Metabolic differences in Zingiber officinale Roscoe by geographical origin determined via multiplatform metabolomics and method for simultaneous analysis of six phenolic compounds.

Ginger, which is the rhizome of Zingiber officinale Roscoe, is widely distributed and consumed. The taste and aroma of ginger differ depending on its geographical origin. To distinguish the origin of ginger, ginger extracts from Korea, Peru, and China were analyzed using ultra-performance liquid chromatography (UPLC) coupled to quadrupole time-of-flight mass spectrometry and nuclear magnetic resonance spectroscopy for metabolomics. Korean ginger contained more 10-gingerol, and Peruvian ginger contained more 6-gingerol and 8-gingerol. Several amino acids negatively correlated with gingerols, suggesting that amino acids are related to the biosynthesis of gingerols. Sugars, which are the main energy source, positively correlated with gingerols. Organic acids and gingerols were also positively correlated, indicating that both organic acids and gingerols are used for adaptation to the environment surrounding the root. We confirmed the features of the primary and secondary metabolites by verifying the correlation between metabolites and differences in metabolites according to ginger origin. We additionally optimized a simultaneous UPLC analytical method of marker compounds for the simple and rapid quality control of ginger. This method exhibits excellent linearity, sensitivity, and reproducibility. Using metabolomics, differences in origin were observed, and a low-end equipment analysis method for quality control can be used in the ginger industry.

Predicting long-term mortality of patients with postoperative acute kidney injury following noncardiac general anesthesia surgery using machine learning.

Background: This study addresses the gap in knowledge regarding the long-term mortality implications of postoperative acute kidney injury (PO-AKI) utilizing advanced machine learning techniques to predict outcomes more accurately than traditional statistical models. Methods: A retrospective cohort study was conducted using data from seven institutions between March 2009 and December 2019. Machine learning models were developed to predict all-cause mortality of PO-AKI patients using 23 preoperative variables and one postoperative variable. Model performance was compared to a traditional statistical approach with Cox regression analysis. The concordance index was used as a predictive performance metric to compare prediction capabilities among different models. Results: Among 199,403 patients, 2,105 developed PO-AKI. During a median follow-up of 144 months (interquartile range, 99.61-170.71 months), 472 in-hospital deaths occurred. Subjects with PO-AKI had a significantly lower survival rate than those without PO-AKI (p < 0.001). For predicting mortality, the XGBoost with an accelerated failure time model had the highest concordance index (0.7521), followed by random survival forest (0.7371), multivariable Cox regression model (0.7318), survival support vector machine (0.7304), and gradient boosting (0.7277). Conclusion: XGBoost with an accelerated failure time model was developed in this study to predict long-term mortality associated with PO-AKI. Its performance was superior to conventional models. The application of machine learning techniques may offer a promising approach to predict mortality following PO-AKI more accurately, providing a basis for developing targeted interventions and clinical guidelines to improve patient outcomes.

Development and Validation of Artificial Intelligence Models for Prognosis Prediction of Juvenile Myoclonic Epilepsy with Clinical and Radiological Features.

Background: Juvenile myoclonic epilepsy (JME) is a common adolescent epilepsy characterized by myoclonic, generalized tonic-clonic, and sometimes absence seizures. Prognosis varies, with many patients experiencing relapse despite pharmacological treatment. Recent advances in imaging and artificial intelligence suggest that combining microstructural brain changes with traditional clinical variables can enhance potential prognostic biomarkers identification. Methods: A retrospective study was conducted on patients with JME at the Severance Hospital, analyzing clinical variables and magnetic resonance imaging (MRI) data. Machine learning models were developed to predict prognosis using clinical and radiological features. Results: The study utilized six machine learning models, with the XGBoost model demonstrating the highest predictive accuracy (AUROC 0.700). Combining clinical and MRI data outperformed models using either type of data alone. The key features identified through a Shapley additive explanation analysis included the volumes of the left cerebellum white matter, right thalamus, and left globus pallidus. Conclusions: This study demonstrated that integrating clinical and radiological data enhances the predictive accuracy of JME prognosis. Combining these neuroanatomical features with clinical variables provided a robust prediction of JME prognosis, highlighting the importance of integrating multimodal data for accurate prognosis.

