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Bruton's tyrosine kinase (BTK) is a critical enzyme which is involved in multiple signaling pathways that regulate cellular survival, activation, and proliferation, making it a major cancer therapeutic target. We applied the novel integrated structure-based pharmacophore modeling, machine learning, and other in silico studies to screen the Korean chemical database (KCB) to identify the potential BTK inhibitors (BTKi). Further evaluation of these inhibitors on three different human cancer cell lines showed significant cell growth inhibitory activity. Among the 13 compounds shortlisted, four demonstrated consistent cell inhibition activity among breast, gastric, and lung cancer cells (IC50 below 3 μM). The selected compounds also showed significant kinase inhibition activity (IC50 below 5 μM). The current study suggests the potential of these inhibitors for targeting BTK malignant tumors.

Glioblastoma is the most aggressive form of brain tumor, accounting for the highest mortality and morbidity rates. Current treatment for patients with glioblastoma includes maximal safe tumor resection followed by radiation therapy with concomitant temozolomide (TMZ) chemotherapy. The addition of TMZ to the conformal radiation therapy has improved the median survival time only from 12 months to 16 months in patients with glioblastoma. Despite these aggressive treatment strategies, patients' prognosis remains poor. This therapeutic failure is primarily attributed to the blood-brain barrier (BBB) that restricts the transport of TMZ from reaching the tumor site. In recent years, nanomedicine has gained considerable attention among researchers and shown promising developments in clinical applications, including the diagnosis, prognosis, and treatment of glioblastoma tumors. This review sheds light on the morphological and physiological complexity of the BBB. It also explains the development of nanomedicine strategies to enhance the permeability of drug molecules across the BBB.

SARS-CoV-2, previously named 2019 novel coronavirus (2019-nCoV), has been associated with the global pandemic of acute respiratory distress syndrome. First reported in December 2019 in the Wuhan province of China, this new RNA virus has several folds higher transmission among humans than its other family member (SARS-CoV and MERS-CoV). The SARS-CoV-2 spike receptor-binding domain (RBD) is the region mediating the binding of the virus to host cells via Angiotensin-converting enzyme 2 (ACE2), a critical step of viral. Here in this study, we have utilized in silico approach for the virtual screening of antiviral library extracted from the Asinex database against the Receptor binding domain (RBD) of the S1 subunit of the SARS-CoV-2 spike glycoprotein. Further, the molecules were ranked based on their binding affinity against RBD, and the top 15 molecules were selected. The affinity of these selected molecules to interrupt the ACE2-Spike interaction was also studied. It was found that the chosen molecules were demonstrating excellent binding affinity against spike protein, and these molecules were also very effectively interrupting the ACE2-RBD interaction.Furthermore, molecular dynamics (MD) simulation studies were utilized to investigate the top 3 selected molecules' stability in the ACE2-RBD complexes. To the best of our knowledge, this is the first study where molecules' inhibitory potential against the Receptor binding domain (RBD) of the S1 subunit of the SARS-CoV-2 spike glycoprotein and their inhibitory potential against the ACE2-Spike has been studied. We believe that these compounds can be further tested as a potential therapeutic option against COVID-19.

Although several pharmacological agents are under investigation to be repurposed as therapeutic against COVID-19, not much success has been achieved yet. So, the search for an effective and active option for the treatment of COVID-19 is still a big challenge. The Spike protein (S), RNA-dependent RNA polymerase (RdRp), and Main protease (Mpro) are considered to be the primary therapeutic drug target for COVID-19. In this study we have screened the drugbank compound library against the Main Protease. But our search was not limited to just Mpro. Like other viruses, SARS-CoV-2, have also acquired unique mutations. These mutations within the active site of these target proteins may be an important factor hindering effective drug candidate development. In the present study we identified important active site mutations within the SARS-CoV-2 Mpro (Y54C, N142S, T190I and A191V). Further the drugbank database was computationally screened against Mpro and the selected mutants. Finally, we came up with the common molecules effective against the wild type (WT) and all the selected Mpro. The study found Imiglitazar, was found to be the most active compound against the wild type of Mpro. While PF-03715455 (Y54C), Salvianolic acid A (N142S and T190I), and Montelukast (A191V) were found to be most active against the other selected mutants. It was also found that some other compounds such as Acteoside, 4-Amino-N- {4-[2-(2,6-Dimethyl-Phenoxy)-Acetylamino]-3-Hydroxy-1-Isobutyl-5-Phenyl-Pentyl}-Benzamide, PF-00610355, 4-Amino-N-4-[2-(2,6-Dimethyl-Phenoxy)-Acetylamino]-3-Hydroxy-1-Isobutyl-5-Phenyl-Pentyl}-Benzamide and Atorvastatin were showing high efficacy against the WT as well as other selected mutants. We believe that these molecules will provide a better and effective option for the treatment of COVID-19 clinical manifestations.

