1,721,109 research outputs found
Nutritional and lifestyle predictors of rectal bleeding in functional constipation: a machine learning approach
No full textBackground: rectal bleeding among young adults is an increasingly common clinical concern often linked with chronic constipation and unhealthy lifestyle habits. Early identification of at-risk individuals through machine learning models-based approach may help in prevention and targeted intervention. Objectives: we aim to identify dietary and lifestyle risk factors for rectal bleeding and to develop machine learning-based models for risk prediction. Methods: a descriptive observational study was conducted on 875 Indian college going participants. A structured questionnaire assessed fiber intake, physical activity, constipation symptoms, and body mass index (BMI). Multiple machine learning algorithms were evaluated, and their performance was assessed using accuracy and area under the receiver operating characteristic curve (ROC-AUC). Results: Low intake of boiled vegetables or oatmeal (<50 g/day) was associated with a 43.92 % bleeding rate (p < 0.001). Participants consuming inadequate whole grains (>25 g/day) showed a 44.81 % bleeding rate. Overweight or obese individuals exhibited a significantly higher bleeding incidence (12.26 %) than those with normal BMI (5.55 %; p = 0.008). The KNeighbors Classifier showed the highest accuracy (98.86 %) and ROC-AUC (0.994). Variables related to symptoms had greater predictive importance than those related to lifestyle. Conclusions: the findings support the role of dietary fiber and BMI in the development of rectal bleeding in constipated individuals. The predictive models demonstrate strong potential for identifying at-risk individuals and is considered a simple and useful tool for predicting rectal bleeding in functional constipation, suggesting preventive health strategies and dietary modifications. This novel algorithm might enable clinicians to perform personalized dietary strategies with improved clinical outcomes. Further validation across larger and more diverse populations is recommended.</p
Artificial intelligence-driven reverse vaccinology for Neisseria gonorrhoeae vaccine: prioritizing epitope-based candidates
No full text
Neisseria gonorrhoeae is the causative agent of the sexually transmitted disease gonorrhea. The increasing prevalence of this disease worldwide, the rise of antibiotic-resistant strains, and the difficulties in treatment necessitate the development of a vaccine, highlighting the significance of preventative measures to control and eradicate the infection. Currently, there is no widely available vaccine, partly due to the bacterium's ability to evade natural immunity and the limited research investment in gonorrhea compared to other diseases. To identify distinct vaccine candidates, we chose to focus on the uncharacterized, hypothetical proteins (HPs) as our initial approach. Using the
in silico method, we first carried out a comprehensive assessment of hypothetical proteins of
Neisseria gonorrhoeae, encompassing assessments of physicochemical properties, cellular localization, secretary pathways, transmembrane regions, antigenicity, toxicity, and prediction of B-cell and T-cell epitopes, among other analyses. Detailed analysis of all HPs resulted in the functional annotation of twenty proteins with a great degree of confidence. Further, using the immuno-informatics approach, the prediction pipeline identified one CD8
+ restricted T-cell epitope, seven linear B-cell epitopes, and seven conformational B-cell epitopes as putative epitope-based peptide vaccine candidates which certainly require further validation in laboratory settings. The study accentuates the promise of functional annotation and immuno-informatics in the systematic design of epitope-based peptide vaccines targeting
Neisseria gonorrhoeae.
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Structure-based drug discovery to identify SARS-CoV2 spike protein-ACE2 interaction inhibitors
No full textAfter the emergence of the COVID-19 pandemic in late 2019, the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has undergone a dynamic evolution driven by the acquisition of genetic modifications, resulting in several variants that are further classified as variants of interest (VOIs), variants under monitoring (VUM) and variants of concern (VOC) by World Health Organization (WHO). Currently, there are five SARS-CoV-2 VOCs (Alpha, Beta, Delta, Gamma and Omicron), two VOIs (Lambda and Mu) and several other VOIs that have been reported globally. In this study, we report a natural compound, Curcumin, as the potential inhibitor to the interactions between receptor binding domain (RBD(S1)) and human angiotensin-converting enzyme 2 (hACE2) domains and showcased its inhibitory potential for the Delta and Omicron variants through a computational approach by implementing state of the art methods. The study for the first time revealed a higher efficiency of Curcumin, especially for hindering the interaction between RBD(S1) and hACE-2 domains of Delta and Omicron variants as compared to other lead compounds. We investigated that the mutations in the RBD(S1) of VOC especially Delta and Omicron variants affect its structure compared to that of the wild type and other variants and therefore altered its binding to the hACE2 receptor. Molecular docking and molecular dynamics (MD) simulation analyses substantially supported the findings in terms of the stability of the docked complexes. This study offers compelling evidence, warranting a more in-depth exploration into the impact of these alterations on the binding of identified drug molecules with the Spike protein. Further investigation into their potential therapeutic effects in vivo is highly recommended.</p
Identification of novel inhibitors of <i>Neisseria gonorrhoeae </i>MurI using homology modeling, structure-based pharmacophore, molecular docking, and molecular dynamics simulation-based approach
No full textMurI is one of the most significant role players in the biosynthesis of the peptidoglycan layer in Neisseria gonorrhoeae (Ng). We attempted to highlight the structural and functional relationship between Ng-MurI and D-glutamate to design novel molecules targeting this interaction. The three-dimensional (3D) model of the protein was constructed by homology modeling and the quality and consistency of generated model were assessed. The binding site of the protein was identified by molecular docking studies and a pharmacophore was identified using the interactions of the control ligand. The structure-based pharmacophore model was validated and employed for high-throughput virtual screening and molecular docking to identify novel Ng-MurI inhibitors. Finally, the model was optimized by molecular dynamics (MD) simulations and the optimized model complex with the substrate glutamate and novel molecules facilitated us to confirm the stability of the protein-ligand docked complexes. The 100 ns MD simulations of the potential lead compounds with protein confirmed that the modeled complexes were stable. This study identifies novel potential compounds with good fitness and docking scores, which made the interactions of biological significance within the protein active site. Hence, the identified compounds may act as new leads to design and develop Ng-MurI inhibitors. Communicated by Ramaswamy H. Sarma.</p
Artificial Intelligence in Predicting, Diagnosing and Preventing Sexually Transmitted Infections (STIs)
No full textSexually transmitted infections (STIs) are major global health challenges, disproportionately affecting women due to complex biological, social and economic factors [...
