Citations

AbstractBackgroundPathological Lewy body disease (LBD) may commonly present clinically as Parkinson’s disease dementia (PDD), Dementia with Lewy bodies (DLB) or Alzheimer’s disease dementia (ADD). Neuropathological definitions also include a group in which ADD is present, but the postmortem Lewy pathology does not meet threshold density and distribution levels for the neuropathological diagnosis of DLB (ADLB). Little is known about the cognitive profiles of patients with ADLB and despite common underlying pathology, studies delineating neuropsychological profiles in LBD are fraught with inconsistencies. We characterized the longitudinal cognitive trajectories defined by neuropsychological profiles in cases along the pathological spectrum of LBD.MethodCases from the Arizona Study of Aging and Neurodegenerative Disorders (AZSAND) with a cognitive diagnosis of dementia at death and who at minimum, had two neuropsychological evaluations were included. Final neuropathological diagnosis was determined as LBD (neocortical or limbic LB ± AD), ADLB, and AD pathology only. LBD cases were categorized by their cognitive clinical syndromes as PDD and DLB. Global cognition was evaluated using MMSE. Neuropsychological domains of Memory, Executive function, language, attention, and visuospatial were evaluated. Mixed linear methods were used to predict longitudinal change for a given baseline MMSE.Result287 participants and 1874 participant‐years of data were included in this study. LBD (n = 115), and ADLB (52) groups were predominantly male, while AD (n = 114) group was primarily female. Baseline MMSE was similar across all pathologic groups. Linear mixed modelling methods showed faster decrease in memory domain scores (decreasing 0.22 units/year – 0.42 units/year faster, p = 0.01) in ADLB group compared to other groups. Significantly faster decline was noted in LBD and ADLB group in clock drawing (p<0.001), fluency scores (declining 1.02 unit/year faster p<0.001) compared to AD group. There was no difference between PDD and DLB clinical subgroups.ConclusionIn autopsy confirmed cases, ADLB had an accelerated trajectory of decline in memory tasks, while AD, and LBD appeared to have similar trajectories. In contrast, all groups with Lewy bodies progressed faster and showed accelerated decline compared to AD in measures of frontal‐subcortical functions. These results may inform antemortem classification of patients and diagnostic accuracy in these groups.

Abstract Background Alcohol use disorder (AUD) has been associated with the development of neurodegenerative diseases, including Alzheimer's disease (AD). However, recent studies demonstrate that moderate alcohol consumption may be protective against dementia and cognitive decline. Methods We examined astrocyte function, low-density lipoprotein (LDL) receptor-related protein 1 (LRP1), and the NF-κB p65 and IKK-α/β signaling pathways in modulating neuroinflammation and amyloid beta (Aβ) deposition. We assessed apolipoprotein E (ApoE) in the mouse brain using IHC and ELISA in response to moderate ethanol exposure (MEE). First, to confirm the intracerebral distribution of ApoE, we co-stained with GFAP, a marker for astrocytes that biosynthesize ApoE. We sought to investigate whether the ethanol-induced upregulation of LRP1 could potentially inhibit the activity of IL-1β and TNF-α induced IKK-α/β towards NF-κB p65, resulting in a reduction of pro-inflammatory cytokines. To evaluate the actual Aβ load in the brains of APP/PS1 mice, we performed with a specific antibody Aβ (Thioflavin S) on both air- and ethanol-exposed groups, subsequently analyzing Aβ levels. We also measured glucose uptake activity using 18F-FDG in APP/PS1 mice. Finally, we investigated whether MEE induced cognitive and memory changes using the Y maze, noble objective recognition (NOR) test, and Morris water maze (MWM). Results Our findings demonstrate that MEE reduced astrocytic glial fibrillary acidic protein (GFAP) and ApoE levels in the cortex and hippocampus in presymptomatic APP/PS1 mice. Interestingly, increased LRP1 protein expression is accompanied by dampening the IKK-α/β-NF-κB p65 pathway, resulting in decreased IL-1β and TNF-α levels in male mice. Notably, female mice show reduced anti-inflammatory cytokines, IL-4, and IL-10 levels without altering IL-1β and TNF-α concentrations. In both males and females, Aβ plaques, a hallmark of AD, were reduced in the cortex and hippocampus of ethanol-exposed presymptomatic APP/PS1 mice. Consistently, MEE increased fluorodeoxyglucose (FDG)-positron emission tomography (PET)-based brain activities and normalized cognitive and memory deficits in the APP/PS1 mice. Conclusions Our findings suggest that MEE may benefit AD pathology via modulating LRP1 expression, potentially reducing neuroinflammation and attenuating Aβ deposition. Our study implies that reduced astrocyte derived ApoE and LDL cholesterol levels are critical for attenuating AD pathology.

