Integration of National Health Insurance claims data and animal models reveals fexofenadine as a promising repurposed drug for Parkinson's disease.

BACKGROUND: Parkinson's disease (PD) is a common and costly progressive neurodegenerative disease of unclear etiology. A disease-modifying approach that can directly stop or slow its progression remains a major unmet need in the treatment of PD. A clinical pharmacology-based drug repositioning strategy is a useful approach for identifying new drugs for PD. METHODS: We analyzed claims data obtained from the National Health Insurance Service (NHIS), which covers a significant portion of the South Korean population, to investigate the association between antihistamines, a class of drugs commonly used to treat allergic symptoms by blocking H1 receptor, and PD in a real-world setting. Additionally, we validated this model using various animal models of PD such as the 6-hydroxydopmaine (6-OHDA), α-synuclein preformed fibrils (PFF) injection, and Caenorhabditis elegans (C. elegans) models. Finally, whole transcriptome data and Ingenuity Pathway Analysis (IPA) were used to elucidate drug mechanism pathways. RESULTS: We identified fexofenadine as the most promising candidate using National Health Insurance claims data in the real world. In several animal models, including the 6-OHDA, PFF injection, and C. elegans models, fexofenadine ameliorated PD-related pathologies. RNA-seq analysis and the subsequent experiments suggested that fexofenadine is effective in PD via inhibition of peripheral immune cell infiltration into the brain. CONCLUSION: Fexofenadine shows promise for the treatment of PD, identified through clinical data and validated in diverse animal models. This combined clinical and preclinical approach offers valuable insights for developing novel PD therapeutics.

Scutellaria baicalensis Attenuated Neurological Impairment by Regulating Programmed Cell Death Pathway in Ischemic Stroke Mice.

Stroke is a major global health problem that causes significant mortality and long-term disability. Post-stroke neurological impairment is a complication that is often underestimated with the risk of persistent neurological deficits. Although traditional Chinese medicines have a long history of being used for stroke, their scientific efficacy remains unclear. Scutellaria baicalensis, an herbal component known for its anti-inflammatory and antioxidant properties, has traditionally been used to treat brain disorders. This study investigated the therapeutic effects of the Scutellaria baicalensis extraction (SB) during the acute stage of ischemic stroke using photothrombotic (PTB)-induced and transient middle cerebral artery occlusion (tMCAO) model mice. We found that SB mitigated ischemic brain injury, as evidenced by a significant reduction in the modified neurological severity score in the acute stage of PTB and both the acute and chronic stages of tMCAO. Furthermore, we elucidated the regulatory role of SB in the necroptosis and pyroptosis pathways during the acute stage of stroke, underscoring its protective effects. Behavioral assessments demonstrated the effectiveness of SB in ameliorating motor dysfunction and cognitive impairment compared to the group receiving the vehicle. Our findings highlight the potential of SB as a promising therapeutic candidate for stroke. SB was found to help modulate the programmed cell death pathways, promote neuroprotection, and facilitate functional recovery.

Evolution of the Stethoscope: Advances with the Adoption of Machine Learning and Development of Wearable Devices.

The stethoscope has long been used for the examination of patients, but the importance of auscultation has declined due to its several limitations and the development of other diagnostic tools. However, auscultation is still recognized as a primary diagnostic device because it is non-invasive and provides valuable information in real-time. To supplement the limitations of existing stethoscopes, digital stethoscopes with machine learning (ML) algorithms have been developed. Thus, now we can record and share respiratory sounds and artificial intelligence (AI)-assisted auscultation using ML algorithms distinguishes the type of sounds. Recently, the demands for remote care and non-face-to-face treatment diseases requiring isolation such as coronavirus disease 2019 (COVID-19) infection increased. To address these problems, wireless and wearable stethoscopes are being developed with the advances in battery technology and integrated sensors. This review provides the history of the stethoscope and classification of respiratory sounds, describes ML algorithms, and introduces new auscultation methods based on AI-assisted analysis and wireless or wearable stethoscopes.