Computational and experimental study of metal–organic frameworks (MOFs) as antimicrobial agents against <i>Neisseria gonorrhoeae</i>
No full textThe emergence of drug-resistant superbugs poses a critical global health threat, necessitating innovative treatment strategies. Neisseria gonorrhoeae (Ng) causes a sexually transmitted disease called gonorrhea, and the bacterium has shown alarming resistance to conventional antibiotics, underscoring the urgent need for novel therapeutic approaches. In the current study, we interfaced computational biology and materials science to investigate the interactions between in-house synthesized metal-organic frameworks (MOFs) and the penicillin-binding protein 2 (PBP2) of Ng, a key target for β-lactam antibiotics. Using molecular docking and interaction analyses, we identified three promising MOFs, namely, Fe-BDC-258445, Cu-BDC-687690, and Ni-BDC-638866, with optimum binding scores and stable interactions. These scores indicated strong interactions with PBP2, suggesting their potential as therapeutic agents. Antimicrobial screening using a standard disk diffusion assay demonstrated that the Cu-BDC MOFs were bactericidal for multiple strains of Ng, whereas the Ni-BDC and Fe-BDC MOFs were nonbactericidal. The Cu-BDC MOF did not kill other Gram-negative bacteria, thus demonstrating specificity for Ng, and showed low toxicity for human Chang conjunctival epithelial cells in vitro. No significant leaching with biological activity was observed for the Cu-BDC MOF, and microscopy demonstrated the loss of gonococcal piliation and damage to the cell membrane. These findings underscore the potential of Cu-BDC MOFs as antimicrobial agents for further development.</p
Critical insights from recent outbreaks of Mycoplasma pneumoniae: decoding the challenges and effective interventions strategies
No full textOBJECTIVES: Mycoplasma pneumoniae (M. pneumoniae) continues to pose a significant disease burden on global public health as a respiratory pathogen. The antimicrobial resistance among M. pneumoniae strains has complicated the outbreak control efforts, emphasizing the need for robust surveillance systems and effective antimicrobial stewardship programs.DESIGN: This review comprehensively investigates studies stemming from previous outbreaks to emphasize the multifaceted nature of M. pneumoniae infections, encompassing epidemiological dynamics, diagnostic innovations, antibiotic resistance, and therapeutic challenges.RESULTS: We explored the spectrum of clinical manifestations associated with M. pneumoniae infections, emphasizing the continuum of disease severity and the challenges in gradating it accurately. Artificial intelligence and machine learning have emerged as promising tools in M. pneumoniae diagnostics, offering enhanced accuracy and efficiency in identifying infections. However, their integration into clinical practice presents hurdles that need to be addressed. Further, we elucidate the pivotal role of pharmacological interventions in controlling and treating M. pneumoniae infections as the efficacy of existing therapies is jeopardized by evolving resistance mechanisms.CONCLUSION: Lessons learned from previous outbreaks underscore the importance of adaptive treatment strategies and proactive management approaches. Addressing these complexities demands a holistic approach integrating advanced technologies, genomic surveillance, and adaptive clinical strategies to effectively combat this pathogen.</p
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Bactericidal activity of esculetin is associated with impaired cell wall synthesis by targeting glutamate racemase of Neisseria gonorrhoeae
No full textNeisseria gonorrhoeae (NG), the causative organism of gonorrhea, has been classified by the World Health Organization as 'Priority' two organism owing to its increased resistance to antibiotics and even failure of recommended dual therapy with ceftriaxone and azithromycin. As a result, the general and reproductive health of infected individuals is severely compromised. The imminent public health catastrophe of antimicrobial-resistant gonococci cannot be understated, as t he of severe complications and sequelae of infection are not only increasing but their treatment has also become more expensive. Tenacious attempts are underway to discover novel drug targets as well as new drugs to fight against NG. Therefore, a considerable number of phytochemicals have been tested for their remedial intercession via targeting bacterial proteins. The MurI gene encodes for an enzyme called glutamate racemase (MurI) that is primarily involved in peptidoglycan (PG) biosynthesis and is specific to the bacterial kingdom and hence can be exploited as a potential drug target for the treatment of bacterial diseases. Accordingly, diverse families of phytochemicals were screened in silico for their binding affinity with N. Gonorrhoeae MurI (NG-MurI) protein. Esculetin, one of the shortlisted compounds, was evaluated for its functional, structural, and anti-bacterial activity. Treatment with esculetin resulted in growth inhibition, cell wall damage, and altered permeability as revealed by fluorescence and electron microscopy. Furthermore, esculetin inhibited the racemization activity of recombinant, purified NG-MurI protein, one of the enzymes required for peptidoglycan biosynthesis. Our results suggest that esculetin could be further explored as a lead compound for developing new drug molecules against multidrug-resistant strains.</p
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