A Nomogram Using Imaging Features to Predict Ipsilateral Breast Tumor Recurrence After Breast-Conserving Surgery for Ductal Carcinoma In Situ.

OBJECTIVE: To develop a nomogram that integrates clinical-pathologic and imaging variables to predict ipsilateral breast tumor recurrence (IBTR) in women with ductal carcinoma in situ (DCIS) treated with breast-conserving surgery (BCS). MATERIALS AND METHODS: This retrospective study included consecutive women with DCIS who underwent BCS at two hospitals. Patients who underwent BCS between 2003 and 2016 in one hospital and between 2005 and 2013 in another were classified into development and validation cohorts, respectively. Twelve clinical-pathologic variables (age, family history, initial presentation, nuclear grade, necrosis, margin width, number of excisions, DCIS size, estrogen receptor, progesterone receptor, radiation therapy, and endocrine therapy) and six mammography and ultrasound variables (breast density, detection modality, mammography and ultrasound patterns, morphology and distribution of calcifications) were analyzed. A nomogram for predicting 10-year IBTR probabilities was constructed using the variables associated with IBTR identified from the Cox proportional hazard regression analysis in the development cohort. The performance of the developed nomogram was evaluated in the external validation cohort using a calibration plot and 10-year area under the receiver operating characteristic curve (AUROC) and compared with the Memorial Sloan-Kettering Cancer Center (MSKCC) nomogram. RESULTS: The development cohort included 702 women (median age [interquartile range], 50 [44-56] years), of whom 30 (4%) women experienced IBTR. The validation cohort included 182 women (48 [43-54] years), 18 (10%) of whom developed IBTR. A nomogram was constructed using three clinical-pathologic variables (age, margin, and use of adjuvant radiation therapy) and two mammographic variables (breast density and calcification morphology). The nomogram was appropriately calibrated and demonstrated a comparable 10-year AUROC to the MSKCC nomogram (0.73 vs. 0.66, P = 0.534) in the validation cohort. CONCLUSION: Our nomogram provided individualized risk estimates for women with DCIS treated with BCS, demonstrating a discriminative ability comparable to that of the MSKCC nomogram.

Development and Validation of Machine Learning Models to Predict Postoperative Delirium Using Clinical Features and Polysomnography Variables.

Background: Delirium affects up to 50% of patients following high-risk surgeries and is associated with poor long-term prognosis. This study employed machine learning to predict delirium using polysomnography (PSG) and sleep-disorder questionnaire data, and aimed to identify key sleep-related factors for improved interventions and patient outcomes. Methods: We studied 912 adults who underwent surgery under general anesthesia at a tertiary hospital (2013-2024) and had PSG within 5 years of surgery. Delirium was assessed via clinical diagnoses, antipsychotic prescriptions, and psychiatric consultations within 14 days postoperatively. Sleep-related data were collected using PSG and questionnaires. Machine learning predictions were performed to identify postoperative delirium, focusing on model accuracy and feature importance. Results: This study divided the 912 patients into an internal training set (700) and an external test set (212). Univariate analysis identified significant delirium risk factors: midazolam use, prolonged surgery duration, and hypoalbuminemia. Sleep-related variables such as fewer rapid eye movement (REM) episodes and higher daytime sleepiness were also linked to delirium. An extreme gradient-boosting-based classification task achieved an AUC of 0.81 with clinical variables, 0.60 with PSG data alone, and 0.84 with both, demonstrating the added value of PSG data. Analysis of Shapley additive explanations values highlighted important predictors: surgery duration, age, midazolam use, PSG-derived oxygen saturation nadir, periodic limb movement index, and REM episodes, demonstrating the relationship between sleep patterns and the risk of delirium. Conclusions: The artificial intelligence model integrates clinical and sleep variables and reliably identifies postoperative delirium, demonstrating that sleep-related factors contribute to its identification. Predicting patients at high risk of developing postoperative delirium and closely monitoring them could reduce the costs and complications associated with delirium.

Enhancement of photovoltaic parameters of thermally stable graphene/LaVO3semitransparent solar cell by employing interfacial graphene quantum dots.

