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Nature Medicine Nov 2023Huntington's disease (HD) is a devastating monogenic neurodegenerative disease characterized by early, selective pathology in the basal ganglia despite the ubiquitous...
Huntington's disease (HD) is a devastating monogenic neurodegenerative disease characterized by early, selective pathology in the basal ganglia despite the ubiquitous expression of mutant huntingtin. The molecular mechanisms underlying this region-specific neuronal degeneration and how these relate to the development of early cognitive phenotypes are poorly understood. Here we show that there is selective loss of synaptic connections between the cortex and striatum in postmortem tissue from patients with HD that is associated with the increased activation and localization of complement proteins, innate immune molecules, to these synaptic elements. We also found that levels of these secreted innate immune molecules are elevated in the cerebrospinal fluid of premanifest HD patients and correlate with established measures of disease burden.In preclinical genetic models of HD, we show that complement proteins mediate the selective elimination of corticostriatal synapses at an early stage in disease pathogenesis, marking them for removal by microglia, the brain's resident macrophage population. This process requires mutant huntingtin to be expressed in both cortical and striatal neurons. Inhibition of this complement-dependent elimination mechanism through administration of a therapeutically relevant C1q function-blocking antibody or genetic ablation of a complement receptor on microglia prevented synapse loss, increased excitatory input to the striatum and rescued the early development of visual discrimination learning and cognitive flexibility deficits in these models. Together, our findings implicate microglia and the complement cascade in the selective, early degeneration of corticostriatal synapses and the development of cognitive deficits in presymptomatic HD; they also provide new preclinical data to support complement as a therapeutic target for early intervention.
Topics: Humans; Animals; Huntington Disease; Neurodegenerative Diseases; Microglia; Synapses; Corpus Striatum; Cognitive Dysfunction; Huntingtin Protein; Complement System Proteins; Disease Models, Animal
PubMed: 37814059
DOI: 10.1038/s41591-023-02566-3 -
Alzheimer's & Dementia : the Journal of... Dec 2023The use of applied modeling in dementia risk prediction, diagnosis, and prognostics will have substantial public health benefits, particularly as "deep phenotyping"... (Review)
Review
INTRODUCTION
The use of applied modeling in dementia risk prediction, diagnosis, and prognostics will have substantial public health benefits, particularly as "deep phenotyping" cohorts with multi-omics health data become available.
METHODS
This narrative review synthesizes understanding of applied models and digital health technologies, in terms of dementia risk prediction, diagnostic discrimination, prognosis, and progression. Machine learning approaches show evidence of improved predictive power compared to standard clinical risk scores in predicting dementia, and the potential to decompose large numbers of variables into relatively few critical predictors.
RESULTS
This review focuses on key areas of emerging promise including: emphasis on easier, more transparent data sharing and cohort access; integration of high-throughput biomarker and electronic health record data into modeling; and progressing beyond the primary prediction of dementia to secondary outcomes, for example, treatment response and physical health.
DISCUSSION
Such approaches will benefit also from improvements in remote data measurement, whether cognitive (e.g., online), or naturalistic (e.g., watch-based accelerometry).
Topics: Humans; Artificial Intelligence; Digital Health; Machine Learning; Dementia
PubMed: 37496259
DOI: 10.1002/alz.13391 -
Hearing Research Oct 2023Learning can induce neurophysiological plasticity in the auditory cortex at multiple timescales. Lasting changes to auditory cortical function that persist over days,... (Review)
Review
Learning can induce neurophysiological plasticity in the auditory cortex at multiple timescales. Lasting changes to auditory cortical function that persist over days, weeks, or even a lifetime, require learning to induce de novo gene expression. Indeed, transcription is the molecular determinant for long-term memories to form with a lasting impact on sound-related behavior. However, auditory cortical genes that support auditory learning, memory, and acquired sound-specific behavior are largely unknown. Using an animal model of adult, male Sprague-Dawley rats, this report is the first to identify genome-wide changes in learning-induced gene expression within the auditory cortex that may underlie long-lasting discriminative memory formation of acoustic frequency cues. Auditory cortical samples were collected from animals in the initial learning phase of a two-tone discrimination sound-reward task known to induce sound-specific neurophysiological and behavioral effects. Bioinformatic analyses on gene enrichment profiles from bulk RNA sequencing identified cholinergic synapse (KEGG rno04725), extra-cellular matrix receptor interaction (KEGG rno04512), and neuroactive receptor interaction (KEGG rno04080) among the top biological pathways are likely to be important for auditory discrimination learning. The findings characterize candidate effectors underlying the early stages of changes in cortical and behavioral function to ultimately support the formation of long-term discriminative auditory memory in the adult brain. The molecules and mechanisms identified are potential therapeutic targets to facilitate experiences that induce long-lasting changes to sound-specific auditory function in adulthood and prime for future gene-targeted investigations.
