MRI features of combined hepatocellular-cholangiocarcinoma.

Combined hepatocellular cholangiocarcinoma (cHCC-CCA) is a rare liver tumor which has a more aggressive behavior and worse survival outcome than hepatocellular carcinoma (HCC), with a prognosis similar to that of intrahepatic cholangiocarcinoma (iCCA). With limited literature on the appearance of this tumor on MRI, it remains a diagnostic challenge. In this review, we looked at the currently described MRI findings in this uncommon entity. Based on studies conducted to date, a mixed pattern at imaging has demonstrated the highest specificity, seen as a combination of areas showing progressive enhancement of the lesion, arterial enhancement with washout, and areas of arterial enhancement without washout and/or hypovascularity. Tumor markers may aid in identification, particularly in cases where the imaging appearance mimics that of isolated HCC or iCCA. Intratumoral heterogeneity leads to difficulties with pathologic diagnosis from sampling due to the possibility of an incorrect diagnosis if the biopsy specimen does not contain adequate tissue comprising both histologic components.

Streptococcus cristatus: An uncommon cause for a common hospital admission.

Common organisms associated with community-acquired pneumonia include Streptococcus pneumoniae, Haemophilus influenzae, and Staphylococcus aureus. Pneumonia can rarely be caused by an organism such as Streptococcus cristatus, as in our case. This organism belongs to the Mitis group within the Streptococcus genus and typically coexists with humans in the oral cavity. We present a case of Streptococcus cristatus bacteremia and community acquired pneumonia in a previously healthy 40-year-old male, for whom infective endocarditis has been ruled out, and who was successfully treated with ceftriaxone. While most reported cases of Streptococcus cristatus involve infective endocarditis, our case is the first identified instance of community acquired pneumonia caused by Streptococcus cristatus. This case highlights that pneumonia with Streptococcus cristatus, typically considered a commensal in the oral mucosa microbiota of humans, is possible, as seen in our case. Unlike previous cases in the literature, our patient did not have infective endocarditis, which is the common presentation of this bacterium. Instead, he solely presented with pneumonia, marking the first reported case in the literature of Streptococcus cristatus causing pneumonia.

Subtractive proteomics-based vaccine targets annotation and reverse vaccinology approaches to identify multiepitope vaccine against Plesiomonas shigelloides.

Plesiomonas shigelloides, an aquatic bacterium belonging to the Enterobacteriaceae family, is a frequent cause of gastroenteritis with diarrhea and gastrointestinal severe disease. Despite decades of research, discovering a licensed and globally accessible vaccine is still years away. Developing a putative vaccine that can combat the Plesiomonas shigelloides infection by boosting population immunity against P. shigelloides is direly needed. In the framework of the current study, the entire proteome of P. shigelloides was explored using subtractive genomics integrated with the immunoinformatics approach for designing an effective vaccine construct against P. shigelloides. The overall stability of the vaccine construct was evaluated using molecular docking, which demonstrated that MEV showed higher binding affinities with toll-like receptors (TLR4: 51.5 ± 10.3, TLR2: 60.5 ± 9.2) and MHC receptors(MHCI: 79.7 ± 11.2 kcal/mol, MHCII: 70.4 ± 23.7). Further, the therapeutic efficacy of the vaccine construct for generating an efficient immune response was evaluated by computational immunological simulation. Finally, computer-based cloning and improvement in codon composition without altering amino acid sequence led to the development of a proposed vaccine. In a nutshell, the findings of this study add to the existing knowledge about the pathogenesis of this infection. The schemed MEV can be a possible prophylactic agent for individuals infected with P. shigelloides. Nevertheless, further authentication is required to guarantee its safeness and immunogenic potential.

Role of small molecules as drug candidates for reprogramming somatic cells into induced pluripotent stem cells: A comprehensive review.

With the use of specific genetic factors and recent developments in cellular reprogramming, it is now possible to generate lineage-committed cells or induced pluripotent stem cells (iPSCs) from readily available and common somatic cell types. However, there are still significant doubts regarding the safety and effectiveness of the current genetic methods for reprogramming cells, as well as the conventional culture methods for maintaining stem cells. Small molecules that target specific epigenetic processes, signaling pathways, and other cellular processes can be used as a complementary approach to manipulate cell fate to achieve a desired objective. It has been discovered that a growing number of small molecules can support lineage differentiation, maintain stem cell self-renewal potential, and facilitate reprogramming by either increasing the efficiency of reprogramming or acting as a genetic reprogramming factor substitute. However, ongoing challenges include improving reprogramming efficiency, ensuring the safety of small molecules, and addressing issues with incomplete epigenetic resetting. Small molecule iPSCs have significant clinical applications in regenerative medicine and personalized therapies. This review emphasizes the versatility and potential safety benefits of small molecules in overcoming challenges associated with the iPSCs reprogramming process.

