Pro- and anti-inflammatory cytokines are the game-changers in childhood obesity-associated metabolic disorders (diabetes and non-alcoholic fatty liver diseases).

Childhood obesity is a chronic inflammatory epidemic that affects children worldwide. Obesity affects approximately 1 in 5 children worldwide. Obesity in children can worsen weight gain and raise the risk of obesity-related comorbidities like diabetes and non-alcoholic fatty liver disease (NAFLD). It can also negatively impact the quality of life for these children. Obesity disrupts immune system function, influencing cytokine (interleukins) balance and expression levels, adipokines, and innate and adaptive immune cells. The altered expression of immune system mediators, including interleukin-1 (IL-1), interleukin-6 (IL-6), interleukin-8 (IL-8), interleukin-17 (IL-17), interleukin-18 (IL-18), transforming growth factor (TGF), tumor necrosis factor (TNF), and others, caused inflammation, progression, and the development of pediatric obesity and linked illnesses such as diabetes and NAFLD. Furthermore, anti-inflammatory cytokines, including interleukin-2 (IL-2), have been shown to have anti-diabetes and IL-1 receptor antagonist (IL-1Ra) anti-diabetic and pro-NAFLFD properties, and interleukin-10 (IL-10) has been shown to have a dual role in managing diabetes and anti-NAFLD. In light of the substantial increase in childhood obesity-associated disorders such as diabetes and NAFLD and the absence of an effective pharmaceutical intervention to inhibit immune modulation factors, it is critical to consider the alteration of immune system components as a preventive and therapeutic approach. Thus, the current review focuses on the most recent information regarding the influence of pro- and anti-inflammatory cytokines (interleukins) and their molecular mechanisms on pediatric obesity-associated disorders (diabetes and NAFLD). Furthermore, we discussed the current therapeutic clinical trials in childhood obesity-associated diseases, diabetes, and NAFLD.

Pro-inflammatory cytokines and CXC chemokines as game-changer in age-associated prostate cancer and ovarian cancer: Insights from preclinical and clinical studies' outcomes.

Prostate cancer (PC) and Ovarian cancer (OC) are two of the most common types of cancer that affect the reproductive systems of older men and women. These cancers are associated with a poor quality of life among the aged population. Therefore, finding new and innovative ways to detect, treat, and prevent these cancers in older patients is essential. Finding biomarkers for these malignancies will increase the chance of early detection and effective treatment, subsequently improving the survival rate. Studies have shown that the prevalence and health of some illnesses are linked to an impaired immune system. However, the age-associated changes in the immune system during malignancies such as PC and OC are poorly understood. Recent research has suggested that the excessive production of inflammatory immune mediators, such as interleukin-6 (IL-6), interleukin-8 (IL-8), transforming growth factor (TGF), tumor necrosis factor (TNF), CXC motif chemokine ligand 1 (CXCL1), CXC motif chemokine ligand 12 (CXCL12), and CXC motif chemokine ligand 13 (CXCL13), etc., significantly impact the development of PC and OC in elderly patients. Our review focuses on the latest functional studies of pro-inflammatory cytokines (interleukins) and CXC chemokines, which serve as biomarkers in elderly patients with PC and OC. Thus, we aim to shed light on how these biomarkers affect the development of PC and OC in elderly patients. We also examine the current status and future perspective of cytokines (interleukins) and CXC chemokines-based therapeutic targets in OC and PC treatment for elderly patients.

Natural products derived from medicinal plants and microbes might act as a game-changer in breast cancer: a comprehensive review of preclinical and clinical studies.

