Integrating Transcriptomic and Structural Insights: Revealing Drug Repurposing Opportunities for Sporadic ALS.

Amyotrophic lateral sclerosis (ALS) is a progressive and devastating neurodegenerative disorder characterized by the loss of upper and lower motor neurons, resulting in debilitating muscle weakness and atrophy. Currently, there are no effective treatments available for ALS, posing significant challenges in managing the disease that affects approximately two individuals per 100,000 people annually. To address the urgent need for effective ALS treatments, we conducted a drug repurposing study using a combination of bioinformatics tools and molecular docking techniques. We analyzed sporadic ALS-related genes from the GEO database and identified key signaling pathways involved in sporadic ALS pathogenesis through pathway analysis using DAVID. Subsequently, we utilized the Clue Connectivity Map to identify potential drug candidates and performed molecular docking using AutoDock Vina to evaluate the binding affinity of short-listed drugs to key sporadic ALS-related genes. Our study identified Cefaclor, Diphenidol, Flubendazole, Fluticasone, Lestaurtinib, Nadolol, Phenamil, Temozolomide, and Tolterodine as potential drug candidates for repurposing in sporadic ALS treatment. Notably, Lestaurtinib demonstrated high binding affinity toward multiple proteins, suggesting its potential as a broad-spectrum therapeutic agent for sporadic ALS. Additionally, docking analysis revealed NOS3 as the gene that interacts with all the short-listed drugs, suggesting its possible involvement in the mechanisms underlying the therapeutic potential of these drugs in sporadic ALS. Overall, our study provides a systematic framework for identifying potential drug candidates for sporadic ALS therapy and highlights the potential of drug repurposing as a promising strategy for discovering new therapies for neurodegenerative diseases.

Network-based drug repurposing identifies small molecule drugs as immune checkpoint inhibitors for endometrial cancer.

Endometrial cancer (EC) is the 6th most common cancer in women around the world. Alone in the United States (US), 66,200 new cases and 13,030 deaths are expected to occur in 2023 which needs the rapid development of potential therapies against EC. Here, a network-based drug-repurposing strategy is developed which led to the identification of 16 FDA-approved drugs potentially repurposable for EC as potential immune checkpoint inhibitors (ICIs). A network of EC-associated immune checkpoint proteins (ICPs)-induced protein interactions (P-ICP) was constructed. As a result of network analysis of P-ICP, top key target genes closely interacting with ICPs were shortlisted followed by network proximity analysis in drug-target interaction (DTI) network and pathway cross-examination which identified 115 distinct pathways of approved drugs as potential immune checkpoint inhibitors. The presented approach predicted 16 drugs to target EC-associated ICPs-induced pathways, three of which have already been used for EC and six of them possess immunomodulatory properties providing evidence of the validity of the strategy. Classification of the predicted pathways indicated that 15 drugs can be divided into two distinct pathway groups, containing 17 immune pathways and 98 metabolic pathways. In addition, drug-drug correlation analysis provided insight into finding useful drug combinations. This fair and verified analysis creates new opportunities for the quick repurposing of FDA-approved medications in clinical trials.

Network-based drug repurposing for HPV-associated cervical cancer.

In women, cervical cancer (CC) is the fourth most common cancer around the world with average cases of 604,000 and 342,000 deaths per year. Approximately 50% of high-grade CC are attributed to human papillomavirus (HPV) types 16 and 18. Chances of CC in HPV-positive patients are 6 times more than HPV-negative patients which demands timely and effective treatment. Repurposing of drugs is considered a viable approach to drug discovery which makes use of existing drugs, thus potentially reducing the time and costs associated with de-novo drug discovery. In this study, we present an integrative drug repurposing framework based on a systems biology-enabled network medicine platform. First, we built an HPV-induced CC protein interaction network named HPV2C following the CC signatures defined by the omics dataset, obtained from GEO database. Second, the drug target interaction (DTI) data obtained from DrugBank, and related databases was used to model the DTI network followed by drug target network proximity analysis of HPV-host associated key targets and DTIs in the human protein interactome. This analysis identified 142 potential anti-HPV repurposable drugs to target HPV induced CC pathways. Third, as per the literature survey 51 of the predicted drugs are already used for CC and 33 of the remaining drugs have anti-viral activity. Gene set enrichment analysis of potential drugs in drug-gene signatures and in HPV-induced CC-specific transcriptomic data in human cell lines additionally validated the predictions. Finally, 13 drug combinations were found using a network based on overlapping exposure. To summarize, the study provides effective network-based technique to quickly identify suitable repurposable drugs and drug combinations that target HPV-associated CC.