The coming era of a new auscultation system for analyzing respiratory sounds.

Auscultation with stethoscope has been an essential tool for diagnosing the patients with respiratory disease. Although auscultation is non-invasive, rapid, and inexpensive, it has intrinsic limitations such as inter-listener variability and subjectivity, and the examination must be performed face-to-face. Conventional stethoscope could not record the respiratory sounds, so it was impossible to share the sounds. Recent innovative digital stethoscopes have overcome the limitations and enabled clinicians to store and share the sounds for education and discussion. In particular, the recordable stethoscope made it possible to analyze breathing sounds using artificial intelligence, especially based on neural network. Deep learning-based analysis with an automatic feature extractor and convoluted neural network classifier has been applied for the accurate analysis of respiratory sounds. In addition, the current advances in battery technology, embedded processors with low power consumption, and integrated sensors make possible the development of wearable and wireless stethoscopes, which can help to examine patients living in areas of a shortage of doctors or those who need isolation. There are still challenges to overcome, such as the analysis of complex and mixed respiratory sounds and noise filtering, but continuous research and technological development will facilitate the transition to a new era of a wearable and smart stethoscope.

Machine learning-based survival rate prediction of Korean hepatocellular carcinoma patients using multi-center data.

AIM: To predict survival time of Korean hepatocellular carcinoma (HCC) patients using multi-center data as a foundation for the development of a predictive artificial intelligence model according to treatment methods based on machine learning. METHODS: Data of patients who underwent treatment for HCC from 2008 to 2015 was provided by Korean Liver Cancer Study Group and Korea Central Cancer Registry. A total of 10,742 patients with HCC were divided into two groups, with Group I (2920 patients) confirmed on biopsy and Group II (5562 patients) diagnosed as HCC according to HCC diagnostic criteria as outlined in Korean Liver Cancer Association guidelines. The data were modeled according to features of patient clinical characteristics. Features effective in predicting survival rate were analyzed retrospectively. Various machine learning methods were used. RESULTS: Target was overall survival time, which divided into approximately 60 months (= /< 60 m, > 60 m). Target distribution in Group I (total 514 samples) was 28.8%: (148 samples) less than 60 months, 71.2% (366 samples) greater than 60 months, and in Group II (total 757 samples) was 66.6% (504 samples) less than 60 months, 33.4% (253 samples) greater than 60 months. Using NG Boost method, its accuracy was 83%, precision 84%, sensitivity 95%, and F1 score 89% for more than 60 months survival time in Group I with surgical resection. Moreover, its accuracy was 79%, precision 82%, sensitivity 87%, and F1 score 84% for less than 60 months survival time in Group II with TACE. The feature importance with gain criterion indicated that pathology, portal vein invasion, surgery, metastasis, and needle biopsy features could be explained as important factors for prediction in case of biopsy (Group I). CONCLUSION: By developing a predictive model using machine learning algorithms to predict prognosis of HCC patients, it is possible to project optimized treatment by case according to liver function and tumor status.

Respiratory sound classification for crackles, wheezes, and rhonchi in the clinical field using deep learning.

Auscultation has been essential part of the physical examination; this is non-invasive, real-time, and very informative. Detection of abnormal respiratory sounds with a stethoscope is important in diagnosing respiratory diseases and providing first aid. However, accurate interpretation of respiratory sounds requires clinician's considerable expertise, so trainees such as interns and residents sometimes misidentify respiratory sounds. To overcome such limitations, we tried to develop an automated classification of breath sounds. We utilized deep learning convolutional neural network (CNN) to categorize 1918 respiratory sounds (normal, crackles, wheezes, rhonchi) recorded in the clinical setting. We developed the predictive model for respiratory sound classification combining pretrained image feature extractor of series, respiratory sound, and CNN classifier. It detected abnormal sounds with an accuracy of 86.5% and the area under the ROC curve (AUC) of 0.93. It further classified abnormal lung sounds into crackles, wheezes, or rhonchi with an overall accuracy of 85.7% and a mean AUC of 0.92. On the other hand, as a result of respiratory sound classification by different groups showed varying degree in terms of accuracy; the overall accuracies were 60.3% for medical students, 53.4% for interns, 68.8% for residents, and 80.1% for fellows. Our deep learning-based classification would be able to complement the inaccuracies of clinicians' auscultation, and it may aid in the rapid diagnosis and appropriate treatment of respiratory diseases.

