PURPOSE: The objective of this study was to investigate the prevalence of antenatal depression in pregnant women and its influencing factors. METHODS: With a cross-sectional survey design, a total of 396 pregnant women were recruited from a local obstetrics and gynecology clinic. Measurements included antenatal depression, perceived stress, predictors of depression during pregnancy, and demographic and obstetric characteristics. RESULTS: Mean antenatal depression score was 8.20 (standard deviation=4.95) out of 30, falling into its normal range. However, the prevalence of antenatal depression was 35.9% when cut-point of 9/10 was used. The prevalence of antenatal depression among women in the first trimester was 31.4%. It was slightly increased to 34.9% in the second trimester but significantly increased to 40.5% in the third trimester. In multiple logistic regression analysis, experiencing prenatal anxiety (odds ratio [OR], 4.16), having no job (OR, 2.90), lower self-esteem (OR, 1.62), and higher perceived stress (OR, 1.32) were significant factors influencing antenatal depression. CONCLUSION: Negative feeling such as feeling anxious, lower self-esteem, and higher perceived stress during pregnancy are key factors affecting antenatal depression. Thus, antenatal nursing intervention focusing on pregnant women's feeling upon their job status is necessary to improve their antenatal psychological well-being.
Accidental Falls ; Anxiety ; Cross-Sectional Studies ; Depression ; Female ; Gynecology ; Humans ; Logistic Models ; Maternal Health ; Nursing ; Obstetrics ; Pregnancy ; Pregnancy Trimester, First ; Pregnancy Trimester, Second ; Pregnancy Trimester, Third ; Pregnant Women ; Prevalence ; Reference Values

Pulmonary hypertension is a rare and progressive illness with a devastating prognosis. Promising research efforts have advanced the understanding and recognition of the pathobiology of pulmonary hypertension. Despite remarkable achievements in terms of improving the survival rate, reducing disease progression, and enhancing quality of life, pulmonary arterial hypertension (PAH) is not completely curable. Therefore, an effective treatment strategy is still needed. Recently, many studies of the underlying molecular mechanisms and technological developments have led to new approaches and paradigms for PAH treatment. Management based on stem cells and related paracrine effects, epigenetic drugs and gene therapies has yielded prospective results for PAH treatment in preclinical research. Further trials are ongoing to optimize these important insights into clinical circumstances.

Background: Pierre Robin sequence (PRS) patients have an increased risk of difficult intubation due to anatomical airway abnormalities, and intubation simulation with a three-dimensional (3D) printed airway model before anesthesia may facilitate safe airway management. Case: We describe the case of a 6.5-year-old boy with a history of PRS (a triad of micrognathia, glossoptosis, and airway obstruction), tracheostomy, and subglottic fibrosis who required general anesthesia. Preparation for this potentially difficult intubation included estimation of endotracheal tube size using a 3D printed airway model derived from 3D computed tomography of the airway, which enabled successful endotracheal intubation via video laryngoscopy. Conclusions If general anesthesia is necessary in patients with dysmorphic features such as PRS and there is a history of tracheal pathology, the possibility of difficult intubation should always be considered and simulation of endotracheal intubation using a 3D printed model of the airway can be helpful clinically in such situations.

Background: Pierre Robin sequence (PRS) patients have an increased risk of difficult intubation due to anatomical airway abnormalities, and intubation simulation with a three-dimensional (3D) printed airway model before anesthesia may facilitate safe airway management. Case: We describe the case of a 6.5-year-old boy with a history of PRS (a triad of micrognathia, glossoptosis, and airway obstruction), tracheostomy, and subglottic fibrosis who required general anesthesia. Preparation for this potentially difficult intubation included estimation of endotracheal tube size using a 3D printed airway model derived from 3D computed tomography of the airway, which enabled successful endotracheal intubation via video laryngoscopy. Conclusions If general anesthesia is necessary in patients with dysmorphic features such as PRS and there is a history of tracheal pathology, the possibility of difficult intubation should always be considered and simulation of endotracheal intubation using a 3D printed model of the airway can be helpful clinically in such situations.

Background: To determine the correct size of endotracheal tubes (ETTs) for endotracheal intubation of pediatric patients, new methods have been investigated. Although the three-dimensional (3D) printing technology has been successful in the field of surgery, there are not many studies in the field of anesthesia. The purpose of this study was to evaluate the accuracy of a 3D airway model for prediction of the correct ETT size, and compare the results with a conventional age-based formula in pediatric patients. Methods: Thirty-five pediatric patients under six years of age who were scheduled for congenital heart surgery were enrolled. In the pre-anesthetic period, the patient’s computed tomography (CT) images were converted to Standard Triangle Language (STL) files using the 3D conversion program. A Fused Deposition Modelling (FDM) type 3D printer was used to print 3D airway models from the sub-glottis to the upper carina. ETT size was selected by inserting various sized cuffed-ETTs to a printed 3D airway model. Results: The 3D method selected the correct ETT size in 21 out of 35 pediatric patients (60%), whereas the age-based formula selected the correct ETT size in 9 patients (26%). Conclusions Prediction of the correct size of ETTs using a printed 3D airway model demonstrated better results than the age-based formula. This suggests that the selection of ETT size using a printed 3D airway model may be feasible for helping minimize re-intubation attempts and complications in patients with congenital heart disease and/or those with an abnormal range of growth and development.

