This study investigates the pharmacokinetics and metabolic characteristics of three marine-derived piericidins as potential drug leads for kidney disease: piericidin A (PA) and its two glycosides (GPAs), glucopiericidin A (GPA) and 13-hydroxyglucopiericidin A (13-OH-GPA). The research aims to facilitate lead selection and optimization for developing a viable preclinical candidate. Rapid absorption of PA and GPAs in mice was observed, characterized by short half-lives and low bioavailability. Glycosides and hydroxyl groups significantly enhanced the absorption rate (13-OH-GPA > GPA > PA). PA and GPAs exhibited metabolic instability in liver microsomes due to Cytochrome P450 enzymes (CYPs) and uridine diphosphoglucuronosyl transferases (UGTs). Glucuronidation emerged as the primary metabolic pathway, with UGT1A7, UGT1A8, UGT1A9, and UGT1A10 demonstrating high elimination rates (30%-70%) for PA and GPAs. This rapid glucuronidation may contribute to the low bioavailability of GPAs. Despite its low bioavailability (2.69%), 13-OH-GPA showed higher kidney distribution (19.8%) compared to PA (10.0%) and GPA (7.3%), suggesting enhanced biological efficacy in kidney diseases. Modifying the C-13 hydroxyl group appears to be a promising approach to improve bioavailability. In conclusion, this study provides valuable metabolic insights for the development and optimization of marine-derived piericidins as potential drug leads for kidney disease.
Animals ; Male ; Mice ; Aquatic Organisms/chemistry* ; Biological Availability ; Cytochrome P-450 Enzyme System/metabolism* ; Glucuronosyltransferase/metabolism* ; Microsomes, Liver/metabolism* ; Molecular Structure ; Biological Products/pharmacokinetics* ; Pyridines/pharmacokinetics*

Advances in novel drugs, therapies, and genetic techniques have revolutionized the diagnosis and treatment of cancers, substantially improving cancer patients' prognosis. Although rare tumors account for a non-negligible number, the practice of precision medicine and development of novel therapies are largely hampered by many obstacles. Their low incidence and drastic regional disparities result in the difficulty of informative evidence-based diagnosis and subtyping. Sample exhaustion due to difficulty in diagnosis also leads to a lack of recommended therapeutic strategies in clinical guidelines, insufficient biomarkers for prognosis/efficacy, and inability to identify potential novel therapies in clinical trials. Herein, by reviewing the epidemiological data of Chinese solid tumors and publications defining rare tumors in other areas, we proposed a definition of rare tumor in China, including 515 tumor types with incidences of less than 2.5/100 000 per year. We also summarized the current diagnosis process, treatment recommendations, and global developmental progress of targeted drugs and immunotherapy agents on the status quo. Lastly, we pinpointed the current recommendation chance for patients with rare tumors to be involved in a clinical trial by NCCN. With this informative report, we aimed to raise awareness on the importance of rare tumor investigations and guarantee a bright future for rare tumor patients.
Humans ; Neoplasms/pathology* ; Biomarkers ; Prognosis ; Oceans and Seas ; China/epidemiology*

Six new bisabolane-type phenolic sesquiterpenoids, including plakordiols A-D (1-4), (7R, 10R)-hydroxycurcudiol (5) and (7R, 10S)-hydroxycurcudiol (6) were isolated from the marine sponge Plakortis simplex collected from the South China Sea. Their structures were determined based on extensive analysis of spectroscopic data. Their configurations were assigned by coupling constant analysis, NOESY correlations, and the modified Mosher's method. Furthermore, their cytotoxic and antibacterial activities were evaluated.
Animals ; Anti-Bacterial Agents/pharmacology* ; China ; Molecular Structure ; Monocyclic Sesquiterpenes/pharmacology* ; Pacific Ocean ; Plakortis/chemistry*

