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Lysine potentiates insulin secretion via AASS-dependent catabolism and regulation of GABA content and signaling
No full textLysine is an essential amino acid with insulinotropic effects in humans. In vitro, it enhances glucose-stimulated insulin secretion (GSIS) in β-cell lines and rodent islets. While lysine is thought to act via membrane depolarization similar to arginine, the role of its intracellular metabolism in β-cell function remains unexplored. Here, we show that lysine acutely potentiates GSIS and that genes encoding enzymes in the lysine degradation pathway, including AminoAdipate-Semialdehyde Synthase (AASS), a key mitochondrial enzyme catalysing the first two steps of lysine catabolism, were present in human pancreatic islets and INS1 832/13 β cells. Some of these genes including AASS, ALDH7A1, DHTKD1, and HADH, were downregulated in pancreatic islets from type 2 diabetes (T2D) versus non-diabetic (ND) donors. Silencing AASS in human islets and INS1 832/13 β cells led to reduced GSIS. Integrated transcriptomics and metabolomics revealed altered expression of GABA metabolism genes, reduced GABA content and accumulation of glutamate in Aass-KD cells. Mitochondrial TCA cycle and OXPHOS function was impaired, evidenced by decreased ATP/ADP ratio, diminished glucose-stimulated mitochondrial respiration, and elevated lactate/pyruvate ratio. Cytosolic calcium responses to glucose and GABA were also disrupted. Pharmacological analyses demonstrated that inhibition of GABA synthesis or degradation did not account for the reduced GSIS, but providing substrates and activation of GDH partially restored insulin secretion, pointing to a diminished glutamate supply as a contributing factor. Remarkably, exogenous GABA restored insulin secretion in β cells and human islets with suppressed AASS-dependent lysine catabolism, supporting a role for GABA as both a metabolic substrate and signaling effector. Together, these findings identify AASS-mediated lysine catabolism as a critical regulator of β-cell metabolic integrity, linking impaired lysine metabolism to GABA depletion, mitochondrial dysfunction, and secretory failure in T2D islets. They also underscore the nutritional importance of essential amino acids such as lysine in sustaining GSIS and glucose homeostasis, and support therapeutic strategies aimed at restoring lysine catabolism or GABA/glutamate balance to maintain β-cell function.</p
Humans can accurately categorise negative but not positive emotional facial expressions in horses
No full textRecognising emotional facial expressions plays a key role in communication, both within and between species. Many non-human animals, including horses, discriminate and react to emotional human facial expressions. This raises the question of whether humans also consider some animals’ facial expressions when determining their emotional states. To address this, the present study aimed to assess human ability to categorise horses’ facial expressions according to their valence (positive/negative) and arousal (high/low), across eight distinct situations likely to elicit emotions (e.g., social isolation, going to a food bucket), and whether prior experience referring to the level of contact with horses improved this ability. An online task, in the form of a quiz, was conducted in which human participants were asked to categorise photographs of horses’ facial expressions based on perceived emotional valence and arousal. Results showed that participants (n = 930) performed well in the valence categorisation of expressions displayed in situations likely to elicit negative emotions (e.g., social isolation: 90 % of correct categorisation, sudden stimulus: 91 %), and that experience with horses improved performance in these cases. However, participants had greater difficulty in categorising the valence of facial expressions emitted in positive contexts (e.g., grooming: 42 %, going to a food bucket: 59 %), and experience, based on their level of contact, did not consistently enhance performance. Low arousal context (resting in the sun: 93 %) was well recognised, while categorisation accuracy for high arousal contexts (e.g., going to a food bucket: 55 %, sudden stimulus: 96 %) was more variable. These findings suggest that humans have a limited ability to recognise horses’ emotional states based on facial expressions, particularly for positive emotions, highlighting the need for increased awareness and caution when interpreting them. Accurate recognition of animals’ emotional facial expressions is therefore crucial, as it contributes directly to the broader assessment of their welfare.</p
Muslim Women and Pious Learning in Denmark
No full textBased on ethnographic fieldwork, this book investigates how and why Danish Muslim women engage as teachers and students in Islamic educational activities. It does so by focusing on the learning trajectories, knowledge disseminating activities, and class interactions of the women, showing that they involve themselves in a variety of activities to stay continuously engaged, and that this is a way of becoming pious. The book makes evident that this becoming is dependent on the embeddedness of the individual in a web of relations to both this- and otherworldly others. As such, the book promotes a relational understanding of piety formation and religious engagement that are informative to studies of religious life beyond Islam
The combined use of cervical ultrasound and deep learning improves the detection of patients at risk for spontaneous preterm delivery