Breast cancer (BC) is the most prevalent neoplasm among women. Genetic and environmental factors lead to BC development and on this basis, several preventive - screening and therapeutic interventions have been developed. Hormones, both in the form of endogenous hormonal signaling or hormonal contraceptives, play an important role in BC pathogenesis and progression. On top of these, breast microbiota includes both species with an immunomodulatory activity enhancing the host's response against cancer cells and species producing proinflammatory cytokines associated with BC development. Identification of novel multitargeted therapeutic agents with poly-pharmacological potential is a dire need to combat advanced and metastatic BC. A growing body of research has emphasized the potential of natural compounds derived from medicinal plants and microbial species as complementary BC treatment regimens, including dietary supplements and probiotics. In particular, extracts from plants such as Artemisia monosperma Delile, Origanum dayi Post, Urtica membranacea Poir. ex Savigny, Krameria lappacea (Dombey) Burdet & B.B. Simpson and metabolites extracted from microbes such as Deinococcus radiodurans and Streptomycetes strains as well as probiotics like Bacillus coagulans and Lactobacillus brevis MK05 have exhibited antitumor effects in the form of antiproliferative and cytotoxic activity, increase in tumors' chemosensitivity, antioxidant activity and modulation of BC - associated molecular pathways. Further, bioactive compounds like 3,3'-diindolylmethane, epigallocatechin gallate, genistein, rutin, resveratrol, lycopene, sulforaphane, silibinin, rosmarinic acid, and shikonin are of special interest for the researchers and clinicians because these natural agents have multimodal action and act via multiple ways in managing the BC and most of these agents are regularly available in our food and fruit diets. Evidence from clinical trials suggests that such products had major potential in enhancing the effectiveness of conventional antitumor agents and decreasing their side effects. We here provide a comprehensive review of the therapeutic effects and mechanistic underpinnings of medicinal plants and microbial metabolites in BC management. The future perspectives on the translation of these findings to the personalized treatment of BC are provided and discussed.

In Silico Drug Discovery for Treatment of Virus Diseases.

Viral infections have remained a serious public health burden despite significant improvements in medical and pharmaceutical research in recent years. In silico approaches for drug discovery and design are fruitful for the management of a plethora of viral diseases. Virtual screening of libraries is performed using various computational tools to search for potential antiviral compounds. For this, a rational approach is used that comprises filtration of the screened compounds using docking, ligand- or pharmacophore-based similarity searches. The selected candidates are then tested in vitro to ascertain their biological activity. This minimizes the overall cost and time incurred in conventional drug designing methods. In this book chapter, we have discussed various methods of drug discovery and design, and their applications for the development of effective antiviral compounds. A descriptive methodology for the management of some common and notorious viral diseases is also outlined.

Vaccines and Immunoinformatics for Vaccine Design.

The host immune system recognizes and responds to the selective antigens or epitopes (immunome) of the intruding pathogen over an entire organism. The immune response so generated is ample to confer the desired immunity and protection to the host. This led to the conception of immunome-derived vaccines that exploit selective genome-derived antigens or epitopes from the pathogen's immunome and not its entire genome or proteome. These are designed to elicit the required immune response and confer protection against future invasions by the same pathogen. Immunoinformatics through its epitope mapping tools allows direct selection of antigens from a pathogen's genome or proteome, which is critical for the generation of an effective vaccine. This paved way for novel vaccine design strategies based on the mapped epitopes for translational applications that includes prophylactic, therapeutic, and personalized vaccines. In this chapter, various Immunoinformatics tools for epitope mapping are presented along with their applications. The methodology for immunoinformatics-assisted vaccine design is also outlined.

Multifaceted Effects of Lycopene: A Boulevard to the Multitarget-Based Treatment for Cancer.

Lycopene is a pigment belonging to the group of carotenoids and it is among the most carefully studied antioxidants found especially in fruit and vegetables. As a carotenoid, lycopene exerts beneficial effects on human health by protecting lipids, proteins, and DNA from damage by oxidation. Lycopene is a powerful oxygen inactivator in the singlet state. This is suggestive of the fact that lycopene harbors comparatively stronger antioxidant properties over other carotenoids normally present in plasma. Lycopene is also reported to hinder cancer cell proliferation. The uncontrolled, rapid division of cells is a characteristic of the metabolism of cancer cells. Evidently, lycopene causes a delay in the progression of the cell cycle, which explains its antitumor activity. Furthermore, lycopene can block cell transformation by reducing the loss of contact inhibition of cancer cells. This paper collects recent studies of scientific evidence that show the multiple beneficial properties of lycopene, which acts with different molecular and cellular mechanisms.

Data-driven microbiota biomarker discovery for personalized drug therapy of cardiovascular disease.