Semitransparent solar cells are attracting attention not only for their visual effects but also for their ability to effectively utilize solar energy. Here, we demonstrate a translucent solar cell composed of bis(trifluoromethane sulfonyl)-amide (TFSA)-doped graphene (Gr), graphene quantum dots (GQDs), and LaVO3. By introducing a GQDs intermediate layer at the TFSA-Gr/LaVO3interface, we can improve efficiency by preventing carrier recombination and promoting charge collection/separation in the device. As a result, the efficiency of the GQDs-based solar cell was 4.35%, which was higher than the 3.52% of the device without GQDs. Furthermore, the average visible transmittance of the device is 28%, making it suitable for translucent solar cells. The Al reflective mirror-based system improved the power conversion efficiency by approximately 7% compared to a device without a mirror. Additionally, the thermal stability of the device remains at 90% even after 2000 h under an environment with a temperature of 60 °C and 40% relative humidity. These results suggest that TFSA-Gr/GQDs/LaVO3-based cells have a high potential for practical use as a next-generation translucent solar energy power source.

Characterization of Inflammation/Immune-, Acute Phase-, Extracellular Matrix-, Adhesion-, and Serine Protease-Related Proteins in the Amniotic Fluid of Women With Early Preterm Prelabor Rupture of Membranes.

PROBLEM: To determine whether altered concentrations of various inflammation/immune-, acute phase-, extracellular matrix-, adhesion-, and serine protease-related proteins in the amniotic fluid (AF) are independently associated with microbial invasion of the amniotic cavity and/or intra-amniotic inflammation (MIAC/IAI), imminent spontaneous preterm delivery (SPTD; ≤7 days), and major neonatal morbidity/mortality (NMM) in women with early preterm prelabor rupture of membranes (PPROM). METHOD OF STUDY: This was a retrospective cohort study involving 111 singleton pregnant women with PPROM (24-31 weeks) undergoing amniocentesis to diagnose MIAC/IAI. The following proteins were measured in stored AF samples by enzyme-linked immunosorbent assay (ELISA): APRIL, DKK-3, Gal-3BP, IGFBP-2, IL-8, VDBP, lumican, MMP-2, MMP-8, SPARC, TGFBI, TGF-ß1, E-selectin, ICAM-5, P-selectin, haptoglobin, hepcidin, SAA1, kallistatin, and uPA. RESULTS: Multivariate logistic regression analyses revealed that (i) elevated APRIL, IL-8, MMP-8, and TGFBI levels in the AF, reduced lumican and SPARC levels in the AF, and high percentages of samples above the lower limit of quantification for AF TGF-ß1 and uPA were significantly associated with MIAC/IAI; (ii) elevated AF levels of IL-8 and MMP-8 were significantly associated with SPTD within 7 days; and (iii) elevated AF IL-6 levels were significantly associated with increased risk for major NMM, when adjusted for baseline covariates. CONCLUSION: ECM (lumican, SPRAC, TGFBI, and TGF-ß1)- and serine protease (uPA)-associated proteins in the AF are involved in the regulation of the host response to infection/inflammation in the amniotic cavity, whereas AF inflammation (IL-8, MMP-8, and IL-6)-associated mediators are implicated in the development of preterm parturition and major NMM in early PPROM.

Microbiome and mycobiome analyses in continuous positive airway pressure devices.

Objectives: Microorganisms are likely present in continuous positive airway pressure (CPAP) devices used daily. Considering the potential risk of infections among CPAP device users, here we aimed to compare the microbiomes in CPAP devices with those in nasal mucosal samples obtained from corresponding individuals using these devices. Methods: We conducted a prospective cohort study at tertiary medical institutes. Samples were collected from the tubes and filters of CPAP devices and the nasal mucosa of corresponding individuals using these devices. Microbiomes and mycobiomes were analyzed using 16S ribosomal RNA and internal transcribed spacer region sequencing. Results were compared according to the sampling site and usage duration for each patient. Results: Overall, 27 paired human nasal mucosa and CPAP samples were analyzed. Bacteria were present in 7 of 27 tubes (29.6%) and 22 of 27 filters (81.5%). Fungi were present in 2 of the 27 tubes (7.4%) and 16 of the 27 filters (59.3%). Actinobacteria and Firmicutes were the predominant phyla among all samples. Fungi were not detected in any of the nasal mucosal samples. However, Basidiomycota and Ascomycota were predominant in the CPAP filters and tube samples. No significant associations were identified among the results according to sampling site and usage duration. Conclusion: Bacteria or fungi can be detected to some extent in CPAP samples even if the CPAP usage period is short. The association between respiratory infections and these microbiomes or mycobiomes was not investigated. Further research might be required to determine the risk posed by CPAP devices as a microbial contamination source.