Organ-on-Chip: Advancing Nutraceutical Testing for Improved Health Outcomes.

The recent global wave of organic food consumption and the vitality of nutraceuticals for human health benefits has driven the need for applying scientific methods for phytochemical testing. Advanced in vitro models with greater physiological relevance than conventional in vitro models are required to evaluate the potential benefits and toxicity of nutraceuticals. Organ-on-chip (OOC) models have emerged as a promising alternative to traditional in vitro models and animal testing due to their ability to mimic organ pathophysiology. Numerous studies have demonstrated the effectiveness of OOC models in identifying pharmaceutically relevant compounds and accurately assessing compound-induced toxicity. This review examines the utility of traditional in vitro nutraceutical testing models and discusses the potential of OOC technology as a preclinical testing tool to examine the biomedical potential of nutraceuticals by reducing the need for animal testing. Exploring the capabilities of OOC models in carrying out plant-based bioactive compounds can significantly contribute to the authentication of nutraceuticals and drug discovery and validate phytochemicals medicinal characteristics. Overall, OOC models can facilitate a more systematic and efficient assessment of nutraceutical compounds while overcoming the limitations of current traditional in vitro models.

Cellular Efficacy of Fattigated Nanoparticles and Real-Time ROS Occurrence Using Microfluidic Hepatocarcinoma Chip System: Effect of Anticancer Drug Solubility and Shear Stress.

The objective of this study was to evaluate the effectiveness of organ-on-chip system investigating simultaneous cellular efficacy and real-time reactive oxygen species (ROS) occurrence of anticancer drug-loaded nanoparticles (NPs) using hepatocarcinoma cells (HepG2) chip system under static and hepatomimicking shear stress conditions (5 dyne/cm2). Then, the role of hepatomimetic shear stress exposed to HepG2 and drug solubility were compared. The highly soluble doxorubicin (DOX) and poorly soluble paclitaxel (PTX) were chosen. Fattigated NPs (AONs) were formed via self-assembly of amphiphilic albumin (HSA)-oleic acid conjugate (AOC). Then, drug-loaded AONs (DOX-AON or PTX-AON) were exposed to a serum-free HepG2 medium at 37 °C and 5% carbon dioxide for 24 h using a real-time ROS sensor chip-based microfluidic system. The cellular efficacy and simultaneous ROS occurrence of free drugs and drug-loaded AONs were compared. The cellular efficacy of drug-loaded AONs varied in a dose-dependent manner and were consistently correlated with real-time of ROS occurrence. Drug-loaded AONs increased the intracellular fluorescence intensity and decreased the cellular efficacy compared to free drugs under dynamic conditions. The half-maximal inhibitory concentration (IC50) values of free DOX (13.4 µg/mL) and PTX (54.44 µg/mL) under static conditions decreased to 11.79 and 38.43 µg/mL, respectively, under dynamic conditions. Furthermore, DOX- and PTX-AONs showed highly decreased IC50 values of 5.613 and 21.86 µg/mL, respectively, as compared to free drugs under dynamic conditions. It was evident that cellular efficacy and real-time ROS occurrence were well-correlated and highly dependent on the drug-loaded nanostructure, drug solubility and physiological shear stress.

Artificial intelligence-assisted repurposing of lubiprostone alleviates tubulointerstitial fibrosis.