Cardiovascular disease (CVD) is the most wide-spread disorder all over the world. The personalized and precision diagnosis, treatment and prevention of CVD is still a challenge. With the developing of metagenome sequencing technologies and the paradigm shifting to data-driven discovery in life science, the computer aided microbiota biomarker discovery for CVD is becoming reality. We here summarize the data resources, knowledgebases and computational models available for CVD microbiota biomarker discovery, and review the present status of the findings about the microbiota patterns associated with the therapeutic effects on CVD. The future challenges and opportunities of the translational informatics on the personalized drug usages in CVD diagnosis, prognosis and treatment are also discussed.

Natural products act as game-changer potentially in treatment and management of sepsis-mediated inflammation: A clinical perspective.

BACKGROUND: Sepsis, a life-threatening condition resulting from uncontrolled host responses to infection, poses a global health challenge with limited therapeutic options. Due to high heterogeneity, sepsis lacks specific therapeutic drugs. Additionally, there remains a significant gap in the clinical management of sepsis regarding personalized and precise medicine. PURPOSE: This review critically examines the scientific landscape surrounding natural products in sepsis and sepsis-mediated inflammation, highlighting their clinical potential. METHODS: Following the PRISMA guidelines, we retrieved articles from PubMed to explore potential natural products with therapeutic effects in sepsis-mediated inflammation. RESULTS: 434 relevant in vitro and in vivo studies were identified and screened. Ultimately, 55 studies were obtained as the supporting resources for the present review. We divided the 55 natural products into three categories: those influencing the synthesis of inflammatory factors, those affecting surface receptors and modulatory factors, and those influencing signaling pathways and the inflammatory cascade. CONCLUSION: Natural products' potential as game-changers in sepsis-mediated inflammation management lies in their ability to modulate hallmarks in sepsis, including inflammation, immunity, and coagulopathy, which provides new therapeutic avenues that are readily accessible and capable of undergoing rapid clinical validation and deployment, offering a gift from nature to humanity. Innovative techniques like bioinformatics, metabolomics, and systems biology offer promising solutions to overcome these obstacles and facilitate the development of natural product-based therapeutics, holding promise for personalized and precise sepsis management and improving patient outcomes. However, standardization, bioavailability, and safety challenges arise during experimental validation and clinical trials of natural products.

Involvement of CXC chemokines (CXCL1-CXCL17) in gastric cancer: Prognosis and therapeutic molecules.

Gastric cancer (GC) is the fifth-most prevalent and second-most deadly cancer worldwide. Due to the late onset of symptoms, GC is frequently treated at a mature stage. In order to improve the diagnostic and clinical decision-making processes, it is necessary to establish more specific and sensitive indicators valuable in the early detection of the disease whenever a cancer is asymptomatic. In this work, we gathered information about CXC chemokines and GC by using scientific search engines including Google Scholar, PubMed, SciFinder, and Web of Science. Researchers believe that GC chemokines, small proteins, class CXC chemokines, and chemokine receptors promote GC inflammation, initiation, and progression by facilitating angiogenesis, tumor transformation, invasion, survival, metastatic spread, host response safeguards, and inter-cell interaction. With our absolute best professionalism, the role of CXC chemokines and their respective receptors in GC diagnosis and prognosis has not been fully explained. This review article updates the general characteristics of CXC chemokines, their unique receptors, their function in the pathological process of GC, and their potential application as possible indicators for GC. Although there have only recently been a few studies focusing on the therapeutic efficacy of CXC chemokine inhibitors in GC, growing experimental evidence points to the inhibition of CXC chemokines as a promising targeted therapy. Therefore, further translational studies are warranted to determine whether specific antagonists or antibodies designed to target CXC chemokines alone or in combination with chemotherapy are useful for diagnosing advanced GC.

Natural products for the treatment of age-related macular degeneration.

BACKGROUND: Age-related macular degeneration (AMD) is a chronic retinal disease that significantly influences the vision of the elderly. PURPOSE: There is no effective treatment and prevention method. The pathogenic process behind AMD is complex, including oxidative stress, inflammation, and neovascularization. It has been demonstrated that several natural products can be used to manage AMD, but systematic summaries are lacking. STUDY DESIGN AND METHODS: PubMed, Web of Science, and ClinicalTrials.gov were searched using the keywords "Biological Products" AND "Macular Degeneration" for studies published within the last decade until May 2023 to summarize the latest findings on the prevention and treatment of age-related macular degeneration through the herbal medicines and functional foods. RESULTS: The eligible studies were screened, and the relevant information about the therapeutic action and mechanism of natural products used to treat AMD was extracted. Our findings demonstrate that natural substances, including retinol, phenols, and other natural products, prevent the development of new blood vessels and protect the retina from oxidative stress in cells and animal models. However, they have barely been examined in clinical studies. CONCLUSION: Natural products could be highly prospective candidate drugs used to treat AMD, and further preclinical and clinical research is required to validate it to control the disease.

