Nashotah House Theological Seminary

Background:Perinatal asphyxia (PA) leads to hypoxemia, hypercarbia, and metabolic acidosis, causing multiorgan dysfunction. Measurement of resistive index (RI) using point-of-care ultrasonography aids in enhancing the prognosis in neonates with PA. The current study was undertaken to evaluate the diagnostic accuracy of RI in assessing hypoxic ischemic encephalopathy (HIE) and its association with therapeutic hypothermia, amplitude-integrated electroencephalography (aEEG) changes, and the Thompson score.Methodology:A hospital-based analytical observational study was conducted with 40 neonates receiving delivery room resuscitation and 40 healthy neonates. RI was measured at 6, 12, 24, and 48 h using Doppler ultrasound.Results:There was a significant correlation between RI and HIE. At 6 h, RI was significantly higher in severe HIE cases as compared to controls (P < 0.001). ROC analysis revealed that the optimal RI cutoff for predicting severe HIE was of 0.87 (75% sensitivity and 100% specificity). RI also showed significant correlation with Thompson scores and aEEG changes. Cases with high and lower RI were more likely to receive therapeutic hypothermia.Conclusions:RI is a reliable biomarker for predicting HIE in neonates requiring delivery room resuscitation. A higher RI is associated with increased HIE severity, abnormal electroencephalogram findings, and the need for therapeutic hypothermia. Early RI measurement may help identify neonates at risk for HIE.

Concordance matrices play a crucial role in input-output analysis, for translating between databases expressed in different sector classifications, or translating many misaligned databases into a common format. These matrices are critical tools in enabling the utilisation of all possible primary data sources for compiling input-output tables, even if those data sources are completely misaligned. Until this date, concordance matrices are constructed manually by interpreting pairwise sector labels, resulting in an often labour-intensive process. In this work, we use artificial intelligence (AI) approaches for the first time to estimate concordance matrices for input-output analysis, offering to significantly reduce the time and labour involved in primary data processing. We show that, when applying deep learning techniques to textual sector labels, AI algorithms are able to grasp intricate linguistic relationships and capture semantic nuances, thus bridging the gap between human language and numerical binary relationships. We use a range of performance evaluation measures and demonstrate the ability to predict a wide range of concordance matrices with up to 85% accuracy.

Quality assessment of manufactured products is vital to ensure performance, safety, and customer satisfaction across industries. Defects in items such as bottle caps, cables, capsules, leather, and metal components can affect functionality and durability. Traditional inspection methods relying on manual visual checks are time-consuming and error-prone. This study proposes an AI-driven framework using the Probabilistic U-Net integrated with a Conditional Variational Autoenco der (CVAE) for automated defect detection. The model introduces stochastic latent variables to generate multiple plausible segmentation maps, enhancing accuracy under ambiguous or noisy conditions. Using the MVTec Anomaly Detection dataset, which includes defects such as scratches and discoloration, the system applies preprocessing steps including resizing, normalization, and data augmentation to enhance the robustness and consistency of the input data. A hybrid loss combining cross-entropy and Kullback-Leibler divergence improves segmentation precision and latent space alignment. Experimental results confirm robust and reliable defect detection across diverse product categories, demonstrating the model's potential for automated manufacturing quality assurance.

Although the widespread adoption of tubectomies has contributed to reducing fertility rates in India, it has also resulted in significant reproductive health challenges. This paper investigates two such interrelated issues: inadequate birth spacing and the decreasing age at which women undergo tubectomies. Using qualitative research techniques, the paper shows how Indian women are reluctant to use reversible modern contraceptives as they disrupt regular menstruation, which further causes them to feel anxious about their fertility status. Coupled with a lack of willingness among men to use condoms, women instead opt for natural contraceptive methods, often leading to unplanned pregnancies and low inter-pregnancy intervals. As a final reprieve from frequent birthing, women who have their first child in their adolescence decide to undergo a tubectomy in their early 20s. Ultimately, this paper argues for reorienting reproductive health policy in India away from fertility control and toward prioritizing reproductive health and rights-based issues.

Trauma and social disadvantage are strongly associated with higher rates of chronic disease, partly driven by modifiable risk factors such as physical inactivity and poor diet. Despite strong evidence supporting exercise and dietary interventions for both physical and mental health, access to allied health professionals-particularly exercise physiologists, physiotherapists and dietitians-remains profoundly inequitable. Current prevention efforts predominantly reach individuals with stable living conditions and sufficient resources, ultimately privileging the privileged and entrenching health disparities. To close these gaps, these workforces must be reoriented: embedded within trusted community settings and delivered earlier in the care pathway, in ways that are trauma-informed and responsive to social context.