Age-related increase in caveolin-1 expression facilitates cell-to-cell transmission of α-synuclein in neurons.

Parkinson's disease (PD) is the second most prevalent neurodegenerative disease, with aging being considered the greatest risk factor for developing PD. Caveolin-1 (Cav-1) is known to participate in the aging process. Recent evidence indicates that prion-like propagation of misfolded α-synuclein (α-syn) released from neurons to neighboring neurons plays an important role in PD progression. In the present study, we demonstrated that cav-1 expression in the brain increased with age, and considerably increased in the brain of A53T α-syn transgenic mice. Cav-1 overexpression facilitated the uptake of α-syn into neurons and formation of additional Lewy body-like inclusion bodies, phosphorylation of cav-1 at tyrosine 14 was found to be crucial for this process. This study demonstrates the relationship between age and α-syn spread and will facilitate our understanding of the molecular mechanism of the cell-to-cell transmission of α-syn.

Machine learning prediction of stone-free success in patients with urinary stone after treatment of shock wave lithotripsy.

BACKGROUND: The aims of this study were to determine the predictive value of decision support analysis for the shock wave lithotripsy (SWL) success rate and to analyze the data obtained from patients who underwent SWL to assess the factors influencing the outcome by using machine learning methods. METHODS: We retrospectively reviewed the medical records of 358 patients who underwent SWL for urinary stone (kidney and upper-ureter stone) between 2015 and 2018 and evaluated the possible prognostic features, including patient population characteristics, urinary stone characteristics on a non-contrast, computed tomographic image. We performed 80% training set and 20% test set for the predictions of success and mainly used decision tree-based machine learning algorithms, such as random forest (RF), extreme gradient boosting trees (XGBoost), and light gradient boosting method (LightGBM). RESULTS: In machine learning analysis, the prediction accuracies for stone-free were 86.0, 87.5, and 87.9%, and those for one-session success were 78.0, 77.4, and 77.0% using RF, XGBoost, and LightGBM, respectively. In predictions for stone-free, LightGBM yielded the best accuracy and RF yielded the best one in those for one-session success among those methods. The sensitivity and specificity values for machine learning analytics are (0.74 to 0.78 and 0.92 to 0.93) for stone-free and (0.79 to 0.81 and 0.74 to 0.75) for one-session success, respectively. The area under curve (AUC) values for machine learning analytics are (0.84 to 0.85) for stone-free and (0.77 to 0.78) for one-session success and their 95% confidence intervals (CIs) are (0.730 to 0.933) and (0.673 to 0.866) in average of methods, respectively. CONCLUSIONS: We applied a selected machine learning analysis to predict the result after treatment of SWL for urinary stone. About 88% accurate machine learning based predictive model was evaluated. The importance of machine learning algorithm can give matched insights to domain knowledge on effective and influential factors for SWL success outcomes.

DJ-1 deficiency impairs synaptic vesicle endocytosis and reavailability at nerve terminals.

Mutations in DJ-1 (PARK7) are a known cause of early-onset autosomal recessive Parkinson's disease (PD). Accumulating evidence indicates that abnormalities of synaptic vesicle trafficking underlie the pathophysiological mechanism of PD. In the present study, we explored whether DJ-1 is involved in CNS synaptic function. DJ-1 deficiency impaired synaptic vesicle endocytosis and reavailability without inducing structural alterations in synapses. Familial mutants of DJ-1 (M26I, E64D, and L166P) were unable to rescue defective endocytosis of synaptic vesicles, whereas WT DJ-1 expression completely restored endocytic function in DJ-1 KO neurons. The defective synaptic endocytosis shown in DJ-1 KO neurons may be attributable to alterations in membrane cholesterol level. Thus, DJ-1 appears essential for synaptic vesicle endocytosis and reavailability, and impairment of this function by familial mutants of DJ-1 may be related to the pathogenesis of PD.

