The role of proinflammatory cytokines and CXC chemokines (CXCL1-CXCL16) in the progression of prostate cancer: insights on their therapeutic management.

Reproductive cancers are malignancies that develop in the reproductive organs. One of the leading cancers affecting the male reproductive system on a global scale is prostate cancer (PCa). The negative consequences of PCa metastases endure and are severe, significantly affecting mortality and life quality for those who are affected. The association between inflammation and PCa has captured interest for a while. Inflammatory cells, cytokines, CXC chemokines, signaling pathways, and other elements make up the tumor microenvironment (TME), which is characterized by inflammation. Inflammatory cytokines and CXC chemokines are especially crucial for PCa development and prognosis. Cytokines (interleukins) and CXC chemokines such as IL-1, IL-6, IL-7, IL-17, TGF-ß, TNF-α, CXCL1-CXCL6, and CXCL8-CXCL16 are thought to be responsible for the pleiotropic effects of PCa, which include inflammation, progression, angiogenesis, leukocyte infiltration in advanced PCa, and therapeutic resistance. The inflammatory cytokine and CXC chemokines systems are also promising candidates for PCa suppression and immunotherapy. Therefore, the purpose of this work is to provide insight on how the spectra of inflammatory cytokines and CXC chemokines evolve as PCa develops and spreads. We also discussed recent developments in our awareness of the diverse molecular signaling pathways of these circulating cytokines and CXC chemokines, as well as their associated receptors, which may one day serve as PCa-targeted therapies. Moreover, the current status and potential of theranostic PCa therapies based on cytokines, CXC chemokines, and CXC receptors (CXCRs) are examined.

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.

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.

PCAO2: an ontology for integration of prostate cancer associated genotypic, phenotypic and lifestyle data.

Disease ontologies facilitate the semantic organization and representation of domain-specific knowledge. In the case of prostate cancer (PCa), large volumes of research results and clinical data have been accumulated and needed to be standardized for sharing and translational researches. A formal representation of PCa-associated knowledge will be essential to the diverse data standardization, data sharing and the future knowledge graph extraction, deep phenotyping and explainable artificial intelligence developing. In this study, we constructed an updated PCa ontology (PCAO2) based on the ontology development life cycle. An online information retrieval system was designed to ensure the usability of the ontology. The PCAO2 with a subclass-based taxonomic hierarchy covers the major biomedical concepts for PCa-associated genotypic, phenotypic and lifestyle data. The current version of the PCAO2 contains 633 concepts organized under three biomedical viewpoints, namely, epidemiology, diagnosis and treatment. These concepts are enriched by the addition of definition, synonym, relationship and reference. For the precision diagnosis and treatment, the PCa-associated genes and lifestyles are integrated in the viewpoint of epidemiological aspects of PCa. PCAO2 provides a standardized and systematized semantic framework for studying large amounts of heterogeneous PCa data and knowledge, which can be further, edited and enriched by the scientific community. The PCAO2 is freely available at https://bioportal.bioontology.org/ontologies/PCAO, http://pcaontology.net/ and http://pcaontology.net/mobile/.

A novel deep generative model for mRNA vaccine development: Designing 5' UTRs with N1-methyl-pseudouridine modification.

Efficient translation mediated by the 5' untranslated region (5' UTR) is essential for the robust efficacy of mRNA vaccines. However, the N1-methyl-pseudouridine (m1Ψ) modification of mRNA can impact the translation efficiency of the 5' UTR. We discovered that the optimal 5' UTR for m1Ψ-modified mRNA (m1Ψ-5' UTR) differs significantly from its unmodified counterpart, highlighting the need for a specialized tool for designing m1Ψ-5' UTRs rather than directly utilizing high-expression endogenous gene 5' UTRs. In response, we developed a novel machine learning-based tool, Smart5UTR, which employs a deep generative model to identify superior m1Ψ-5' UTRs in silico. The tailored loss function and network architecture enable Smart5UTR to overcome limitations inherent in existing models. As a result, Smart5UTR can successfully design superior 5' UTRs, greatly benefiting mRNA vaccine development. Notably, Smart5UTR-designed superior 5' UTRs significantly enhanced antibody titers induced by COVID-19 mRNA vaccines against the Delta and Omicron variants of SARS-CoV-2, surpassing the performance of vaccines using high-expression endogenous gene 5' UTRs.