Trends for Syndromic Surveillance of Norovirus in Emergency Department Data Based on Chief Complaints.

BACKGROUND: This study compared trends in norovirus cases to determine whether chief complaint-based emergency department (ED) visit data could reflect trends of norovirus in Korea. METHODS: The ED visits from the National Emergency Department Information System database and the weekly reported number of noroviruses from the sentinel surveillance system were collected between August 2017 and December 2020. The correlation between weekly norovirus cases and weekly ED visits considering the chief complaint and discharge diagnosis code was estimated using a 3-week moving average. RESULTS: In total, 6 399 774 patients with chief complaints related to digestive system disease visited an ED. A higher correlation between reported norovirus cases and ED visit with chief complaint of vomiting and discharge diagnosis code of gastroenteritis and colitis of unspecified origin or other and unspecified gastroenteritis and colitis of infectious origin was observed (R = 0.88, P < .0001). The correlation was highest for the age group 0-4 years (R = 0.89, P < .0001). However, no correlation was observed between the reported norovirus cases and the number of ED visits with norovirus identified as a discharge diagnosis code. CONCLUSIONS: ED visit data considering a combination of chief complaints and discharged diagnosis code would be useful for early detection of infectious disease trends.

Prospective validation of a mobile health application for blood pressure management in patients with hypertensive disorders of pregnancy: study protocol for a randomized controlled trial.

BACKGROUND: Hypertensive disorders of pregnancy (HDP) pose significant risks to both maternal and fetal health, contributing to global morbidity and mortality. Management of HDP is complex, particularly because of concerns regarding potential negative effects on utero-placental circulation and limited therapeutic options due to fetal safety. Our study investigates whether blood pressure monitoring through a mobile health (mHealth) application can aid in addressing the challenges of blood pressure management in pregnant individuals with HDP. Additionally, we aim to assess whether this intervention can improve short-term maternal and fetal outcomes and potentially mitigate long-term cardiovascular consequences. METHODS: This prospective, randomized, single-center trial will include 580 pregnant participants who meet the HDP criteria or who have a heightened risk of pregnancy-related hypertension due to factors such as multiple pregnancies, obesity, diabetes, or a history of HDP in prior pregnancies leading to preterm birth. Participants will be randomized to either the mHealth intervention group or the standard care group. The primary endpoint is the difference in systolic blood pressure from enrollment to 1 month after childbirth. The secondary endpoints include various blood pressure parameters, obstetric outcomes, body mass index trajectory, step counts, mood assessment, and drug adherence. CONCLUSIONS: This study emphasizes the potential of mHealth interventions, such as the Heart4U application, to improve blood pressure management in pregnant individuals with HDP. By leveraging technology to enhance engagement, communication, and monitoring, this study aims to positively impact maternal, fetal, and postpartum outcomes associated with HDP. This innovative approach demonstrates the potential of personalized technology-driven solutions for managing complex health conditions. TRIAL REGISTRATION: ClinicalTrials.gov NCT05995106. Registered on 16 August 2023.

Metabolomics study to reveal cognitive improvement with treatment of Scrophularia buergeriana.

Population aging around the world is rapidly progressing; as a result, cognitive decline developing into dementia is becoming a social problem. There is no drug that can cure dementia, and though drugs that alleviate the symptoms of dementia have been developed, they also have side effects. Therefore, we conducted a study on improving cognitive function using natural products that have secured safety. We confirmed the effect of an extract of Scrophularia buergeriana on scopolamine-induced cognitive impairment through mouse behavioral experiments, and we observed metabolic changes in the cortex and hippocampus via brain tissue dissection after the behavioral experiment. Mitigating effects of S. buergeriana on cognitive impairment caused by scopolamine were observed in passive avoidance and Morris water maze tests. A metabolic analysis revealed biomarkers related to the alleviating effect of cognitive impairment. Niacinamide, tyrosine, uridine, and valine in the cortex and GABA, choline, creatine, formate, fumarate, hypoxanthine, leucine, myo-inositol, pyroglutamate, and taurine in the hippocampus were identified as biomarker candidates for recovering cognitive impairment. In addition to behavioral experiments, this metabolomics study using specific regions of the brain may be helpful in understanding the effects of cognitive improvement.