HD047703, a New Promising Anti-Diabetic Drug Candidate: In Vivo Preclinical Studies.

G-protein coupled receptor 119 (GPR119) has emerged as a novel target for the treatment of type 2 diabetes mellitus. GPR119 is involved in glucose-stimulated insulin secretion (GSIS) from the pancreatic ß-cells and intestinal cells. In this study, we identified a novel small-molecule GPR119 agonist, HD047703, which raises intracellular cAMP concentrations in pancreatic ß-cells and can be expected to potentiate glucose-stimulated insulin secretion from human GPR119 receptor stably expressing cells (CHO cells). We evaluated the acute efficacy of HD047703 by the oral glucose tolerance test (OGTT) in normal C57BL/6J mice. Then, chronic administrations of HD047703 were performed to determine its efficacy in various diabetic rodent models. Single administration of HD047703 caused improved glycemic control during OGTT in a dose-dependent manner in normal mice, and the plasma GLP-1 level was also increased. With respect to chronic efficacy, we observed a decline in blood glucose levels in db/db, ob/ob and DIO mice. These results suggest that HD047703 may be a potentially promising anti-diabetic agent.

S100A9 knockout decreases the memory impairment and neuropathology in crossbreed mice of Tg2576 and S100A9 knockout mice model.

Our previous study presented evidence that the inflammation-related S100A9 gene is significantly upregulated in the brains of Alzheimer's disease (AD) animal models and human AD patients. In addition, experiments have shown that knockdown of S100A9 expression improves cognition function in AD model mice (Tg2576), and these animals exhibit reduced amyloid plaque burden. In this study, we established a new transgenic animal model of AD by crossbreeding the Tg2576 mouse with the S100A9 knockout (KO) mouse. We observed that S100A9KO/Tg2576 (KO/Tg) mice displayed an increased spatial reference memory in the Morris water maze task and Y-maze task as well as decreased amyloid beta peptide (Aß) neuropathology because of reduced levels of Aß, C-terminal fragments of amyloid precursor protein (APP-CT) and phosphorylated tau and increased expression of anti-inflammatory IL-10 and also decreased expression of inflammatory IL-6 and tumor neurosis factor (TNF)-α when compared with age-matched S100A9WT/Tg2576 (WT/Tg) mice. Overall, these results suggest that S100A9 is responsible for the neurodegeneration and cognitive deficits in Tg2576 mice. The mechanism of S100A9 is able to coincide with the inflammatory process. These findings indicate that knockout of S100A9 is a potential target for the pharmacological therapy of AD.

Novel GPR119 agonist HD0471042 attenuated type 2 diabetes mellitus.

In type 2 diabetes mellitus (T2DM) patients, the gradual loss of pancreatic ß-cell function is a characteristic feature of disease progression that is associated with sustained hyperglycemia. Recently, G protein-coupled receptor 119 (GPR119) has been identified as a promising anti-diabetic therapeutic target. It is predominantly expressed in pancreatic ß-cells, directly promotes glucose stimulated insulin secretion and indirectly increases glucagon-like peptide 1 (GLP-1) levels reducing appetite and food intake. Activation of GPR119 leads to insulin release in ß-cells by increasing intracellular cAMP. Here, we identified a novel structural class of small-molecule GPR119 agonists, HD0471042, consisting of substituted a 3-isopropyl-1,2,4-oxadiazol-piperidine derivative with promising potential for the treatment of T2DM. The GPR119 agonist, HD0471042 increased intracellular cAMP levels in stably human GPR119 expressing CHO cell lines and HIT-T15 cell lines, hamster ß-cell line expressing endogenously GPR119. HD0471042, significantly elevated insulin release in INS-1 cells of rat pancreatic ß-cell line. In in vivo experiments, a single dose of HD0471042 improved glucose tolerance. Insulin and GLP-1 level were increased in a dose-dependent manner. Treatment with HD0471042 for 6 weeks in diet induced obesity mice and for 4 weeks in ob/ob and db/db mice improved glycemic control and also reduced weight gain in a dose-dependent manner. These data demonstrate that the novel GPR119 agonist, HD0471042, not only effectively controlled glucose levels, but also had an anti-obesity effect, a feature observed with GLP-1. We therefore suggest that HD0471042 represents a new type of anti-diabetes agent with anti-obesity potential for the effective treatment of type 2 diabetes.