PURPOSE: Despite the well-known anti-inflammatory effects of vitamin D in periodontal health, its mechanism has not been fully elucidated. In the present study, the effect of vitamin D on strengthening E-cadherin junctions (ECJs) was explored in human gingival keratinocytes (HGKs). ECJs are the major type of intercellular junction within the junctional epithelium, where loose intercellular junctions develop and microbial invasion primarily occurs. METHODS: HOK-16B cells, an immortalized normal human gingival cell line, were used for the study. To mimic the inflammatory environment, cells were treated with tumor necrosis factor-alpha (TNF-α). Matrix metalloproteinases (MMPs) in the culture medium were assessed by an MMP antibody microarray and gelatin zymography. The expression of various molecules was investigated using western blotting. The extent of ECJ development was evaluated by comparing the average relative extent of the ECJs around the periphery of each cell after immunocytochemical E-cadherin staining. Vitamin D receptor (VDR) expression was examined via immunohistochemical analysis. RESULTS: TNF-α downregulated the development of the ECJs of the HGKs. Dissociation of the ECJs by TNF-α was accompanied by the upregulation of MMP-9 production and suppressed by a specific MMP-9 inhibitor, Bay 11-7082. Exogenous MMP-9 decreased the development of ECJs. Vitamin D reduced the production of MMP-9 and attenuated the breakdown of ECJs in the HGKs treated with TNF-α. In addition, vitamin D downregulated TNF-α-induced nuclear factor kappa B (NF-κB) signaling in the HGKs. VDR was expressed in the gingival epithelium, including the junctional epithelium. CONCLUSIONS: These results suggest that vitamin D may avert TNF-α-induced downregulation of the development of ECJs in HGKs by decreasing the production of MMP-9, which was upregulated by TNF-α. Vitamin D may reinforce ECJs by downregulating NF-κB signaling, which is upregulated by TNF-α. Strengthening the epithelial barrier may be a way for vitamin D to protect the periodontium from bacterial invasion.
Bays ; Blotting, Western ; Cadherins ; Cell Line ; Down-Regulation ; Epithelial Attachment ; Epithelium ; Gelatin ; Humans ; Intercellular Junctions ; Keratinocytes ; Matrix Metalloproteinase 9 ; Matrix Metalloproteinases ; NF-kappa B ; Periodontium ; Receptors, Calcitriol ; Tumor Necrosis Factor-alpha ; Up-Regulation ; Vitamin D ; Vitamins

Inferior meatal antrostomy (IMA) is a widely performed surgical technique to treat postoperative maxillary mucocele. The method is safe and easy to perform, without major complications compared with other approaches. Facial pain after IMA is a rare clinical entity that can be challenging to diagnose and treat. The authors present an unusual case of acute facial neuralgia triggered by cold air that developed after IMA. The antrostomy was located at the anterior-most part of the inferior meatus, and the inlet size was relatively large compared with the size of the remaining sinus. Surgical narrowing of the antrostomy inlet using endoscopy dramatically reduced the symptoms, and symptom relief was maintained for up to one year after surgery.
Bays ; Cartilage ; Endoscopy ; Facial Neuralgia ; Facial Pain ; Methods ; Mucocele ; Transplants

OBJECTIVE: Enhanced technology in computer and internet has driven scale and quality of data to be improved in various areas including healthcare sectors. Machine Learning (ML) has played a pivotal role in efficiently analyzing those big data, but a general misunderstanding of ML algorithms still exists in applying them (e.g., ML techniques can settle a problem of small sample size, or deep learning is the ML algorithm). This paper reviewed the research of diagnosing mental illness using ML algorithm and suggests how ML techniques can be employed and worked in practice. METHODS: Researches about mental illness diagnostic using ML techniques were carefully reviewed. Five traditional ML algorithms-Support Vector Machines (SVM), Gradient Boosting Machine (GBM), Random Forest, Naïve Bayes, and K-Nearest Neighborhood (KNN)-frequently used for mental health area researches were systematically organized and summarized. RESULTS: Based on literature review, it turned out that Support Vector Machines (SVM), Gradient Boosting Machine (GBM), Random Forest, Naïve Bayes, and K-Nearest Neighborhood (KNN) were frequently employed in mental health area, but many researchers did not clarify the reason for using their ML algorithm though every ML algorithm has its own advantages. In addition, there were several studies to apply ML algorithms without fully understanding the data characteristics. CONCLUSION: Researchers using ML algorithms should be aware of the properties of their ML algorithms and the limitation of the results they obtained under restricted data conditions. This paper provides useful information of the properties and limitation of each ML algorithm in the practice of mental health.
Bays ; Forests ; Health Care Sector ; Internet ; Learning ; Machine Learning ; Mental Health ; Residence Characteristics ; Sample Size ; Support Vector Machine

OBJECTIVES: To estimate the number and risk of imported infections resulting from people visiting Asian and Latin American countries.METHODS: The dataset of visitors to 5 Asian countries with dengue were analyzed for 2016 and 2017, and in the Philippines, Thailand and Vietnam, imported cases of zika virus infection were also reported. For zika virus, a single imported case was reported from Brazil in 2016, and 2 imported cases reported from the Maldives in 2017. To understand the transmissibility in 5 Southeast Asian countries, the estimate of the force of infection, i.e., the hazard of infection per year and the average duration of travel has been extracted. Outbound travel numbers were retrieved from the World Tourism Organization, including business travelers.RESULTS: The incidence of imported dengue in 2016 was estimated at 7.46, 15.00, 2.14, 4.73 and 2.40 per 100,000 travelers visiting Philippines, Indonesia, Thailand, Malaysia and Vietnam, respectively. Similarly, 2.55, 1.65, 1.53, 1.86 and 1.70 per 100,000 travelers in 2017, respectively. It was estimated that there were 60.1 infections (range: from 16.8 to 150.7 infections) with zika virus in Brazil, 2016, and 345.6 infections (range: from 85.4 to 425.5 infections) with zika virus in the Maldives, 2017.CONCLUSION: This study emphasizes that dengue and zika virus infections are mild in their nature, and a substantial number of infections may go undetected. An appropriate risk assessment of zika virus infection must use the estimated total size of infections.
Asia, Southeastern ; Asian Continental Ancestry Group ; Brazil ; Commerce ; Dataset ; Dengue ; Humans ; Incidence ; Indian Ocean Islands ; Indonesia ; Korea ; Latin America ; Malaysia ; Philippines ; Risk Assessment ; Thailand ; Vietnam ; Zika Virus ; Zika Virus Infection