We synthesized bioinspired sericin encapsulated gold nanoparticles (SGNPs) using HAuCl4 as the starting material in a bottom-up approach. Further, two-dimensional (2D) and three-dimensional (3D) conformational changes (folding and unfolding) in sericin were studied using circular dichroism (CD) and fluorescence spectroscopy, respectively, during and after the synthesis of particles. Finally, the synthesized SGNPs were characterized using several physical techniques to ensure their correct synthesis and study the size, stability, and charge over the surface of particles. At the beginning of the reaction, when gold was in the ionic form (Au+3), sericin exhibited maximum electrostatic interaction and underwent unfolding. Au+3 reduced to Au during the reaction, and sericin regained its 3D confirmation due to a decrease in its native electrostatic interactions. However, CD revealed the same patterns of unfolding and folding; a decrease in α helix and an increase inβ3 pleated sheets were noticed. Although the 3D structure of sericin was restored after the synthesis of SGNPs, it was substantially altered. In addition, certain changes in the 2D structure were observed; however, these did not alter the activity of sericin. Furthermore, Fourier-transform infrared spectroscopy (FTIR) confirmed these findings. The SGNPs were found to be effective against lung cancer (A549 cells), with an IC50 of 145.49 βM, without exerting any toxic effects on normal cells (NRK cells). The effectiveness of SGNPs was examined by MTT cytotoxicity and nuclear fragmentation assays. Furthermore, we assessed their ability to produce excessive ROS and release Cyt-c from the mitochondria for caspase-3-mediated apoptosis.

Diabetes and its related complications are responsible for severe morbidity and deaths round the globe. Diabetic foot infections (DFIs) are severe and dreadful complication of diabetes mellitus. DFI/DFUs are colonized with numerous pathogenic, multi-drug-resistant microorganisms and progression in colonization of these microorganism leads to biofilm establishment making treatment challenging. There is an indispensable requirement for alternative approaches to combat this threat. In the present assignment, titanium oxide nanoparticles (TiO2-NPs) were fabricated from the leaf extract of Ochradenus arabicus and inhibition of biofilm and related functions was examined against multi-drug-resistant strains of P. aeruginosa and methicillin-resistant S. aureus (MRSA) isolated from foot ulcers. Bio-fabricated TiO2-NPs reduced biofilm formation in the selected strains by 22–70% at tested sub-MICs. Significant reduction in alginate and severely impaired motility of P. aeruginosa strains was also observed. Key functions such as cell surface hydrophobicity and EPS production that contribute in the attachment and maintenance of biofilm, respectively, were also inhibited considerably in test strains at sub-MICs. Statistically significant reduction (51–63%) in the preformed biofilms of all strains was also recorded. Interaction of bacterial cell and TiO2-NPs resulted in increased levels of ROS, and this could be the plausible the reason for the biofilm inhibitory action of NPs. These NPs could be exploited in the current treatment strategies to combat the threat of biofilm-based drug-resistant diabetic foot infections and facilitate wound healing.