In vivo efficacy of HD0471953: a novel GPR119 agonist for the treatment of type 2 diabetes mellitus.

G-protein coupled receptor 119 (GPR119) has emerged as a promising new target for the treatment of type 2 diabetes mellitus. The expression of GPR119 on the pancreatic B cells and intestinal L cells provides a unique opportunity for a single drug to promote insulin and GLP-1 secretion. In this study, we identified a novel small molecule GPR119 agonist, HD0471953, from our large library of synthetic compounds based on its ability to anti-hyperglycemic effects on T2DM murine models. We have tested the acute efficacy of HD0471953 by the oral glucose tolerance test (OGTT) with normal C57BL/6J mice. Then, chronic administrations of HD0471953 were performed to evaluate the efficacy on various diabetic rodent models. Single administration of HD0471953 showed improved glycemic control with a dose-dependent manner in OGTT with normal mice, and the insulin and GLP-1 were also increased. To identify chronic efficacy, we have observed a decline of blood glucose and fasting insulin in a dose-dependent manner of 10, 20, and 50 mpk in db/db mice. The results suggest that HD0471953 may be a potentially promising anti-hyperglycemic agent for the treatment of patients with type 2 diabetes mellitus.

S100a9 knockdown decreases the memory impairment and the neuropathology in Tg2576 mice, AD animal model.

Inflammation, insoluble protein deposition and neuronal cell loss are important features in the Alzheimer's disease (AD) brain. To investigate the regulatory genes responsible for the neuropathology in AD, we performed microarray analysis with APP(V717I)-CT100 transgenic mice, an animal model of AD, and isolated the S100a9 gene, which encodes an inflammation-associated calcium binding protein. In another AD animal model, Tg2576 mouse brain, and in human AD brain, induction of S100a9 was confirmed. The endogenous expression of S100a9 was induced by treatment with Abeta or CT peptides in a microglia cell line, BV2 cells. In these cells, silencing study of S100a9 showed that the induction of S100a9 increased the intracellular calcium level and up-regulated the inflammatory cytokines (IL-1beta and TNFalpha) and iNOS. S100a9 lentiviral short hairpin RNA (sh-S100a9) was injected into the hippocampus region of the brains of 13-month-old Tg2576 mice. At two months after injection, we found that knockdown of S100a9 expression had improved the cognition decline of Tg2576 mice in the water maze task, and had reduced amyloid plaque burden. These results suggest that S100a9 induced by Abeta or CT contributes to cause inflammation, which then affects the neuropathology including amyloid plaques burden and impairs cognitive function. Thus, the inhibition of S100a9 is a possible target for AD therapy.

Phosphorylation of amyloid precursor protein (APP) at Thr668 regulates the nuclear translocation of the APP intracellular domain and induces neurodegeneration.

Amyloid precursor protein (APP) has eight potential phosphorylation sites in its cytoplasmic domain. Recently, it has demonstrated that the constitutive phosphorylation of APP at T668 (APP695 isoform numbering) was observed specifically in the brain. Neuron-specific phosphorylation of APP at T668 is thought to be important for neuronal functions of APP, although its exact physiological significance remains to be clarified. In this study, we show that the phosphorylation of the APP intracellular domain (AICD) at T668 is essential for its binding to Fe65 and its nuclear translocation and affects the resultant neurotoxicity, possibly mediated through the induction of glycogen synthase kinase 3beta and tau phosphorylation by enhancing the formation of a ternary complex with Fe65 and CP2 transcription factor. Taken together, these results suggest that the phosphorylation of AICD at T668 contributes to the neuronal degeneration in Alzheimer's disease (AD) by regulating its translocation into the nucleus and then affects neurodegeneration; therefore, the specific inhibitor of T668 phosphorylation might be the target of AD therapy.