Efficacy and safety of ciprofol versus propofol for induction and maintenance of general anesthesia: a systematic review and meta-analysis.

BACKGROUND: Propofol has been the gold standard for anesthesia induction and maintenance due to its rapid onset and favorable pharmacokinetic properties. However, the search for alternative agents with improved safety and efficacy has led to the emergence of ciprofol (HSK3486), a structural analog of propofol. This systematic review and meta-analysis aim to comprehensively assess the safety and efficacy of ciprofol compared to propofol for anesthesia induction and maintenance in adult patients undergoing surgical procedures. METHODS: This study included only double-arm RCTs in which participants were aged eighteen or older undergoing surgery. For the statistical analysis of the extracted data, we employed RevMan 5.4.1. RESULTS: Ciprofol demonstrated a promising trend of higher anesthesiologists' satisfaction during the induction phase (MD 0.14, 95%, CI - 0.28 to 0.56, p = 0.51), whereas Propofol was favored during maintenance. Propofol also exhibited advantages with a shorter time to successful anesthesia induction (MD 0.08 min, 95% CI 0.00 to 0.15, p = 0.04), and quicker attainment of full alertness (MD 0.11 min, 95% CI - 1.29 to 1.52, p = 0.87), suggesting its efficiency in clinical practice. Importantly, there were no significant disparities in the success rate of anesthesia. CONCLUSION: Both ciprofol and propofol demonstrate comparable efficacy and safety for anesthesia induction and maintenance in adult patients undergoing surgery. While propofol provides a faster onset of induction, ciprofol exhibits advantages in terms of pain management. Clinicians should consider these findings when selecting anesthetic agents, and tailoring choices to individual patient needs and clinical scenarios.

Incidence of acute kidney injury in patients with acute ischaemic stroke undergoing CT angiography (CTA) and CT perfusion (CTP): a systematic review and meta-analysis.

Background and purpose: We conducted a systematic review and meta-analysis to assess the incidence of acute kidney injury (AKI) in patients undergoing CT angiography (CTA) and CT perfusion (CTP) for acute ischaemic stroke (AIS). Concerns over contrast-induced nephropathy (CIN) often lead medical centres to mandate pre-imaging serum creatinine level assessments, causing unnecessary delays. We aim to confirm further the practice of conducting CTA/CTP without first testing creatinine. Methods: We searched PubMed, Cochrane Central and Scopus from inception until March 2023 for studies reporting on AKI in patients with AIS receiving CTA/CTP. Outcomes of interest were (1) the odds of AKI in patients receiving CTA/CTP versus non-contrast CT and (2) the overall incidence of AKI and haemodialysis in patients with AIS undergoing CTA/CTP. Results: Results were pooled using a random effects model. 13 studies were included (5 cohort and 8 single-arm studies) with 5104 patients in total, out of which 4347 patients received CTA/CTP and 757 patients received no contrast. In case-control studies, 4.8% (OR=0.66, 95% CI 0.35 to 1.22, Z=1.32, p=0.19) of patients who received CTA/CTP developed AKI, compared with 7.7% of patients in the control group. Temporary haemodialysis was required for two patients in the analysed studies. Conclusions: Non-randomised evidence suggests that CTA/CTP is not associated with a statistically significant increase in the risk of AKI in patients with stroke. Further well-designed prospective studies are required to explore potential risk factors of CIN in specific patient populations such as diabetes mellitus and chronic kidney disease.

Multitarget Mechanisms of Monocarbonyl Curcuminoid Analogues against HL-60 Cancer Cells: In Vitro and Network Pharmacology-Based Approach.