It’s been more than 2 years since the first outbreak of COVID-19, and still the pandemic is devastating the world and is standing as significant threat to the worldwide medical settings. The reports of new variants, absence of globally approved therapeutics are the major reasons responsible for the continuously increasing number of cases and mortalities. The main protease (Mpro) of SARS-CoV-2 is a well reported and a key therapeutic target for designing drugs to treat COVID-19. The medicinal properties of mushrooms and its derived natural compounds are well established. Here in this study, we have performed the virtual screening of mushroom-derived compounds against the SARS-CoV-2 Mpro. Cordycepin was found to be the most active compound against Mpro. The compound was further isolated from C. militaris and in vitro assay was performed to confirm its antivirals potential against SARS-CoV-2. Our findings show cordycepin to be a promising candidate against COVID-19.

Early detection of bacteremia is important to prevent antibiotic abuse. Therefore, we aimed to develop a clinically applicable bacteremia prediction model using machine learning technology. Data from two tertiary medical centers’ electronic medical records during a 12-year-period were extracted. Multi-layer perceptron (MLP), random forest, and gradient boosting algorithms were applied for machine learning analysis. Clinical data within 12 and 24 hours of blood culture were analyzed and compared. Out of 622,771 blood cultures, 38,752 episodes of bacteremia were identified. In MLP with 128 hidden layer nodes, the area under the receiver operating characteristic curve (AUROC) of the prediction performance in 12- and 24-h data models was 0.762 (95% confidence interval (CI); 0.7617–0.7623) and 0.753 (95% CI; 0.7520–0.7529), respectively. AUROC of causative-pathogen subgroup analysis predictive value for Acinetobacter baumannii bacteremia was the highest at 0.839 (95% CI; 0.8388–0.8394). Compared to primary bacteremia, AUROC of sepsis caused by pneumonia was highest. Predictive performance of bacteremia was superior in younger age groups. Bacteremia prediction using machine learning technology appeared possible for acute infectious diseases. This model was more suitable especially to pneumonia caused by Acinetobacter baumannii. From the 24-h blood culture data, bacteremia was predictable by substituting only the continuously variable values.

The recent emergence of novel SARS-CoV-2 variants has threatened the efforts to contain the COVID-19 pandemic. The emergence of these “variants of concern” has increased viral transmissibility or immune escape and has supplanted the ancestral strains. The novel variants harbored by the B.1.617 lineage (Kappa and Delta) carry mutations within the receptor-binding domain of spike (S) protein (L452R + E484Q and L452R + T478K), the region binding to the host receptor. The double mutations carried by these novel variants are primarily responsible for an upsurge number of COVID-19 cases in India. In this study, we thoroughly investigated the impact of these double mutations on the binding capability to the human host receptor. We performed several structural analyses and found that the studied double mutations increase the binding affinity of the spike protein to the human host receptor (ACE2). Furthermore, our study showed that these double mutants might be a dominant contributor enhancing the receptor-binding affinity of SARS-CoV-2 and consequently making it more stable. We also investigated the impact of these mutations on the binding affinity of two monoclonal antibodies (Abs) (2-15 and LY-CoV555) and found that the presence of the double mutations also hinders its binding with the studied Abs. The principal component analysis, free energy landscape, intermolecular interaction, and other investigations provided a deeper structural insight to better understand the molecular mechanism responsible for increased viral transmissibility of these variants.

The global coronavirus pandemic has burdened the human population with mass fatalities and disastrous socio-economic consequences. The frequent occurrence of these new variants has fueled the already prevailing challenge. There is still a necessity for highly effective small molecular agents to prevent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Here, we targeted the human transmembrane surface protease TMPRSS2, which is essential for proteolytic activation of SARS-CoV-2. Camostat is a well-known inhibitor of serine proteases and an effective TMPRSS2 inhibitor. A virtual library of camostat-like compounds was computationally screened against the catalytic site of TMPRSS2. Following a sequential in-depth molecular docking and dynamics simulation, we report the compounds that exhibited promising efficacy against TMPRSS2. The molecular docking and MM/PBSA free energy calculation study indicates these compounds carry excellent binding affinity against TMPRSS2 and found them more effective than camostat. The study will open doors for the effective treatment of coronavirus disease 2019.

