Dynamics of the airway microbiome in response to exposure to particulate matter 2.5 in patients with chronic obstructive pulmonary disease.

BACKGROUND: Particulate matter (PM) and air pollution have been suggested to be associated with chronic obstructive pulmonary disease (COPD), contributing significantly to global respiratory disease-related mortality. This study aimed to investigate whether seasonal exposure to PM influences dysbiosis in the respiratory microbiota of patients with COPD. METHODS: Sputum samples were collected four times over 1 year from 102 patients with COPD, and 16S rRNA sequencing was performed. The dynamics of the airway microbiota were analyzed depending on PM exposure levels and season. RESULTS: The PM-low exposure group had higher α-diversity compared to the PM-high exposure group, particularly noted in spring. Some bacterial groups, including seven species such as Treponema socranskii, were more abundant in the low exposure group. Additionally, the bacterial community structure in summer significantly differed from that in other seasons, with significantly increased α-diversity in this season. The difference in the airway microbiome due to PM exposure was prominent in patients with moderate COPD. CONCLUSIONS: PM exposure may influence changes in the sputum microbiome depending on exposure levels and seasonal variations. Our results suggest that airway microbiomes could vary with PM exposure according to seasonal trends.

Rescue Cerclage in Women With Acute Cervical Insufficiency and Intra-Amniotic Inflammation: A Retrospective Cohort Study.

BACKGROUND: To assess the effectiveness of rescue cerclage concerning pregnancy and neonatal outcomes in women with acute cervical insufficiency (CI) complicated with intra-amniotic inflammation (IAI) compared with those managed expectantly. METHODS: This retrospective cohort study included 87 consecutive singleton pregnant women (17-25 weeks) with acute CI who underwent amniocentesis to assess IAI. Amniotic fluid (AF) samples were assayed for interleukin-6 to define IAI (≥ 2.6 ng/mL). Primary and secondary outcomes were assessed in a subset of CI patients with IAI. The primary outcome measures were spontaneous preterm birth (SPTB) at < 28 and < 34 weeks, and the secondary outcomes were interval from sampling to delivery, neonatal survival, neonatal birth weight, and histologic and clinical chorioamnionitis. Macrolide antibiotics were prescribed depending on the type of microorganism isolated from the AF, clinically suspected IAI, and the discretion of the attending clinician. RESULTS: IAI was identified in 65.5% (57/87) of patients with CI, of whom 73.6% (42/57) were treated with macrolide antibiotics. Among the CI patients with IAI (n = 57), 40 underwent rescue cerclage and 17 were expectantly managed. The rates of SPTBs at < 28 and < 34 weeks were significantly lower and the latency period was significantly longer in the cerclage group than in the group that was managed expectantly. The median birth weight and neonatal survival rate were significantly higher in the cerclage group than in the group that was managed expectantly. However, the rates of histologic and clinical chorioamnionitis did not differ between the groups. Multivariable analyses revealed that rescue cerclage placement and administration of macrolide antibiotics were significantly associated with a decrease in SPTBs at < 28 and < 34 weeks, prolonged gestational latency, and increased likelihood of neonatal survival, after adjusting for possible confounding parameters; however, macrolide antibiotic administration did not reach statistical significance with respect to SPTB at < 34 weeks and neonatal survival (P = 0.076 and 0.063, respectively). CONCLUSION: Rescue cerclage along with macrolide antibiotic treatment may positively impact pregnancy and neonatal outcomes in women with CI complicated by IAI, compared with expectant management. These findings suggest the benefit of cerclage placement even in patients with CI complicated by IAI.

Effects of Leaf Extracts from Genetic Resource of Capsicum spp. on Neuroprotection and Anti-Neuroinflammation in HT22 and in BV2 Cells.

To develop functional varieties of Capsicum spp. leaves, 40 genetic resources were collected and extracted with 30% aqueous-fermented ethanol. We investigated the protective effects of extracts from 40 genetic resources of Capsicum spp. on glutamate-induced HT22 and LPS-induced BV2 cells. The results showed that the five extracts exhibited cell-protective activities. We also investigated the anti-inflammatory effects of these five extracts on LPS-induced BV2 cell neuroinflammation and found that 23OM18 exhibited superior anti-inflammatory effects. We further investigated the protective activity and anti-inflammatory mechanisms of 23OM18 in these two cell models. In addition, the profiles of 16 metabolites were compared between the representative accessions and among the five genetic resources using ultra-high-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS). The results showed that 23OM18 protected HT22 cells by inhibiting reactive oxygen species generation and regulating the MAPK-JNK signaling pathway, thereby reducing LPS-induced BV2 cell neuroinflammation by regulating the NF-κB and MAPK signaling pathways. Based on these results, 23OM18 has the potential to be developed as a functional food for the treatment of neurodegenerative diseases.

