles (EVs) are heterogeneous, cell-derived, membrane-bound vesicles involved in the intercellular communication and transport of cell-specific cargos, such as proteins, Ribonucleic acid (RNA), and lipids. The types of EVs include exosomes, microvesicles, and apoptotic bodies based on their size and origin of biogenesis. A growing body of evidence suggests that intercellular communication mediated through EVs is responsible for disseminating important proteins implicated in the progression of traumatic brain injury (TBI) and other NDs. Some studies showed that TBI is a risk factor for different NDs. In terms of therapeutic potential, EVs outperform the alternative synthetic drug delivery methods because they can transverse the blood–brain barrier (BBB) without inducing immunogenicity, impacting neuroinflammation, immunological responses, and prolonged bio-distribution. Furthermore, EV production varies across different cell types and represents intracellular processes. Moreover, proteomic markers, which can represent a variety of pathological processes, such as cellular damage or neuroinflammation, have been frequently studied in neurotrauma research. However, proteomic blood-based biomarkers have short half-lives as they are easily susceptible to degradation. EV-based biomarkers for TBI may represent the complex genetic and neurometabolic abnormalities that occur post-TBI. These biomarkers are not caught by proteomics, less susceptible to degradation and hence more reflective of these modifications (cellular damage and neuroinflammation). In the current narrative and comprehensive review, we sought to discuss the contemporary knowledge and better understanding the EV-based research in TBI, and thus its applications in modern medicine. These applications include the utilization of circulating EVs as biomarkers for diagnosis, developments of EV-based therapies, and managing their associated challenges and opportunities.
Published: 2022
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12. Decreased Interfacial Dynamics Caused by the N501Y Mutation in the SARS-CoV-2 S1 Spike:ACE2 Complex

Author: Wesam S. Ahmed, Angelin M. Philip, and Kabir H. Biswas
Subjects: ACE2, COVID-19, molecular dynamics simulation, SARS-CoV-2, S1 spike protein, N501Y mutant, Biology (General), QH301-705.5
Abstract: Coronavirus Disease of 2019 (COVID-19) caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has resulted in a massive health crisis across the globe, with some genetic variants gaining enhanced infectivity and competitive fitness, and thus significantly aggravating the global health concern. In this regard, the recent SARS-CoV-2 alpha, beta, and gamma variants (B.1.1.7, B.1.351, and P.1 lineages, respectively) are of great significance in that they contain several mutations that increase their transmission rates as evident from clinical reports. By the end of March 2021, these variants were accounting for about two-thirds of SARS-CoV-2 variants circulating worldwide. Specifically, the N501Y mutation in the S1 spike receptor binding domain (S1-RBD) of these variants have been reported to increase its affinity for ACE2, although the basis for this is not entirely clear yet. Here, we dissect the mechanism underlying the increased binding affinity of the N501Y mutant for ACE2 using molecular dynamics (MD) simulations of the available ACE2-S1-RBD complex structure (6M0J) and show a prolonged and stable interfacial interaction of the N501Y mutant S1-RBD with ACE2 compared to the wild type S1-RBD. Additionally, we find that the N501Y mutant S1-RBD displays altered dynamics that likely aids in its enhanced interaction with ACE2. By elucidating a mechanistic basis for the increased affinity of the N501Y mutant S1-RBD for ACE2, we believe that the results presented here will aid in developing therapeutic strategies against SARS-CoV-2 including designing of therapeutic agents targeting the ACE2-S1-RBD interaction.
Published: 2022
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13. An experimental study on high-pressure water jets for paraffin scale removal in partially blocked production tubings

Author: Kabir H. Yar’Adua, Idoko J. John, Abubakar J. Abbas, Kazeem A. Lawal, and Aisha Kabir
Subjects: Flow assurance, Oil field descaling, Paraffin scales, Descaling efficiency, High-pressure water jets, Soft scales, Petroleum refining. Petroleum products, TP690-692.5, Petrology, QE420-499
Abstract: Abstract The common practice of using chemicals and solid-entrained liquids to remove soft scales in production tubings is associated with a high risk of contaminating the environment and eroding pipe internal surfaces. Due to the suspended solids, the current practices are also characterized by high pumping costs and are more problematic to rotating parts of machinery than freshwater. As a cheap and less risky alternative to these corrosive chemicals and liquids, this paper investigates the feasibility of utilizing multiple high-pressure (HP) water jets for the same objective. A total of 54 experimental trials were conducted to study the effects of four factors on the efficiency of scale removal with multiple flat-fan nozzles at an orientation of 25°. The factors investigated are (1) number of nozzles; (2) spray injection pressure; (3) stand-off distance between the spray nozzle and target scale; and (4) condition of the production tubing: ambient and pressurized. Details of the experimental set-up, equipment and procedure are provided. The results of these controlled experiments show a positive correlation between descaling efficiency and spray injection pressure. The same set of experiments reveals a negative correlation between descaling efficiency and nozzle count, as well as between descaling efficiency and spray stand-off. However, for the scale samples and range of parameters investigated in this study, descaling efficiency did not exhibit significant dependency on the chamber conditions, i.e. ambient versus pressurized. The results of this study provide some insights into the feasibility of multiple HP water jets as a cleaner alternative to the use of corrosive chemicals and solid-entrained liquids to remove soft scales in production tubings in oil fields and other applications.
Published: 2020
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14. Understanding the Mechanism of Dysglycemia in a Fanconi-Bickel Syndrome Patient