Introduction: Selonsertib, the most recently developed selective inhibitor of apoptosis signal-regulating kinase 1. We elucidated the binding characteristics, mechanism of interaction, and dynamic behaviors of selonsertib with human serum albumin (HSA), a major circulatory transport protein. Method: Different biophysical approaches (fluorescence quenching and isothermal titration calo-rimetry (ITC) were combined with various in silico techniques to examine the binding of selonsertib to HSA. Molecular docking results, analysis of molecular dynamics trajectories, and essential dy-namics investigations indicated the stable binding of selonsertib to HSA. Further in vitro studies were performed to validate the observed interaction. Result: ITC results confirmed the robust binding and high affinity of selonsertib and HSA. Like-wise, the fluorescence quenching results highlighted the binding affinity of selonsertib and HSA. Collectively, our findings offer deeper insight into the binding mechanism of selonsertib and HSA, emphasizing the selonsertib-mediated structural changes within HSA, along with a comprehensive rationale for the biological transport and accumulation of selonsertib in the blood plasma. Conclusion: Therefore, considering the bioavailability and effectiveness of selonsertib, assessing the interactions of this inhibitor with carrier proteins is crucial to elucidate its biological processes at the molecular level. This evidence carries the considerable scientific potential for future drug de-sign.

Introduction: Apoptosis signal-regulating kinase 1 (ASK1), also known as MAP3K5, is a member of mitogen-activated protein kinase kinase kinase (MAP3K) family and is well reported as crucial in the regulation of the JNK and P38 pathways. ASK1 is activated in response to a diverse array of stresses such as endoplasmic reticulum stress, lipopolysaccharides, tumor necrosis factor alpha, and reactive oxygen species. The activation of ASK1 induces various stress responses. Areas covered: Considering ASK1 as an important therapeutic drug target, here we have discussed the role of ASK1 in the progression of various diseases. We have also provided an overview of the available inhibitors for ASK1. The success of computational-based approaches toward ASK1 inhibitor design has also been discussed. Expert opinion: A number of reports have outlined the prominent role of ASK1 in the pathogenesis of several diseases. The discovery of novel ASK1 inhibitors would have a wide range of applications in medical science. In-silico techniques have been successfully used in the design of some novel ASK1 inhibitors. The use of machine learning-based approaches in combination with structure-based virtual screening (SBVS) and ligand-based virtual screening (LBVS) will be helpful toward the development of potent ASK1 inhibitors.

The COVID-19 outbreak continues to spread worldwide at a rapid rate. Currently, the absence of any effective antiviral treatment is the major concern for the global population. The reports of the occurrence of various point mutations within the important therapeutic target protein of SARS-CoV-2 has elevated the problem. The SARS-CoV-2 main protease (Mpro) is a major therapeutic target for new antiviral designs. In this study, the efficacy of PF-00835231 was investigated (a Mpro inhibitor under clinical trials) against the Mpro and their reported mutants. Various in silico approaches were used to investigate and compare the efficacy of PF-00835231 and five drugs previously documented to inhibit the Mpro. Our study shows that PF-00835231 is not only effective against the wild type but demonstrates a high affinity against the studied mutants as well.

The ongoing coronavirus disease 2019 (COVID-19) pandemic has become a serious global threat. Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), the virus responsible for this pandemic has imposed a severe burden on the medical settings. The spike (S) protein of SARS-CoV-2 is an important structural protein playing a key role in the viral entry. This protein is responsible for the receptor recognition and cell membrane fusion process. The recent reports of the appearance and spread of new SARS-CoV-2 strain has raised alarms. It was reported that this new variant containing the prominent active site mutation in the RBD (N501Y) was rapidly spreading within the population. The reported N501Y mutation within the spike's essential part, known as the ‘receptor-binding domain’ has raised several questions. Here in this study we have tried to explore the effect of N501Y mutation within the spike protein using several in silico approaches

