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Article ; Online: Prediction of Phage Virion Proteins Using Machine Learning Methods.

Barman, Ranjan Kumar / Chakrabarti, Alok Kumar / Dutta, Shanta

2023 Volume 28, Issue 5

Abstract: Antimicrobial resistance (AMR) is a major problem and an immediate alternative to antibiotics is the need of the hour. Research on the possible alternative products to tackle bacterial infections is ongoing worldwide. One of the most promising ... ...

Abstract	Antimicrobial resistance (AMR) is a major problem and an immediate alternative to antibiotics is the need of the hour. Research on the possible alternative products to tackle bacterial infections is ongoing worldwide. One of the most promising alternatives to antibiotics is the use of bacteriophages (phage) or phage-driven antibacterial drugs to cure bacterial infections caused by AMR bacteria. Phage-driven proteins, including holins, endolysins, and exopolysaccharides, have shown great potential in the development of antibacterial drugs. Likewise, phage virion proteins (PVPs) might also play an important role in the development of antibacterial drugs. Here, we have developed a machine learning-based prediction method to predict PVPs using phage protein sequences. We have employed well-known basic and ensemble machine learning methods with protein sequence composition features for the prediction of PVPs. We found that the gradient boosting classifier (GBC) method achieved the best accuracy of 80% on the training dataset and an accuracy of 83% on the independent dataset. The performance on the independent dataset is better than other existing methods. A user-friendly web server developed by us is freely available to all users for the prediction of PVPs from phage protein sequences. The web server might facilitate the large-scale prediction of PVPs and hypothesis-driven experimental study design.
MeSH term(s)	Humans ; Bacteriophages ; Computational Biology/methods ; Proteins/metabolism ; Bacterial Infections/microbiology ; Virion/metabolism ; Machine Learning ; Anti-Bacterial Agents/metabolism
Chemical Substances	Proteins ; Anti-Bacterial Agents
Language	English
Publishing date	2023-02-28
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	1413402-0
ISSN	1420-3049 ; 1431-5165 ; 1420-3049
ISSN (online)	1420-3049
ISSN	1431-5165 ; 1420-3049
DOI	10.3390/molecules28052238
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Article: Screening of Potential

Barman, Ranjan Kumar / Chakrabarti, Alok Kumar / Dutta, Shanta

Frontiers in microbiology

2022 Volume 13, Page(s) 803933

Abstract: Cholera continues to be a major burden for developing nations, especially where sanitation, quality of water supply, and hospitalization have remained an issue. Recently, growing antimicrobial-resistant strains ... ...

Abstract	Cholera continues to be a major burden for developing nations, especially where sanitation, quality of water supply, and hospitalization have remained an issue. Recently, growing antimicrobial-resistant strains of
Language	English
Publishing date	2022-03-29
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2587354-4
ISSN	1664-302X
ISSN	1664-302X
DOI	10.3389/fmicb.2022.803933
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Article ; Online: Prediction of Phage Virion Proteins Using Machine Learning Methods

Ranjan Kumar Barman / Alok Kumar Chakrabarti / Shanta Dutta

Molecules, Vol 28, Iss 2238, p

2023 Volume 2238

Abstract	Antimicrobial resistance (AMR) is a major problem and an immediate alternative to antibiotics is the need of the hour. Research on the possible alternative products to tackle bacterial infections is ongoing worldwide. One of the most promising alternatives to antibiotics is the use of bacteriophages (phage) or phage-driven antibacterial drugs to cure bacterial infections caused by AMR bacteria. Phage-driven proteins, including holins, endolysins, and exopolysaccharides, have shown great potential in the development of antibacterial drugs. Likewise, phage virion proteins (PVPs) might also play an important role in the development of antibacterial drugs. Here, we have developed a machine learning-based prediction method to predict PVPs using phage protein sequences. We have employed well-known basic and ensemble machine learning methods with protein sequence composition features for the prediction of PVPs. We found that the gradient boosting classifier (GBC) method achieved the best accuracy of 80% on the training dataset and an accuracy of 83% on the independent dataset. The performance on the independent dataset is better than other existing methods. A user-friendly web server developed by us is freely available to all users for the prediction of PVPs from phage protein sequences. The web server might facilitate the large-scale prediction of PVPs and hypothesis-driven experimental study design.
Keywords	AMR ; bacteriophage ; phage virion protein ; machine learning ; phage therapy ; web server ; Organic chemistry ; QD241-441
Subject code	500
Language	English
Publishing date	2023-02-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Nerve identification in open inguinal hernioplasty: A meta-analysis.