Author: Sanaa Sharari, Mustapha Aouida, Idris Mohammed, Basma Haris, Ajaz Ahmad Bhat, Iman Hawari, Sabah Nisar, Igor Pavlovski, Kabir H. Biswas, Najeeb Syed, Selma Maacha, Jean-Charles Grivel, Maryam Saifaldeen, Johan Ericsson, and Khalid Hussain
Subjects: Fanconi-Bickel syndrome (FBS), dysglycemia, glucose transporter 2 (GLUT2), clustered regularly interspaced short palindromic repeats (CRISPR)- Cas9, sodium-glucose transport protein 2 (SGLT2), Diseases of the endocrine glands. Clinical endocrinology, RC648-665
Abstract: Fanconi–Bickel Syndrome (FBS) is a rare disorder of carbohydrate metabolism that is characterized mainly by the accumulation of glycogen in the liver and kidney. It is inherited as an autosomal recessive disorder caused by mutations in the SLC2A2 gene, which encodes for GLUT2. Patients with FBS have dysglycemia but the molecular mechanisms of dysglycemia are still not clearly understood. Therefore, we aimed to understand the underlying molecular mechanisms of dysglycemia in a patient with FBS. Genomic DNA was isolated from a peripheral blood sample and analyzed by whole genome and Sanger sequencing. CRISPR-Cas9 was used to introduce a mutation that mimics the patient’s mutation in a human kidney cell line expressing GLUT2 (HEK293T). Mutant cells were used for molecular analysis to investigate the effects of the mutation on the expression and function of GLUT2, as well as the expression of other genes implicated in dysglycemia. The patient was found to have a homozygous nonsense mutation (c.901C>T, R301X) in the SLC2A2 gene. CRISPR-Cas9 successfully mimicked the patient’s mutation in HEK293T cells. The mutant cells showed overexpression of a dysfunctional GLUT2 protein, resulting in reduced glucose release activity and enhanced intracellular glucose accumulation. In addition, other glucose transporters (SGLT1 and SGLT2 in the kidney) were found to be induced in the mutant cells. These findings suggest the last loops (loops 9-12) of GLUT2 are essential for glucose transport activity and indicate that GLUT2 dysfunction is associated with dysglycemia in FBS.
Published: 2022
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15. Structure-Based Virtual Screening and Functional Validation of Potential Hit Molecules Targeting the SARS-CoV-2 Main Protease

Author: Balasubramanian Moovarkumudalvan, Anupriya Madhukumar Geethakumari, Ramya Ramadoss, Kabir H. Biswas, and Borbala Mifsud
Subjects: COVID-19, SARS-CoV-2 main protease, structure-based virtual screening, molecular docking, FDA-approved drugs, natural products, Microbiology, QR1-502
Abstract: The recent global health emergency caused by the coronavirus disease 2019 (COVID-19) pandemic has taken a heavy toll, both in terms of lives and economies. Vaccines against the disease have been developed, but the efficiency of vaccination campaigns worldwide has been variable due to challenges regarding production, logistics, distribution and vaccine hesitancy. Furthermore, vaccines are less effective against new variants of the SARS-CoV-2 virus and vaccination-induced immunity fades over time. These challenges and the vaccines’ ineffectiveness for the infected population necessitate improved treatment options, including the inhibition of the SARS-CoV-2 main protease (Mpro). Drug repurposing to achieve inhibition could provide an immediate solution for disease management. Here, we used structure-based virtual screening (SBVS) to identify natural products (from NP-lib) and FDA-approved drugs (from e-Drug3D-lib and Drugs-lib) which bind to the Mpro active site with high-affinity and therefore could be designated as potential inhibitors. We prioritized nine candidate inhibitors (e-Drug3D-lib: Ciclesonide, Losartan and Telmisartan; Drugs-lib: Flezelastine, Hesperidin and Niceverine; NP-lib: three natural products) and predicted their half maximum inhibitory concentration using DeepPurpose, a deep learning tool for drug–target interactions. Finally, we experimentally validated Losartan and two of the natural products as in vitro Mpro inhibitors, using a bioluminescence resonance energy transfer (BRET)-based Mpro sensor. Our study suggests that existing drugs and natural products could be explored for the treatment of COVID-19.
Published: 2022
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16. Determination of Heavy Metal Residues in Slaughtered Camels at Sokoto and Gusau Modern Abattoirs, Nigeria