Tubulointerstitial fibrosis (TIF) is the most prominent cause which leads to chronic kidney disease (CKD) and end-stage renal failure. Despite extensive research, there have been many clinical trial failures, and there is currently no effective treatment to cure renal fibrosis. This demonstrates the necessity of more effective therapies and better preclinical models to screen potential drugs for TIF. In this study, we investigated the antifibrotic effect of the machine learning-based repurposed drug, lubiprostone, validated through an advanced proximal tubule on a chip system and in vivo UUO mice model. Lubiprostone significantly downregulated TIF biomarkers including connective tissue growth factor (CTGF), extracellular matrix deposition (Fibronectin and collagen), transforming growth factor (TGF-ß) downstream signaling markers especially, Smad-2/3, matrix metalloproteinase (MMP2/9), plasminogen activator inhibitor-1 (PAI-1), EMT and JAK/STAT-3 pathway expression in the proximal tubule on a chip model and UUO model compared to the conventional 2D culture. These findings suggest that the proximal tubule on a chip model is a more physiologically relevant model for studying and identifying potential biomarkers for fibrosis compared to conventional in vitro 2D culture and alternative of an animal model. In conclusion, the high throughput Proximal tubule-on-chip system shows improved in vivo-like function and indicates the potential utility for renal fibrosis drug screening. Additionally, repurposed Lubiprostone shows an effective potency to treat TIF via inhibiting 3 major profibrotic signaling pathways such as TGFß/Smad, JAK/STAT, and epithelial-mesenchymal transition (EMT), and restores kidney function.

Revolutionizing drug development: harnessing the potential of organ-on-chip technology for disease modeling and drug discovery.

The inefficiency of existing animal models to precisely predict human pharmacological effects is the root reason for drug development failure. Microphysiological system/organ-on-a-chip technology (organ-on-a-chip platform) is a microfluidic device cultured with human living cells under specific organ shear stress which can faithfully replicate human organ-body level pathophysiology. This emerging organ-on-chip platform can be a remarkable alternative for animal models with a broad range of purposes in drug testing and precision medicine. Here, we review the parameters employed in using organ on chip platform as a plot mimic diseases, genetic disorders, drug toxicity effects in different organs, biomarker identification, and drug discoveries. Additionally, we address the current challenges of the organ-on-chip platform that should be overcome to be accepted by drug regulatory agencies and pharmaceutical industries. Moreover, we highlight the future direction of the organ-on-chip platform parameters for enhancing and accelerating drug discoveries and personalized medicine.

Functionalized graphene oxide-based lamellar membranes for organic solvent nanofiltration applications.

In this study, two-dimensional graphene oxide-based novel membranes were fabricated by modifying the surface of graphene oxide nanosheets with six-armed poly(ethylene glycol) (PEG) at room conditions. The as-modified PEGylated graphene oxide (PGO) membranes with unique layered structures and large interlayer spacing (∼1.12 nm) were utilized for organic solvent nanofiltration applications. The as-prepared 350 nm-thick PGO membrane offers a superior separation (>99%) against evans blue, methylene blue and rhodamine B dyes along with high methanol permeance ∼ 155 ± 10 L m-2 h-1, which is 10-100 times high compared to pristine GO membranes. Additionally, these membranes are stable for up to 20 days in organic solvent. Hence the results suggested that the as-synthesized PGO membranes with superior separation efficiency for dye molecules in organic solvent can be used in future for organic solvent nanofiltration application.

A systematic review of computational approaches to understand cancer biology for informed drug repurposing.

Cancer is the second leading cause of death globally, trailing only heart disease. In the United States alone, 1.9 million new cancer cases and 609,360 deaths were recorded for 2022. Unfortunately, the success rate for new cancer drug development remains less than 10%, making the disease particularly challenging. This low success rate is largely attributed to the complex and poorly understood nature of cancer etiology. Therefore, it is critical to find alternative approaches to understanding cancer biology and developing effective treatments. One such approach is drug repurposing, which offers a shorter drug development timeline and lower costs while increasing the likelihood of success. In this review, we provide a comprehensive analysis of computational approaches for understanding cancer biology, including systems biology, multi-omics, and pathway analysis. Additionally, we examine the use of these methods for drug repurposing in cancer, including the databases and tools that are used for cancer research. Finally, we present case studies of drug repurposing, discussing their limitations and offering recommendations for future research in this area.

