RESUMEN
Background Anemia is a severe public health problem in India affecting more than half of the population. To reduce its burden on the population, the Government of India under the Anemia Mukt Bharat program has adopted a monitoring strategy for the diagnosis and treatment of anemia. Point-of-care testing (POCT) devices play a pivotal role in testing hemoglobin at a community level where sophisticated laboratory instruments are not available. The majority of the currently available POCT devices are invasive in nature which have their own limitations. A non-invasive method of hemoglobin estimation will address many of the limitations of an invasive POCT instrument, which will further improve people's acceptability for hemoglobin testing. The Non-Invasive Anemia Detection App (NiADA) (Monere AI Private Limited, Kolkata, West Bengal, India), a non-invasive POCT application, uses artificial intelligence (AI) to predict the hemoglobin level from lower eyelid images. This real-time, point-of-care, low-cost solution uses a custom computer vision deep-learning algorithm to determine blood hemoglobin value. Method The study validates an AI-based smartphone application NiADA against laboratory hemoglobin estimation and a widely used point-of-care hemoglobin estimation instrument (HemoCue Hb 301; HemoCue AB, Angelholm, Skane County, Sweden). The study was conducted in a tertiary care hospital in Eastern India and recruited a total of 556 participants. These included 58 pediatric patients, 51 pregnant women, 214 adult females, and 224 adult males. Statistical analysis was performed using Python (Python Software Foundation, Wilmington, Delaware, United States). A p-value of < 0.05 was taken to be significant. Result The mean difference observed between NiADA and laboratory-estimated hemoglobin values came out to be -0.29 g/dL and -0.89 g/dL for adult females and males respectively, and 0.61 g/dL for pregnant women and -0.69g/dL for the pediatric population. The limits of agreement for NiADA were narrow at 2.77 to -2.18 g/dL for adult females, 3.76 to -1.96 g/dL for adult males, 1.89 to -3.29 g/dL for pregnant women, and 3.28 to -2.08 g/dL for the pediatric population. The sensitivity and specificity of the NiADA application against the laboratory estimation method were 75.8% and 53.8% for adult females, 70.0% and 48.3% for adult males, 23.8% and 90% for pregnant females, and 75% and 57% for the pediatric population. Conclusion As a non-invasive application, NiADA's performance is satisfactory and comparable with minimally invasive tools like HemoCue Hb 301 and other POCT devices.
RESUMEN
Rab4 GTPase organizes endosomal sorting essential for maintaining the balance between recycling and degradative pathways. Rab4 localizes to many cargos whose transport in neurons is critical for regulating neurotransmission and neuronal health. Furthermore, elevated Rab4 levels in the CNS are associated with synaptic atrophy and neurodegeneration in Drosophila and humans, respectively. However, how the transport of Rab4-associated vesicles is regulated in neurons remains unknown. Using in vivo time-lapse imaging of Drosophila larvae, we show that activation of insulin signaling via Dilp2 and dInR increases the anterograde velocity, run length, and flux of Rab4 vesicles in the axons. Molecularly, we show that activation of neuronal insulin signaling further activates Vps34, elevates the levels of PI(3)P on Rab4-associated vesicles, recruits Klp98A (a PI(3)P-binding kinesin-3 motor) and activates their anterograde transport. Together, these observations delineate the role of insulin signaling in regulating axonal transport and synaptic homeostasis.