This study addressed the cytotoxic potential of four compounds: monocarbonyl curcuminoid, ethyl (2E)-2-benzylidene-3-oxobutanoate 1, 1,2-dimethoxy-12-methyl-13H- [1,3] benzodioxolo[5,6-c] phenanthridine 2, 3,5-dibenzyloxybenzyl bromide 3, and (E)-4-(4-chlorobenzylidene)-1-(4-nitrophenyl)hexan-3-one 4. In vitro cytotoxic assays were carried out in HL-60 and BJ cells using the MTT assay along with analysis of apoptosis with the annexin V detection kit. Additional network pharmacology and docking analyses were carried out. In the in vitro assays, compounds 2 and 4 displayed significant antiproliferative effects in HL-60 cells, exhibiting IC50 values of 5.02 and 9.50 µM, respectively. Compound 1 showed no activity, and compound 3 displayed toxicity in BJ cells. In addition, both compounds 2 and 4 induced apoptosis in HL-60 cells. Network pharmacology and docking analyses indicated that compounds 2 and 4 had synergistic effects targeting the CASP3 and PARP1 proteins. Notably, these proteins play pivotal roles in cancer-related pathways. Thus, by modulating these proteins, monocarbonyl curcuminoid has the potential to influence various cancer-related pathways. In summary, our novel findings provide valuable insights into the potential of these compounds to serve as novel anticancer therapeutic agents, warranting further mechanistic studies and clinical exploration.

Brucellosis an Unusual presentation as isolated septic mono-arthritis of the knee joint: A case report.

Brucellosis is a zoonotic infection that is widely spread across the world. It is becoming more common in Middle Eastern countries such as Qatar, Saudi Arabia, and the Mediterranean region. Despite this, we need to remain vigilant as it is still prevalent in many parts of the world. The most common presentation is musculoskeletal, but it can also present as septic arthritis in the sacroiliac, hip, or knee joints. Brucella melitensis was only found in one extended culture of synovial fluid. Treatment involved a combination of antimicrobial therapy using gentamycin, doxycycline, and rifampin. A high level of suspicion for brucellosis is necessary for any patient coming from an endemic region with non-specific and chronic arthritis to ensure early diagnosis and treatment. In this case, we present a 28-year-old male who was diagnosed with Brucellosis after developing acute septic arthritis.

Bioactivity predictions and virtual screening using machine learning predictive model.

Recently, there has been significant attention on machine learning algorithms for predictive modeling. Prediction models for enzyme inhibitors are limited, and it is essential to account for chemical biases while developing them. The lack of repeatability in available models and chemical bias issues constrain drug discovery and development. A new prediction model for enzyme inhibitors has been developed, and the model efficacy was checked using Dipeptidyl peptidase 4 (DPP-4) inhibitors. A Python script was prepared and can be provided for personal use upon request. Among various machine learning algorithms, it was found that Random Forest offers the best accuracy. Two models were compared, one with diverse training and test data and the other with a random split. It was concluded that machine learning predictive models based on the Murcko scaffold can address chemical bias concerns. In-silico screening of the Drug Bank database identified two molecules against DPP-4, which are previously proven hit molecules. The approach was further validated through molecular docking studies and molecular dynamics simulations, demonstrating the credibility and relevance of the developed model for future investigations and potential translation into clinical applications.Communicated by Ramaswamy H. Sarma.

Deep-HPI-pred: An R-Shiny applet for network-based classification and prediction of Host-Pathogen protein-protein interactions.

Host-pathogen interactions (HPIs) are vital in numerous biological activities and are intrinsically linked to the onset and progression of infectious diseases. HPIs are pivotal in the entire lifecycle of diseases: from the onset of pathogen introduction, navigating through the mechanisms that bypass host cellular defenses, to its subsequent proliferation inside the host. At the heart of these stages lies the synergy of proteins from both the host and the pathogen. By understanding these interlinking protein dynamics, we can gain crucial insights into how diseases progress and pave the way for stronger plant defenses and the swift formulation of countermeasures. In the framework of current study, we developed a web-based R/Shiny app, Deep-HPI-pred, that uses network-driven feature learning method to predict the yet unmapped interactions between pathogen and host proteins. Leveraging citrus and CLas bacteria training datasets as case study, we spotlight the effectiveness of Deep-HPI-pred in discerning Protein-protein interaction (PPIs) between them. Deep-HPI-pred use Multilayer Perceptron (MLP) models for HPI prediction, which is based on a comprehensive evaluation of topological features and neural network architectures. When subjected to independent validation datasets, the predicted models consistently surpassed a Matthews correlation coefficient (MCC) of 0.80 in host-pathogen interactions. Remarkably, the use of Eigenvector Centrality as the leading topological feature further enhanced this performance. Further, Deep-HPI-pred also offers relevant gene ontology (GO) term information for each pathogen and host protein within the system. This protein annotation data contributes an additional layer to our understanding of the intricate dynamics within host-pathogen interactions. In the additional benchmarking studies, the Deep-HPI-pred model has proven its robustness by consistently delivering reliable results across different host-pathogen systems, including plant-pathogens (accuracy of 98.4% and 97.9%), human-virus (accuracy of 94.3%), and animal-bacteria (accuracy of 96.6%) interactomes. These results not only demonstrate the model's versatility but also pave the way for gaining comprehensive insights into the molecular underpinnings of complex host-pathogen interactions. Taken together, the Deep-HPI-pred applet offers a unified web service for both identifying and illustrating interaction networks. Deep-HPI-pred applet is freely accessible at its homepage: https://cbi.gxu.edu.cn/shiny-apps/Deep-HPI-pred/ and at github: https://github.com/tahirulqamar/Deep-HPI-pred.