Lamellar Graphene Oxide-Based Composite Membranes for Efficient Separation of Heavy Metal Ions and Desalination of Water.

Sufficient efforts have been carried out to fabricate highly efficient graphene oxide (GO) lamellar membranes for heavy metal ion separation and desalination of water. However, selectivity for small ions remains a major problem. Herein, GO was modified by using onion extractive (OE) and a bioactive phenolic compound, i.e., quercetin. The as-prepared modified materials were fabricated into membranes and used for separation of heavy metal ions and water desalination. The GO/onion extract (GO/OE) composite membrane with a thickness of 350 nm shows an excellent rejection efficiency for several heavy metal ions such as Cr6+ (∼87.5%), As3+ (∼89.5%), Cd2+ (∼93.0%), and Pb2+ (∼99.5%) and a good water permeance of ∼460 ± 20 L m-2 h-1 bar-1. In addition, a GO/quercetin (GO/Q) composite membrane is also fabricated from quercetin for comparative studies. Quercetin is an active ingredient of onion extractives (2.1% w/w). The GO/Q composite membranes show good rejection up to ∼78.0, ∼80.5, ∼88.0, and 95.2% for Cr6+, As3+, Cd2+, and Pb2+, respectively, with a DI water permeance of ∼150 ± 10 L m-2 h-1 bar-1. Further, both membranes are used for water desalination by measuring rejection of small ions such as NaCl, Na2SO4, MgCl2, and MgSO4. The resulting membranes show >70% rejection for small ions. In addition, both membranes are used for filtration of Indus River water and the GO/Q membrane shows remarkably high separation efficiency and makes river water suitable for drinking purpose. Furthermore, the GO/QE composite membrane is highly stable up to ∼25 days under acidic, basic, and neutral environments as compared to GO/Q composite and pristine GO-based membranes.

Drug repurposing for viral cancers: A paradigm of machine learning, deep learning, and virtual screening-based approaches.

Cancer management is major concern of health organizations and viral cancers account for approximately 15.4% of all known human cancers. Due to large number of patients, efficient treatments for viral cancers are needed. De novo drug discovery is time consuming and expensive process with high failure rate in clinical stages. To address this problem and provide treatments to patients suffering from viral cancers faster, drug repurposing emerges as an effective alternative which aims to find the other indications of the Food and Drug Administration approved drugs. Applied to viral cancers, drug repurposing studies following the niche have tried to find if already existing drugs could be used to treat viral cancers. Multiple drug repurposing approaches till date have been introduced with successful results in viral cancers and many drugs have been successfully repurposed various viral cancers. Here in this study, a critical review of viral cancer related databases, tools, and different machine learning, deep learning and virtual screening-based drug repurposing studies focusing on viral cancers is provided. Additionally, the mechanism of viral cancers is presented along with drug repurposing case study specific to each viral cancer. Finally, the limitations and challenges of various approaches along with possible solutions are provided.

Influence of Operating and Electrochemical Parameters on PEMFC Performance: A Simulation Study.

Proton exchange membrane fuel cell, or polymer electrolyte fuel cell, (PEMFC) has received a significant amount of attention for green energy applications due to its low carbon emission and less other toxic pollution capacity. Herein, we develop a three-dimensional (3D) computational fluid dynamic model. The values of temperature, pressure, relative humidity, exchange coefficient, reference current density (RCD), and porosity values of the gas diffusion layer (GDL) were taken from the published literature. The results demonstrate that the performance of the cell is improved by modifying temperature and operating pressure. Current density is shown to degrade with the rising temperature as explored in this study. The findings show that at 353 K, the current density decreases by 28% compared to that at 323 K. In contrast, studies have shown that totally humidified gas passing through the gas channel results in a 10% higher current density yield, and that an evaluation of a 19% higher RCD value results in a similar current density yield.