Expression of nitric oxide synthase isoforms in the porcine ovary during follicular development.

The expression of nitric oxide synthase (NOS) isoforms in the ovaries of pigs was examined to study the involvement of nitric oxide, a product of NOS activity, in the function of the ovary. Western blot analysis detected three types of NOS in the ovary, including constitutive neuronal NOS (nNOS), endothelial NOS (eNOS) and inducible NOS (iNOS); eNOS immunoreactivity was more intense compared with that of iNOS or nNOS. Immunohistochemical studies demonstrated the presence of nNOS and eNOS in the surface epithelium, stroma, oocytes, thecal cells, and endothelial cells of blood vessels. Positive immunoreactions for nNOS and iNOS were detected in the granulosa cells from multilaminar and antral follicles, but not in those of unilaminar follicles. iNOS was detected in the surface epithelium, oocytes, and theca of multilaminar and antral follicles. Taking all of the findings into consideration, the observed differential expression of the three NOS isoforms in the ovary suggests a role for nitric oxide in modulating reproduction in pigs.

Expression of constitutive endothelial, neuronal and inducible nitric oxide synthase in the testis and epididymis of horse.

The expression of three isoforms of nitric oxide synthase (NOS) were examined in the testis and epididymis of a thoroughbred horse. Immunohistochemical studies demonstrated the presence of eNOS immunostaining in some germ cells in the seminiferous tubules and in vascular endothelial cells in the interstitial tissues. Interstitial cells, most likely Leydig cells, were also intensely immunopositive for eNOS. The pattern of immunostaining for nNOS was similar to that for eNOS in the testis. Weak expression of iNOS was detected in the seminiferous tubules of the testis, but intense expression was found in interstitial cells. Inducible NOS was also strongly detected in stereocilia, sperm, epithelium and connective tissue of the epididymis of normal horses. These findings suggest that three isoforms of NOS are expressed in the testis and epididymis of horse and that they play important roles in the biology of interstitial cells that produce testosterone, as well as in spermatogenesis in the seminiferous tubules.

Histochemical detection of glycoconjugates in the male reproductive system of the horse.

Lectins are glycoproteins of plant and animal origin that have the ability to bind specific carbohydrate residues of cell glycoconjugates, particularly in terminal positions. In this study, the binding of lectins, Dolichos biflorus agglutinin (DBA), soybean agglutinin (SBA), Bandeiraea simplicifolia BS-1 (isolectin B4), Triticum vulgaris (WGA), Arachis hypogaea (PNA), and Ulex europaeus (UEA-I), was studied in the reproductive systems of male thoroughbred horses.DBA was detected in the stereocilia of the caput and corpus epididymis, and in the vas deferens. It was weakly detected in connective tissue of the corpus epididymis. Strong SBA staining was seen in epithelial cells in the testis, stereocilia of the corpus and cauda epididymis, and in the vas deferens. There were intense positive reactions for isolectin B4 in interstitial cells in all tissue and serosa of the vas deferens. PNA staining was seen only in stereocilia in the caput and corpus epididymis, and in the vas deferens. Strong WGA staining was seen throughout the testis, except in Sertoli cells, stereocilia, and connective tissue. UEA-I was detected in secondary spermatids, stereocilia, and epithelial cells of the cauda epididymis. These results show that degenerating cells in the testis, epididymal tubules, and vas deferens have differential affinities for lectins, and suggest that lectins play a role in the reproductive system of the horse. The heterogeneity of the lectin staining pattern in the reproductive tubules of adult horses suggests that the carbohydrate composition of each cell type is region specific.