Warfarin-A natural anticoagulant: A review of research trends for precision medication.

BACKGROUND: Warfarin is a widely prescribed anticoagulant in the clinic. It has a more considerable individual variability, and many factors affect its variability. Mathematical models can quantify the quantitative impact of these factors on individual variability. PURPOSE: The aim is to comprehensively analyze the advanced warfarin dosing algorithm based on pharmacometrics and machine learning models of personalized warfarin dosage. METHODS: A bibliometric analysis of the literature retrieved from PubMed and Scopus was performed using VOSviewer. The relevant literature that reported the precise dosage of warfarin calculation was retrieved from the database. The multiple linear regression (MLR) algorithm was excluded because a recent systematic review that mainly reviewed this algorithm has been reported. The following terms of quantitative systems pharmacology, mechanistic model, physiologically based pharmacokinetic model, artificial intelligence, machine learning, pharmacokinetic, pharmacodynamic, pharmacokinetics, pharmacodynamics, and warfarin were added as MeSH Terms or appearing in Title/Abstract into query box of PubMed, then humans and English as filter were added to retrieve the literature. RESULTS: Bibliometric analysis revealed important co-occuring MeShH and index keywords. Further, the United States, China, and the United Kingdom were among the top countries contributing in this domain. Some studies have established personalized warfarin dosage models using pharmacometrics and machine learning-based algorithms. There were 54 related studies, including 14 pharmacometric models, 31 artificial intelligence models, and 9 model evaluations. Each model has its advantages and disadvantages. The pharmacometric model contains biological or pharmacological mechanisms in structure. The process of pharmacometric model development is very time- and labor-intensive. Machine learning is a purely data-driven approach; its parameters are more mathematical and have less biological interpretation. However, it is faster, more efficient, and less time-consuming. Most published models of machine learning algorithms were established based on cross-sectional data sourced from the database. CONCLUSION: Future research on personalized warfarin medication should focus on combining the advantages of machine learning and pharmacometrics algorithms to establish a more robust warfarin dosage algorithm. Randomized controlled trials should be performed to evaluate the established algorithm of warfarin dosage. Moreover, a more user-friendly and accessible warfarin precision medicine platform should be developed.

Role of MMP-9 and NF-κB in Diabetic Retinopathy: Progression and Potential Role of Bioflavonoids in the Mitigation of Diabetic Retinopathy.

"Diabetes mellitus" is a chronic metabolic disorder manifested by elevated blood glucose levels, primarily due to insufficient insulin production or resistance to insulin. Long-term diabetes results in persistent complications like retinopathy, cardiomyopathy, nephropathy, and neuropathy, causing significant health risks. The most alarming microvascular consequence allied with diabetes is "diabetic retinopathy," distinguished by the proliferation of anomalous blood vessels in the eye, mainly in the retina, resulting in visual impairment, diabetic macular edema, and retinal detachment if left untreated. According to estimates, 27.0% of people with diabetes worldwide have retinopathy, which leads to 0.4 million blindness cases. It is believed that mitochondrial damage and the production of inflammatory mediators are the early indicators of diabetic retinopathy before any histological changes occur in the retina. Moreover, it is evident that augmented oxidative stress in the retina further initiates the NF-κB/MMP-9 downstream signaling pathway. Interestingly, these downstream regulators, Nuclear Factor Kappa B [NF- kB] and matrix metalloproteinases 9 [MMP-9], have been recognized as important regulators of the inception and advancement of diabetic retinopathy. This diabetes and oxidative stress-induced MMP-9 are believed to regulate various cellular functions, including angiogenesis and apoptosis, causing blood-retinal barrier breakdown and tight junction protein degradation that further leads to diabetic retinopathy. Thus, there is an emergency need for the treatment of diabetic retinopathy. Emerging treatment options include anti-VEGF, laser treatment, and eye surgery, but these have certain limitations. This comprehensive review explores the mechanisms of MMP-9 and NF-kB involvement in diabetic retinopathy and bioflavonoids' therapeutic potential and mechanisms of action in inhibiting MMP-9 activity and suppressing NF-kB-mediated inflammation. Clinical evidence supporting the use of bioflavonoids in mitigating diabetic complications and future perspectives are also examined.