Sinha, Mithilesh Kumar / Barman, Apurba / Tripathy, Prabhas Ranjan / Shettar, Ankit

Turkish journal of surgery

2022 Volume 38, Issue 4, Page(s) 315–326

Abstract: Objectives: In open inguinal hernioplasty, three inguinal nerves are encountered in the surgical field. It is advisable to identify these nerves as careful dissection reduces the chances of debilitating post-operative inguinodynia. Recognizing nerves ... ...

Abstract	Objectives: In open inguinal hernioplasty, three inguinal nerves are encountered in the surgical field. It is advisable to identify these nerves as careful dissection reduces the chances of debilitating post-operative inguinodynia. Recognizing nerves during surgery can be challenging. Limited surgical studies have reported on the identification rates of all nerves. This study aimed to calculate the pooled prevalence of each nerve from these studies. Material and methods: We searched PubMed, CENTRAL, CINAHL, ClinicalTrials.gov and Research Square. We selected articles that reported on the prevalence of all three nerves during surgery. A meta-analysis was performed on the data from eight studies. IVhet model from the software MetaXL was used for preparing the forest plot. Subgroup analysis was performed to understand the cause of heterogeneity. Results: The pooled prevalence rates for Ilioinguinal nerve (IIN), Iliohypogastric nerve (IHN), and genital branch of genitofemoral nerve (GB) were 84% (95% CI 67-97%), 71% (95% CI 51-89%) and 53% (95% CI 31-74%), respectively. On subgroup analysis, the identification rates were higher in single centre studies and studies with a single primary objective as nerve identification. The heterogeneity was significant in all pooled values, excluding the subgroup analysis of IHN identification rates in single-centre studies. Conclusion: The pooled values indicate low identification rates for IHN and GB. Significant heterogeneity and large confidence intervals reduce the importance of these values as quality standards. Better results are observed in single-centre studies and studies which are focused on nerve identification.
Language	English
Publishing date	2022-12-20
Publishing country	Turkey
Document type	Journal Article
ISSN	2564-6850
ISSN	2564-6850
DOI	10.47717/turkjsurg.2022.5882
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Article ; Online: A network biology approach to identify crucial host targets for COVID-19.

Barman, Ranjan Kumar / Mukhopadhyay, Anirban / Maulik, Ujjwal / Das, Santasabuj

Methods (San Diego, Calif.)

2022 Volume 203, Page(s) 108–115

Abstract: The ongoing global pandemic of COVID-19, caused by SARS-CoV-2 has killed more than 5.9 million individuals out of ∼43 million confirmed infections. At present, several parts of the world are encountering the 3rd wave. Mass vaccination has been started in ...