Author: Akawu Bala, Abdulkadir Usman Junaidu, Mohammed Danlami Salihu, Bello Mohammed Agaie, Mahmud Abdulahi Saulawa, Aliyu Ibrahim Musawa, and Kabir H. Ahmad
Subjects: abattoir, camels, kidney, liver, muscle, hide, blood, Environmental technology. Sanitary engineering, TD1-1066
Abstract: Background. Heavy metals can pose health risks to both animals and humans. Objectives. To determine the concentrations of lead (Pb), cadmium (Cd), and chromium (Cr) in samples taken from the kidney, liver, muscle, hide, and blood of camels slaughtered at both Sokoto and Gusau modern abattoirs. Methods. The concentrations of Pb, Cd, and Cr in tissues and organs of camels slaughtered at both Sokoto and Gusau modern abattoirs were determined using atomic absorption spectrophotometry. A total of 120 samples were collected. Results. All the samples collected tested positive for Pb, Cd and Cr. The overall mean concentrations of Pb, Cd and Cr in tissues and organs of slaughtered camels at Sokoto modern abattoir ranged from 0.11 mg/kg to 0.35 mg/kg, 0.05 to 0.8 mg/kg and 0.41 to 0.59 mg/kg, respectively, while at Gusau modern abattoir, the overall mean concentrations of Pb, Cd and Cr ranged from 0.20 mg/kg to 1.17 mg/kg, 0.01 to 0.14 mg/kg and 0.13 to 0.51 mg/kg, respectively. Discussion. The concentration of Pb in the tissues and organs of camels slaughtered at Gusau modern abattoir was high compared to in camels slaughtered at Sokoto modern abattoir, while the concentrations of Cd and Cr in the tissues and organs of camels slaughtered at Sokoto modern abattoir were high compared to those in tissues and organs of camels slaughtered at Gusau modern abattoir. There were significant differences (P
Published: 2018
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17. Toward security quantification of serverless computing

Author: Ni, Kan, Mondal, Subrota Kumar, Kabir, H M Dipu, Tan, Tian, and Dai, Hong-Ning
Published: 2024
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18. Regulation of α-catenin conformation at cadherin adhesions

Author: Kabir H BISWAS
Subjects: α-catenin, actin, β-catenin, cadherin, conformation, mechanical signaling, phosphorylation, Science, Mechanical engineering and machinery, TJ1-1570
Abstract: Cells in our body utilize a variety of adaptor proteins for transmitting context specific signals that arise from the cellular microenvironment. Adaptor proteins lack enzymatic activity and typically perform their function by acting as scaffolds that bind other signaling proteins. While most adaptor proteins are functionally modulated by biochemical alterations such as phosphorylation, a subset of adaptor proteins are functionally modulated by a mechanical alteration in their structure that makes cryptic sites available for binding to downstream signaling proteins. α-catenin is one such adaptor protein that localizes to cadherin-based cell adhesions by binding the membrane-localized cadherin-β-catenin complex at one side and the cytosolic F-actin on the other side. An increase in actomyosin tension is directly relayed to α-catenin resulting in a change in its conformation making cryptic binding sites accessible to its interacting partners. Here, I describe an updated view of the mechanical regulation of α-catenin in the context of cellular adhesion, including the role of cadherin clustering in its activation.
Published: 2018
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19. Resource allocation problem and artificial intelligence: the state-of-the-art review (2009–2023) and open research challenges

Author: Hassannataj Joloudari, Javad, Mojrian, Sanaz, Saadatfar, Hamid, Nodehi, Issa, Fazl, Fatemeh, Khanjani Shirkharkolaie, Sahar, Alizadehsani, Roohallah, Kabir, H. M. Dipu, Tan, Ru-San, and Acharya, U. Rajendra
Published: 2024
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20. Reduction of Class Activation Uncertainty with Background Information

Author: Kabir, H M Dipu
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Multitask learning is a popular approach to training high-performing neural networks with improved generalization. In this paper, we propose a background class to achieve improved generalization at a lower computation compared to multitask learning to help researchers and organizations with limited computation power. We also present a methodology for selecting background images and discuss potential future improvements. We apply our approach to several datasets and achieve improved generalization with much lower computation. Through the class activation mappings (CAMs) of the trained models, we observed the tendency towards looking at a bigger picture with the proposed model training methodology. Applying the vision transformer with the proposed background class, we receive state-of-the-art (SOTA) performance on CIFAR-10C, Caltech-101, and CINIC-10 datasets. Example scripts are available in the `CAM' folder of the following GitHub Repository: github.com/dipuk0506/UQ
Published: 2023