A comprehensive review of key factors affecting the efficacy of antibody drug conjugate.

Antibody Drug Conjugate (ADC) is an emerging technology to overcome the limitations of chemotherapy by selectively targeting the cancer cells. ADC binds with an antigen, specifically over expressed on the surface of cancer cells, results decrease in bystander effect and increase in therapeutic index. The potency of an ideal ADC is entirely depending on several physicochemical factors such as site of conjugation, molecular weight, linker length, Steric hinderance, half-life, conjugation method, binding energy and so on. Inspite of the fact that there is more than 100 of ADCs are in clinical trial only 14 ADCs are approved by FDA for clinical use. However, to design an ideal ADC is still challenging and there is much more to be done. Here in this review, we have discussed the key components along with their significant role or contribution towards the efficacy of an ADC. Moreover, we also explained about the recent advancement in the conjugation method. Additionally, we spotlit the mode of action of an ADC, recent challenges, and future perspective regarding ADC. The profound knowledge regarding key components and their properties will help in the synthesis or production of different engineered ADCs. Therefore, contributes to develop an ADC with low safety concern and high therapeutic index. We hope this review will improve the understanding and encourage the practicing of research in anticancer ADCs development.

Drug repurposing in psoriasis, performed by reversal of disease-associated gene expression profiles.

Psoriasis is a skin disease which results in scales on the skin caused by flaky patches. Psoriasis is triggered by various conditions such as drug reactions, trauma, and skin infection etc. Globally, there are 125 million people affected by psoriasis and yet there is no effective treatment available, and it emphasizes the need for discovery of efficacious treatments. De-novo drug development takes 10-17 years and $2-$3 billion of investment with <10 % success rate to bring drug from concept to a market ready product. A possible alternative is drug repurposing, which aims at finding other indications of already approved drugs. In this study, a computational drug repurposing framework is developed and applied to differential gene expressions of Psoriasis targets obtained from the publicly available database (GEO). This strategy uses the gene expression signatures of the Psoriasis and compares it with perturbagen available in the CMap. Based on the connected signature drugs are ranked which could possibly reverse the signatures to stop the psoriasis. The drugs with most negative connectivity scores are ranked efficient and vice versa. The top hit drugs are verified using the literature survey of the peer reviewed journal, electronic health records, patents, and hospital database. As a result, 50/150 and 37/150 drugs are confirmed to have anti-psoriasis efficacy in two datasets. Top 10 drugs are suggested as potential repurposable drugs for psoriasis. This study offers, a powerful yet simple approach for rapid identification of potential drug repurposing candidates in Psoriasis and any disease of interest.

SperoPredictor: An Integrated Machine Learning and Molecular Docking-Based Drug Repurposing Framework With Use Case of COVID-19.

The global spread of the SARS coronavirus 2 (SARS-CoV-2), its manifestation in human hosts as a contagious disease, and its variants have induced a pandemic resulting in the deaths of over 6,000,000 people. Extensive efforts have been devoted to drug research to cure and refrain the spread of COVID-19, but only one drug has received FDA approval yet. Traditional drug discovery is inefficient, costly, and unable to react to pandemic threats. Drug repurposing represents an effective strategy for drug discovery and reduces the time and cost compared to de novo drug discovery. In this study, a generic drug repurposing framework (SperoPredictor) has been developed which systematically integrates the various types of drugs and disease data and takes the advantage of machine learning (Random Forest, Tree Ensemble, and Gradient Boosted Trees) to repurpose potential drug candidates against any disease of interest. Drug and disease data for FDA-approved drugs (n = 2,865), containing four drug features and three disease features, were collected from chemical and biological databases and integrated with the form of drug-disease association tables. The resulting dataset was split into 70% for training, 15% for testing, and the remaining 15% for validation. The testing and validation accuracies of the models were 99.3% for Random Forest and 99.03% for Tree Ensemble. In practice, SperoPredictor identified 25 potential drug candidates against 6 human host-target proteomes identified from a systematic review of journals. Literature-based validation indicated 12 of 25 predicted drugs (48%) have been already used for COVID-19 followed by molecular docking and re-docking which indicated 4 of 13 drugs (30%) as potential candidates against COVID-19 to be pre-clinically and clinically validated. Finally, SperoPredictor results illustrated the ability of the platform to be rapidly deployed to repurpose the drugs as a rapid response to emergent situations (like COVID-19 and other pandemics).