Mice lacking the ethanol‐sensitive equilibrative nucleoside adenosine transporter 1 (ENT1 –/– ) display enhanced goal‐directed behaviors due to decreased adenosine A 2A R function. However, the role of A 2A R signaling in the regulation of impulsivity remains unknown. Using Pavlovian conditioning, second‐order schedule discrimination, and the 5‐choice serial reaction time task (5‐CSRTT), which incorporates differential‐reward of low rate (DRL) schedules, we reveal that deficits in A 2A R function promote impulsivity. ENT1 –/– mice display exaggerated impulsive responses and faster reaction times to retrieve reward during conditioning and extinction. Interestingly, our results show that basal ERK1/2 phosphorylation is reduced in the dorsal hippocampus (dHPC) of ENT1 –/– mice and that impulsive Pavlovian conditioning significantly increases ERK1/2 phosphorylation. Subsequently, we validate that inhibition of A 2A R promotes impulsivity during Pavlovian conditioning and the 5‐CSRTT. In addition to increased ERK1/2 phosphorylation, mice demonstrating exaggerated impulsivity as a result of A 2A R inhibition exhibit enhanced expression of doublecortin and increased incorporation of BrdU within the HPC. Taken together, these findings indicate that impaired ENT1/A 2A R signaling promotes impulsivity during cue‐induced conditioning and contributes to reward seeking during extinction. More importantly, these results demonstrate that impulsive behavior is associated with aberrant hippocampal neurogenesis, which may contribute to the development of maladaptive reward‐seeking behaviors. Support or Funding Information NIH R01 AA018779

Abstract Introduction As total neoadjuvant therapy for locally advanced rectal cancer (LARC) emerged, the possibility of skipping radiotherapy for poorly responsive patients arose. Machine-learning algorithms have focused on the radiopathologic features of tumor segmentation in order to predict responsiveness to radiotherapy. However, in addition to the tumor itself, there are several factors related to responsiveness, such as vasculature affecting hypoxia or MRI-detected extramural vascular invasion status. We aimed to predict poor responders using pretreatment rectal MRI images without segmentation and to identify which factors mainly contribute to the prediction algorithm. Methods Between Jan 1, 2000, and Dec 30, 2020, 689 consecutive patients were retrospectively included in two hospitals. Poor responders were defined by tumor regression grades (TRG) 2 and 3 that were determined through surgical resection. The ResNet-50 model was trained to predict poor responders from pretreatment rectal MRIs (T2-weighted axial, sagittal, and coronal images). We adopted a tenfold cross-validation for training and testing the model and used Gradient-weighted Class Activation Mapping (Grad-CAM) to highlight the important regions in the MRI scans that help predict poor responders. Results The number in each group of TRG was 108 (15.7%), 250 (36.3%), 265 (38.5%), and 66 (9.6%) for TRG0, TRG1, TRG2, and TRG3, respectively. There were 618 patients in the training cohort and 71 patients in the validation cohort. In the training and validation cohort, the accuracy for the prediction of poor responders was 85.6% (area under the curve (AUC) 0.856 [95% CI 0.761-0.950]) and 70.2% (AUC 0.703 [0.682-0.724]), although without segmentation. Our prediction model achieved a sensitivity of 0.724 (95% CI 0.700-0.748), a specificity of 0.684 (0.658-0.710), a positive predictive value of 0.697 (0.656-0.737), and a negative predictive value of 0.708 (0.666-0.751) in the validation cohort. Grad-CAM showed that the most important part of the accurately predicted images to contribute to the prediction was not the tumor (7/355, 1.9%) but the pelvic vasculature (353/355, 99.4%), including iliac vessels, femoral vessels, and presacral plexus, and followed by the mesorectum (38/355, 10.7%). Conclusion The pelvic vasculature contributes more to predicting poor responders to radiotherapy than the tumor itself. Therefore, when creating a prediction model for responsiveness to radiotherapy in LARC, this should be considered. Citation Format: Rumi Shin, Byunho Jo, Inyeop Jang, Cheong-Il Shin, Jin Sun Choi, Seung-Yong Jeong, Seung Chul Heo, Ji Won Park, Min Jung Kim, Tae Hyun Hwang. Why only focus on the tumor?: The crucial role of the extra-tumor environment to predict poor responders for locally advanced rectal cancer. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5374.