21. Uncertainty Aware Neural Network from Similarity and Sensitivity

Author: Kabir, H M Dipu, Mondal, Subrota Kumar, Khanam, Sadia, Khosravi, Abbas, Rahman, Shafin, Qazani, Mohammad Reza Chalak, Alizadehsani, Roohallah, Asadi, Houshyar, Mohamed, Shady, Nahavandi, Saeid, and Acharya, U Rajendra
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Neural and Evolutionary Computing
Abstract: Researchers have proposed several approaches for neural network (NN) based uncertainty quantification (UQ). However, most of the approaches are developed considering strong assumptions. Uncertainty quantification algorithms often perform poorly in an input domain and the reason for poor performance remains unknown. Therefore, we present a neural network training method that considers similar samples with sensitivity awareness in this paper. In the proposed NN training method for UQ, first, we train a shallow NN for the point prediction. Then, we compute the absolute differences between prediction and targets and train another NN for predicting those absolute differences or absolute errors. Domains with high average absolute errors represent a high uncertainty. In the next step, we select each sample in the training set one by one and compute both prediction and error sensitivities. Then we select similar samples with sensitivity consideration and save indexes of similar samples. The ranges of an input parameter become narrower when the output is highly sensitive to that parameter. After that, we construct initial uncertainty bounds (UB) by considering the distribution of sensitivity aware similar samples. Prediction intervals (PIs) from initial uncertainty bounds are larger and cover more samples than required. Therefore, we train bound correction NN. As following all the steps for finding UB for each sample requires a lot of computation and memory access, we train a UB computation NN. The UB computation NN takes an input sample and provides an uncertainty bound. The UB computation NN is the final product of the proposed approach. Scripts of the proposed method are available in the following GitHub repository: github.com/dipuk0506/UQ
Published: 2023
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22. Engineering β-catenin-derived peptides for α-catenin binding

Author: Uddin, S. M. Nasir, Rasool, Saad, Geethakumari, Anupriya M., Ahmed, Wesam S., and Biswas, Kabir H.
Published: 2024
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23. CoV-TI-Net: Transferred Initialization with Modified End Layer for COVID-19 Diagnosis

Author: Khanam, Sadia, Qazani, Mohammad Reza Chalak, Mondal, Subrota Kumar, Kabir, H M Dipu, Sabyasachi, Abadhan S., Asadi, Houshyar, Kumar, Keshav, Tabarsinezhad, Farzin, Mohamed, Shady, Khorsavi, Abbas, and Nahavandi, Saeid
Subjects: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract: This paper proposes transferred initialization with modified fully connected layers for COVID-19 diagnosis. Convolutional neural networks (CNN) achieved a remarkable result in image classification. However, training a high-performing model is a very complicated and time-consuming process because of the complexity of image recognition applications. On the other hand, transfer learning is a relatively new learning method that has been employed in many sectors to achieve good performance with fewer computations. In this research, the PyTorch pre-trained models (VGG19\_bn and WideResNet -101) are applied in the MNIST dataset for the first time as initialization and with modified fully connected layers. The employed PyTorch pre-trained models were previously trained in ImageNet. The proposed model is developed and verified in the Kaggle notebook, and it reached the outstanding accuracy of 99.77% without taking a huge computational time during the training process of the network. We also applied the same methodology to the SIIM-FISABIO-RSNA COVID-19 Detection dataset and achieved 80.01% accuracy. In contrast, the previous methods need a huge compactional time during the training process to reach a high-performing model. Codes are available at the following link: github.com/dipuk0506/SpinalNet
Published: 2022

24. The state-of-the-art review on resource allocation problem using artificial intelligence methods on various computing paradigms

Author: Joloudari, Javad Hassannataj, Mojrian, Sanaz, Saadatfar, Hamid, Nodehi, Issa, Fazl, Fatemeh, shirkharkolaie, Sahar Khanjani, Alizadehsani, Roohallah, Kabir, H M Dipu, Tan, Ru-San, and Acharya, U Rajendra
Subjects: Computer Science - Artificial Intelligence, Computer Science - Networking and Internet Architecture
Abstract: With the increasing growth of information through smart devices, increasing the quality level of human life requires various computational paradigms presentation including the Internet of Things, fog, and cloud. Between these three paradigms, the cloud computing paradigm as an emerging technology adds cloud layer services to the edge of the network so that resource allocation operations occur close to the end-user to reduce resource processing time and network traffic overhead. Hence, the resource allocation problem for its providers in terms of presenting a suitable platform, by using computational paradigms is considered a challenge. In general, resource allocation approaches are divided into two methods, including auction-based methods(goal, increase profits for service providers-increase user satisfaction and usability) and optimization-based methods(energy, cost, network exploitation, Runtime, reduction of time delay). In this paper, according to the latest scientific achievements, a comprehensive literature study (CLS) on artificial intelligence methods based on resource allocation optimization without considering auction-based methods in various computing environments are provided such as cloud computing, Vehicular Fog Computing, wireless, IoT, vehicular networks, 5G networks, vehicular cloud architecture,machine-to-machine communication(M2M),Train-to-Train(T2T) communication network, Peer-to-Peer(P2P) network. Since deep learning methods based on artificial intelligence are used as the most important methods in resource allocation problems; Therefore, in this paper, resource allocation approaches based on deep learning are also used in the mentioned computational environments such as deep reinforcement learning, Q-learning technique, reinforcement learning, online learning, and also Classical learning methods such as Bayesian learning, Cummins clustering, Markov decision process., Comment: 23 pages, 9 figures
Published: 2022