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.

Elevated incidence of somatic mutations at prevalent genetic sites.

The common loci represent a distinct set of the human genome sites that harbor genetic variants found in at least 1% of the population. Small somatic mutations occur at the common loci and non-common loci, i.e. csmVariants and ncsmVariants, are presumed with similar probabilities. However, our work revealed that within the coding region, common loci constituted only 1.03% of all loci, yet they accounted for 5.14% of TCGA somatic mutations. Furthermore, the small somatic mutation incidence rate at these common loci was 2.7 times that observed in the non-common. Notably, the csmVariants exhibited an impressive recurrent rate of 36.14%, which was 2.59 times of the ncsmVariants. The C-to-T transition at the CpG sites accounted for 32.41% of the csmVariants, which was 2.93 times for the ncsmVariants. Interestingly, the aging-related mutational signature contributed to 13.87% of the csmVariants, 5.5 times that of ncsmVariants. Moreover, 35.93% of the csmVariants contexts exhibited palindromic features, outperforming ncsmVariant contexts by 1.84 times. Notably, cancer patients with higher csmVariants rates had better progression-free survival. Furthermore, cancer patients with high-frequency csmVariants enriched with mismatch repair deficiency were also associated with better progression-free survival. The accumulation of csmVariants during cancerogenesis is a complex process influenced by various factors. These include the presence of a substantial percentage of palindromic sequences at csmVariants sites, the impact of aging and DNA mismatch repair deficiency. Together, these factors contribute to the higher somatic mutation incidence rates of common loci and the overall accumulation of csmVariants in cancer development.

RARPKB: A knowledge-guide decision support platform for personalized robot-assisted surgery in prostate cancer.

BACKGROUND: Robot-assisted radical prostatectomy (RARP) has emerged as a pivotal surgical intervention for the treatment of prostate cancer. However, the complexity of clinical cases, heterogeneity of prostate cancer, and limitations in physician expertise pose challenges to rational decision-making in RARP. To address these challenges, we aimed to organize the knowledge of previously complex cohorts and establish an online platform named the RARP Knowledge Base (RARPKB) to provide reference evidence for personalized treatment plans. MATERIALS AND METHODS: PubMed searches over the past two decades were conducted to identify publications describing RARP. We collected, classified, and structured surgical details, patient information, surgical data, and various statistical results from the literature. A knowledge-guided decision-support tool was established using MySQL, DataTable, ECharts, and JavaScript. ChatGPT-4 and two assessment scales were used to validate and compare the platform. RESULTS: The platform comprised 583 studies, 1589 cohorts, 1 911 968 patients, and 11 986 records, resulting in 54 834 data entries. The knowledge-guided decision support tool provide personalized surgical plan recommendations and potential complications on the basis of patients' baseline and surgical information. Compared with ChatGPT-4, RARPKB outperformed in authenticity (100% versus [vs.] 73%), matching (100% vs. 53%), personalized recommendations (100% vs. 20%), matching of patients (100% vs. 0%), and personalized recommendations for complications (100% vs. 20%). Post-use, the average System Usability Scale score was 88.88±15.03, and the Net Promoter Score of RARPKB was 85. The knowledge base is available at http://rarpkb.bioinf.org.cn. CONCLUSIONS: We introduced the pioneering RARPKB, the first knowledge base for robot-assisted surgery, with an emphasis on prostate cancer. RARPKB can assist in personalized and complex surgical planning for prostate cancer to improve its efficacy. RARPKB provides a reference for the future applications of artificial intelligence in clinical practice.

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.

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.

Performance of ChatGPT on Chinese national medical licensing examinations: a five-year examination evaluation study for physicians, pharmacists and nurses.

