Faculty Bibliography

PURPOSE OF REVIEW/OBJECTIVE:The 2023 CONVINCE trial demonstrated improved survival with high-dose hemodiafiltration (HDF), prompting discussions about widespread adoption. However, this clinical advancement occurs amid growing awareness of healthcare's environmental impact, particularly dialysis treatments that consume extensive water and energy resources. This review examines the environmental implications of HDF adoption, synthesizing recent evidence on resource consumption and emerging sustainability solutions in the context of the climate crisis facing nephrology. RECENT FINDINGS/RESULTS:Life cycle assessments indicate HDF has a carbon footprint 30-40% higher than conventional hemodialysis, consuming an additional 10 300 L of water per patient annually. However, recent technological innovations show promise: expanded hemodialysis (HDx) using medium cut-off membranes reduces water usage by >20% and energy consumption by >30% compared to HDF while potentially achieving similar clinical outcomes. Water conservation technologies, including reverse osmosis, reject water reuse and reduced dialysate flow protocols, can decrease environmental impact by 30-50% without any difference in patient outcomes. SUMMARY/CONCLUSIONS:The adoption of HDF represents a critical test case for sustainable healthcare innovation. While the potential benefits should not be ignored, technology is not static and, if confirmed, additional sustainability work and comprehensive policy frameworks integrating environmental impact assessments into technology evaluation are urgently needed. The nephrology community must balance clinical excellence with planetary stewardship through technological innovation, resource optimization, and evidence-based environmental guidelines that benefit, not compromise, patient care.

PURPOSE/OBJECTIVE: METHODS:Like many other MRI simulators, ours discretizes magnetic fields in space. However, we extend the MR signal simulation at each grid point from the 0th-order approximation, which assumes piecewise constant fields, to a 1st-order approximation, which assumes piecewise linear fields. We solve the signal equation by analytically integrating over each grid cube, assuming linear field variations, and then summing over all cubes. We provide analytical integrals for several pulse sequences. RESULTS:The 1st-order approximation captures strongly varying fields and associated intravoxel dephasing more accurately, avoiding severe "ringing" artifacts present in the usual 0th-order simulations. This enables simulations on a much coarser grid, facilitating computational feasibility. CONCLUSION/CONCLUSIONS:The first-order simulator enables the evaluation of unconventional scanner designs with strongly varying magnetic fields.

OBJECTIVES/OBJECTIVE:Despite its high negative predictive value (NPV) for clinically significant prostate cancer (csPCa), MRI suffers from a substantial number of false positives, especially for intermediate-risk cases. In this work, we determine whether a deep learning model trained with PI-RADS-guided representation learning can disambiguate the PI-RADS 3 classification, detect csPCa from bi-parametric prostate MR images, and avoid unnecessary benign biopsies. MATERIALS AND METHODS/METHODS:This study included 28,263 MR examinations and radiology reports from 21,938 men imaged for known or suspected prostate cancer between 2015 and 2023 at our institution (21 imaging locations with 34 readers), with 6352 subsequent biopsies. We trained a deep learning model, a representation learner (RL), to learn how radiologists interpret conventionally acquired T2-weighted and diffusion-weighted MR images, using exams in which the radiologists are confident in their risk assessments (PI-RADS 1 and 2 for the absence of csPCa vs. PI-RADS 4 and 5 for the presence of csPCa, n=21,465). We then trained biopsy-decision models to detect csPCa (Gleason score ≥7) using these learned image representations, and compared them to the performance of radiologists, and of models trained on other clinical variables (age, prostate volume, PSA, and PSA density) for treatment-naïve test cohorts consisting of only PI-RADS 3 (n=253, csPCa=103) and all PI-RADS (n=531, csPCa=300) cases. RESULTS:On the 2 test cohorts (PI-RADS-3-only, all-PI-RADS), RL-based biopsy-decision models consistently yielded higher AUCs in detecting csPCa (AUC=0.73 [0.66, 0.79], 0.88 [0.85, 0.91]) compared with radiologists (equivocal, AUC=0.79 [0.75, 0.83]) and the clinical model (AUCs=0.69 [0.62, 0.75], 0.78 [0.74, 0.82]). In the PIRADS-3-only cohort, all of whom would be biopsied using our institution's standard of care, the RL decision model avoided 41% (62/150) of benign biopsies compared with the clinical model (26%, P<0.001), and improved biopsy yield by 10% compared with the PI-RADS ≥3 decision strategy (0.50 vs. 0.40). Furthermore, on the all-PI-RADS cohort, RL decision model avoided 27% of additional benign biopsies (138/231) compared to radiologists (33%, P<0.001) with comparable sensitivity (93% vs. 92%), higher NPV (0.87 vs. 0.77), and biopsy yield (0.75 vs. 0.64). The combination of clinical and RL decision models further avoided benign biopsies (46% in PI-RADS-3-only and 62% in all-PI-RADS) while improving NPV (0.82, 0.88) and biopsy yields (0.52, 0.76) across the 2 test cohorts. CONCLUSIONS:Our PI-RADS-guided deep learning RL model learns summary representations from bi-parametric prostate MR images that can provide additional information to disambiguate intermediate-risk PI-RADS 3 assessments. The resulting RL-based biopsy decision models also outperformed radiologists in avoiding benign biopsies while maintaining comparable sensitivity to csPCa for the all-PI-RADS cohort. Such AI models can easily be integrated into clinical practice to supplement radiologists' reads in general and improve biopsy yield for any equivocal decisions.

