A pairwise cytokine code explains the organism-wide response to sepsis.

Sepsis is a systemic response to infection with life-threatening consequences. Our understanding of the molecular and cellular impact of sepsis across organs remains rudimentary. Here, we characterize the pathogenesis of sepsis by measuring dynamic changes in gene expression across organs. To pinpoint molecules controlling organ states in sepsis, we compare the effects of sepsis on organ gene expression to those of 6 singles and 15 pairs of recombinant cytokines. Strikingly, we find that the pairwise effects of tumor necrosis factor plus interleukin (IL)-18, interferon-gamma or IL-1ß suffice to mirror the impact of sepsis across tissues. Mechanistically, we map the cellular effects of sepsis and cytokines by computing changes in the abundance of 195 cell types across 9 organs, which we validate by whole-mouse spatial profiling. Our work decodes the cytokine cacophony in sepsis into a pairwise cytokine message capturing the gene, cell and tissue responses of the host to the disease.

Organism-Level Analysis of Vaccination Reveals Networks of Protection across Tissues.

A fundamental challenge in immunology is to decipher the principles governing immune responses at the whole-organism scale. Here, using a comparative infection model, we observe immune signal propagation within and between organs to obtain a dynamic map of immune processes at the organism level. We uncover two inter-organ mechanisms of protective immunity mediated by soluble and cellular factors. First, analyzing ligand-receptor connectivity across tissues reveals that type I IFNs trigger a whole-body antiviral state, protecting the host within hours after skin vaccination. Second, combining parabiosis, single-cell analyses, and gene knockouts, we uncover a multi-organ web of tissue-resident memory T cells that functionally adapt to their environment to stop viral spread across the organism. These results have implications for manipulating tissue-resident memory T cells through vaccination and open up new lines of inquiry for the analysis of immune responses at the organism level.

FEATS: feature selection-based clustering of single-cell RNA-seq data.

MOTIVATION: Advances in next-generation sequencing have made it possible to carry out transcriptomic studies at single-cell resolution and generate vast amounts of single-cell RNA sequencing (RNA-seq) data rapidly. Thus, tools to analyze this data need to evolve as well as to improve accuracy and efficiency. RESULTS: We present FEATS, a Python software package, that performs clustering on single-cell RNA-seq data. FEATS is capable of performing multiple tasks such as estimating the number of clusters, conducting outlier detection and integrating data from various experiments. We develop a univariate feature selection-based approach for clustering, which involves the selection of top informative features to improve clustering performance. This is motivated by the fact that cell types are often manually determined using the expression of only a few known marker genes. On a variety of single-cell RNA-seq datasets, FEATS gives superior performance compared with the current tools, in terms of adjusted Rand index and estimating the number of clusters. It achieves a 22% improvement in clustering and more accurately estimates the number of clusters when compared with other tools. In addition to cluster estimation, FEATS also performs outlier detection and data integration while giving an excellent computational performance. Thus, FEATS is a comprehensive clustering tool capable of addressing the challenges during the clustering of single-cell RNA-seq data. AVAILABILITY: The installation instructions and documentation of FEATS is available at https://edwinv87.github.io/feats/. SUPPLEMENTARY DATA: Supplementary data are available online at https://academic.oup.com/bib.

Wear behaviour of lithography ceramic manufactured dental zirconia.

OBJECTIVE: The study aims to evaluate the wear surface using 3D surface roughness and other material characterization of zirconia fabricated using photopolymerization based Lithography-based Ceramic Manufacturing (LCM). METHOD: LCM technology was used to fabricate zirconia specimens of size 10 × 10 × 2mm3. Scanning Electron Microscope, 3D-profilometer, X-ray Diffraction, and hardness test characterized the samples before and after wear and Coefficient of friction (COF) was monitored. RESULT: The COF was around 0.7 and did not differ much between the horizontally and vertically printed specimens. However, the surface roughness after wear for horizontally printed specimen was 0.567 ± 0.139 µm, while that for vertically printed specimen was 0.379 ± 0.080 µm. The reduced valley depth and the dale void volume were low for the vertically printed zirconia specimen, indicating lesser voids and low fluid retention. In addition, it was observed that the hardness value of the vertically printed sample was better. The scanning electron microscopic images and 3D surface profiles of the zirconia specimens depicted the surface topography and revealed the wear track. CONCLUSION: The study shows that zirconia fabricated using LCM technology possesses surface roughness of about 0.5 µm with no machining scars that are usually associated with CAD/CAM dentistry and also indicating agreement with clinically acceptable values for minimal surface roughness of dental restorations. Dental restorations using LCM fabricated zirconia redues the requirement of post-processing work flow that is part of CAD/CAM dentistry.