A comprehensive review of artificial intelligence and network based approaches to drug repurposing in Covid-19.

Conventional drug discovery and development is tedious and time-taking process; because of which it has failed to keep the required pace to mitigate threats and cater demands of viral and re-occurring diseases, such as Covid-19. The main reasons of this delay in traditional drug development are: high attrition rates, extensive time requirements, and huge financial investment with significant risk. The effective solution to de novo drug discovery is drug repurposing. Previous studies have shown that the network-based approaches and analysis are versatile platform for repurposing as the network biology is used to model the interactions between variety of biological concepts. Herein, we provide a comprehensive background of machine learning and deep learning in drug repurposing while specifically focusing on the applications of network-based approach to drug repurposing in Covid-19, data sources, and tools used. Furthermore, use of network proximity, network diffusion, and AI on network-based drug repurposing for Covid-19 is well-explained. Finally, limitations of network-based approaches in general and specific to network are stated along with future recommendations for better network-based models.

Renal Hypoxic Reperfusion Injury-on-Chip Model for Studying Combinational Vitamin Therapy.

Renal ischemic-reperfusion injury decreases the chances of long-term kidney graft survival and may lead to the loss of a transplanted kidney. During organ excision, the cycle of warm ischemia from the donor and cold ischemia is due to storage in a cold medium after revascularization following organ transplantation. The reperfusion of the kidney graft activates several pathways that generate reactive oxygen species, forming a hypoxic-reperfusion injury. Animal models are generally used to model and investigate renal hypoxic-reperfusion injury. However, these models face ethical concerns and present a lack of robustness and intraspecies genetic variations, among other limitations. We introduce a microfluidics-based renal hypoxic-reperfusion (RHR) injury-on-chip model to overcome current limitations. Primary human renal proximal tubular epithelial cells and primary human endothelial cells were cultured on the apical and basal sides of a porous membrane. Hypoxic and normoxic cell culture media were used to create the RHR injury-on-chip model. The disease model was validated by estimating various specific hypoxic biomarkers of RHR. Furthermore, retinol, ascorbic acid, and combinational doses were tested to devise a therapeutic solution for RHR. We found that combinational vitamin therapy can decrease the chances of RHR injury. The proposed RHR injury-on-chip model can serve as an alternative to animal testing for injury investigation and the identification of new therapies.

High performance inkjet printed embedded electrochemical sensors for monitoring hypoxia in a gut bilayer microfluidic chip.

Sensing devices have shown tremendous potential for monitoring state-of-the-art organ chip devices. However, challenges like miniaturization while maintaining higher performance, longer operating times for continuous monitoring, and fabrication complexities limit their use. Herein simple, low-cost, and solution-processible inkjet dispenser printing of embedded electrochemical sensors for dissolved oxygen (DO) and reactive oxygen species (ROS) is proposed for monitoring developmental (initially normoxia) and induced hypoxia in a custom-developed gut bilayer microfluidic chip platform for 6 days. The DO sensors showed a high sensitivity of 31.1 nA L mg-1 with a limit of detection (LOD) of 0.67 mg L-1 within the 0-9 mg L-1 range, whereas the ROS sensor had a higher sensitivity of 1.44 nA µm-1 with a limit of detection of 1.7 µm within the 0-300 µm range. The dynamics of the barrier tight junctions are quantified with the help of an in-house developed trans-epithelial-endothelial electrical impedance (TEEI) sensor. Immunofluorescence staining was used to evaluate the expressions of HIF-1α and tight junction protein (TJP) ZO-1. This platform can also be used to enhance bioavailability assays, drug transport studies under an oxygen-controlled environment, and even other barrier organ models, as well as for various applications like toxicity testing, disease modeling and drug screening.