Astrocytes differentiate and mature during postnatal development, but the molecular mechanisms linking their maturation to neuronal function remain unclear. We investigated the role of Wnt/β-catenin signaling and its effector, the transcription factor TCF7L2, in postnatal astrocytes using single-nucleus RNA sequencing, imaging, morphometric analysis, microdialysis, and electrophysiology in Tcf7l2 conditional knockout (cKO) mice. Loss of Tcf7l2 caused widespread transcriptional dysregulation in astrocytes, particularly in genes related to amino acid and ion transport, as well as membrane potential regulation. These mice showed disrupted amino acid homeostasis, astrocyte swelling, and impaired extracellular potassium clearance in the somatosensory cortex. These astrocytic changes were accompanied by altered gene expression in cortical pyramidal neurons, reduced excitability, and a hyperpolarized resting membrane potential. Our results suggest that astrocytic TCF7L2 is crucial in coordinating ion and amino acid transport in adulthood, thereby contributing to maintaining extracellular homeostasis and supporting neuronal function. This study identifies TCF7L2 as a key regulator of astrocyte-mediated neurophysiological support and underscores the importance of its role in astrocyte maturation during postnatal development.

Prior expectation powerfully shapes perception, yet its effects have been notoriously difficult to characterize due to the confounding influence of attention. In this study, we systematically investigated expectation's influence on conscious visual object recognition while carefully disentangling it from attentional effects. Across three experiments, we observed that expectation's effect varied markedly depending on the experimental context. When expectation was manipulated in isolation, it enhanced recognition sensitivity, mirroring the effects of attention. However, when expectation and attention were orthogonally manipulated, a surprising interaction effect emerged whereby observers were less likely to report recognition of an expected stimulus in the unattended condition-an effect attributed to swap errors. Finally, a stronger expectation cue in both space and time reduced swap errors and increased the likelihood of observers reporting seeing the expected stimuli. These findings reveal a remarkable degree of flexibility and context dependence in expectation's influence on perception and shed new light on how attention and expectation jointly shape conscious object recognition.