BACKGROUND: Large language models like ChatGPT have revolutionized the field of natural language processing with their capability to comprehend and generate textual content, showing great potential to play a role in medical education. This study aimed to quantitatively evaluate and comprehensively analysis the performance of ChatGPT on three types of national medical examinations in China, including National Medical Licensing Examination (NMLE), National Pharmacist Licensing Examination (NPLE), and National Nurse Licensing Examination (NNLE). METHODS: We collected questions from Chinese NMLE, NPLE and NNLE from year 2017 to 2021. In NMLE and NPLE, each exam consists of 4 units, while in NNLE, each exam consists of 2 units. The questions with figures, tables or chemical structure were manually identified and excluded by clinician. We applied direct instruction strategy via multiple prompts to force ChatGPT to generate the clear answer with the capability to distinguish between single-choice and multiple-choice questions. RESULTS: ChatGPT failed to pass the accuracy threshold of 0.6 in any of the three types of examinations over the five years. Specifically, in the NMLE, the highest recorded accuracy was 0.5467, which was attained in both 2018 and 2021. In the NPLE, the highest accuracy was 0.5599 in 2017. In the NNLE, the most impressive result was shown in 2017, with an accuracy of 0.5897, which is also the highest accuracy in our entire evaluation. ChatGPT's performance showed no significant difference in different units, but significant difference in different question types. ChatGPT performed well in a range of subject areas, including clinical epidemiology, human parasitology, and dermatology, as well as in various medical topics such as molecules, health management and prevention, diagnosis and screening. CONCLUSIONS: These results indicate ChatGPT failed the NMLE, NPLE and NNLE in China, spanning from year 2017 to 2021. but show great potential of large language models in medical education. In the future high-quality medical data will be required to improve the performance.

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.

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.

From molecular mechanisms of prostate cancer to translational applications: based on multi-omics fusion analysis and intelligent medicine.

Prostate cancer is the most common cancer in men worldwide and has a high mortality rate. The complex and heterogeneous development of prostate cancer has become a core obstacle in the treatment of prostate cancer. Simultaneously, the issues of overtreatment in early-stage diagnosis, oligometastasis and dormant tumor recognition, as well as personalized drug utilization, are also specific concerns that require attention in the clinical management of prostate cancer. Some typical genetic mutations have been proved to be associated with prostate cancer's initiation and progression. However, single-omic studies usually are not able to explain the causal relationship between molecular alterations and clinical phenotypes. Exploration from a systems genetics perspective is also lacking in this field, that is, the impact of gene network, the environmental factors, and even lifestyle behaviors on disease progression. At the meantime, current trend emphasizes the utilization of artificial intelligence (AI) and machine learning techniques to process extensive multidimensional data, including multi-omics. These technologies unveil the potential patterns, correlations, and insights related to diseases, thereby aiding the interpretable clinical decision making and applications, namely intelligent medicine. Therefore, there is a pressing need to integrate multidimensional data for identification of molecular subtypes, prediction of cancer progression and aggressiveness, along with perosonalized treatment performing. In this review, we systematically elaborated the landscape from molecular mechanism discovery of prostate cancer to clinical translational applications. We discussed the molecular profiles and clinical manifestations of prostate cancer heterogeneity, the identification of different states of prostate cancer, as well as corresponding precision medicine practices. Taking multi-omics fusion, systems genetics, and intelligence medicine as the main perspectives, the current research results and knowledge-driven research path of prostate cancer were summarized.

Evolutionarily new genes in humans with disease phenotypes reveal functional enrichment patterns shaped by adaptive innovation and sexual selection.

New genes (or young genes) are structural novelties pivotal in mammalian evolution. Their phenotypic impacts on humans, however, remain elusive due to the technical and ethical complexities in functional studies. Through combining gene age dating with Mendelian disease phenotyping, our research reveals a steady integration of new genes with biomedical phenotypes into the human genome over macroevolutionary timescales (~0.07% per million years). Despite this stable pace, we observe distinct patterns in phenotypic enrichment, pleiotropy, and selective pressures shaped by different gene ages. Notably, young genes show significant enrichment in the male reproductive system, indicating strong sexual selection. Young genes also exhibit functions in tissues and systems potentially linked to human phenotypic innovations, such as increased brain size, musculoskeletal phenotypes, and color vision. Our findings further reveal increasing levels of pleiotropy over evolutionary time, which accompanies stronger selective constraints. We propose a "pleiotropy-barrier" model that delineates different potentials for phenotypic innovation between young and older genes subject to natural selection. Our study demonstrates that evolutionary new genes are critical in influencing human reproductive evolution and adaptive phenotypic innovations driven by sexual and natural selection, with low pleiotropy as a selective advantage.