Background: To determine the correct size of endotracheal tubes (ETTs) for endotracheal intubation of pediatric patients, new methods have been investigated. Although the three-dimensional (3D) printing technology has been successful in the field of surgery, there are not many studies in the field of anesthesia. The purpose of this study was to evaluate the accuracy of a 3D airway model for prediction of the correct ETT size, and compare the results with a conventional age-based formula in pediatric patients. Methods: Thirty-five pediatric patients under six years of age who were scheduled for congenital heart surgery were enrolled. In the pre-anesthetic period, the patient’s computed tomography (CT) images were converted to Standard Triangle Language (STL) files using the 3D conversion program. A Fused Deposition Modelling (FDM) type 3D printer was used to print 3D airway models from the sub-glottis to the upper carina. ETT size was selected by inserting various sized cuffed-ETTs to a printed 3D airway model. Results: The 3D method selected the correct ETT size in 21 out of 35 pediatric patients (60%), whereas the age-based formula selected the correct ETT size in 9 patients (26%). Conclusions Prediction of the correct size of ETTs using a printed 3D airway model demonstrated better results than the age-based formula. This suggests that the selection of ETT size using a printed 3D airway model may be feasible for helping minimize re-intubation attempts and complications in patients with congenital heart disease and/or those with an abnormal range of growth and development.

Endocannabinoids can affect multiple cellular targets, such as cannabinoid (CB) receptors, transient receptor potential cation channel, subfamily V, member 1 (TRPV1) and peroxisome proliferator-activated receptor gamma (PPARgamma). The stimuli to induce adipocyte differentiation in hBM-MSCs increase the gene transcription of the CB1 receptor, TRPV1 and PPARgamma. In this study, the effects of three endocannabinoids, N-arachidonoyl ethanolamine (AEA), N-arachidonoyl dopamine (NADA) and 2-arachidonoyl glycerol (2-AG), on adipogenesis in hBM-MSCs were evaluated. The adipocyte differentiation was promoted by AEA whereas inhibited by NADA. No change was observed by the treatment of non-cytotoxic concentrations of 2-AG. The difference between AEA and NADA in the regulation of adipogenesis is associated with their effects on PPARgamma transactivation. AEA can directly activate PPARgamma. The effect of AEA on PPARgamma in hBM-MSCs may prevail over that on the CB1 receptor mediated signal transduction, giving rise to the AEA-induced promotion of adipogenesis. In contrast, NADA had no effect on the PPARgamma activity in the PPARgamma transactivation assay. The inhibitory effect of NADA on adipogenesis in hBM-MSCs was reversed not by capsazepine, a TRPV1 antagonist, but by rimonabant, a CB1 antagonist/inverse agonist. Rimonabant by itself promoted adipogenesis in hBM-MSCs, which may be interpreted as the result of the inverse agonism of the CB1 receptor. This result suggests that the constantly active CB1 receptor may contribute to suppress the adipocyte differentiation of hBM-MSCs. Therefore, the selective CB1 agonists that are unable to affect cellular PPARgamma activity inhibit adipogenesis in hBM-MSCs.
Adipocytes* ; Adipogenesis ; Dopamine* ; Endocannabinoids ; Ethanolamine ; Felodipine ; Glycerol ; Humans ; Mesenchymal Stromal Cells* ; PPAR gamma ; Receptor, Cannabinoid, CB1 ; Receptors, Cannabinoid* ; Signal Transduction ; Transcriptional Activation

Background: Preeclampsia (PE) is a hypertensive pregnancy disorder linked to placental dysfunction, often involving pathological lesions like acute atherosis, decidual vasculopathy, accelerated villous maturation, and fibrinoid deposition. However, there is no gold standard for the pathological diagnosis of PE and this limits the ability of clinicians to distinguish between PE and non-PE pregnancies. Recent advances in computational pathology have provided the opportunity to automate pathological analysis for diagnosis, classification, prediction, and prediction of disease progression. In this study, we assessed whether computational pathology could be used to identify PE placentas. Methods: A total of 168 placental whole-slide images (WSIs) of patients from Seoul National University Hospital (comprising 84 PE cases and 84 normal controls) were used for model development and internal validation. For external validation of the model, 76 placental slides (including 38 PE cases and 38 normal controls) were obtained from the Boramae Medical Center (BMC). To establish standard criteria for diagnosing PE and distinguishing it from controls using placental WSIs, patch characteristics and quantification of terminal and intermediate villi were employed. In unsupervised learning, K-means clustering was conducted as a feature obtained through an Auto Encoder to extract the ratio of each cluster for each WSI. For supervised learning, quantitative assessments of the villi were obtained using a U-Net-based segmentation algorithm. The prediction model was developed using an ensemble method and was compared with a clinical feature model developed by using placental size features. Results: Using ensemble modeling, we developed a model to identify PE placentas.The model showed good performance (area under the precision-recall curve [AUPRC], 0.771; 95% confidence interval [CI], 0.752–0.790), with 77.3% of sensitivity and 71.1% of specificity, whereas the clinical feature model showed an AUPRC 0.713 (95% CI, 0.694–0.732) with 55.6% sensitivity and 86.8% specificity. External validation of the predictive model employing the BMC-derived set of placental slides also showed good discrimination (AUPRC, 0.725; 95% CI, 0.720–0.730). Conclusion The proposed computational pathology model demonstrated a strong ability to identify preeclamptic placentas. Computational pathology has the potential to improve the identification of PE placentas.