3D QSAR study on substituted 1, 2, 4 triazole derivatives as anticancer agents by kNN MFA approach.

Background and objectives: Researchers have recently focused on the biological and synthetic effects of 1, 2, and 4-triazole fused heterocyclic molecules because they have tremendous medicinal value. The objective of the present study was to carry out the 3D QSAR evaluation on the substituted 1,2, and 4 triazole derivatives for anticancer potential using k-Nearest Neighbor-Molecular Field Analysis (kNN-MFA) method. Methods: Using the molecular design suite, a three-dimensional quantitative structure-activity relationship (3D-QSAR) analysis was undertaken on a series of 4-amino-5-(pyridin3yl)-4H-1, 2, and 4-triazole-3-thiol anticancer drugs (Vlife MDS). This study used a genetic algorithm and a manual selection approach on 20 substituted 1, 2, and 4-triazole derivatives. Based on the genetic algorithm (GA), the 3D-QSAR model was generated. Statistical significance and predictive capacity were evaluated using internal and external validation. Results: The most significant model has a correlation coefficient of 0.9334 (squared correlation coefficient r2 = 0.8713), showing that biological activity and descriptors have a strong relationship. The model exhibited internal predictivity of 74.45 percent (q2 = 0.2129), external predictivity of 81.09 percent (pred r2 = 0.8417), and the smallest error term for the predictive correlation coefficient (pred r2se = 0.1255). The model revealed steric (S 1047--0.0780--0.0451S 927) and electrostatic (E 1002) data points that contribute remarkably to anticancer activity. A molecular field study demonstrates a link between the structural features of substituted triazole derivatives and their activities. Conclusion: The good-to-moderate anticancer potential of compounds confirms the significant pharmacological role of 1,2,4-triazole derivatives. These results could lead to the identification of potential chemical compounds with optimal anticancer activity and minimal side effects.

Facile one pot microbe-mediatedin situsynthesis and antibacterial activity of reduced graphene oxide-silver nanocomposite.

The present research deals with the development of a novel bioinspiredin situfabrication of reduced graphene oxide (rGO)-silver nanoparticle (AgNPs) nanocomposite (rGO@AgNCs) using microbes namelyPseudomonas aeruginosa(PA) andStaphylococcus aureus(SA). The fabricated rGO@AgNCs were characterized using Ultraviolet-visible (UV-Vis) spectroscopy, Fourier-transform infrared spectroscopy (FTIR), particle size analysis, polydispersity index (PDI), zeta potential analysis, energy dispersive x-ray analysis (EDAX), Raman spectroscopy, powder x-ray diffraction (PXRD), high-resolution transmission electron microscopy (HR-TEM) analysis, etc. Furthermore, the rGO@AgNCs-PA and rGO@AgNCs-SA interaction with serum protein, pH stability study, andin vitrodissolution of AgNPs were also performed. The research findings of the proposed study demonstrated the simultaneous reduction of graphene oxide (GO) and AgNPs and the formation of rGO@AgNCs in the presence of microbes. Thein vitrodissolution studies of rGO@AgNCs composites showed better AgNPs dissolution with controlled release and offered remarkable matrix integrity throughout the dissolution period. The size and stability of rGO@AgNCs-PA and rGO@AgNCs-SA had no significant changes at physiological pH 7.4. A minimal decrease in the zeta potential of rGO@AgNCs was observed, which may be due to the weak interaction of nanocomposites and albumin. The antibacterial application of the synthesized nanocomposite was evaluated against a pathogenic mastitis-forming bacterium. The obtained results suggested an admirable antibacterial activity of synthesized nanocomposites against the tested microbes. This knowledge will assist the scientific fraternity in designing novel antibacterial agents with enhanced antibacterial activity against various veterinary pathogens in near future.

AI-driven streamlined modeling: experiences and lessons learned from multiple domains.