AbstractBackgroundWe previously reported that in our autopsy‐confirmed cohort of Dementia with Lewy Bodies (DLB), Parkinson’s Disease Dementia (PDD) and Alzheimer’s Disease (AD), DLB with parkinsonism (DLB+Park) predicted faster progression of UPDRS‐III scores compared to PDD, DLB without parkinsonism (DLB‐Park), and AD. This study examined annualized rate of change of UPDRS‐III subscales in our cohort.MethodsData included were from participants in Arizona Study of Aging and Neurodegenerative Diseases (AZSAND) with clinicopathologic diagnosis of DLB (n = 48), PDD (n = 98) or pure AD (n = 48), and longitudinal movement exams. Mixed effects model (accounting for age, education, and sex) was used to determine predicted mean UPDRS‐III and subscale scores (0‐4) for all groups. Individual UPDRS‐III subscale scores were derived for: Body Bradykinesia, Limb Bradykinesia, Gait, Postural Instability, Limb Rest Tremor, Neck Rigidity and Limb Rigidity. Where applicable, scores for multiple limbs involved were averaged.ResultsLongitudinal data was analyzed for over 1430‐participant years. Parkinsonism was present in 65.6% of DLB participants. PDD participants were more likely to be treated with dopaminergic agents (87.8%) compared to (p<0.001) DLB+Park (57.6%), DLB‐park (0%) and AD (2.1%). Frequency (29.4% vs. 29.0%) and severity (mean±SE = 0.4±0.07 and 0.4±0.12) of Limb Rest Tremor was similar for PDD and DLB+park. Limb Bradykinesia frequency and severity were PDD: 100%, 2.4±0.11, DLB+Park: 96.8%, 2.1±0.18, DLB‐Park: 20%, 0.4±0.23, and AD: 62%, 1.1±0.16. Frequency of gait abnormality was 98.5% in PDD, 90.3% in DLB+park, 60% in DLB‐Park and 57.8% in AD. Rigidity was most frequent in the PDD group (88.2%), followed by DLB+Park (71%), AD (19.6%), and none DLB‐park (0%). Over 8 years trajectories for limb bradykinesia and gait were greater for DLB+park than PDD (Figure 1, Cohen’s d range 0.89‐2.18 and 0.63‐1.99 over 8 years, p<0.001).ConclusionOur results suggest that DLB+Park group have faster motor progression predominantly related to gait abnormalities and limb bradykinesia. Future analyses will explore more convergent data, integrated effects of various measures and their differential contributions in this cohort.