Study of the Anticancer Potential of Plant Extracts Using Liver Tumor Microphysiological System.

BACKGROUND: Plants have been considered a vital source of modern pharmaceutics since the paleolithic age. Contemporary chemotherapeutic drugs for cancer therapy are chemical entities sourced from plants. However, synthetic drugs or their derivatives come with severe to moderate side effects for human health. Hence, the quest to explore and discover plant-based novel anticancer drugs is ongoing. Anticancer activities are the primary method to estimate the potential and efficacy of an extract or compound for drug discovery. However, traditional in vitro anticancer activity assays often show poor efficacy due to the lack of in-vivo-like cellular environment. In comparison, the animal-based in vivo assays lack human genetic makeup and have ethical concerns. AIM: This study aimed to overcome the limitations of traditional cell-culture-based anticancer assays and find the most suitable assay for anticancer activity of plant extracts. We first reported utilizing a liver tumor microphysiological system in the anticancer effect assessment of plant extracts. METHODOLOGY: Methanolic extracts of Acer cappadocicum Gled were used to assess anticancer activity against liver tumor microphysiological system (MPS), and cell viability, liver function tests, and antioxidant enzyme activities were performed. Additionally, an embedded transepithelial electrical resistance sensor was utilized for the real-time monitoring of the liver tumor MPS. The results were also compared with the traditional cell culture model. RESULTS: The study demonstrated the superiority of the TEER sensor-based liver tumor MPS by its better anticancer activity based on cell viability and biomarker analysis compared to the traditional in vitro cell culture model. The anticancer effects of the plant extracts were successfully observed in real time, and methanolic extracts of Acer cappadocicum Gled increased the alanine transaminase and aspartate aminotransferase secretion, which may reveal the different mechanisms of these extracts and suggest a clue for the future molecular study of the anticancer pathways. CONCLUSION: Our results show that the liver tumor microphysiological system could be a better platform for plant-based anticancer activity assessment than traditional cell culture models.

Frequency and Molecular Characterization of Staphylococcus aureus from Placenta of Mothers with Term and Preterm Deliveries.

Globally, prematurity is the leading cause of neonatal mortality (babies in the first four weeks of life) and now the second leading cause of mortality after pneumonia in children under age five. The neonatal gut microbial colonization is crucial in the human life cycle. Placental microbiota transmits from the gut microbiota plays a significant role in association with kinship. Simultaneously, this transition is being made from mother to infant. This comparative study explored the diversity of microbiota associated with term and preterm neonates by evaluating the placental samples. The study found that 16/68 (23.5%) full-term placental samples were positive for S. aureus; on the other hand, 4/16 (25%) preterm placental samples confirmed culture growth for S. aureus. Antimicrobial susceptibility patterns showed that Staphylococcusaureus (S. aureus) isolates from both types of samples were resistant to Ofloxacin, Trimethoprim-sulfamethoxazole, Oxacillin, and Cefoxitin. However, Methicillin-Resistant Staphylococcus aureus (MRSA) detection was 43.75% in full-term and 75% in preterm placental samples. Moreover, two isolates were positive for both mecA and PVL virulent genes, and the rest were positive only for the mecA gene. Interestingly few isolates lacked both characteristic MRSA genes, mecA and PVL. Notably, resistances were more inclined towards preterm samples for antimicrobial susceptibility and MRSA screening. It may be concluded that there is a significant presence of S. aureus in the placenta of mothers with term and preterm deliveries which might be responsible for preterm deliveries. Therefore, judicious use of antibiotics during pregnancies may help prevent preterm births.