Early-Term Neonates Demonstrate a Higher Likelihood of Requiring Phototherapy Compared to Those Born Full-Term.

Early-term neonates (with a gestational age (GA) of 37 and 0/7 weeks to 38 and 6/7 weeks) face higher morbidities, including respiratory and neurodevelopmental issues, than full-term (39 and 0/7 weeks to 40 and 6/7 weeks) infants. This study explores whether hyperbilirubinemia necessitating phototherapy also differs between these groups. A retrospective study was conducted on neonates born from January 2021-June 2022, excluding those with specific conditions. Evaluated factors included GA, birth weight, bilirubin levels, glucose-6-phosphate dehydrogenase (G6PD) deficiency, and feeding type, with phototherapy given as per AAP guidelines. Of 1085 neonates, 356 met the criteria. When stratifying the neonates based on the need for phototherapy, a higher proportion of early-term neonates required phototherapy compared to full-term (p < 0.05). After factoring in various risks (GA; birth weight; gender; feeding type; G6PD deficiency; transcutaneous bilirubin levels at 24 h and 24-48 h postpartum; maternal diabetes; and the presence of caput succedaneum or cephalohematoma), early-term neonates were more likely to need phototherapy than full-term babies (OR: 2.15, 95% CI: 1.21 to 3.80). The optimal cut-off for transcutaneous bilirubin levels 24-48 h postpartum that were used to predict phototherapy need was 9.85 mg/dl. In conclusion, early-term neonates are at a greater risk for developing jaundice and requiring phototherapy than full-term neonates. Monitoring bilirubin 24-48 h postpartum enhances early prediction and intervention.

Explainable machine learning model for predicting skeletal muscle loss during surgery and adjuvant chemotherapy in ovarian cancer.

BACKGROUND: Skeletal muscle loss during treatment is associated with poor survival outcomes in patients with ovarian cancer. Although changes in muscle mass can be assessed on computed tomography (CT) scans, this labour-intensive process can impair its utility in clinical practice. This study aimed to develop a machine learning (ML) model to predict muscle loss based on clinical data and to interpret the ML model by applying SHapley Additive exPlanations (SHAP) method. METHODS: This study included the data of 617 patients with ovarian cancer who underwent primary debulking surgery and platinum-based chemotherapy at a tertiary centre between 2010 and 2019. The cohort data were split into training and test sets based on the treatment time. External validation was performed using 140 patients from a different tertiary centre. The skeletal muscle index (SMI) was measured from pre- and post-treatment CT scans, and a decrease in SMI ≥ 5% was defined as muscle loss. We evaluated five ML models to predict muscle loss, and their performance was determined using the area under the receiver operating characteristic curve (AUC) and F1 score. The features for analysis included demographic and disease-specific characteristics and relative changes in body mass index (BMI), albumin, neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR). The SHAP method was applied to determine the importance of the features and interpret the ML models. RESULTS: The median (inter-quartile range) age of the cohort was 52 (46-59) years. After treatment, 204 patients (33.1%) experienced muscle loss in the training and test datasets, while 44 (31.4%) patients experienced muscle loss in the external validation dataset. Among the five evaluated ML models, the random forest model achieved the highest AUC (0.856, 95% confidence interval: 0.854-0.859) and F1 score (0.726, 95% confidence interval: 0.722-0.730). In the external validation, the random forest model outperformed all ML models with an AUC of 0.874 and an F1 score of 0.741. The results of the SHAP method showed that the albumin change, BMI change, malignant ascites, NLR change, and PLR change were the most important factors in muscle loss. At the patient level, SHAP force plots demonstrated insightful interpretation of our random forest model to predict muscle loss. CONCLUSIONS: Explainable ML model was developed using clinical data to identify patients experiencing muscle loss after treatment and provide information of feature contribution. Using the SHAP method, clinicians may better understand the contributors to muscle loss and target interventions to counteract muscle loss.

Association of Body Composition with Type 2 Diabetes: A Retrospective Chart Review Study.

This study analyzed the body composition of individuals with type 2 diabetes (T2DM). In this retrospective chart review study, body composition was measured through multifrequency bioelectrical impedance analysis (InBody 770). Body composition assessments were conducted in individuals with T2DM, who were aged ≥18 years. The parameters included body mass index (BMI), body fat mass (BFM), fat-free mass (FFM), visceral fat area, percent body fat (PBF), appendicular skeletal muscle mass (ASM), and skeletal muscle index (SMI). One-way ANOVA and independent t-tests were used to calculate differences in body composition distribution by age and sex. A total of 2404 participants were recruited. The prevalence of overall low muscle mass and sarcopenic obesity was 28.0% and 18.7%, respectively, which increased with age. The overall prevalence of obesity when PBF was used was 71.5%, which was higher than that when BMI was applied (32.4%). The normal BMI group exhibited a prevalence of low muscle mass of 55.6% and sarcopenic obesity of 34.8%. For both men and women, bodyweight, BFM, FFM, ASM, and SMI all decreased with age. The prevalence of low muscle mass and sarcopenic obesity was high in older adults and people with normal BMI. Using BMI to assess obesity and determine insufficient muscle mass underestimates the prevalence of obesity and neglects the problems of sarcopenia and high body fat in people with normal BMI.