Abstract	The ongoing global pandemic of COVID-19, caused by SARS-CoV-2 has killed more than 5.9 million individuals out of ∼43 million confirmed infections. At present, several parts of the world are encountering the 3rd wave. Mass vaccination has been started in several countries but they are less likely to be broadly available for the current pandemic, repurposing of the existing drugs has drawn highest attention for an immediate solution. A recent publication has mapped the physical interactions of SARS-CoV-2 and human proteins by affinity-purification mass spectrometry (AP-MS) and identified 332 high-confidence SARS-CoV-2-human protein-protein interactions (PPIs). Here, we taken a network biology approach and constructed a human protein-protein interaction network (PPIN) with the above SARS-CoV-2 targeted proteins. We utilized a combination of essential network centrality measures and functional properties of the human proteins to identify the critical human targets of SARS-CoV-2. Four human proteins, namely PRKACA, RHOA, CDK5RAP2, and CEP250 have emerged as the best therapeutic targets, of which PRKACA and CEP250 were also found by another group as potential candidates for drug targets in COVID-19. We further found candidate drugs/compounds, such as guanosine triphosphate, remdesivir, adenosine monophosphate, MgATP, and H-89 dihydrochloride that bind the target human proteins. The urgency to prevent the spread of infection and the death of diseased individuals has prompted the search for agents from the pool of approved drugs to repurpose them for COVID-19. Our results indicate that host targeting therapy with the repurposed drugs may be a useful strategy for the treatment of SARS-CoV-2 infection.
MeSH term(s)	Antiviral Agents/pharmacology ; Antiviral Agents/therapeutic use ; Autoantigens ; Cell Cycle Proteins ; Drug Repositioning ; Humans ; Nerve Tissue Proteins ; Pandemics ; SARS-CoV-2 ; COVID-19 Drug Treatment
Chemical Substances	Antiviral Agents ; Autoantigens ; CDK5RAP2 protein, human ; Cell Cycle Proteins ; CEP250 protein, human ; Nerve Tissue Proteins
Language	English
Publishing date	2022-03-29
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1066584-5
ISSN	1095-9130 ; 1046-2023
ISSN (online)	1095-9130
ISSN	1046-2023
DOI	10.1016/j.ymeth.2022.03.016
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Article ; Online: Machine Learning Approaches for Discriminating Bacterial and Viral Targeted Human Proteins

Ranjan Kumar Barman / Anirban Mukhopadhyay / Ujjwal Maulik / Santasabuj Das

Processes, Vol 10, Iss 291, p

2022 Volume 291

Abstract: Infectious diseases are one of the core biological complications for public health. It is important to recognize the pathogen-specific mechanisms to improve our understanding of infectious diseases. Differentiations between bacterial- and viral-targeted ... ...

Abstract	Infectious diseases are one of the core biological complications for public health. It is important to recognize the pathogen-specific mechanisms to improve our understanding of infectious diseases. Differentiations between bacterial- and viral-targeted human proteins are important for improving both prognosis and treatment for the patient. Here, we introduce machine learning-based classifiers to discriminate between the two groups of human proteins. We used the sequence, network, and gene ontology features of human proteins. Among different classifiers and features, the deep neural network (DNN) classifier with amino acid composition (AAC), dipeptide composition (DC), and pseudo-amino acid composition (PAAC) (445 features) achieved the best area under the curve (AUC) value (0.939), F1-score (94.9%), and Matthews correlation coefficient (MCC) value (0.81). We found that each of the selected top 100 of the bacteria- and virus-targeted human proteins from a candidate pool of 1618 and 3916 proteins, respectively, were part of distinct enriched biological processes and pathways. Our proposed method will help to differentiate between the bacterial and viral infections based on the targeted human proteins on a global scale. Furthermore, identification of the crucial pathogen targets in the human proteome would help us to better understand the pathogen-specific infection strategies and develop novel therapeutics.
Keywords	infectious diseases ; pathogen-specific infection ; machine learning ; host-pathogen interactions ; classification ; deep learning ; Chemical technology ; TP1-1185 ; Chemistry ; QD1-999
Subject code	006
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: A network biology approach to identify crucial host targets for COVID-19