Establishing and Validating an Innovative Focal Adhesion-Linked Gene Signature for Enhanced Prognostic Assessment in Endometrial Cancer.

Studies have highlighted the significant role of focal adhesion signaling in cancer. Nevertheless, its specific involvement in the pathogenesis of endometrial cancer and its clinical significance remains uncertain. We analyzed TCGA-UCEC and GSE119041 datasets with corresponding clinical data to investigate focal adhesion-related gene expression and their clinical significance. A signature, "FA-riskScore," was developed using LASSO regression in the TCGA cohort and validated in the GSE dataset. The FA-riskScore was compared with four existing models in terms of their prediction performance. We employed univariate and multivariate Cox regression analyses towards FA-riskScore to assess its independent prognostic value. A prognostic evaluation nomogram based on our model and clinical indexes was established subsequently. Biological and immune differences between high- and low-risk groups were explored through functional enrichment, PPI network analysis, mutation mining, TME evaluation, and single-cell analysis. Sensitivity tests on commonly targeted drugs were performed on both groups, and Connectivity MAP identified potentially effective molecules for high-risk patients. qRT-PCR validated the expressions of FA-riskScore genes. FA-riskScore, based on FN1, RELN, PARVG, and PTEN, indicated a poorer prognosis for high-risk patients. Compared with published models, FA-riskScore achieved better and more stable performance. High-risk groups exhibited a more challenging TME and suppressive immune status. qRT-PCR showed differential expression in FN1, RELN, and PTEN. Connectivity MAP analysis suggested that BU-239, potassium-canrenoate, and tubocurarine are effective for high-risk patients. This study introduces a novel prognostic model for endometrial cancer and offers insights into focal adhesion's role in cancer pathogenesis.

Fermented formulation of Silybum marianum seeds: Optimization, heavy metal analysis, and hepatoprotective assessment.

BACKGROUND: Fermented formulations are extensively used in Ayurveda due to several benefits like improved palatability, bioavailability, pharmacological potential, and shelf life. These formulations can also quench the heavy metals from the plant material and thus reduce the toxicity. Seeds of Silybum marianum (L.) Gaertn. are widely used for the management of many liver diseases. STUDY DESIGN AND METHODS: In the present study, we developed a novel fermented formulation of S. marianum seeds and evaluated parameters like safety (heavy metal analysis) and effectiveness (hepatoprotective). As the developed formulation's validation is crucial, the critical process variables (time, pH, and sugar concentration) are optimized for alcohol and silybin content using the Box-Behnken design (BBD). RESULTS: The response surface methodology coupled with BBD predicted the optimized conditions (fermentation time (28 days), pH 5.6, and sugar concentration (22.04%)) for the development of a fermented formulation of the selected herb. Moreover, the alcohol content (6.5 ± 0.9%) and silybin concentration (26.1 ± 2.1%) were confirmed in optimized formulation by GC-MS and HPTLC analysis. The optimized formulation was also analyzed for heavy metals (Pb, As, Hg, and Cd); their concentration is significantly less than the decoction of herbs. Further, the comparative evaluation of the developed formulation with the marketed formulation also confirmed that the fermented formulation's silybin concentration and percentage release were significantly enhanced. In addition, the developed fermented formulation's percentage recovery of HepG2 cell lines after treatment with CCl4 was significantly improved compared with the marketed formulation. CONCLUSION: It can be summarized that the developed fermented formulation improves safety and effectiveness compared to other market formulations. Finally, it can be concluded that the developed fermented formulation could be further explored as a better alternative for developing Silybum marianum preparation.

Unlocking the potential of oncology biomarkers: advancements in clinical theranostics.