Model-driven technologies (MD*), considered beneficial through abstraction and automation, have not enjoyed widespread adoption in the industry. In keeping with the recent trends, using AI techniques might help the benefits of MD* outweigh their costs. Although the modeling community has started using AI techniques, it is, in our opinion, quite limited and requires a change in perspective. We provide such a perspective through five industrial case studies where we use AI techniques in different modeling activities. We discuss our experiences and lessons learned, in some cases evolving purely modeling solutions with AI techniques, and in others considering the AI aids from the beginning. We believe that these case studies can help the researchers and practitioners make sense of various artifacts and data available to them and use applicable AI techniques to enhance suitable modeling activities.

Emergence, Transmission, and Potential Therapeutic Targets for the COVID-19 Pandemic Associated with the SARS-CoV-2.

The pandemic of the severe acute respiratory syndrome coronavirus (SARS-CoV)-2 at the end of 2019 marked the third outbreak of a highly pathogenic coronavirus affecting the human population in the past twenty years. Cross-species zoonotic transmission of SARS-CoV-2 has caused severe pathogenicity and led to more than 655,000 fatalities worldwide until July 28, 2020. Outbursts of this virus underlined the importance of controlling infectious pathogens across international frontiers. Unfortunately, there is currently no clinically approved antiviral drug or vaccine against SARS-CoV-2, although several broad-spectrum antiviral drugs targeting multiple RNA viruses have shown a positive response and improved recovery in patients. In this review, we compile our current knowledge of the emergence, transmission, and pathogenesis of SARS-CoV-2 and explore several features of SARS-CoV-2. We emphasize the current therapeutic approaches used to treat infected patients. We also highlight the results of in vitro and in vivo data from several studies, which have broadened our knowledge of potential drug candidates for the successful treatment of patients infected with and discuss possible virus and host-based treatment options against SARS-CoV-2.

Recent Advancement in Bio-precursor derived graphene quantum dots: Synthesis, Characterization and Toxicological Perspective.

Graphene quantum dots (GQDs), impressive materials with enormous future potential, are reviewed from their inception, including different precursors. Considering the increasing burden of industrial and ecological bio-waste, there is an urgency to develop techniques which will convert biowaste into active moieties of interest. Amongst the various materials explored, we selectively highlight the use of potential carbon containing bioprecursors (e.g. plant-based, amino acids, carbohydrates), and industrial waste and its conversion into GQDs with negligible use of chemicals. This review focuses on the effects of different processing parameters that affect the properties of GQDs, including the surface functionalization, paradigmatic characterization, toxicity and biocompatibility issues of bioprecursor derived GQDs. This review also examines current challenges and s the ongoing exploration of potential bioprecursors for ecofriendly GQD synthesis for future applications. This review sheds further light on the electronic and optical properties of GQDs along with the effects of doping on the same. This review may aid in future design approaches and applications of GQDs in the biomedical and materials design fields.

OPAL+: Length-Specific MoRF Prediction in Intrinsically Disordered Protein Sequences.

Intrinsically disordered proteins (IDPs) contain long unstructured regions, which play an important role in their function. These intrinsically disordered regions (IDRs) participate in binding events through regions called molecular recognition features (MoRFs). Computational prediction of MoRFs helps identify the potentially functional regions in IDRs. In this study, OPAL+, a novel MoRF predictor, is presented. OPAL+ uses separate models to predict MoRFs of varying lengths along with incorporating the hidden Markov model (HMM) profiles and physicochemical properties of MoRFs and their flanking regions. Together, these features help OPAL+ achieve a marginal performance improvement of 0.4-0.7% over its predecessor for diverse MoRF test sets. This performance improvement comes at the expense of increased run time as a result of the requirement of HMM profiles. OPAL+ is available for download at https://github.com/roneshsharma/OPAL-plus/wiki/OPAL-plus-Download.

Discovering MoRFs by trisecting intrinsically disordered protein sequence into terminals and middle regions.

BACKGROUND: Molecular Recognition Features (MoRFs) are short protein regions present in intrinsically disordered protein (IDPs) sequences. MoRFs interact with structured partner protein and upon interaction, they undergo a disorder-to-order transition to perform various biological functions. Analyses of MoRFs are important towards understanding their function. RESULTS: Performance is reported using the MoRF dataset that has been previously used to compare the other existing MoRF predictors. The performance obtained in this study is equivalent to the benchmarked OPAL predictor, i.e., OPAL achieved AUC of 0.815, whereas the model in this study achieved AUC of 0.819 using TEST set. CONCLUSION: Achieving comparable performance, the proposed method can be used as an alternative approach for MoRF prediction.