Andrographolide inhibits hypoxia-induced hypoxia-inducible factor 1α and endothelin 1 expression through the heme oxygenase 1/CO/cGMP/MKP-5 pathways in EA.hy926 cells.

Andrographolide is a potent anti-inflammatory agent found in Andrographis paniculata. Endothelin 1 (ET-1) is an endothelium-derived vasoconstrictor with pro-inflammatory properties secreted in response to hypoxia. Mitogen-activated protein kinase phosphatase 5 (MKP-5) is a dual-specificity phosphatase that dephosphorylates threonine and tyrosine residues of MAPKs. We showed previously that hypoxia-induced HIF-1α expression and ET-1 secretion are dependent on p38 MAPK in EA.hy926 cells. Here, we investigate what role MKP-5 plays in andrographolide's inhibition of hypoxia-induced expression of HIF-1α and ET-1. Hypoxic conditions were created using the hypoxia-mimetic agent CoCl2 . Andrographolide enhanced HO-1 and MKP-5 expression and cellular cGMP content in addition to inhibiting hypoxia-induced ROS generation. Concomitantly, the HO-1 byproduct CO and the cGMP analogue 8-bromoguanosine 3',5'-cyclic monophosphate (8-Br-cGMP) increased MKP-5 expression, and pretreatment with CO and 8-Br-cGMP inhibited hypoxia-induced HIF-1α and ET-1 expression. Transfection of HO-1 siRNA or pretreatment with the HO-1 inhibitor ZnPP-9 or 1H-[1,2,4]oxadiazolo[4,3-a]quinoxalin-1-one, a specific inhibitor of soluble guanylate cyclase, reduced andrographolide-induced MKP-5 expression. Moreover, silencing MKP-5 or treatment with the phosphatase inhibitor vanadate abrogated andrographolide's suppressing hypoxia-induced p38 MAPK activation and HIF-1α expression. The inhibition of hypoxia-induced HIF-1α and ET-1 expression by andrographolide is likely associated with HO-1/CO/cGMP/MKP-5 pathways, which is involved in inhibiting hypoxia-induced p38 MAPK activation.

Andrographolide inhibits hypoxia-induced HIF-1α-driven endothelin 1 secretion by activating Nrf2/HO-1 and promoting the expression of prolyl hydroxylases 2/3 in human endothelial cells.

Andrographolide, the main bioactive component of the medicinal plant Andrographis paniculata, has been shown to possess potent anti-inflammatory activity. Endothelin 1 (ET-1), a potent vasoconstrictor peptide produced by vascular endothelial cells, displays proinflammatory property. Hypoxia-inducible factor 1α (HIF-1α), the regulatory member of the transcription factor heterodimer HIF-1α/ß, is one of the most important molecules that responds to hypoxia. Changes in cellular HIF-1α protein level are the result of altered gene transcription and protein stability, with the latter being dependent on prolyl hydroxylases (PHDs). In this study, inhibition of pro-inflammatory ET-1 expression and changes of HIF-1α gene transcription and protein stability under hypoxia by andrographolide in EA.hy926 endothelial-like cells were investigated. Hypoxic conditions were created using the hypoxia-mimetic agent CoCl2. We found that hypoxia stimulated the production of reactive oxygen species (ROS), the expression of HIF-1α mRNA and protein, and the expression and secretion of ET-1. These effects, however, were attenuated by co-exposure to andrographolide, bilirubin, and RuCO. Silencing Nrf2 and heme oxygenase 1 (HO-1) reversed the inhibitory effects of andrographolide on hypxoia-induced HIF-1α mRNA and protein expression. Moreover, andrographolide increased the expression of prolyl hydroxylases (PHD) 2/3, which hydroxylate HIF-1α and promotes HIF-1α proteasome degradation, with an increase in HIF-1α hydroxylation was noted under hypoxia. Inhibition of p38 MAPK abrogated the hypoxia-induced increases in HIF-1α mRNA and protein expression as well as ET-1 mRNA expression and secretion. Taken together, these results suggest that andrographolide suppresses hypoxia-induced pro-inflammatory ET-1 expression by activating Nrf2/HO-1, inhibiting p38 MAPK signaling, and promoting PHD2/3 expression. © 2016 Wiley Periodicals, Inc. Environ Toxicol 32: 918-930, 2017.

Photoplethysmography variability as an alternative approach to obtain heart rate variability information in chronic pain patient.

Heart rate variability (HRV) is a well-known method for the assessment of autonomic nervous function of the heart. Previous study suggested that pulse rate variability (PRV) determined by photoplethysmography could be used instead of HRV to more simply assess autonomic nervous function. However, most research studies included healthy subjects. Thus, the aim of this study was to investigate the feasibility for PRV as a surrogate index for patients with chronic pain. This study investigated the correlation coefficient (by Pearson correlation) and agreement (by Bland-Altman analysis) between PRV and HRV in chronic pain patients in the clinical setting. The results showed high significant correlations (p < 0.001, r > 0.86) between all the HRV and PRV parameters and good agreements (ratio < 0.1) between the parameters in terms of HR, mean RR, VLF, LF, nLF, nHF, and SD1/SD2. Our study suggests that HRV can also be reliably estimated using the photoplethysmography-based PP interval in elderly patients with chronic pain.