Integrated virtual screening and molecular dynamics simulation approaches revealed potential natural inhibitors for DNMT1 as therapeutic solution for triple negative breast cancer.

Triple negative breast cancers (TNBC) are clinically heterogeneous but mostly aggressive malignancies devoid of expression of the estrogen, progesterone, and HER2 (ERBB2 or NEU) receptors. It accounts for 15-20% of all cases. Altered epigenetic regulation including DNA hypermethylation by DNA methyltransferase 1 (DNMT1) has been implicated as one of the causes of TNBC tumorigenesis. The antitumor effect of DNMT1 has also been explored in TNBC that currently lacks targeted therapies. However, the actual treatment for TNBC is yet to be discovered. This study is attributed to the identification of novel drug targets against TNBC. A comprehensive docking and simulation analysis was performed to optimize promising new compounds by estimating their binding affinity to the target protein. Molecular dynamics simulation of 500 ns well complemented the binding affinity of the compound and revealed strong stability of predicted compounds at the docked site. Calculation of binding free energies using MMPBSA and MMGBSA validated the strong binding affinity between compound and binding pockets of DNMT1. In a nutshell, our study uncovered that Beta-Mangostin, Gancaonin Z, 5-hydroxysophoranone, Sophoraflavanone L, and Dorsmanin H showed maximum binding affinity with the active sites of DNMT1. Furthermore, all of these compounds depict maximum drug-like properties. Therefore, the proposed compounds can be a potential candidate for patients with TNBC, but, experimental validation is needed to ensure their safety.Communicated by Ramaswamy H. Sarma.

FDA approved fused-pyrimidines as potential PI3K inhibitors: a computational repurposing approach.

Fused pyrimidine scaffold is present in several US FDA-approved drugs for various therapeutic indications. Drug repurposing (or drug repositioning) involves the analysis of existing clinically approved drugs for new therapeutic indications. Phosphoinositide-3-kinase (PI3K), via the regulatory PI3K pathway, is involved in cell growth, proliferation, differentiation, survival, and angiogenesis. It is also considered a target in anticancer drug development as it promotes the growth of cancerous cells and increases resistance to anticancer therapy. The present work employed computational techniques like molecular docking, MMGBSA analysis, and molecular dynamics simulations to explore the PI3K inhibition by FDA-approved drugs with fused pyrimidine scaffold. The work identifies Lapatinib as a pan-class I PI3K inhibitor and Dipyridamole as an γ isoform-specific PI3K inhibitor and is reported here.Communicated by Ramaswamy H. Sarma.

Unveiling the multi-target compounds of Rhazya stricta: Discovery and inhibition of novel target genes for the treatment of clear cell renal cell carcinoma.