OPAL: prediction of MoRF regions in intrinsically disordered protein sequences.

Motivation: Intrinsically disordered proteins lack stable 3-dimensional structure and play a crucial role in performing various biological functions. Key to their biological function are the molecular recognition features (MoRFs) located within long disordered regions. Computationally identifying these MoRFs from disordered protein sequences is a challenging task. In this study, we present a new MoRF predictor, OPAL, to identify MoRFs in disordered protein sequences. OPAL utilizes two independent sources of information computed using different component predictors. The scores are processed and combined using common averaging method. The first score is computed using a component MoRF predictor which utilizes composition and sequence similarity of MoRF and non-MoRF regions to detect MoRFs. The second score is calculated using half-sphere exposure (HSE), solvent accessible surface area (ASA) and backbone angle information of the disordered protein sequence, using information from the amino acid properties of flanks surrounding the MoRFs to distinguish MoRF and non-MoRF residues. Results: OPAL is evaluated using test sets that were previously used to evaluate MoRF predictors, MoRFpred, MoRFchibi and MoRFchibi-web. The results demonstrate that OPAL outperforms all the available MoRF predictors and is the most accurate predictor available for MoRF prediction. It is available at http://www.alok-ai-lab.com/tools/opal/. Contact: ashwini@hgc.jp or alok.sharma@griffith.edu.au. Supplementary information: Supplementary data are available at Bioinformatics online.

TimeXNet Web: identifying cellular response networks from diverse omics time-course data.

Summary: Condition-specific time-course omics profiles are frequently used to study cellular response to stimuli and identify associated signaling pathways. However, few online tools allow users to analyze multiple types of high-throughput time-course data. TimeXNet Web is a web server that extracts a time-dependent gene/protein response network from time-course transcriptomic, proteomic or phospho-proteomic data, and an input interaction network. It classifies the given genes/proteins into time-dependent groups based on the time of their highest activity and identifies the most probable paths connecting genes/proteins in consecutive groups. The response sub-network is enriched in activated genes/proteins and contains novel regulators that do not show any observable change in the input data. Users can view the resultant response network and analyze it for functional enrichment. TimeXNet Web supports the analysis of high-throughput data from multiple species by providing high quality, weighted protein-protein interaction networks for 12 model organisms. Availability and implementation: http://txnet.hgc.jp/. Supplementary information: Supplementary data are available at Bioinformatics online.

Generation of tumor antigen-specific murine CD8+ T cells with enhanced anti-tumor activity via highly efficient CRISPR/Cas9 genome editing.

Immunotherapies have led to the successful development of novel therapies for cancer. However, there is increasing concern regarding the adverse effects caused by non-tumor-specific immune responses. Here, we report an effective strategy to generate high-avidity tumor-antigen-specific CTLs, using Cas9/single-guide RNA (sgRNA) ribonucleoprotein (RNP) delivery. As a proof-of-principle demonstration, we selected the gp100 melanoma-associated tumor antigen, and cloned the gp100-specific high-avidity TCR from gp100-immunized mice. To enable rapid structural dissection of the TCR, we developed a 3D protein structure modeling system for the TCR/antigen-major histocompatibility complex (pMHC) interaction. Combining these technologies, we efficiently generated gp100-specific PD-1(-) CD8+ T cells, and demonstrated that the genetically engineered CD8+ T cells have high avidity against melanoma cells both in vitro and in vivo. Our methodology offers computational prediction of the TCR response, and enables efficient generation of tumor antigen-specific CD8+ T cells that can neutralize tumor-induced immune suppression leading to a potentially powerful cancer therapeutic.

Protective effect of omeprazole and lansoprazole on ß-receptor stimulated myocardial infarction in Wistar rats.