Background: Preeclampsia (PE) is a hypertensive pregnancy disorder linked to placental dysfunction, often involving pathological lesions like acute atherosis, decidual vasculopathy, accelerated villous maturation, and fibrinoid deposition. However, there is no gold standard for the pathological diagnosis of PE and this limits the ability of clinicians to distinguish between PE and non-PE pregnancies. Recent advances in computational pathology have provided the opportunity to automate pathological analysis for diagnosis, classification, prediction, and prediction of disease progression. In this study, we assessed whether computational pathology could be used to identify PE placentas. Methods: A total of 168 placental whole-slide images (WSIs) of patients from Seoul National University Hospital (comprising 84 PE cases and 84 normal controls) were used for model development and internal validation. For external validation of the model, 76 placental slides (including 38 PE cases and 38 normal controls) were obtained from the Boramae Medical Center (BMC). To establish standard criteria for diagnosing PE and distinguishing it from controls using placental WSIs, patch characteristics and quantification of terminal and intermediate villi were employed. In unsupervised learning, K-means clustering was conducted as a feature obtained through an Auto Encoder to extract the ratio of each cluster for each WSI. For supervised learning, quantitative assessments of the villi were obtained using a U-Net-based segmentation algorithm. The prediction model was developed using an ensemble method and was compared with a clinical feature model developed by using placental size features. Results: Using ensemble modeling, we developed a model to identify PE placentas.The model showed good performance (area under the precision-recall curve [AUPRC], 0.771; 95% confidence interval [CI], 0.752–0.790), with 77.3% of sensitivity and 71.1% of specificity, whereas the clinical feature model showed an AUPRC 0.713 (95% CI, 0.694–0.732) with 55.6% sensitivity and 86.8% specificity. External validation of the predictive model employing the BMC-derived set of placental slides also showed good discrimination (AUPRC, 0.725; 95% CI, 0.720–0.730). Conclusion The proposed computational pathology model demonstrated a strong ability to identify preeclamptic placentas. Computational pathology has the potential to improve the identification of PE placentas.

Background: Preeclampsia (PE) is a hypertensive pregnancy disorder linked to placental dysfunction, often involving pathological lesions like acute atherosis, decidual vasculopathy, accelerated villous maturation, and fibrinoid deposition. However, there is no gold standard for the pathological diagnosis of PE and this limits the ability of clinicians to distinguish between PE and non-PE pregnancies. Recent advances in computational pathology have provided the opportunity to automate pathological analysis for diagnosis, classification, prediction, and prediction of disease progression. In this study, we assessed whether computational pathology could be used to identify PE placentas. Methods: A total of 168 placental whole-slide images (WSIs) of patients from Seoul National University Hospital (comprising 84 PE cases and 84 normal controls) were used for model development and internal validation. For external validation of the model, 76 placental slides (including 38 PE cases and 38 normal controls) were obtained from the Boramae Medical Center (BMC). To establish standard criteria for diagnosing PE and distinguishing it from controls using placental WSIs, patch characteristics and quantification of terminal and intermediate villi were employed. In unsupervised learning, K-means clustering was conducted as a feature obtained through an Auto Encoder to extract the ratio of each cluster for each WSI. For supervised learning, quantitative assessments of the villi were obtained using a U-Net-based segmentation algorithm. The prediction model was developed using an ensemble method and was compared with a clinical feature model developed by using placental size features. Results: Using ensemble modeling, we developed a model to identify PE placentas.The model showed good performance (area under the precision-recall curve [AUPRC], 0.771; 95% confidence interval [CI], 0.752–0.790), with 77.3% of sensitivity and 71.1% of specificity, whereas the clinical feature model showed an AUPRC 0.713 (95% CI, 0.694–0.732) with 55.6% sensitivity and 86.8% specificity. External validation of the predictive model employing the BMC-derived set of placental slides also showed good discrimination (AUPRC, 0.725; 95% CI, 0.720–0.730). Conclusion The proposed computational pathology model demonstrated a strong ability to identify preeclamptic placentas. Computational pathology has the potential to improve the identification of PE placentas.