Clear cell renal cell carcinoma (ccRCC) is a prevalent kidney malignancy with a pressing need for innovative therapeutic strategies. In this context, emerging research has focused on exploring the medicinal potential of plants such as Rhazya stricta. Nevertheless, the complex molecular mechanisms underlying its potential therapeutic efficacy remain largely elusive. Our study employed an integrative approach comprising data mining,network pharmacology,tissue cell type analysis, and molecular modelling approaches to identify potent phytochemicals from R. stricta, with potential relevance for ccRCC treatments. Initially, we collected data on R. stricta's phytochemical from public databases. Subsequently, we integrated this information with differentially expressed genes (DEGs) in ccRCC, which were derived from microarray datasets(GSE16441,GSE66270, and GSE76351). We identified potential intersections between R. stricta and ccRCC targets, which enabled us to construct a compound-genes-pathway network using Cytoscape software. This helped illuminate R. stricta's multi-target pharmacological effects on ccRCC. Moreover, tissue cell type analysis added another layer of insight into the cellular specificity of potential therapeutic targets in the kidney. Through further Kaplan-Meier survival analysis, we pinpointed MMP9,ACE,ERBB2, and HSP90AA1 as prospective diagnostic and prognostic biomarkers for ccRCC. Notably, our study underscores the potential of R. stricta derived compounds-namely quebrachamine,corynan-17-ol, stemmadenine,strictanol,rhazinilam, and rhazimolare-to impede ccRCC progression by modulating the activity of MMP9,ACE,ERBB2, and HSP90AA1 genes. Further, molecular docking and dynamic simulations confirmed the plausible binding affinities of these compounds. Despite these promising findings, we recognize the need for comprehensive in vivo and in vitro studies to further investigate the pharmacokinetics and biosafety profiles of these compounds.

Systematic elucidation of the multi-target pharmacological mechanism of Terminalia arjuna against congestive cardiac failure via network pharmacology and molecular modelling approaches.

Congestive cardiac failure (CCF) is a pathophysiologic state when the heart is not able to maintain its cardiac output to meet the demand of metabolising tissues. CCF is responsible for approximately 2.9 million deaths worldwide. The heterogeneous nature of CCF draws the attention of researchers to find more enthralling and promising diagnostic and treatment options. Terminalia arjuna (Arjuna) is an evergreen, deciduous tree exhibited various astringent, anti-bacterial, and anti-microbial properties. T. arjuna is being used in various regions for anginal pain, hypertension, congestive heart failure, and dyslipidemia. Although previous in vitro studies have demonstrated the therapeutic potential of T. arjuna, the exact molecular mechanism underlying its protective effect on the heart remains unclear. In this study, a network pharmacology technique was used to explore the active ingredients, potential targets in T. arjuna for the treatment of CCF. In the framework of this study, we explored the active ingredient-target-pathway network and figured out that oleanolic acid, arjunolic acid, luteolin, kaempferol, cholesterol, ellagic acid 4-O-xylopyranoside 3,3'-dimethyl ether, and cyclohexyl (2,4-dimethyl phenyl) methanone contributed significantly to the development of CCF by affecting AKT1, MAPK14, TNF, IL6, ESR1, and HSP90AA1 genes. Molecular docking analysis further validated the activities of these compounds against potential targets. To sum up, integrated network pharmacology and docking analysis revealed that T. arjuna exerts its cardioprotective effect by acting on various signalling pathways, including the thyroid hormone, VEGF signalling pathway, AGE-RAGE signalling pathway in diabetic complications, HIF signalling pathway, sphingolipid signalling pathway, and oestrogen signalling pathways. Overall, this study provides valuable insights into the molecular mechanism of T. arjuna in CCF and highlights its potential as a promising preventive treatment for this condition.

Identification and characterization of codon usage pattern and influencing factors in HFRS-causing hantaviruses.

Hemorrhagic fever with renal syndrome (HFRS) is an acute viral zoonosis carried and transmitted by infected rodents through urine, droppings, or saliva. The etiology of HFRS is complex due to the involvement of viral factors and host immune and genetic factors which hinder the development of potential therapeutic solutions for HFRS. Hantaan virus (HTNV), Dobrava-Belgrade virus (DOBV), Seoul virus (SEOV), and Puumala virus (PUUV) are predominantly found in hantaviral species that cause HFRS in patients. Despite ongoing prevention and control efforts, HFRS remains a serious economic burden worldwide. Furthermore, recent studies reported that the hantavirus nucleocapsid protein is a multi-functional protein and plays a major role in the replication cycle of the hantavirus. However, the precise mechanism of the nucleoproteins in viral pathogenesis is not completely understood. In the framework of the current study, various in silico approaches were employed to identify the factors influencing the codon usage pattern of hantaviral nucleoproteins. Based on the relative synonymous codon usage (RSCU) values, a comparative analysis was performed between HFRS-causing hantavirus and their hosts, suggesting that HTNV, DOBV, SEOV, and PUUV, were inclined to evolve their codon usage patterns that were comparable to those of their hosts. The results indicated that most of the overrepresented codons had AU-endings, which revealed that mutational pressure is the major force shaping codon usage patterns. However, the influence of natural selection and geographical factors cannot be ignored on viral codon usage bias. Further analysis also demonstrated that HFRS causing hantaviruses adapted host-specific codon usage patterns to sustain successful replication and transmission chains within hosts. To our knowledge, no study to date reported the factors influencing the codon usage pattern within hantaviral nucleoproteins. Thus, the proposed computational scheme can help in understanding the underlying mechanism of codon usage patterns in HFRS-causing hantaviruses which lend a helping hand in designing effective anti-HFRS treatments in future. This study, although comprehensive, relies on in silico methods and thus necessitates experimental validation for more solid outcomes. Beyond the identified factors influencing viral behavior, there could be other yet undiscovered influences. These potential factors should be targets for further research to improve HFRS therapeutic strategies.