From multi-omics approaches to personalized medicine in myocardial infarction.

Myocardial infarction (MI) is a prevalent cardiovascular disease characterized by myocardial necrosis resulting from coronary artery ischemia and hypoxia, which can lead to severe complications such as arrhythmia, cardiac rupture, heart failure, and sudden death. Despite being a research hotspot, the etiological mechanism of MI remains unclear. The emergence and widespread use of omics technologies, including genomics, transcriptomics, proteomics, metabolomics, and other omics, have provided new opportunities for exploring the molecular mechanism of MI and identifying a large number of disease biomarkers. However, a single-omics approach has limitations in understanding the complex biological pathways of diseases. The multi-omics approach can reveal the interaction network among molecules at various levels and overcome the limitations of the single-omics approaches. This review focuses on the omics studies of MI, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, and other omics. The exploration extended into the domain of multi-omics integrative analysis, accompanied by a compilation of diverse online resources, databases, and tools conducive to these investigations. Additionally, we discussed the role and prospects of multi-omics approaches in personalized medicine, highlighting the potential for improving diagnosis, treatment, and prognosis of MI.

Pathophysiological impact of CXC and CX3CL1 chemokines in preeclampsia and gestational diabetes mellitus.

Diabetes-related pathophysiological alterations and various female reproductive difficulties were common in pregnant women with gestational diabetes mellitus (GDM), who had 21.1 million live births. Preeclampsia (PE), which increases maternal and fetal morbidity and mortality, affects approximately 3%-5% of pregnancies worldwide. Nevertheless, it is unclear what triggers PE and GDM to develop. Therefore, the development of novel moderator therapy approaches is a crucial advancement. Chemokines regulate physiological defenses and maternal-fetal interaction during healthy and disturbed pregnancies. Chemokines regulate immunity, stem cell trafficking, anti-angiogenesis, and cell attraction. CXC chemokines are usually inflammatory and contribute to numerous reproductive disorders. Fractalkine (CX3CL1) may be membrane-bound or soluble. CX3CL1 aids cell survival during homeostasis and inflammation. Evidence reveals that CXC and CX3CL1 chemokines and their receptors have been the focus of therapeutic discoveries for clinical intervention due to their considerable participation in numerous biological processes. This review aims to give an overview of the functions of CXC and CX3CL1 chemokines and their receptors in the pathophysiology of PE and GDM. Finally, we examined stimulus specificity for CXC and CX3CL1 chemokine expression and synthesis in PE and GDM and preclinical and clinical trials of CXC-based PE and GDM therapies.

Opuntia ficus-indica (L.) Mill. - anticancer properties and phytochemicals: current trends and future perspectives.

Cancer is a leading cause of mortality worldwide, and conventional cancer therapies such as chemotherapy and radiotherapy often result in undesirable and adverse effects. Natural products have emerged as a promising alternative for cancer treatment, with comparatively fewer side effects reported. Opuntia ficus-indica (L.) Mill., a member of the Cactaceae family, contains a diverse array of phytochemicals, including flavonoids, polyphenols, betalains, and tannins, which have been shown to exhibit potent anticancer properties. Various parts of the Opuntia plant, including the fruits, stems/cladodes, and roots, have demonstrated cytotoxic effects against malignant cell lines in numerous studies. This review comprehensively summarizes the anticancer attributes of the phytochemicals found in Opuntia ficus-indica (L.) Mill., highlighting their potential as natural cancer prevention and treatment agents. Bibliometric metric analysis of PubMed and Scopus-retrieved data using VOSviewer as well as QDA analysis provide further insights and niche to be explored. Most anticancer studies on Opuntia ficus-indica and its purified metabolites are related to colorectal/colon cancer, followed by melanoma and breast cancer. Very little attention has been paid to leukemia, thyroid, endometrial, liver, and prostate cancer, and it could be considered an opportunity for researchers to explore O. ficus-indica and its metabolites against these cancers. The most notable mechanisms expressed and validated in those studies are apoptosis, cell cycle arrest (G0/G1 and G2/M), Bcl-2 modulation, antiproliferative, oxidative stress-mediated mechanisms, and cytochrome c. We have also observed that cladodes and fruits of O. ficus-indica have been more studied than other plant parts, which again opens the opportunity for the researchers to explore. Further, cell line-based studies dominated, and very few studies were related to animal-based experiments. The Zebrafish model is another platform to explore. However, it seems like more in-depth studies are required to ascertain clinical utility of this biosustainable resource O. ficus-indica.