25. A Comprehensive Study on Torchvision Pre-trained Models for Fine-grained Inter-species Classification

Author: Albardi, Feras, Kabir, H M Dipu, Bhuiyan, Md Mahbub Islam, Kebria, Parham M., Khosravi, Abbas, and Nahavandi, Saeid
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract: This study aims to explore different pre-trained models offered in the Torchvision package which is available in the PyTorch library. And investigate their effectiveness on fine-grained images classification. Transfer Learning is an effective method of achieving extremely good performance with insufficient training data. In many real-world situations, people cannot collect sufficient data required to train a deep neural network model efficiently. Transfer Learning models are pre-trained on a large data set, and can bring a good performance on smaller datasets with significantly lower training time. Torchvision package offers us many models to apply the Transfer Learning on smaller datasets. Therefore, researchers may need a guideline for the selection of a good model. We investigate Torchvision pre-trained models on four different data sets: 10 Monkey Species, 225 Bird Species, Fruits 360, and Oxford 102 Flowers. These data sets have images of different resolutions, class numbers, and different achievable accuracies. We also apply their usual fully-connected layer and the Spinal fully-connected layer to investigate the effectiveness of SpinalNet. The Spinal fully-connected layer brings better performance in most situations. We apply the same augmentation for different models for the same data set for a fair comparison. This paper may help future Computer Vision researchers in choosing a proper Transfer Learning model., Comment: Accepted
Published: 2021

26. Machine translation and its evaluation: a study

Author: Mondal, Subrota Kumar, Zhang, Haoxi, Kabir, H. M. Dipu, Ni, Kan, and Dai, Hong-Ning
Published: 2023
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27. A slow but steady nanoLuc: R162A mutation results in a decreased, but stable, nanoLuc activity

Author: Ahmed, Wesam S., Geethakumari, Anupriya M., Sultana, Asfia, Fatima, Asma, Philip, Angelin M., Uddin, S.M. Nasir, and Biswas, Kabir H.
Published: 2024
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28. SpinalNet: Deep Neural Network with Gradual Input

Author: Kabir, H M Dipu, Abdar, Moloud, Jalali, Seyed Mohammad Jafar, Khosravi, Abbas, Atiya, Amir F, Nahavandi, Saeid, and Srinivasan, Dipti
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing, Electrical Engineering and Systems Science - Image and Video Processing
Abstract: Deep neural networks (DNNs) have achieved the state of the art performance in numerous fields. However, DNNs need high computation times, and people always expect better performance in a lower computation. Therefore, we study the human somatosensory system and design a neural network (SpinalNet) to achieve higher accuracy with fewer computations. Hidden layers in traditional NNs receive inputs in the previous layer, apply activation function, and then transfer the outcomes to the next layer. In the proposed SpinalNet, each layer is split into three splits: 1) input split, 2) intermediate split, and 3) output split. Input split of each layer receives a part of the inputs. The intermediate split of each layer receives outputs of the intermediate split of the previous layer and outputs of the input split of the current layer. The number of incoming weights becomes significantly lower than traditional DNNs. The SpinalNet can also be used as the fully connected or classification layer of DNN and supports both traditional learning and transfer learning. We observe significant error reductions with lower computational costs in most of the DNNs. Traditional learning on the VGG-5 network with SpinalNet classification layers provided the state-of-the-art (SOTA) performance on QMNIST, Kuzushiji-MNIST, EMNIST (Letters, Digits, and Balanced) datasets. Traditional learning with ImageNet pre-trained initial weights and SpinalNet classification layers provided the SOTA performance on STL-10, Fruits 360, Bird225, and Caltech-101 datasets. The scripts of the proposed SpinalNet are available at the following link: https://github.com/dipuk0506/SpinalNet
Published: 2020
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29. Regulation of Tight Junction by Cadherin Adhesion and Its Implication in Inflammation and Cancer

Author: Nasir Uddin, S. M., Sultana, Asfia, Fatima, Asma, Geethakumari, Anupriya M., Biswas, Kabir H., Bhat, Ajaz A., editor, Haris, Mohammad, editor, Macha, Muzafar A., editor, and Dhawan, Punita, editor
Published: 2023
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30. Optimal Uncertainty-guided Neural Network Training

Author: Kabir, H M Dipu, Khosravi, Abbas, Kavousi-Fard, Abdollah, Nahavandi, Saeid, and Srinivasan, Dipti
Subjects: Statistics - Machine Learning, Computer Science - Machine Learning
Abstract: The neural network (NN)-based direct uncertainty quantification (UQ) methods have achieved the state of the art performance since the first inauguration, known as the lower-upper-bound estimation (LUBE) method. However, currently-available cost functions for uncertainty guided NN training are not always converging and all converged NNs are not generating optimized prediction intervals (PIs). Moreover, several groups have proposed different quality criteria for PIs. These raise a question about their relative effectiveness. Most of the existing cost functions of uncertainty guided NN training are not customizable and the convergence of training is uncertain. Therefore, in this paper, we propose a highly customizable smooth cost function for developing NNs to construct optimal PIs. The optimized average width of PIs, PI-failure distances and the PI coverage probability (PICP) are computed for the test dataset. The performance of the proposed method is examined for the wind power generation and the electricity demand data. Results show that the proposed method reduces variation in the quality of PIs, accelerates the training, and improves convergence probability from 99.2% to 99.8%.
Published: 2019
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31. An optimised deep learning method for the prediction of dynamic viscosity of MXene-based nanofluid