Integrated molecular modeling and dynamics approaches revealed potential natural inhibitors of NF-κB transcription factor as breast cancer therapeutics.

Breast cancer is a silent killer malady among women and a serious economic burden in health care management. A case of breast cancer is diagnosed among women every 19 s, and every 74 s, a woman dies of breast cancer somewhere in the world. Despite the pop-up of progressive research, advanced treatment approaches, and preventive measures, breast cancer remains amplifying ailment. The nuclear factor kappa B (NF-κB) is a key transcription factor that links inflammation with cancer and is demonstrated as being involved in the tumorigenesis of breast cancer. The NF-κB transcription factor family in mammals consists of five proteins; c-Rel, RelA(p65), RelB, NF-κB1(p50), and NF-κB2(p52). The antitumor effect of NF-κB has also been explored in breast cancer, however, the actual treatment for breast cancer is yet to be discovered. This study is attributed to the identification of novel drug targets against breast cancer by targeting c-Rel, RelA(p65), RelB, NF-κB1(p50), and NF-κB2(p52) proteins. To identify the putative active compounds, a structure-based 3D pharmacophore model to the protein active site cavity was generated followed by virtual screening, molecular docking, and molecular dynamics (MD) simulation. Initially, a library of 45000 compounds were docked against the target protein and five compounds namely Z56811101, Z653426226, Z1097341967, Z92743432, and Z464101066 were selected for further analysis. The relative binding affinity of Z56811101, Z653426226, Z1097341967, Z92743432, and Z464101066 with NF-κB1 (p50), NF-κB2 (p52), RelA (p65), RelB, and c-Rel proteins were -6.8, -8, -7.0, -6.9, and -7.2 kcal/mol, respectively which remained stable throughout the simulations of 200 ns. Furthermore, all of these compounds depict maximum drug-like properties. Therefore, the proposed compounds can be a potential candidate for patients with breast cancer, but, experimental validation is needed to ensure their safety.Communicated by Ramaswamy H. Sarma.

Discovering common pathogenic processes between COVID-19 and HFRS by integrating RNA-seq differential expression analysis with machine learning.

Zoonotic virus spillover in human hosts including outbreaks of Hantavirus and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) imposes a serious impact on the quality of life of patients. Recent studies provide a shred of evidence that patients with Hantavirus-caused hemorrhagic fever with renal syndrome (HFRS) are at risk of contracting SARS-CoV-2. Both RNA viruses shared a higher degree of clinical features similarity including dry cough, high fever, shortness of breath, and certain reported cases with multiple organ failure. However, there is currently no validated treatment option to tackle this global concern. This study is attributed to the identification of common genes and perturbed pathways by combining differential expression analysis with bioinformatics and machine learning approaches. Initially, the transcriptomic data of hantavirus-infected peripheral blood mononuclear cells (PBMCs) and SARS-CoV-2 infected PBMCs were analyzed through differential gene expression analysis for identification of common differentially expressed genes (DEGs). The functional annotation by enrichment analysis of common genes demonstrated immune and inflammatory response biological processes enriched by DEGs. The protein-protein interaction (PPI) network of DEGs was then constructed and six genes named RAD51, ALDH1A1, UBA52, CUL3, GADD45B, and CDKN1A were identified as the commonly dysregulated hub genes among HFRS and COVID-19. Later, the classification performance of these hub genes were evaluated using Random Forest (RF), Poisson Linear Discriminant Analysis (PLDA), Voom-based Nearest Shrunken Centroids (voomNSC), and Support Vector Machine (SVM) classifiers which demonstrated accuracy >70%, suggesting the biomarker potential of the hub genes. To our knowledge, this is the first study that unveiled biological processes and pathways commonly dysregulated in HFRS and COVID-19, which could be in the next future used for the design of personalized treatment to prevent the linked attacks of COVID-19 and HFRS.