An artificial intelligence method for predicting postoperative urinary incontinence based on multiple anatomic parameters of MRI.

Background: Deep learning methods are increasingly applied in the medical field; however, their lack of interpretability remains a challenge. Captum is a tool that can be used to interpret neural network models by computing feature importance weights. Although Captum is an interpretable model, it is rarely used to study medical problems, and there is a scarcity of data regarding MRI anatomical measurements for patients with prostate cancer after undergoing Robotic-Assisted Radical Prostatectomy (RARP). Consequently, predictive models for continence that use multiple types of anatomical MRI measurements are limited. Methods: We explored the energy efficiency of deep learning models for predicting continence by analyzing MRI measurements. We analyzed and compared various statistical models and provided reference examples for the clinical application of interpretable deep-learning models. Patients who underwent RARP at our institution between July 2019 and December 2020 were included in this study. A series of clinical MRI anatomical measurements from these patients was used to discover continence features, and their impact on continence was primarily evaluated using a series of statistical methods and computational models. Results: Age and six other anatomical measurements were identified as the top seven features of continence by the proposed model UINet7 with an accuracy of 0.97, and the first four of these features were also found by primary statistical analysis. Conclusions: This study fills the gaps in the in-depth investigation of continence features after RARP due to the limitations of clinical data and applicable models. We provide a pioneering example of the application of deep-learning models to clinical problems. The interpretability analysis of deep learning models has the potential for clinical applications.

The panda-derived Lactiplantibacillus plantarum BSG201683 improves LPS-induced intestinal inflammation and epithelial barrier disruption in vitro.

Captive pandas are suffering from intestinal infection due to intestinal microbiota characterized by a high abundance of Enterobacteriaceae induced by long-term captivity. Probiotic supplements showed improvement in intestinal barrier function and inflammation. However, the effects of panda-derived probiotics on the intestinal epithelium and inflammation have not been elucidated. In the present study, lipopolysaccharide (LPS) impaired Caco-2 and RAW264.7 inflammatory models were applied to assess the protection of Lactiplantibacillus plantarum BSG201683 (L. plantarum G83) on barrier disruption and inflammation. The results showed that treatment with L. plantarum G83 significantly decreased the paracellular permeability to fluorescein isothiocyanate conjugated dextran (MW 4000, FITC-D4) after LPS induction. Meanwhile, L. plantarum G83 alleviated the reduction in tight junction (TJ) proteins and downregulated proinflammatory cytokines caused by LPS in Caco-2 cells. L. plantarum G83 also significantly decreased the expression and secretion of pro-inflammatory cytokines in LPS-induced RAW264.7 cells. In addition, the IL-10 increased in both Caco-2 and RAW264.7 cells after L. plantarum G83 treatment. The phagocytosis activity of RAW264.7 cells was significantly increased after L. plantarum G83 treatment. Toll-like receptor 4/ nuclear factor kappa-B (TLR4/NF-κB) signaling pathways were significantly down-regulated after L. plantarum G83 intervention, and the phosphorylation of NF-κB/p65 was consistent with this result. Our findings suggest that L. plantarum G83 improves intestinal inflammation and epithelial barrier disruption in vitro.