Author: Qazani, Mohammad Reza Chalak, Aslfattahi, Navid, Kulish, Vladimir, Asadi, Houshyar, Schmirler, Michal, Said, Zafar, Afzal, Asif, Kabir, H. M. Dipu, and Arıcı, Müslüm
Published: 2023
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32. Metabolomics of diabetes and cardiovascular disease

Author: Rahman, Mahbuba, primary, Qureshi, Rizwan, additional, Bruginski, Estevan, additional, Alazmi, Meshari, additional, Soufan, Othman, additional, Biswas, Kabir H., additional, and Alam, Tanvir, additional
Published: 2023
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33. Contributors

Author: Alam, Tanvir, primary, Alasmael, Noura, additional, Alazmi, Meshari, additional, Almahasheer, Hanan, additional, Al-Talla, Zeyad A., additional, Al-Younis, Inas, additional, Azad, Abul Kalam, additional, Bastaki, Kholoud, additional, Biswas, Kabir H., additional, Bruginski, Estevan, additional, Dhahri, Manel, additional, Emwas, Abdul-Hamid, additional, Hakim, Al, additional, Jaremko, Łukasz, additional, Jaremko, Mariusz, additional, Jithesh, Puthen Veettil, additional, Lachowicz, Joanna Izabela, additional, Qureshi, Rizwan, additional, Rahman, Mahbuba, additional, Ramaswamy, Mamatha, additional, Sohag, Md. Mehadi Hasan, additional, Soufan, Othman, additional, Szczepski, Kacper, additional, and Umlai, Umm-Kulthum Ismail, additional
Published: 2023
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34. A Comprehensive Exploration of 6G Wireless Communication Technologies.

Author: Siddiky, Md Nurul Absar, Rahman, Muhammad Enayetur, Uzzal, Md Shahriar, and Kabir, H. M. Dipu
Subjects: WIRELESS communications, TELECOMMUNICATION, QUANTUM communication, TECHNOLOGICAL innovations, SPECTRUM allocation, TERAHERTZ technology
Abstract: As the telecommunications landscape braces for the post-5G era, this paper embarks on delineating the foundational pillars and pioneering visions that define the trajectory toward 6G wireless communication systems. Recognizing the insatiable demand for higher data rates, enhanced connectivity, and broader network coverage, we unravel the evolution from the existing 5G infrastructure to the nascent 6G framework, setting the stage for transformative advancements anticipated in the 2030s. Our discourse navigates through the intricate architecture of 6G, highlighting the paradigm shifts toward superconvergence, non-IP-based networking protocols, and information-centric networks, all underpinned by a robust 360-degree cybersecurity and privacy-by-engineering design. Delving into the core of 6G, we articulate a systematic exploration of the key technologies earmarked to revolutionize wireless communication including terahertz (THz) waves, optical wireless technology, and dynamic spectrum management while elucidating the intricate trade-offs necessitated by the integration of such innovations. This paper not only lays out a comprehensive 6G vision accentuated by high security, affordability, and intelligence but also charts the course for addressing the pivotal challenges of spectrum efficiency, energy consumption, and the seamless integration of emerging technologies. In this study, our goal is to enrich the existing discussions and research efforts by providing comprehensive insights into the development of 6G technology, ultimately supporting the creation of a thoroughly connected future world that meets evolving demands. [ABSTRACT FROM AUTHOR]
Published: 2025
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35. Competition for shared downstream signaling molecules establishes indirect negative feedback between EGFR and EphA2

Author: Oh, Dongmyung, Chen, Zhongwen, Biswas, Kabir H., Bai, Funing, Ong, Hui Ting, Sheetz, Michael P., and Groves, Jay T.
Published: 2022
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36. Aleatory-aware deep uncertainty quantification for transfer learning

Author: Kabir, H M Dipu, Khanam, Sadia, Khozeimeh, Fahime, Khosravi, Abbas, Mondal, Subrota Kumar, Nahavandi, Saeid, and Acharya, U Rajendra
Published: 2022
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37. ANN-Based LUBE Model for Interval Prediction of Compressive Strength of Concrete

Author: Akbari, Mahmood, Kabir, H. M. Dipu, Khosravi, Abbas, and Nasirzadeh, Farnad
Published: 2022
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38. Gulf Cooperation Council Clinical Trials in the Pursuit of Medications for COVID-19.

Author: Bassam Ali Jaber, Mowafa S. Househ, Zubair Shah, M. Sohel Rahman, Mounir Hamdi, Kabir H. Biswas, and Tanvir Alam
Published: 2021
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39. Kubernetes in IT administration and serverless computing: An empirical study and research challenges

Author: Mondal, Subrota Kumar, Pan, Rui, Kabir, H M Dipu, Tian, Tan, and Dai, Hong-Ning
Published: 2022
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40. NN-based Prediction Interval for Nonlinear Processes Controller

Author: Hosen, Mohammad Anwar, Khosravi, Abbas, Kabir, H. M. Dipu, Johnstone, Michael, Creighton, Douglas, Nahavandi, Saeid, and Shi, Peng
Published: 2021
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41. Phosphodiesterase 5 (PDE5): Structure-function regulation and therapeutic applications of inhibitors

Author: Ahmed, Wesam S., Geethakumari, Anupriya M., and Biswas, Kabir H.
Published: 2021
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42. Gulf Cooperation Council Clinical Trials in the Pursuit of Medications for COVID-19

Author: Jaber, Bassam Ali, primary, Househ, Mowafa, additional, Shah, Zubair, additional, Rahman, M.Sohel, additional, Hamdi, Mounir, additional, Biswas, Kabir H, additional, and Alam, Tanvir, additional
Published: 2022
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43. A potential allosteric inhibitor of SARS-CoV-2 main protease (Mpro) identified through metastable state analysis.