Autophagy is a process essential for cellular energy consumption, survival, and defense mechanisms. The role of autophagy in several types of human cancers has been explicitly explained; however, the underlying molecular mechanism of autophagy in glioblastoma remains ambiguous. Autophagy is thought to be a “double-edged sword”, and its effect on tumorigenesis varies with cell type. On the other hand, autophagy may play a significant role in the resistance mechanisms against various therapies. Therefore, it is of the utmost importance to gain insight into the molecular mechanisms deriving the autophagy-mediated therapeutic resistance and designing improved treatment strategies for glioblastoma. In this review, we discuss autophagy mechanisms, specifically its pro-survival and growth-suppressing mechanisms in glioblastomas. In addition, we try to shed some light on the autophagy-mediated activation of the cellular mechanisms supporting radioresistance and chemoresistance in glioblastoma. This review also highlights autophagy’s involvement in glioma stem cell behavior, underlining its role as a potential molecular target for therapeutic interventions.

Cyclin-dependent kinase 2 (CDK2) is an essential protein kinase involved in the cell cycle regulation. The abnormal activity of CDK2 is associated with cancer progression and metastasis. Here, we have performed structure-based virtual screening of the PubChem database to identify potent CDK2 inhibitors. First, we retrieved all compounds from the PubChem database having at least 90% structural similarity with the known CDK2 inhibitors. The selected compounds were subjected to structure-based molecular docking studies to investigate their pattern of interaction and estimate their binding affinities with CDK2. Selected compounds were further filtered out based on their physicochemical and ADMET properties. Detailed interaction analysis revealed that selected compounds interact with the functionally important residues of the active site pocket of CDK2. All-atom molecular dynamics simulation was performed to evaluate conformational changes, stability and the interaction mechanism of CDK2 in-complex with the selected compound. We found that binding of 6-N,6-N-dimethyl-9-(2-phenylethyl)purine-2,6-diamine stabilizes the structure of CDK2 and causes minimal conformational change. Finally, we suggest that the compound (PubChem ID 101874157) would be a promising scaffold to be further exploited as a potential inhibitor of CDK2 for therapeutic management of cancer after required validation.

An adequate model for predicting bacteraemia has not yet been developed. This study aimed to evaluate the performance of an artificial neural network (ANN)-based prediction model in comparison with previous statistical models. The performance of multi-layer perceptron (MLP), a representative ANN model, was verified via comparison with a non-neural network model. A total of 1260 bacteraemia episodes were identified in 13,402 patients. In MLP with 128 hidden layer nodes, the area under the receiver operating characteristic curve (AUC) of the prediction performance was 0.729 (95% confidence interval [CI], 0.712&ndash, 0.728), while in MLP with 256 hidden layer nodes, it was 0.727 (95% CI, 0.713&ndash, 0.727). In a conventional Bayesian statistical method, the AUC was 0.7. The aforementioned two MLP models exhibited the highest sensitivity (0.810). The ranking of clinical variables was used to describe the influential power of the prediction. Serum alkaline phosphatase was one of the most influential clinical variables, and one-out search was the best ranking method for measuring the influence of the clinical variables. Furthermore, adding variables beyond the 10 top-ranking ones did not significantly affect the prediction of bacteraemia. The ANN model is not inferior to conventional statistical approaches. Bacteraemia can be predicted using only the top 10 clinical variables determined by a ranking method, and the model can be used in clinical practice by applying real-time monitoring.

This study aimed to investigate the relationship between employment status and insulin resistance in the Korean elderly population. The study included 2325 healthy participants (862 men and 1463 women) who did not have diabetes, were 60–74 years old, and participated in the 2007–2010 Korea National Health and Nutrition Examination Survey. Their employment status was categorized as employed or unemployed. The homeostasis model assessment of insulin resistance (HOMA-IR) was used to assess insulin sensitivity. Insulin resistance was defined as values greater than the 75th percentile of the HOMA-IR (> 2.67 in men and > 2.87 in women). Mean HOMA-IR values were significantly higher for both sexes in the unemployed group than in the employed group. Compared with the unemployed group, the odds ratios (95% confidence intervals) for insulin resistance in the employed group were 0.55 (0.34–0.87) in men and 0.68 (0.47–0.98) in women after adjusting for confounders. Being employed was significantly associated with a lower risk of insulin resistance in the Korean elderly population. These findings suggest that insulin sensitivity could be increased by increasing physical activity through occupational activities in the elderly population.