Background and Objectives: The number of patients who undergo multiple operations on a knee is increasing. The objective of this study was to develop a deep learning algorithm that could detect 17 different surgical implants on plain knee radiographs. Materials and Methods: An internal dataset consisted of 5206 plain knee antero-posterior X-rays from a single, tertiary institute for model development. An external set contained 238 X-rays from another tertiary institute. A total of 17 different types of implants including total knee arthroplasty, unicompartmental knee arthroplasty, plate, and screw were labeled. The internal dataset was approximately split into a train set, a validation set, and an internal test set at a ratio of 7:1:2. You Only look Once (YOLO) was selected as the detection network. Model performances with the validation set, internal test set, and external test set were compared. Results: Total accuracy, total sensitivity, total specificity value of the validation set, internal test set, and external test set were (0.978, 0.768, 0.999), (0.953, 0.810, 0.990), and (0.956, 0.493, 0.975), respectively. Means ± standard deviations (SDs) of diagonal components of confusion matrix for these three subsets were 0.858 ± 0.242, 0.852 ± 0.182, and 0.576 ± 0.312, respectively. True positive rate of total knee arthroplasty, the most dominant class of the dataset, was higher than 0.99 with internal subsets and 0.96 with an external test set. Conclusion: Implant identification on plain knee radiographs could be automated using a deep learning technique. The detection algorithm dealt with overlapping cases while maintaining high accuracy on total knee arthroplasty. This could be applied in future research that analyzes X-ray images with deep learning, which would help prompt decision-making in clinics.

Habitual reward‐seeking behavior is a hallmark of addictive behaviors. Although habitual behavior could be reversed to goal‐directed behavior, this shifting is not well studied. Here, we identify a role of astrocyte activation in the dorsomedial striatum (DMS) on transition from habitual to goal‐directed reward‐seeking behavior. Utilizing the Inscopix endomicroscopy/fiber photometry in vivo calcium imaging and the cell‐type specific activation of designer receptors exclusively activated by designer drugs (DREADDs), we confirmed that GFAP‐driven activation of hM3Dq DREADDs increases intensity and frequency of calcium signaling in the DMS astrocytes of ALDH1L1‐GCaMP6s expressing mice. We found that this chemogenetic activation of the DMS astrocytes increase and decrease neuronal excitability of direct pathway Medium Spiny Neurons (dMSNs) and indirect pathway Medium Spiny Neurons (iMSNs), respectively. Specifically, it reduced the frequency of spontaneous excitatory postsynaptic currents (sEPSCs) in the dMSNs, whereas it increased the amplitude of the sEPSCs and decreased the frequency of spontaneous inhibitory postsynaptic currents (sIPSCs) in the iMSNs. The changes in synaptic events induced by astrocytic activation were significantly inhibited by either of the pretreatments of DPCPX (Adenosine A1R antagonist, 1 μM), PSB12379 (Inhibitor of ectonucleotidase, CD73, 10 μM), NBTI [Inhibitor of the astrocytic adenosine transporter, equilibrative nucleoside transporter 1 (ENT1, slc29a1), 10 μM], and genetic deletion of ENT1. These data suggest that astrocytic activation‐induced synaptic activities are, in part, adenosine‐dependent. In the evaluation tests of goal‐directed and habitual reward‐seeking, the chemogenetic activation of the DMS astrocytes shifted the habitual behavior to goal‐directed action in sucrose reward‐seeking for WT, but not ENT1 KO mice. The astrocytic activation‐induced shifting was rescued when ENT1 expression was normalized in the DMS astrocytes of the ENT1 KO mice. On the other hand, when mice were trained to have goal‐directed behavior, the astrocytic activation in the DMS did not change the reward‐seeking behavioral pattern. Together, our results indicate that astrocyte‐regulated adenosine signaling in the DMS determines goal‐directed and habitual behaviors by selectively regulating synapses in the MSNs. The interaction between astrocytes and neurons via adenosine signaling could be a potential therapeutic target for maladaptive reward‐seeking behaviors. Support or Funding Information This work was supported by the Samuel C. Johnson for Genomics of Addiction Program at Mayo Clinic, the Ulm Foundation, and National Institute on Alcohol Abuse and Alcoholism (K01 AA027773 to SK, R01 AA018779 to DSC).