Organisms evolve behavioral and morphological traits to adapt to their ecological niches, yet the genetic basis of adaptation remains largely unknown. Drosophila suzukii has evolved a distinctive oviposition preference for ripe fruit, unlike most Drosophila species such as D. melanogaster, which prefer overripe fruit. Carbon dioxide (CO2), a metabolic volatile that increases as fruit ripens and decays, may act as a critical ecological cue shaping these preferences. Here, we focus on D. suzukii and its sister species D. subpulchrella, which shows an intermediate preference, to investigate the genetic basis of CO2 responses. We report a previously unrecognized shift in CO2-guided oviposition: D. suzukii and D. subpulchrella readily lay eggs on CO2-enriched substrates, unlike the strong aversion displayed by D. melanogaster. Electrophysiological recordings revealed a species-specific sensory tuning, characterized by an early spike in CO2-evoked neuronal firing in D. suzukii and D. subpulchrella-a temporal response feature absent in D. melanogaster. To dissect the genetic basis of this shift, we generated transgenic D. melanogaster expressing either the D. suzukii Gr63a coding sequence or the D. subpulchrella Gr63a cis-regulatory element. Remarkably, both manipulations reproduced the early-onset firing pattern of CO2 sensitivity, demonstrating that either receptor function or expression can independently drive this sensitivity adaptation. Our findings reveal that evolution can shape ecological adaptation through distinct genetic mechanisms, leading to convergent physiological traits among closely related species.

Developing populations of connected neurons often share spatial and/or temporal features that anticipate their assembly. A unifying spatiotemporal motif might link sensory, central, and motor populations that comprise an entire circuit. In the sensorimotor reflex circuit that stabilizes vertebrate gaze, central and motor partners are paired in time (birthdate) and space (dorso-ventral). To determine if birthdate and/or dorso-ventral organization could align the entire circuit, we measured the spatial and temporal development of the sensory circuit node: the vestibular ganglion neurons. We discovered that progressive dorsal-to-ventral organization closely predicts vestibular ganglion development, with additional organization along its functional (rostrocaudal) axis. With an acute optical lesion and calcium imaging paradigm, we found that this common temporal axis anticipated functional sensory-to-central partner matching. We propose a "first-come, first-served" model in which birthdate organizes and assembles the sensory, central, and motor populations that comprise the gaze stabilization circuit, a general strategy for poly-synaptic circuit assembly across embryonically-diverse neural populations.

Despite extensive research on hemispheric asymmetries, the mechanisms regulating lateralized brain functions are incompletely understood. Growing evidence suggests that lateralized neural circuits are side-specifically controlled, in part, by neuropeptides acting as neuromodulators, paracrine factors, and neurohormones. This review highlights evidence supporting this concept in the contexts of lateralized pain processing in the amygdala, control of auditory signaling, lateralized interoceptive signaling, and side-specific endocrine regulation. Our focus is primarily on rodent studies, with supporting data from humans and nonmammalian species, including turtles and nematodes. Left-right side-specific control may be rooted in a bipartite, lateralized organization of neuropeptide systems. Neuropeptides with asymmetric actions may act locally within specific brain regions or be coordinated across the neuraxis. These findings converge on a model in which neuropeptides enable lateralized control through interconnected mechanisms spanning gene expression, neural circuits, and behavioral outcomes.

Early identification of high-risk trauma patients in the prehospital setting is crucial for optimizing resource allocation and improving survival. We developed and externally validated a real-time AI model predicting emergency room mortality using 21 prehospital variables. Model development and internal validation utilized the Korean Trauma Data Bank (KTDB; 204,189 patients), and external validation included four South Korean trauma centers (8,358 patients) and one Australian Level 1 center (3,578 patients). Our Prehospital-AI model, an ensemble of XGBoost, LightGBM, and random forest, achieved an AUROC of 0.923 (sensitivity: 0.780, specificity: 0.880) on the test set, outperforming the shock index (AUROC: 0.712). External validation yielded AUROCs of 0.925-0.956 across South Korean centers and 0.895 in the Australian center. Here we show that the Prehospital-AI model enables accurate, real-time risk assessment in the prehospital setting, outperforming traditional triage tools and improving trauma system efficiency. Nonetheless, additional multinational studies are warranted to further evaluate its generalizability across diverse trauma care systems.