Background Smoking is one of the risk factors to exacerbate allergic diseases, and it may affect serum immunoglobulin E (IgE) levels. However, few studies have relied on an objective biomarker to examine the effect of tobacco smoking on serum IgE levels. Method A nationwide cross-sectional study was conducted to examine the relationship between urinary cotinine (Ucot) concentrations and IgE levels in 973 males using data from the 2010 Korean National Health and Nutrition Examination Survey (KNHANES). Ucot was classified into four groups based on concentration (ng/mL) as follows: nonsmoker group (Ucot Results We found a significant and positive dose-related effect of cigarette smoking as measured by Ucot concentrations on the total serum IgE level. The multivariate adjusted means of total serum IgE levels (SE) were 321.0 (36.3), 404.4 (102.7), 499.2 (79.2), and 534.7 (82.7) IU/mL, after adjusting for age, body mass index, alcohol ingestion, physical exercise, job, and household income. The regression coefficient β for total serum IgE was β = 68.6 with increasing level of Ucot group after adjusting for the same covariables (p = .009). Conclusion These findings suggest that the amount of smoking may have a dose-dependent effect on total serum IgE levels. Implication Smoking is one of the risk factors to exacerbate allergic diseases, and it may affect serum immunoglobulin E (IgE) levels, which is closely related to type 1 mediated allergic diseases. However, few studies have relied on an objective biomarker to examine the effect of tobacco smoking on serum IgE levels. We found that tobacco exposure, as measured by Ucot concentrations, increased the serum IgE levels in a dose-response manner in a representative sample of Korean adult males.

Background By applying machine-learning-based algorithm using artificial intelligence to massive medical data, we are trying to build a real-time monitoring system for prediction of diseases to support accurate and efficient clinical decision making in time. In the previous study, we presented a model for predicting bacteremia using Bayesian statistical approach. Now, we have developed various machine-learning technique-based prediction model to achieve better prediction performance. Methods We retrospectively analyzed 13,402 febrile patients who were admitted to Gangnam Severance Hospital, a tertiary center in Seoul, South Korea. The training data were 11,061 patients with admission date from July 2008 to August 2011, and validation data were 2,341 patients from September 2011 to February 2012. The primary outcome was bacteremia, and the training data were analyzed to make prediction model with conventional Bayesian approach, Support Vector Machine (SVM), Random Forest (RF) and multi-layer perceptron (MLP), a representative artificial neural network (ANN) model, respectively. The performance of prediction was assessed based on the area under the curve (AUC) and sensitivity from validation data. We used 20 clinical variables for predictors of bacteremia same as Bayesian approach. The difference from the previous model was that each variable had been stratified, but in this study, they were trained by continuous number as it is. Results A total of 1,538 bacteremia episodes were identified from 13,402 febrile patients. The AUC of bacteremia prediction performance in SVM model was lowest with the result of 0.699 (95%CI; 0.687–0.700), even though it was 0.7 in conventional Bayesian statistical method. The highest results were 0.732 (95% CI; 0.722–0.733) in RF model and in MLP with 128 nodes of hidden layer model, the AUC was 0.719 (95% CI; 0.712–0.728) and in MLP with 256 nodes, it was 0.727 (95% CI; 0.713–0.727). In comparison with sensitivity, MLP models (0.810, 95% CI 0.772–0.747 in 128 nodes, 0.810, 95% CI, 0.782–0.837 in 256 nodes) were the highest but in RF model, the sensitivity was the lowest. Conclusion Compared with conventional statistical model, ANN-based bacteremia prediction model-MLP showed better predictive value. In order to improve the performance of prediction, further larger amount of clinical data is needed to be analyzed. Disclosures All authors: No reported disclosures.