Advanced network pharmacology study reveals multi-pathway and multi-gene regulatory molecular mechanism of Bacopa monnieri in liver cancer based on data mining, molecular modeling, and microarray data analysis.

Liver cancer is a malignant tumor that grows on the surface or inside the liver. The leading cause is a viral infection with hepatitis B or C virus. Natural products and their structural analogues have historically made a major contribution to pharmacotherapy, especially for cancer. A list of studies evidences the therapeutic efficacy of Bacopa monnieri against liver cancer, but the precise molecular mechanism is yet to be discovered. This study combines data mining, network pharmacology, and molecular docking analysis to potentially revolutionize liver cancer treatment by identifying effective phytochemicals. Initially, the information on active constituents of B. monnieri and target genes of both liver cancer and B. monnieri were retrieved from literature as well as from publicly available databases. Based on the matching results between B. monnieri potential targets and liver cancer targets, the protein-protein interaction (PPI) network was constructed using the STRING database and imported into Cytoscape for screening of hub genes based on their degree of connectivity. Later, the interactions network between compounds and overlapping genes was constructed using Cytoscape software to analyze the network pharmacological prospective effects of B. monnieri on liver cancer. Gene Ontology (GO) and KEGG pathway analysis of hub genes revealed that these genes are involved in the cancer-related pathway. Lastly, the expression level of core targets was analyzed using microarray data (GSE39791, GSE76427, GSE22058, GSE87630, and GSE112790). Further, the GEPIA server and PyRx software were used for survival and molecular docking analysis, respectively. In summary, we proposed that quercetin, luteolin, apigenin, catechin, epicatechin, stigmasterol, beta-sitosterol, celastrol, and betulic acid inhibit tumor growth by affecting tumor protein 53 (TP53), interleukin 6 (IL6), RAC-alpha serine/threonine protein kinases 1 (AKT1), caspase-3 (CASP3), tumor necrosis factor (TNF), jun proto-oncogene (JUN), heat shot protein 90 AA1 (HSP90AA1), vascular endothelial growth factor A (VEGFA), epidermal growth factor receptor (EGFR), and SRC proto-oncogene (SRC). Through, microarray data analysis, the expression level of JUN and IL6 were found to be upregulated while the expression level of HSP90AA1 was found to be downregulated. Kaplan-Meier survival analysis indicated that HSP90AA1 and JUN are promising candidate genes that can serve as diagnostic and prognostic biomarkers for liver cancer. Moreover, the molecular docking and molecular dynamic simulation of 60ns well complemented the binding affinity of the compound and revealed strong stability of predicted compounds at the docked site. Calculation of binding free energies using MMPBSA and MMGBSA validated the strong binding affinity between the compound and binding pockets of HSP90AA1 and JUN. Despite that, in vivo and in vitro studies are mandatory to unveil pharmacokinetics and biosafety profiles to completely track the candidature status of B. monnieri in liver cancer.

Machine learning for synergistic network pharmacology: a comprehensive overview.

Network pharmacology is an emerging area of systematic drug research that attempts to understand drug actions and interactions with multiple targets. Network pharmacology has changed the paradigm from 'one-target one-drug' to highly potent 'multi-target drug'. Despite that, this synergistic approach is currently facing many challenges particularly mining effective information such as drug targets, mechanism of action, and drug and organism interaction from massive, heterogeneous data. To overcome bottlenecks in multi-target drug discovery, computational algorithms are highly welcomed by scientific community. Machine learning (ML) and especially its subfield deep learning (DL) have seen impressive advances. Techniques developed within these fields are now able to analyze and learn from huge amounts of data in disparate formats. In terms of network pharmacology, ML can improve discovery and decision making from big data. Opportunities to apply ML occur in all stages of network pharmacology research. Examples include screening of biologically active small molecules, target identification, metabolic pathways identification, protein-protein interaction network analysis, hub gene analysis and finding binding affinity between compounds and target proteins. This review summarizes the premier algorithmic concepts of ML in network pharmacology and forecasts future opportunities, potential applications as well as several remaining challenges of implementing ML in network pharmacology. To our knowledge, this study provides the first comprehensive assessment of ML approaches in network pharmacology, and we hope that it encourages additional efforts toward the development and acceptance of network pharmacology in the pharmaceutical industry.