We investigated the effect of omeprazole (OPZ) and lansoprazole (LPZ) on the pathophysiology of myocardial necrosis in rats by inspecting a series of indicators like hemodynamic parameters, biochemical estimations and histopathological changes in the myocardial tissue. Rats received either OPZ, LPZ (50 mg/kg/day, p.o.) individually for 7 days with concurrent administration of isoproterenol (ISO) (150 mg/kg, s.c.) on 6th and 7th day of study period to induce myocardial infarction. On the 8th day after measuring hemodynamic parameters, rats were killed and parameters were evaluated. ECG waves were found to be normal in the treatment group. ISO control rats revealed escalation in the oxidative stress as evidenced by depletion in the content of SOD, GSH, catalase and increase in the level of MDA and NO as compared with the normal rats. Treatment with OPZ and LPZ significantly reduced the ROS, indicated by an increase in the endogenous antioxidants and a decrease in NO and MDA levels. ISO control rats showed a significant elevation in the levels of pro-inflammatory cytokine TNF-α as compared to the normal and treatment group of rats. Administration of OPZ and LPZ does not exhibit any significant toxicity. Our findings reveal that multiple doses of OPZ and LPZ may have distinctly minimized the ISO-induced myocardial necrosis by declining the hmodynamic parameters, oxidative stress and pro-inflammatory cytokine TNF-α in myocardial infarcted rats.

MoRFPred-plus: Computational Identification of MoRFs in Protein Sequences using Physicochemical Properties and HMM profiles.

MOTIVATION: Intrinsically Disordered Proteins (IDPs) lack stable tertiary structure and they actively participate in performing various biological functions. These IDPs expose short binding regions called Molecular Recognition Features (MoRFs) that permit interaction with structured protein regions. Upon interaction they undergo a disorder-to-order transition as a result of which their functionality arises. Predicting these MoRFs in disordered protein sequences is a challenging task. METHOD: In this study, we present MoRFpred-plus, an improved predictor over our previous proposed predictor to identify MoRFs in disordered protein sequences. Two separate independent propensity scores are computed via incorporating physicochemical properties and HMM profiles, these scores are combined to predict final MoRF propensity score for a given residue. The first score reflects the characteristics of a query residue to be part of MoRF region based on the composition and similarity of assumed MoRF and flank regions. The second score reflects the characteristics of a query residue to be part of MoRF region based on the properties of flanks associated around the given residue in the query protein sequence. The propensity scores are processed and common averaging is applied to generate the final prediction score of MoRFpred-plus. RESULTS: Performance of the proposed predictor is compared with available MoRF predictors, MoRFchibi, MoRFpred, and ANCHOR. Using previously collected training and test sets used to evaluate the mentioned predictors, the proposed predictor outperforms these predictors and generates lower false positive rate. In addition, MoRFpred-plus is a downloadable predictor, which makes it useful as it can be used as input to other computational tools. AVAILABILITY: https://github.com/roneshsharma/MoRFpred-plus/wiki/MoRFpred-plus:-Download.

Predicting MoRFs in protein sequences using HMM profiles.

BACKGROUND: Intrinsically Disordered Proteins (IDPs) lack an ordered three-dimensional structure and are enriched in various biological processes. The Molecular Recognition Features (MoRFs) are functional regions within IDPs that undergo a disorder-to-order transition on binding to a partner protein. Identifying MoRFs in IDPs using computational methods is a challenging task. METHODS: In this study, we introduce hidden Markov model (HMM) profiles to accurately identify the location of MoRFs in disordered protein sequences. Using windowing technique, HMM profiles are utilised to extract features from protein sequences and support vector machines (SVM) are used to calculate a propensity score for each residue. Two different SVM kernels with high noise tolerance are evaluated with a varying window size and the scores of the SVM models are combined to generate the final propensity score to predict MoRF residues. The SVM models are designed to extract maximal information between MoRF residues, its neighboring regions (Flanks) and the remainder of the sequence (Others). RESULTS: To evaluate the proposed method, its performance was compared to that of other MoRF predictors; MoRFpred and ANCHOR. The results show that the proposed method outperforms these two predictors. CONCLUSIONS: Using HMM profile as a source of feature extraction, the proposed method indicates improvement in predicting MoRFs in disordered protein sequences.

Linking transcriptional changes over time in stimulated dendritic cells to identify gene networks activated during the innate immune response.