Barman, Ranjan Kumar / Mukhopadhyay, Anirban / Maulik, Ujjwal / Das, Santasabuj

Methods. 2022 Mar. 27,

2022

Abstract	The ongoing global pandemic of COVID-19, caused by SARS-CoV-2 has killed more than 5.9 million individuals out of ∼43 million confirmed infections. At present, several parts of the world are encountering the 3rd wave. Mass vaccination has been started in several countries but they are less likely to be broadly available for the current pandemic, repurposing of the existing drugs has drawn highest attention for an immediate solution. A recent publication has mapped the physical interactions of SARS-CoV-2 and human proteins by affinity-purification mass spectrometry (AP-MS) and identified 332 high-confidence SARS-CoV-2-human protein-protein interactions (PPIs). Here, we taken a network biology approach and constructed a human protein-protein interaction network (PPIN) with the above SARS-CoV-2 targeted proteins. We utilized a combination of essential network centrality measures and functional properties of the human proteins to identify the critical human targets of SARS-CoV-2. Four human proteins, namely PRKACA, RHOA, CDK5RAP2, and CEP250 have emerged as the best therapeutic targets, of which PRKACA and CEP250 were also found by another group as potential candidates for drug targets in COVID-19. We further found candidate drugs/compounds, such as guanosine triphosphate, remdesivir, adenosine monophosphate, MgATP, and H-89 dihydrochloride that bind the target human proteins. The urgency to prevent the spread of infection and the death of diseased individuals has prompted the search for agents from the pool of approved drugs to repurpose them for COVID-19. Our results indicate that host targeting therapy with the repurposed drugs may be a useful strategy for the treatment of SARS-CoV-2 infection.
Keywords	COVID-19 infection ; Severe acute respiratory syndrome coronavirus 2 ; adenosine monophosphate ; death ; drugs ; guanosine triphosphate ; humans ; mass spectrometry ; pandemic ; protein-protein interactions ; vaccination
Language	English
Dates of publication	2022-0327
Publishing place	Elsevier Inc.
Document type	Article
Note	Pre-press version
ZDB-ID	1066584-5
ISSN	1095-9130 ; 1046-2023
ISSN (online)	1095-9130
ISSN	1046-2023
DOI	10.1016/j.ymeth.2022.03.016
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Efficient Synergistic Antibacterial Activity of α-MSH Using Chitosan-Based Versatile Nanoconjugates.

Barman, Sourav / Chakraborty, Asmita / Saha, Sujata / Sikder, Kunal / Maitra Roy, Sayoni / Modi, Barkha / Bahadur, Sabarnee / Khan, Ali Hossain / Manna, Dipak / Bag, Pousali / Sarkar, Ankan Kumar / Bhattacharya, Rishi / Basu, Arnab / Maity, Amit Ranjan

ACS omega

2023 Volume 8, Issue 14, Page(s) 12865–12877

Abstract: The application of antimicrobial peptides has emerged as an alternative therapeutic tool to encounter against multidrug resistance of different pathogenic organisms. α-Melanocyte stimulating hormone (α-MSH), an endogenous neuropeptide, is found to be ... ...

Abstract	The application of antimicrobial peptides has emerged as an alternative therapeutic tool to encounter against multidrug resistance of different pathogenic organisms. α-Melanocyte stimulating hormone (α-MSH), an endogenous neuropeptide, is found to be efficient in eradicating infection of various kinds of
Language	English
Publishing date	2023-03-30
Publishing country	United States
Document type	Journal Article
ISSN	2470-1343
ISSN (online)	2470-1343
DOI	10.1021/acsomega.2c08209
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Article ; Online: Pharmacological screening of fenofibrate-loaded solid dispersion in fructose-induced diabetic rat.

Ghosh, Milon Kumar / Wahed, Mir Imam Ibne / Khan, Rafiqul Islam / Habib, Anwar / Barman, Ranjan Kumar

The Journal of pharmacy and pharmacology

2020 Volume 72, Issue 7, Page(s) 909–915

Abstract: Objectives: Hyperlipidaemia is a common phenomenon in diabetes mellitus. Fenofibrate (FF) is a good candidate for the treatment of lipid abnormalities in patients with type 2 diabetes. But the bioavailability as well as therapeutic efficacy of this drug ...