Plasma Kallistatin and Progranulin as Predictive Biomarkers of Intraamniotic Inflammation, Microbial Invasion of the Amniotic Cavity, and Composite Neonatal Morbidity/Mortality in Women With Preterm Premature Rupture of Membranes.

PROBLEM: To explore the clinical utility of nine inflammatory immune-, adhesion-, and extracellular matrix-related mediators in the plasma for predicting intraamniotic inflammation and/or microbial invasion of the amniotic cavity (IAI/MIAC) and composite neonatal morbidity and/or mortality (CNMM) in women with preterm premature rupture of membranes (PPROM) when used alone or in combination with conventional blood-, ultrasound-, and clinical-based factors. METHODS OF STUDY: This retrospective cohort comprised 173 singleton pregnant women with PPROM (24 + 0 - 33 + 6 weeks), who underwent amniocentesis. Amniotic fluid was cultured for microorganisms and assayed for IL-6 levels. Plasma levels of AFP, CXCL14, E-selectin, Gal-3BP, kallistatin, progranulin, P-selectin, TGFBI, and VDBP were determined by ELISA. Ultrasonographic cervical length (CL) and neutrophil-to-lymphocyte ratio (NLR) were measured. RESULTS: Multivariate logistic regression analyses revealed significant associations between (i) decreased plasma kallistatin levels and IAI/MIAC and (ii) decreased plasma progranulin levels and increased CNMM risk after adjusting for baseline variables (e.g., gestational age at sampling [or delivery] and parity). Using stepwise regression analysis, noninvasive prediction models for IAI/MIAC and CNMM risks were developed, which included plasma progranulin levels, NLR, CL, and gestational age at sampling, and provided a good prediction of the corresponding endpoints (area under the curve: 0.79 and 0.87, respectively). CONCLUSIONS: Kallistatin and progranulin are potentially valuable plasma biomarkers for predicting IAI/MIAC and CNMM in women with PPROM. Particularly, the combination of these plasma biomarkers with conventional blood-, ultrasound-, and clinical-based factors can significantly support the diagnosis of IAI/MIAC and CNMM.

Identification and characterization of plasma proteins associated with intra-amniotic inflammation and/or infection in women with preterm labor.

This study aimed to identify plasma proteins that could serve as potential biomarkers for microbial invasion of the amniotic cavity (MIAC) or intra-amniotic inflammation (IAI) in women with preterm labor (PTL). A retrospective cohort comprised singleton pregnant women with PTL (24-34 weeks) who underwent amniocentesis. Pooled plasma samples were analyzed by label-free liquid chromatography-tandem mass spectrometry for proteome profiling in a nested case-control study (concomitant MIAC/IAI cases vs. non-MIAC/IAI controls [n = 10 per group]). Eight target proteins associated with MIAC/IAI were further verified by immunoassays in a large cohort (n = 230). Shotgun proteomic analysis revealed 133 differentially expressed proteins (fold change > 1.5, P < 0.05) in the plasma of MIAC/IAI cases. Further quantification confirmed that the levels of AFP were higher and those of kallistatin and TGFBI were lower in the plasma of women with MIAC and that the levels of kallistatin and TGFBI were lower in the plasma of women with IAI than in those without these conditions. The area under the curves of plasma AFP, kallistatin, and TGFBI ranged within 0.67-0.81 with respect to each endpoint. In summary, plasma AFP, kallistatin, and TGFBI may represent valuable non-invasive biomarkers for predicting MIAC or IAI in women with PTL.

Prognosis of remaining fetus in twin pregnancy after demise of one fetus according to its location.