Topics: Male; Rats; Animals; Rats, Sprague-Dawley; Auditory Cortex; Learning; Discrimination Learning; Brain
PubMed: 37659220
DOI: 10.1016/j.heares.2023.108878 -
Behavioral Ecology : Official Journal... 2024Cognitive flexibility can enhance the ability to adjust to changing environments. Here, we use learning simulations to investigate the possible advantages of flexible...
Cognitive flexibility can enhance the ability to adjust to changing environments. Here, we use learning simulations to investigate the possible advantages of flexible learning in volatile (changing) environments. We compare two established learning mechanisms, one with constant learning rates and one with rates that adjust to volatility. We study an ecologically relevant case of volatility, based on observations of developing cleaner fish that experience a transition from a simpler to a more complex foraging environment. There are other similar transitions in nature, such as migrating to a new and different habitat. We also examine two traditional approaches to volatile environments in experimental psychology and behavioral ecology: reversal learning, and learning set formation (consisting of a sequence of different discrimination tasks). These provide experimental measures of cognitive flexibility. Concerning transitions to a complex world, we show that both constant and flexible learning rates perform well, losing only a small proportion of available rewards in the period after a transition, but flexible rates perform better than constant rates. For reversal learning, flexible rates improve the performance with each successive reversal because of increasing learning rates, but this does not happen for constant rates. For learning set formation, we find no improvement in performance with successive shifts to new stimuli to discriminate for either flexible or constant learning rates. Flexible learning rates might thus explain increasing performance in reversal learning but not in learning set formation, and this can shed light on the nature of cognitive flexibility in a given system.
PubMed: 38162692
DOI: 10.1093/beheco/arad109 -
JAMA Network Open Aug 2023Delays in starting cancer treatment disproportionately affect vulnerable populations and can influence patients' experience and outcomes. Machine learning algorithms...
IMPORTANCE
Delays in starting cancer treatment disproportionately affect vulnerable populations and can influence patients' experience and outcomes. Machine learning algorithms incorporating electronic health record (EHR) data and neighborhood-level social determinants of health (SDOH) measures may identify at-risk patients.
OBJECTIVE
To develop and validate a machine learning model for estimating the probability of a treatment delay using multilevel data sources.
DESIGN, SETTING, AND PARTICIPANTS
This cohort study evaluated 4 different machine learning approaches for estimating the likelihood of a treatment delay greater than 60 days (group least absolute shrinkage and selection operator [LASSO], bayesian additive regression tree, gradient boosting, and random forest). Criteria for selecting between approaches were discrimination, calibration, and interpretability/simplicity. The multilevel data set included clinical, demographic, and neighborhood-level census data derived from the EHR, cancer registry, and American Community Survey. Patients with invasive breast, lung, colorectal, bladder, or kidney cancer diagnosed from 2013 to 2019 and treated at a comprehensive cancer center were included. Data analysis was performed from January 2022 to June 2023.
EXPOSURES
Variables included demographics, cancer characteristics, comorbidities, laboratory values, imaging orders, and neighborhood variables.
MAIN OUTCOMES AND MEASURES
The outcome estimated by machine learning models was likelihood of a delay greater than 60 days between cancer diagnosis and treatment initiation. The primary metric used to evaluate model performance was area under the receiver operating characteristic curve (AUC-ROC).
RESULTS
A total of 6409 patients were included (mean [SD] age, 62.8 [12.5] years; 4321 [67.4%] female; 2576 [40.2%] with breast cancer, 1738 [27.1%] with lung cancer, and 1059 [16.5%] with kidney cancer). A total of 1621 (25.3%) experienced a delay greater than 60 days. The selected group LASSO model had an AUC-ROC of 0.713 (95% CI, 0.679-0.745). Lower likelihood of delay was seen with diagnosis at the treating institution; first malignant neoplasm; Asian or Pacific Islander or White race; private insurance; and lacking comorbidities. Greater likelihood of delay was seen at the extremes of neighborhood deprivation. Model performance (AUC-ROC) was lower in Black patients, patients with race and ethnicity other than non-Hispanic White, and those living in the most disadvantaged neighborhoods. Though the model selected neighborhood SDOH variables as contributing variables, performance was similar when fit with and without these variables.
CONCLUSIONS AND RELEVANCE
In this cohort study, a machine learning model incorporating EHR and SDOH data was able to estimate the likelihood of delays in starting cancer therapy. Future work should focus on additional ways to incorporate SDOH data to improve model performance, particularly in vulnerable populations.
Topics: Humans; Middle Aged; Cohort Studies; Risk Assessment; Bayes Theorem; Carcinoma, Renal Cell; Kidney Neoplasms
PubMed: 37578796
DOI: 10.1001/jamanetworkopen.2023.28712 -
Frontiers in Immunology 2023Immune checkpoint inhibitor (ICI)-related pneumonitis (IRP) is a common and potentially fatal clinical adverse event. The identification and prediction of the risk of...
BACKGROUND
Immune checkpoint inhibitor (ICI)-related pneumonitis (IRP) is a common and potentially fatal clinical adverse event. The identification and prediction of the risk of ICI-related IRP is a major clinical issue. The objective of this study was to apply a machine learning method to explore risk factors and establish a prediction model.