The innate immune response is primarily mediated by the Toll-like receptors functioning through the MyD88-dependent and TRIF-dependent pathways. Despite being widely studied, it is not yet completely understood and systems-level analyses have been lacking. In this study, we identified a high-probability network of genes activated during the innate immune response using a novel approach to analyze time-course gene expression profiles of activated immune cells in combination with a large gene regulatory and protein-protein interaction network. We classified the immune response into three consecutive time-dependent stages and identified the most probable paths between genes showing a significant change in expression at each stage. The resultant network contained several novel and known regulators of the innate immune response, many of which did not show any observable change in expression at the sampled time points. The response network shows the dominance of genes from specific functional classes during different stages of the immune response. It also suggests a role for the protein phosphatase 2a catalytic subunit α in the regulation of the immunoproteasome during the late phase of the response. In order to clarify the differences between the MyD88-dependent and TRIF-dependent pathways in the innate immune response, time-course gene expression profiles from MyD88-knockout and TRIF-knockout dendritic cells were analyzed. Their response networks suggest the dominance of the MyD88-dependent pathway in the innate immune response, and an association of the circadian regulators and immunoproteasomal degradation with the TRIF-dependent pathway. The response network presented here provides the most probable associations between genes expressed in the early and the late phases of the innate immune response, while taking into account the intermediate regulators. We propose that the method described here can also be used in the identification of time-dependent gene sub-networks in other biological systems.

Design of lactoferrin functionalized carboxymethyl dextran coated egg albumin nanoconjugate for targeted delivery of capsaicin: Spectroscopic and cytotoxicity studies.

The increased mortality rates associated with colorectal cancer highlight the pressing need for improving treatment approaches. While capsaicin (CAP) has shown promising anticancer activity, its efficacy is hampered due to low solubility, rapid metabolism, suboptimal bioavailability, and a short half-life. Therefore, this study aimed to prepare a lactoferrin-functionalized carboxymethyl dextran-coated egg albumin nanoconjugate (LF-CMD@CAP-EGA-NCs) for the targeted CAP delivery to enhance its potential for colorectal cancer therapy. Briefly, LF-CMD was synthesized through an esterification reaction involving LF as a receptor and CMD as a shell. Concurrently, CAP was incorporated into an EGA carrier using gelation and hydrophobic interactions. The subsequent production of LF-CMD@CAP-EGA-NCs was achieved through the Maillard reaction. Spectral characterizations confirmed the successful synthesis of smooth and spherical-shaped LF-CMD@CAP-EGA-NCs using LF-CMD and EGA-CAP nanoparticles, with high entrapment efficiency and satisfactory drug content. Furthermore, LF-CMD@CAP-EGA-NCs demonstrated a sustained release of CAP (76.52 ± 1.01 % in 24 h, R2 = 0.9966) in pH 5.8 buffer with anomalous transport (n = 0.68) owing to the shell of the CMD and EGA matrix. The nanoconjugate exhibited enhanced cytotoxicity in HCT116 and LoVo cell lines, which is attributed to the overexpression of LF receptors in colorectal HCT116 cells. Additionally, LF-CMD@CAP-EGA-NCs demonstrated excellent biocompatibility, as observed in the FHC-CRL-1831 cell line. In conclusion, LF-CMD@CAP-EGA-NCs can be considered as a promising approach for targeted delivery of CAP and other anticancer agents in colorectal cancer treatment.

Concept mapping a potential pedagogical strategy to foster meaningful learning in physiology students.

BACKGROUND: Understanding of human physiology is critical for clinical practice and disease management. Escalating the teaching-learning method to improve conceptual knowledge may help the students to apply their knowledge in clinical scenarios. The present study was conducted to teach the use of concept mapping as a learning strategy to foster meaningful learning in physiology, compare its impact as a learning tool with traditional methods on meaningful learning, assess the cognitive gain, and find student's perception regarding concept mapping. MATERIALS AND METHODS: The interventional study was conducted on first-year MBBS students. Depending upon marks obtained in previous internal assessments, the students were classified into "rapid learners" (RL) and "potential learners" (PL). By simple random sampling technique, both groups were divided into interventional (concept mapping) and control groups (question-answer discussion). After a pretest, all students had a lecture on glomerular filtration. The assignment was given to the interventional group to prepare a concept map on glomerular filtration, and question-answer were discussed for control groups. A surprise posttest was conducted after 2-3 days. RESULT: In our study, all four groups showed significant differences in the pretest and posttest scores using a paired t-test (P < 0.05). The mean score of gain in learning, raw gain (G0), absolute learning gain, relative learning gain, and average normalized gain compared between the interventional group and controls group showed statistically significant performance improvement in both RL and PL groups. CONCLUSION: The concept mapping strategy was more efficacious than the question-answer discussion. Concept mapping is an impactful learning tool to improve cognitive gain and potential pedagogical strategy to foster meaningful learning in physiology students.