Author: Fatima, Asma, Geethakumari, Anupriya M., Ahmed, Wesam S., and Biswas, Kabir H.
Published: 2024
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44. Evaluation of cement soundness using the ASTM C151 autoclave expansion test

Author: Kabir, H., Hooton, R.D., and Popoff, N.J.
Published: 2020
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45. Evaluating soundness of concrete containing shrinkage-compensating MgO admixtures

Author: Kabir, H. and Hooton, R.D.
Published: 2020
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46. Sustained α-catenin Activation at E-cadherin Junctions in the Absence of Mechanical Force

Author: Biswas, Kabir H, Hartman, Kevin L, Zaidel-Bar, Ronen, and Groves, Jay T
Subjects: Biochemistry and Cell Biology, Biological Sciences, Bioengineering, 1.1 Normal biological development and functioning, Underpinning research, Generic health relevance, Actins, Antigens, CD, Cadherins, Cell Adhesion, Cell Culture Techniques, Cell Line, Tumor, Green Fluorescent Proteins, Humans, Lipid Bilayers, Mechanotransduction, Cellular, Microscopy, Confocal, Microscopy, Fluorescence, Myosin Light Chains, Stress, Mechanical, Vinculin, alpha Catenin, rho-Associated Kinases, Physical Sciences, Chemical Sciences, Biophysics, Biological sciences, Chemical sciences, Physical sciences
Abstract: Mechanotransduction at E-cadherin junctions has been postulated to be mediated in part by a force-dependent conformational activation of α-catenin. Activation of α-catenin allows it to interact with vinculin in addition to F-actin, resulting in a strengthening of junctions. Here, using E-cadherin adhesions reconstituted on synthetic, nanopatterned membranes, we show that activation of α-catenin is dependent on E-cadherin clustering, and is sustained in the absence of mechanical force or association with F-actin or vinculin. Adhesions were formed by filopodia-mediated nucleation and micron-scale assembly of E-cadherin clusters, which could be distinguished as either peripheral or central assemblies depending on their relative location at the cell-bilayer adhesion. Whereas F-actin, vinculin, and phosphorylated myosin light chain associated only with the peripheral assemblies, activated α-catenin was present in both peripheral and central assemblies, and persisted in the central assemblies in the absence of actomyosin tension. Impeding filopodia-mediated nucleation and micron-scale assembly of E-cadherin adhesion complexes by confining the movement of bilayer-bound E-cadherin on nanopatterned substrates reduced the levels of activated α-catenin. Taken together, these results indicate that although the initial activation of α-catenin requires micron-scale clustering that may allow the development of mechanical forces, sustained force is not required for maintaining α-catenin in the active state.
Published: 2016

47. A molecular assembly phase transition and kinetic proofreading modulate Ras activation by SOS

Author: Huang, William Y. C., Alvarez, Steven, Kondo, Yasushi, Lee, Young Kwang, Chung, Jean K., Lam, Hiu Yue Monatrice, Biswas, Kabir H., Kuriyan, John, and Groves, Jay T.
Published: 2019

48. Molecular Mobility-Mediated Regulation of E-Cadherin Adhesion

Author: Biswas, Kabir H.
Published: 2020
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49. Role of Actin Cytoskeleton in E-cadherin-Based Cell–Cell Adhesion Assembly and Maintenance

Author: Rasool, Saad, Geethakumari, Anupriya M., and Biswas, Kabir H.
Published: 2021
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50. Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic : a comprehensive demographic analysis for the Global Burden of Disease Study 2021