BACKGROUND: To investigate the prognosis of the remaining fetus in twin pregnancy after experiencing one fetal demise in the first trimester according to the location of the demised fetus. METHODS: This was a retrospective study of twin pregnancies with one fetal demise after the first trimester (14 weeks of gestation) delivered between September 2004 and September 2022. The study population was divided into two groups based on the location of the demised fetus as determined by the last recorded ultrasonography results: Group 1 included twin pregnancies where the presenting fetus was demised (n = 36) and Group 2 included twin pregnancies where the non-presenting fetus was demised (n = 44). The obstetric and neonatal outcomes were also reviewed. RESULTS: A total of 80 pregnant women were included. The median gestational age for the diagnosis of fetal demise was 24.1 weeks. The gestational age of the demised fetus was not different between Groups 1 and 2; however, the gestational age of the remaining fetus at delivery was significantly earlier in Group 1 than it was in Group 2 (33.8 vs. 37.3 weeks, P = .004). The rate of preterm birth before 28 weeks was almost five times higher in Group 1 than in Group 2 (22.2% vs. 4.5%, P = .037). Regression analysis demonstrated significant differences between Groups 1 and 2. Respiratory distress syndrome, bronchopulmonary dysplasia, patent ductus arteriosus, retinopathy of prematurity, and jaundice were more common in Group 1 than in Group 2; however, the association was not significant after adjusting for gestational age at delivery. CONCLUSIONS: When the presenting fetus is demised in a twin pregnancy, the remaining fetus tends to be delivered earlier than when the non-presenting fetus is demised.

Comparison of HIV characteristics across 3 datasets: the Korea HIV/AIDS Cohort Study prospective, retrospective, and national reporting system.

OBJECTIVES: The Korea HIV/AIDS Cohort Study has been conducted prospectively for 18 years. However, it faces limitations in representing the entire population of patients with human immunodeficiency virus (HIV) in Korea. To address these limitations and validate the study design, we analyzed characteristics across several HIV datasets. METHODS: We compared epidemiological and clinical characteristics from 3 datasets: the Korea HIV/AIDS Cohort Study (dataset 1, n=1,562), retrospective cohort data (dataset 2, n=2,665), and the national HIV reporting system of the Korea Disease Control and Prevention Agency (KDCA) (dataset 3, n=17,403). RESULTS: The demographic characteristics of age, sex, and age at HIV diagnosis did not differ significantly across datasets. However, dataset 3 contained a higher proportion of patients diagnosed after 2008 (69.5%) than the other datasets. Regarding transmission routes, same-sex contact accounted for a greater proportion of dataset 1 (59.8%) compared to datasets 2 (20.9%) and 3 (32.6%). The proportion of patients with CD4 T-cell counts below 200/mm3 at HIV diagnosis was higher in datasets 1 (39.4%) and 2 (33.3%) compared to dataset 3 (16.3%). Initial HIV viral load measurements were not obtained for dataset 3. CONCLUSIONS: The Korea HIV/AIDS Cohort Study demonstrated representativeness regarding the demographic characteristics of Korean patients. Of the sources, dataset 1 contained the most data on transmission routes. While the KDCA data encompassed all HIV patients, it lacked detailed clinical information. To improve the representativeness of the Korea HIV/AIDS Cohort Study, we propose expanding and revising the cohort design and enrolling more patients who have been recently diagnosed.

Deep learning model for differentiating nasal cavity masses based on nasal endoscopy images.

BACKGROUND: Nasal polyps and inverted papillomas often look similar. Clinically, it is difficult to distinguish the masses by endoscopic examination. Therefore, in this study, we aimed to develop a deep learning algorithm for computer-aided diagnosis of nasal endoscopic images, which may provide a more accurate clinical diagnosis before pathologic confirmation of the nasal masses. METHODS: By performing deep learning of nasal endoscope images, we evaluated our computer-aided diagnosis system's assessment ability for nasal polyps and inverted papilloma and the feasibility of their clinical application. We used curriculum learning pre-trained with patches of nasal endoscopic images and full-sized images. The proposed model's performance for classifying nasal polyps, inverted papilloma, and normal tissue was analyzed using five-fold cross-validation. RESULTS: The normal scores for our best-performing network were 0.9520 for recall, 0.7900 for precision, 0.8648 for F1-score, 0.97 for the area under the curve, and 0.8273 for accuracy. For nasal polyps, the best performance was 0.8162, 0.8496, 0.8409, 0.89, and 0.8273, respectively, for recall, precision, F1-score, area under the curve, and accuracy. Finally, for inverted papilloma, the best performance was obtained for recall, precision, F1-score, area under the curve, and accuracy values of 0.5172, 0.8125, 0.6122, 0.83, and 0.8273, respectively. CONCLUSION: Although there were some misclassifications, the results of gradient-weighted class activation mapping were generally consistent with the areas under the curve determined by otolaryngologists. These results suggest that the convolutional neural network is highly reliable in resolving lesion locations in nasal endoscopic images.