As an important branch of traditional medicines,medicinal marine organisms have many advantages,including biological diversity,remarkable biological activity,especial for the treatment of anti-cancer,anti-virus,anti-coagulation,analgesia,anti-bacterial,cardiovascular and cerebrovascular diseases. In recent years,with the continuous exploration of marine organisms by human beings,many marine organisms with specific biological activities and medicinal prospects have been found,which have attracted great attention around the world and thus called " new hope" to solve human health problems. However,due to the rapid development of modern industry,heavy metal pollution not only poses a great threat to medicinal marine living resources,but also hinders the development of marine biomedical industry and threatens human health. In view of this,this paper introduced the development trend of medicinal marine organisms and the current situation of heavy metal pollution and focusing on the analysis technology and chemical removal technology of heavy metals in medicinal marine organisms,which is to provide reference for the heavy metals control in marine medicines and the development and utilization of marine medicines.
Aquatic Organisms ; China ; Environmental Monitoring ; Medicine, Chinese Traditional ; Metals, Heavy/analysis* ; Water Pollutants, Chemical/analysis*

Parkinson's disease (PD) is a widespread degenerative syndrome that affects the nervous system. Its early appearing symptoms include tremor, rigidity, and vocal impairment (dysphonia). Consequently, speech indicators are important in the identification of PD based on dysphonic signs. In this regard, computer-aided-diagnosis systems based on machine learning can be useful in assisting clinicians in identifying PD patients. In this work, we evaluate the performance of machine learning based techniques for PD diagnosis based on dysphonia symptoms. Several machine learning techniques were considered and trained with a set of twenty-two voice disorder measurements to classify healthy and PD patients. These machine learning methods included linear discriminant analysis (LDA), k nearest-neighbors (k-NN), naïve Bayes (NB), regression trees (RT), radial basis function neural networks (RBFNN), support vector machine (SVM), and Mahalanobis distance classifier. We evaluated the performance of these methods by means of a tenfold cross validation protocol. Experimental results show that the SVM classifier achieved higher average performance than all other classifiers in terms of overall accuracy, G-mean, and area under the curve of the receiver operating characteristic plot. The SVM classifier achieved higher performance measures than the majority of the other classifiers also in terms of sensitivity, specificity, and F-measure statistics. The LDA, k-NN and RT achieved the highest average precision. The RBFNN method yielded the highest F-measure.; however, it performed poorly in terms of other performance metrics. Finally, t tests were performed to evaluate statistical significance of the results, confirming that the SVM outperformed most of the other classifiers on the majority of performance measures. SVM is a promising method for identifying PD patients based on classification of dysphonia measurements.
Bays ; Classification ; Diagnosis ; Dysphonia ; Humans ; Machine Learning ; Methods ; Nervous System ; Parkinson Disease ; ROC Curve ; Sensitivity and Specificity ; Support Vector Machine ; Trees ; Tremor ; Voice Disorders

The prevalence of metabolic syndrome (MS) in the nonobese population is not low. However, the identification and risk mitigation of MS are not easy in this population. We aimed to develop an MS prediction model using genetic and clinical factors of nonobese Koreans through machine learning methods. A prediction model for MS was designed for a nonobese population using clinical and genetic polymorphism information with five machine learning algorithms, including naïve Bayes classification (NB). The analysis was performed in two stages (training and test sets). Model A was designed with only clinical information (age, sex, body mass index, smoking status, alcohol consumption status, and exercise status), and for model B, genetic information (for 10 polymorphisms) was added to model A. Of the 7,502 nonobese participants, 647 (8.6%) had MS. In the test set analysis, for the maximum sensitivity criterion, NB showed the highest sensitivity: 0.38 for model A and 0.42 for model B. The specificity of NB was 0.79 for model A and 0.80 for model B. In a comparison of the performances of models A and B by NB, model B (area under the receiver operating characteristic curve [AUC] = 0.69, clinical and genetic information input) showed better performance than model A (AUC = 0.65, clinical information only input). We designed a prediction model for MS in a nonobese population using clinical and genetic information. With this model, we might convince nonobese MS individuals to undergo health checks and adopt behaviors associated with a preventive lifestyle.
Alcohol Drinking ; Bays ; Body Mass Index ; Classification ; Life Style ; Machine Learning ; Polymorphism, Genetic ; Prevalence ; ROC Curve ; Sensitivity and Specificity ; Smoke ; Smoking