Author: Schumacher, A. E., Kyu, H. H., Antony, C. M., Aravkin, A. Y., Azhar, G. S., Bisignano, C., Burkart, K., Cercy, K. M., Chung, E., Coberly, K., Comfort, H., Cousin, E., Culbreth, G. T., Cunningham, M., Weaver, N. D., Degenhardt, L., Deitesfeld, L., Dirac, M. A., Estep, K., Feigin, V. L., Flaxman, A. D., Flor, L. S., Force, L. M., Fuller, J. E., Gakidou, E., Hay, S. I., Ikuta, K. S., Jones, D. P., Kassebaum, N. J., Kassel, M. B., Keller, C., Kinzel, K. E., Krohn, K. J., Lozano, R., May, E. A., McKowen, A. W., McLaughlin, S. A., Mehlman, M. L., Mestrovic, T., Mokdad, A. H., Mosser, J. F., Mougin, V., Naghavi, M., Nesbit, O. D., Novotney, A., Ozten, Y., Pease, S. A., Pigott, D. M., Reiner, R. C., Robinson-Oden, H. E., Shaw, D. H., Slepak, E. L. N., Sorensen, R. J. D., Verghese, N. A., Vollset, S., Vongpradith, A., Vos, T., Wang, D., Watson, S., Weaver, M. R., Wells, K. M., Wilson, S., Wool, E. E., Zheng, P., Lim, S. S., Murray, C. J. L., Boyko, E. J., Chahine, Y., Kalani, R., Krishnamoorthy, V., Khalil, I. A., Minja, N. W., Morrison, S. D., Nafukho, F. M., Olivas-Martinez, A., Orellana, E., Tram, K., Aali, A., Rahnavard, N., Ahmadzade, A., Mohammad-Pour, S., Morovatdar, N., Pourali, G., Zafari, N., Abbafati, C., Cattaruzza, M. S., Abbas, J., Phillips, M. R., Abbasgholizadeh, R., Emamverdi, M., Friedman, J., Abbasi, M. A., Daneshvar, S., Dashti, M., Ghasemzadeh, A., Mirza-Aghazadeh-Attari, M., Doshmangir, L., Ghafourifard, M., Lotfi, M., Hosseini, M., Jadidi-Niaragh, F., Kalankesh, L. R., Karimi, S., Mohammad-Alizadeh-Charandabi, S., Khalafi, M., Mirghafourvand, M., Mirinezhad, S., Heris, R. M., Mousavi, S., Kafil, H. S., Abbasian, M., Momenzadeh, K., Santos, F. C. D., Carr, S., Chi, G., Elgendy, I. Y., Feroze, A. H., Kempen, J. H., Kubeisy, C. M., Li, Z., Rohloff, P., Liu, X., Natto, Z. S., Olanipekun, T. O., Pigeolet, M., Pradhan, P. M. S., Zweck, E., Sharfaei, S., Sheikh, A., Abtahi, D., Salimi, S., Shakeri, A., Tajbakhsh, A., Aghamiri, S., Ahmadzade, M., Hashemi, M. B., Ajami, M., Asgary, S., Ghasemi, M., Hassanian-Moghaddam, H., Kolahi, A., Nikoobar, A., Heidari-Foroozan, M., Montazeri, F., Nejadghaderi, S., Rahmani, S., Ziaeefar, P., Hesami, H., Zahir, M., Jahankhani, K., Rasouli-Saravani, A., Jahanmehr, N., Kashani, H. K., Nasiri, M., Raee, P., Rezaee, M., Safi, S., Shool, S., Tabatabai, S., ElHafeez, S. A., Tantawi, M. E., Elmeligy, O. A. A., Ghazy, R. M., Talaat, I. M., Abdelmasseh, M., Sanabria, J., Abd-Elsalam, S., Abdelwahab, A., Abdollahi, M., Abolhassani, H., Rezaei, N., Saghazadeh, A., Ala, M., Noroozi, N., Bahri, R. A., Khatami, F., Ayyoubzadeh, S., Azadnajafabad, S., Keykhaei, M., Momtazmanesh, S., Mousavi, P., Behnoush, A., Farrokhpour, H., Karimi, H., Khalaji, A., Khanmohammadi, S., Mayeli, M., Mohammadi, S., Eskandarieh, S., Sahraian, M., Fahimi, S., Malekzadeh, R., Mansouri, V., Sepanlou, S. G., Ghassemi, F., Haddadi, M., Kazemian, S., Khadembashiri, M., Khamesipour, F., Khavandegar, A., Khormali, M., Salamati, P., Kompani, F., Larijani, B., Mirdamadi, N., Seyedi, S., Malazy, O. T., Mahmoudi, E., Rashedi, S., Rad, E. M., Mansournia, M., Nasab, E. M., Mohammadshahi, M., Mostafavi, H., SeyedAlinaghi, S., Shafie, M., Shahbandi, A., Vahdani, A. M., Sharifan, A., Shirkoohi, R., Sohrabi, H., Tavangar, S., Heidari-Soureshjani, R., Abdoun, M., Abdullahi, A., Awotidebe, A. W., Borodo, S. B., Gadanya, M. A., Ladan, M., Ali, M. U., Tyrovolas, S., Abdurehman, A. M., Debele, A. T., Getachew, T., Gudeta, M. D., Misgana, T., Sertsu, A., Abebe, M., Ayele, G. M., Afework, A., Lerango, T. L., Tebeje, T. M., Sibhat, M. M., Tesfaye, S. H., Abedi, A., Gholamrezanezhad, A., Salehi, S., Athari, S., Hanifi, N., Abegaz, T. M., Kifle, Z. D., Zuñiga, R. A. A., Abhilash, E. S., Abiodun, O. O., Aboagye, R. G., Amu, H., Ayanore, M. A., Dowou, R. K., Immurana, M., Klu, D., Orish, V. N., Carrero, J. 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Abstract: Background: Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period. Methods: 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, ant
Published: 2024
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