INTRODUCTION: Cancer biomarkers have revolutionized the field of oncology by providing valuable insights into tumor changes and aiding in screening, diagnosis, prognosis, treatment prediction, and risk assessment. The emergence of "omic" technologies has enabled biomarkers to become reliable and accurate predictors of outcomes during cancer treatment. CONTENT: In this review, we highlight the clinical utility of biomarkers in cancer identification and motivate researchers to establish a personalized/precision approach in oncology. By extending a multidisciplinary technology-based approach, biomarkers offer an alternative to traditional techniques, fulfilling the goal of cancer therapeutics to find a needle in a haystack. SUMMARY AND OUTLOOK: We target different forms of cancer to establish a dynamic role of biomarkers in understanding the spectrum of malignancies and their biochemical and molecular characterization, emphasizing their prospective contribution to cancer screening. Biomarkers offer a promising avenue for the early detection of human cancers and the exploration of novel technologies to predict disease severity, facilitating maximum survival and minimum mortality rates. This review provides a comprehensive overview of the potential of biomarkers in oncology and highlights their prospects in advancing cancer diagnosis and treatment.

Machine learning guided prediction of warfarin blood levels for personalized medicine based on clinical longitudinal data from cardiac surgery patients: a prospective observational study.

BACKGROUND: Warfarin is a common oral anticoagulant, and its effects vary widely among individuals. Numerous dose-prediction algorithms have been reported based on cross-sectional data generated via multiple linear regression or machine learning. This study aimed to construct an information fusion perturbation theory and machine learning prediction model of warfarin blood levels based on clinical longitudinal data from cardiac surgery patients. METHODS AND MATERIAL: The data of 246 patients were obtained from electronic medical records. Continuous variables were processed by calculating the distance of the raw data with the moving average (MA ∆vki(sj)), and categorical variables in different attribute groups were processed using Euclidean distance (ED ǁ∆vk(sj)ǁ). Regression and classification analyses were performed on the raw data, MA ∆vki(sj), and ED ǁ∆vk(sj)ǁ. Different machine-learning algorithms were chosen for the STATISTICA and WEKA software. RESULTS: The random forest (RF) algorithm was the best for predicting continuous outputs using the raw data. The correlation coefficients of the RF algorithm were 0.978 and 0.595 for the training and validation sets, respectively, and the mean absolute errors were 0.135 and 0.362 for the training and validation sets, respectively. The proportion of ideal predictions of the RF algorithm was 59.0%. General discriminant analysis (GDA) was the best algorithm for predicting the categorical outputs using the MA ∆vki(sj) data. The GDA algorithm's total true positive rate (TPR) was 95.4% and 95.6% for the training and validation sets, respectively, with MA ∆vki(sj) data. CONCLUSIONS: An information fusion perturbation theory and machine learning model for predicting warfarin blood levels was established. A model based on the RF algorithm could be used to predict the target international normalized ratio (INR), and a model based on the GDA algorithm could be used to predict the probability of being within the target INR range under different clinical scenarios.

Unlocking the Immunomodulatory Potential of Rosmarinic Acid Isolated from Punica granatum L. Using Bioactivity-Guided Approach: In Silico, In Vitro, and In Vivo Approaches.

BACKGROUND: Punica granatum L. is well-known for its multifaceted therapeutic potential, including anti-inflammatory and immunomodulatory activities. AIM: This study aimed to characterize an immunomodulatory compound isolated from Punica granatum L. using a bioactivity-guided approach. METHODS: Chromatographic techniques were adopted for isolation and purification of secondary metabolites. In silico, in vitro, and in vivo methods were performed to characterize the therapeutic potential of the isolated compound. RESULTS: Using preparative thin-layer chromatography, rosmarinic acid was isolated from F4 (column chromatography product obtained from a butanolic fraction of the extract). The impact of rosmarinic acid was assessed in rats using the neutrophil adhesion test, DTH response, and phagocytic index. In immunized rats, rosmarinic acid demonstrated significant immunomodulatory potential. Computational experiments, like molecular docking and molecular dynamics, were also conducted against two targeted receptors, Cereblon (PDB ID: 8AOQ) and human CD22 (PDB ID: 5VKM). Computational studies suggested that an increase in phagocytic index by rosmarinic acid could be attributed to inhibiting Cereblon and CD22. Pharmacokinetics and toxicity prediction also suggested the drug-likeness of rosmarinic acid. CONCLUSION: Rosmarinic acid is a potential candidate, but extensive research needs to be done to translate this molecule from bench to bedside.

Exploring the pathophysiological influence of heme oxygenase-1 on neuroinflammation and depression: A study of phytotherapeutic-based modulation.