Mume Fructus reduces interleukin-1 beta-induced cartilage degradation via MAPK downregulation in rat articular chondrocytes.

Osteoarthritis is the most prevalent type of degenerative arthritis. It is characterized by persistent pain, joint dysfunction, and physical disability. Pain relief and inflammation control are prioritised during osteoarthritis treatment Mume Fructus (Omae), a fumigated product of the Prunus mume fruit, is used as a traditional medicine in several Asian countries. However, its therapeutic mechanism of action and effects on osteoarthritis and articular chondrocytes remain unknown. In this study, we analyzed the anti-osteoarthritis and articular regenerative effects of Mume Fructus extract on rat chondrocytes. Mume Fructus treatment reduced the interleukin-1ß-induced expression of matrix metalloproteinase 3, matrix metalloproteinase 13, and a disintegrin and metalloproteinase with thrombospondin type 1 motifs 5. Additionally, it enhanced collagen type II alpha 1 chain and aggrecan accumulation in rat chondrocytes. Furthermore, Mume Fructus treatment regulated the inflammatory cytokine levels, mitogen-activated protein kinase phosphorylation, and nuclear factor-kappa B activation. Overall, our results demonstrated that Mume Fructus inhibits osteoarthritis progression by inhibiting the nuclear factor-kappa B and mitogen-activated protein kinase pathways to reduce the levels of inflammatory cytokines and prevent cartilage degeneration. Therefore, Mume Fructus may be a potential therapeutic option for osteoarthritis.

Stroke recurrence and osteoporotic conditions in postmenopausal patients with atherosclerotic ischemic stroke.

Recurrence after stroke is common, and associated with a high mortality rate. Degradation of the elastic tissue in the arterial wall has been shown to aggravate atherosclerosis in blood vessels. Considering that type 1 collagen is present in both bone and vascular smooth muscle cells, we explored whether osteoporotic conditions affect the likelihood of stroke recurrence in postmenopausal women following atherosclerotic ischemic stroke. To determine actual bone mineral density (BMD), the Hounsfield unit values in the frontal skull were evaluated using brain computed tomography (CT) scans taken at admission. A multivariate Cox regression analysis was also performed to examine if osteoporosis could independently predict stroke recurrence in postmenopausal patients with large artery atherosclerosis (LAA) or small vessel occlusion (SVO) stroke. This study included 2130 consecutive patients (both males and females aged 50 and older) with acute LAA or SVO strokes. After adjusting for all covariates, hypothetical osteoporosis was identified as an independent predictor of stroke recurrence in female patients ≥50 years with acute LAA or SVO stroke (hazard ratio, 1.84; 95 % confidence interval, 1.05 to 3.24; p = 0.034). Our findings showed that osteoporosis could potentially affect the recurrence of ischemic stroke in postmenopausal patients with LAA or SVO stroke.

Advancement of Electro-Optical Properties through Changes in the Physicochemical Composition of Hybrid Thin Films by Doping Reduced Graphene Oxide into a Sol-Gel Process Solution.

A hybrid thin film was fabricated by doping graphene oxide into a sol-gel solution containing a mixture of zirconium, bismuth, and indium oxide. The thin film was fabricated using a brush coating process. The graphene oxide doping ratios used were 0, 5, and 15 wt%. During the thin film fabrication process, the produced sol-gel solution generates a contractile force due to the shear stress of the brush bristles, resulting in a microgroove structure. This structure was confirmed through scanning electron microscopy analysis, which revealed the clear presence of rGO. Comparing the electrical properties of a zirconium bismuth indium oxide thin film without graphene oxide doping and a thin film doped with 15 wt% graphene oxide, the electro-optical properties were significantly improved with graphene oxide doping. In general, the threshold voltage decreased by approximately 0.42 V. In addition, bandgap measurements confirmed the improved conductivity characteristics with graphene oxide doping. Since this improvement in electro-optical properties is associated with the reduction process due to graphene oxide doping, X-ray photoelectron spectroscopy analysis was performed to assess the intensity change of each element. Based on these observations, hybrid thin films doped with graphene oxide emerge as promising candidates for next generation thin film.