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.

Enhanced WGAN Model for Diagnosing Laryngeal Carcinoma.

This study modifies the U-Net architecture for pixel-based segmentation to automatically classify lesions in laryngeal endoscopic images. The advanced U-Net incorporates five-level encoders and decoders, with an autoencoder layer to derive latent vectors representing the image characteristics. To enhance performance, a WGAN was implemented to address common issues such as mode collapse and gradient explosion found in traditional GANs. The dataset consisted of 8171 images labeled with polygons in seven colors. Evaluation metrics, including the F1 score and intersection over union, revealed that benign tumors were detected with lower accuracy compared to other lesions, while cancers achieved notably high accuracy. The model demonstrated an overall accuracy rate of 99%. This enhanced U-Net model shows strong potential in improving cancer detection, reducing diagnostic errors, and enhancing early diagnosis in medical applications.

Renal microRNA-144-3p is associated with transforming growth factor-ß1-induced oxidative stress and fibrosis by suppressing the NRF2 pathway in hypertensive diabetic kidney disease.

Chronic kidney disease (CKD) is a global health problem characterized by progressive renal fibrosis and excessive extracellular matrix deposition. Oxidative stress and epigenetic regulation, particularly through microRNAs (miRNAs), play crucial roles in the pathogenesis of CKD. In this study, we investigated the role of urinary miR-144-3p, which is upregulated in rats with CKD induced by diabetes and hypertension, in renal fibrosis progression, particularly its regulation of the nuclear factor erythroid-2-related factor 2 (NRF2) pathway. Our findings revealed elevated miR-144-3p levels and reduced NRF2 and target gene levels in kidney tissues of streptozotocin-treated spontaneously hypertensive rats. In vitro experiments demonstrated that miR-144-3p directly binds to the 3'-untranslated region of nrf2, suppressing the NRF2 pathway in renal tubular epithelial cells. Additionally, the profibrogenic factor transforming growth factor (TGF)-ß1 increased miR-144-3p expression. TGF-ß1-induced NRF2 suppression and reactive oxygen species elevation were found to be mediated through miR-144-3p upregulation. In vivo, cilostazol, an antiplatelet drug with an NRF2-activating effect, ameliorated renal injury in diabetic hypertensive rats by decreasing TGF-ß1 and miR-144-3p levels while increasing NRF2 and its target gene levels in the kidneys. These findings highlight the potential therapeutic value of targeting the miR-144-3p/NRF2 pathway to attenuate CKD progression in hypertensive diabetic conditions.

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.

Enhanced production of turanose using a mutant amylosucrase from Bifidobacterium thermophilum immobilized on silica carriers.

Turanose (α-d-glucopyranosyl-(1→3)-α-d-fructose) is a rare disaccharide that is a potential low-calorigenic sweetener. This novel sucrose isomer has been efficiently synthesized by the amylosucrase from Bifidobacterium thermophilum (BtAS). In this study, we aimed to enhance turanose biosynthesis by designing a BtAS variant (BtAS-G374S) with improved thermal stability. The BtAS variant was immobilized on porosity-controlled silica carrier, and its enzymatic properties were thoroughly investigated. Using response surface methodology with central composite design, optimal immobilization conditions were determined to significantly boost the biosynthetic efficiency. The BtAS-G374S showed 1.6-fold higher specific activity (2.2 U/mg) than the wild-type enzyme (1.4 U/mg). Additionally, the turanose production yield of BtAS-G374S was significantly enhanced, reaching 65 %, compared to 25 % for the wild-type enzyme when reacting with 2 M sucrose. Immobilization of BtAS-G374S was optimized on controlled porosity carrier (CPC) silica carrier using Response Surface Methodology, achieving an enzyme activity of 7.89 U and an immobilization efficiency of 68.98 % under optimal conditions. Immobilization of BtAS-G374S on CPC silica carriers enhanced its pH and thermal stability. The immobilized enzyme showed a half-life of 50.23 h at 55 °C and retained 68 % of its initial biosynthetic yield after 10 reuses. These properties suggest its potential for efficient industrial turanose production.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.