BACKGROUND: The heme oxygenase (HO) system plays a significant role in neuroprotection and reduction of neuroinflammation and neurodegeneration. The system, via isoforms HO-1 and HO-2, regulates cellular redox balance. HO-1, an antioxidant defense enzyme, is highlighted due to its association with depression, characterized by heightened neuroinflammation and impaired oxidative stress responses. METHODOLOGY: We observed the pathophysiology of HO-1 and phytochemicals as its modulator. We explored Science Direct, Scopus, and PubMed for a comprehensive literature review. Bibliometric and temporal trend analysis were done using VOSviewer. RESULTS: Several phytochemicals can potentially alleviate neuroinflammation and oxidative stress-induced depressive symptoms. These effects result from inhibiting the MAPK and NK-κB pathways - both implicated in the overproduction of pro-inflammatory factors - and from the upregulation of HO-1 expression mediated by Nrf2. Bibliometric and temporal trend analysis further validates these associations. CONCLUSION: In summary, our findings suggest that antidepressant agents can mitigate neuroinflammation and depressive disorder pathogenesis via the upregulation of HO-1 expression. These agents suppress pro-inflammatory mediators and depressive-like symptoms, demonstrating that HO-1 plays a significant role in the neuroinflammatory process and the development of depression.

Revolutionizing pediatric neuroblastoma treatment: unraveling new molecular targets for precision interventions.

Neuroblastoma (NB) is the most frequent solid tumor in pediatric cases, contributing to around 15% of childhood cancer-related deaths. The wide-ranging genetic, morphological, and clinical diversity within NB complicates the success of current treatment methods. Acquiring an in-depth understanding of genetic alterations implicated in the development of NB is essential for creating safer and more efficient therapies for this severe condition. Several molecular signatures are being studied as potential targets for developing new treatments for NB patients. In this article, we have examined the molecular factors and genetic irregularities, including those within insulin gene enhancer binding protein 1 (ISL1), dihydropyrimidinase-like 3 (DPYSL3), receptor tyrosine kinase-like orphan receptor 1 (ROR1) and murine double minute 2-tumor protein 53 (MDM2-P53) that play an essential role in the development of NB. A thorough summary of the molecular targeted treatments currently being studied in pre-clinical and clinical trials has been described. Recent studies of immunotherapeutic agents used in NB are also studied in this article. Moreover, we explore potential future directions to discover new targets and treatments to enhance existing therapies and ultimately improve treatment outcomes and survival rates for NB patients.

Microbiota dysbiosis and myasthenia gravis: Do all roads lead to Rome?

Dysregulated immune system with a failure to recognize self from non-self-antigens is one of the common pathogeneses seen in autoimmune diseases. The complex interplay of genetic and environmental factors is important for the occurrence and development of the disease. Among the environmental factors, disturbed gut microbiota (gut dysbiosis) has recently attracted particular attention, especially with advancement in human microbiome research. Although the alterations in microbiota have been seen in various autoimmune diseases, including those of nervous system, there is paucity of information on neuromuscular system diseases. Myasthenia gravis (MG) is one such rare autoimmune disease of neuromuscular junction, and is caused by generation of pathogenic autoantibodies to components of the postsynaptic muscle endplate. In the recent years, accumulating evidences have endorsed the key role of host microbiota, particularly those of gut, in the pathogenesis of MG. Differential microbiota composition, characterized by increased abundance of Fusobacteria, Bacteroidetes, and Proteobacteria, and decreased abundance of Actinobacteria and Firmicutes, has been seen in MG patients in comparison to healthy subjects. Disturbance of microbiota composition, particularly reduced ratio of Firmicutes/Bacteroidetes, alter the gut permeability, subsequently triggering the immunological response. Resultant reduction in levels of short chain fatty acids (SCFAs) is another factor contributing to the immunological response in MG patients. Modulation of gut microbiota via intervention of probiotics, prebiotics, synbiotics, postbiotics (metabiotics), and fecal microbiota transplantation (FMT) is considered to be the futuristic approach for the management of MG. This review summarizes the role of gut microbiota and their metabolites (postbiotics) in the progression of MG. Also, various bacteriotherapeutic approaches involving gut microbiota are discussed for the prevention of MG progression.