Abstract	Objectives: Hyperlipidaemia is a common phenomenon in diabetes mellitus. Fenofibrate (FF) is a good candidate for the treatment of lipid abnormalities in patients with type 2 diabetes. But the bioavailability as well as therapeutic efficacy of this drug is limited to its dissolution behaviour. Here, the authors assess the therapeutic efficacy of a newly formulated solid dispersion of fenofibrate (SDF) having enhanced dissolution profiles in contrast to pure FF using fructose-induced diabetic rat model. Methods: Fructose-induced diabetic rat model was developed to assess the pharmacological efficacy of the formulated SDF, and the results were compared with the effects of conventional FF therapy. Key findings: The 14 days treatment showed better improvement in lipid-lowering potency of SDF than pure FF. SDF containing one-third dose of pure FF showed similar effect in terms of triglyceride, total cholesterol and low-density lipoprotein lowering efficacy, whereas increased high-density lipoprotein at same extent. The similar dose of SDF produced more prominent effect than FF. Histological studies also demonstrated the enhanced lipid clearance from liver by SDF than FF that was concordant with the biochemical results. Conclusions: This newly formulated SDF would be a promising alternative for conventional fenofibrate in treating hyperlipidaemia.
MeSH term(s)	Animals ; Cholesterol/analysis ; Diabetes Mellitus, Experimental/drug therapy ; Diabetes Mellitus, Experimental/metabolism ; Drug Compounding/methods ; Fenofibrate/pharmacokinetics ; Hepatobiliary Elimination/drug effects ; Hyperlipidemias/drug therapy ; Hyperlipidemias/metabolism ; Hypolipidemic Agents/pharmacokinetics ; Lipoproteins, LDL/analysis ; Metabolic Clearance Rate ; Rats ; Solubility ; Treatment Outcome ; Triglycerides/analysis
Chemical Substances	Hypolipidemic Agents ; Lipoproteins, LDL ; Triglycerides ; Cholesterol (97C5T2UQ7J) ; Fenofibrate (U202363UOS)
Language	English
Publishing date	2020-04-19
Publishing country	England
Document type	Journal Article
ZDB-ID	3107-0
ISSN	2042-7158 ; 0022-3573 ; 0373-1022
ISSN (online)	2042-7158
ISSN	0022-3573 ; 0373-1022
DOI	10.1111/jphp.13267
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Article: Assessment of Knowledge, Attitude, and Practice of Dental and Medical Interns toward Toothbrush Maintenance and Replacement in Bhubaneswar City, Odisha, India.

Kumar, Gunjan / Sethi, Alok Kumar / Tripathi, Ranjan Mani / Pratik / Barman, Diplina

Journal of pharmacy & bioallied sciences

2018 Volume 10, Issue 2, Page(s) 77–82

Abstract: Background: Toothbrushes are an important medium for maintaining good oral hygiene, and hence there arises a need to maintain and replace toothbrushes at a regular interval. Assessing the knowledge, attitude, and practice (KAP) of the medical and dental ...

Abstract	Background: Toothbrushes are an important medium for maintaining good oral hygiene, and hence there arises a need to maintain and replace toothbrushes at a regular interval. Assessing the knowledge, attitude, and practice (KAP) of the medical and dental interns would help the society in promoting oral hygiene in a broader aspect. Materials and methods: A cross-sectional questionnaire study was conducted among 759 medical and dental interns residing in Bhubaneswar, Odisha, India. The data on oral health KAP were collected using a self-structured questionnaire. Descriptive statistics was evaluated using SPSS software package, version 19. Results: Of 759 participants, 445 were dental interns and 314 were medical interns. Knowledge about toothbrush maintenance was seen to be more in the dental interns. The attitude toward maintenance was seen to be better among the dental interns compared with the medical interns. The practice of toothbrush maintenance was seen in both the groups but more dominantly in the dental interns. Conclusion: Education regarding the effective use and maintenance of the toothbrush would help improve the KAP toward toothbrush maintenance and replacement. The lack of knowledge holds back the attitude of properly maintaining the toothbrush in a regular basis.
Language	English
Publishing date	2018-06-14
Publishing country	India
Document type	Journal Article
ZDB-ID	2573569-X
ISSN	0975-7406 ; 0976-4879
ISSN (online)	0975-7406
ISSN	0976-4879
DOI	10.4103/JPBS.JPBS_22_18
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