No full textBackground Preterm birth is the leading cause of neonatal mortality and morbidity. While ultrasound-based cervical length measurement is the current standard for predicting preterm birth, its performance is limited. Artificial intelligence has shown potential in ultrasound analysis, yet few small-scale studies have evaluated its use for predicting preterm birth. Objective To develop and validate an artificial intelligence model for spontaneous preterm birth prediction from cervical ultrasound images and compare its performance to cervical length. Study design In this multicenter study, we developed a deep learning–based artificial intelligence model using data from women who underwent cervical ultrasound scans as part of antenatal care between 2008 and 2018 in Denmark. Indications for ultrasound were not systematically recorded, and scans were likely performed due to risk factors or symptoms of preterm labor. We compared the performance of the artificial intelligence model with cervical length measurement for spontaneous preterm birth prediction by assessing the area under the curve, sensitivity, specificity, and likelihood ratios. Subgroup analyses evaluated model performance across baseline characteristics, and saliency heat maps identified anatomical features that influenced artificial intelligence model predictions the most. Results The final dataset included 4224 pregnancies and 7862 cervical ultrasound images, with 50% resulting in spontaneous preterm birth. The artificial intelligence model surpassed cervical length for predicting spontaneous preterm birth before 37 weeks with a sensitivity of 0.51 (95% confidence interval, 0.50–0.53) versus 0.41 (0.39–0.42) at a fixed specificity at 0.85, P <0.001 and a higher area under the curve of 0.75 (0.74–0.76) versus 0.67 (0.66–0.68), P <0.001. For identifying late preterm births at 34 to 37 weeks, the artificial intelligence model had 36.6% higher sensitivity than cervical length (0.47 versus 0.34, P <0.001). The artificial intelligence model achieved higher area under the curves across all subgroups, especially at earlier gestational ages. Saliency heat maps indicated that in 70% of preterm birth cases, the artificial intelligence model focused on the inner lining of the lower uterine segment, suggesting it incorporates more data than cervical length alone. Conclusion To our knowledge, this is the first large-scale multicenter study demonstrating that artificial intelligence is more sensitive than cervical length measurement in identifying spontaneous preterm births across multiple characteristics, 19 hospital sites, and different ultrasound machines. The artificial intelligence model performs particularly well at earlier gestational ages, enabling more timely prophylactic interventions.</p
Poor appetite and growth differentiation factor-15 as predictors of insufficient energy and protein intake during and after hospitalization in older adults with acute medical illness:Exploratory analysis of a randomized controlled trial
No full textBACKGROUND & AIMS: Poor appetite is a key contributor to malnutrition in older adults, partly due to reduced dietary intake. While nutritional deficits are well recognized, the biological mechanisms driving poor appetite remain incompletely understood. Growth Differentiation Factor-15 (GDF-15) and the Simplified Nutritional Appetite Questionnaire (SNAQ) may help identify patients at risk of insufficient intake. This study examines the association between GDF-15 and insufficient energy and protein intake and evaluates the predictive performance of GDF-15 and SNAQ - individually and in combination - to identify insufficient energy and protein intake (<75 % and <100 % of estimated requirements, respectively) in acutely admitted older adults.METHODS: This exploratory study included 130 older adults (≥65 years) with or at risk of malnutrition, admitted for acute medical illness and assessed at baseline, and 8 and 16 weeks post-discharge (FW8 and FW16). GDF-15 plasma concentrations were measured from blood samples, SNAQ scores from validated questionnaire, and energy and protein intake were evaluated through 3-day dietary records. Associations were analyzed using regression models and predictive performance was evaluated using Receiver Operating Characteristic analysis.RESULTS: A doubling of GDF-15 showed a 6.73 % (-13.98-0.51) and 5.07 % (-12.18-2.05) lower baseline energy and protein intake, and results decreased after discharge. Using predefined cut-offs of ≥1500 pg/mL for GDF-15 and ≤14 for SNAQ, the highest positive predictive values (PPVs) were 86 (95 % CI: 0.83-0.89) and 90 (0.79-1.00) at baseline and FW8 for insufficient protein intake, respectively, with corresponding sensitivities of 84 (0.75-0.92) and 60 (0.44-0.74). For estimated cut-offs of 2095 pg/mL for GDF-15 at baseline and 18.5 for SNAQ at FW8, the highest PPVs were 89 (0.84-0.94) and 87 (0.86-0.87), with sensitivities of 73 (0.62-0.84) and 99 (0.96-1.00), both for insufficient protein intake, respectively. Combining GDF-15 and SNAQ yielded improved PPVs at the expense of reduced sensitivity in most models. All results were non-significant.CONCLUSION: Higher GDF-15 levels showed a non-significant trend toward lower baseline energy and protein intake. GDF-15 and SNAQ offer limited ability to identify insufficient energy and protein intake, based on predictive performance. These exploratory findings should therefore be interpreted cautiously. Larger studies are needed to validate and further refine the use of GDF-15 and SNAQ in clinical settings.</p
Echoes of colonial disruption: historicizing vulnerability, raiding, and violence in Northwestern Kenya
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A Syd and RUFY dynein adaptor complex mediates axonal circulation of dense core vesicles
No full textNeuropeptide-containing dense core vesicles (DCVs) generated in neuronal somata are circulated in axons to supply distal release sites, depending on kinesin-1, kinesin-3, and dynein, but how the motors are recruited remains unclear. Here we use proximity proteomics in the living Drosophila nervous system to identify the protein complex responsible for recruitment of kinesin-1 and dynein on DCVs. We find that the dynein and kinesin-1 adaptor Sunday driver (Syd/dJIP3/4) interact with the DCV-located GTPase Rab2 and also bind the Arl8 effector RUFY. Disrupting Rab2, Syd, RUFY, the Arl8 activator BORC, or dynein impedes retrograde DCV flux and induces axonal accumulation of immobile DCVs. Our data suggest that dynein is recruited and activated by a Syd/RUFY complex anchored to DCVs by Rab2 and Arl8. Rab2 loss but not disruption of Syd, RUFY, or dynein causes missorting of DCV membrane proteins into vesicle aggregates in motor neuron somata, suggesting that Rab2 employs separate effectors in DCV biogenesis and motility.</p