METHODS
We retrospectively analyzed 48 patients with IRP (IRP group) and 142 patients without IRP (control group) who were treated with ICIs. An Elastic Net model was constructed using a repeated k-fold cross-validation framework (repeat = 10; k = 3). The prediction models were validated internally and the final prediction model was built on the entire training set using hyperparameters with the best interval validation performance. The generalizability of the final prediction model was assessed by applying it to an independent test set. The overall performance, discrimination, and calibration of the prediction model were evaluated.
RESULTS
Eleven predictors were included in the final predictive model: sindillizumab, number of ≥2 underlying diseases, history of lung diseases, tirelizumab, non-small cell lung cancer (NSCLC), percentage of CD4 lymphocytes, body temperature, KPS score ≤70, hemoglobin, cancer stage IV, and history of antitumor therapy. The external validation of the risk prediction model on an independent test set of 37 patients and showed good discrimination and acceptable calibration ability: with AUC of 0.81 (95% CI 0.58-0.90), AP of 0.76, scaled Brier score of 0.31, and Spiegelhalter-z of -0.29 (P-value:0.77). We also designed an online IRP risk calculator for use in clinical practice.
CONCLUSION
The prediction model of ICI-related IRP provides a tool for accurately predicting the occurrence of IRP in patients with cancer who received ICIs.
Topics: Humans; Immune Checkpoint Inhibitors; Carcinoma, Non-Small-Cell Lung; Retrospective Studies; Lung Neoplasms; Pneumonia; Machine Learning
PubMed: 37457722
DOI: 10.3389/fimmu.2023.1138489 -
Molecular Psychiatry Dec 2023The brain's ability to associate threats with external stimuli is vital to execute essential behaviours including avoidance. Disruption of this process contributes...
The brain's ability to associate threats with external stimuli is vital to execute essential behaviours including avoidance. Disruption of this process contributes instead to the emergence of pathological traits which are common in addiction and depression. However, the mechanisms and neural dynamics at the single-cell resolution underlying the encoding of associative learning remain elusive. Here, employing a Pavlovian discrimination task in mice we investigate how neuronal populations in the lateral habenula (LHb), a subcortical nucleus whose excitation underlies negative affect, encode the association between conditioned stimuli and a punishment (unconditioned stimulus). Large population single-unit recordings in the LHb reveal both excitatory and inhibitory responses to aversive stimuli. Additionally, local optical inhibition prevents the formation of cue discrimination during associative learning, demonstrating a critical role of LHb activity in this process. Accordingly, longitudinal in vivo two-photon imaging tracking LHb calcium neuronal dynamics during conditioning reveals an upward or downward shift of individual neurons' CS-evoked responses. While recordings in acute slices indicate strengthening of synaptic excitation after conditioning, support vector machine algorithms suggest that postsynaptic dynamics to punishment-predictive cues represent behavioral cue discrimination. To examine the presynaptic signaling in LHb participating in learning we monitored neurotransmitter dynamics with genetically-encoded indicators in behaving mice. While glutamate, GABA, and serotonin release in LHb remain stable across associative learning, we observe enhanced acetylcholine signaling developing throughout conditioning. In summary, converging presynaptic and postsynaptic mechanisms in the LHb underlie the transformation of neutral cues in valued signals supporting cue discrimination during learning.
Topics: Male; Animals; Mice, Inbred C57BL; Association Learning; Habenula; Neuronal Plasticity; Punishment; Neurons; Cues; Acetylcholine; Synapses
PubMed: 37414924
DOI: 10.1038/s41380-023-02155-3 -
Frontiers in Neuroscience 2023Aging is associated with impairments in learning, memory, and cognitive flexibility, as well as a gradual decline in hippocampal neurogenesis. We investigated the...
Aging is associated with impairments in learning, memory, and cognitive flexibility, as well as a gradual decline in hippocampal neurogenesis. We investigated the performance of 6-and 14-month-old mice (considered mature adult and late middle age, respectively) in learning and memory tasks based on the Morris water maze (MWM) and determined their levels of preceding and current neurogenesis. While both age groups successfully performed in the spatial version of MWM (sMWM), the older mice were less efficient compared to the younger mice when presented with modified versions of the MWM that required a reassessment of the previously acquired experience. This was detected in the reversal version of MWM (rMWM) and was particularly evident in the context discrimination MWM (cdMWM), a novel task that required integrating various distal cues, local cues, and altered contexts and adjusting previously used search strategies. Older mice were impaired in several metrics that characterize rMWM and cdMWM, however, they showed improvement and narrowed the performance gap with the younger mice after additional training. Furthermore, we analyzed the adult-born mature and immature neurons in the hippocampal dentate gyrus and found a significant correlation between neurogenesis levels in individual mice and their performance in the tasks demanding cognitive flexibility. These results provide a detailed description of the age-related changes in learning and memory and underscore the importance of hippocampal neurogenesis in supporting cognitive flexibility.
PubMed: 37645372
DOI: 10.3389/fnins.2023.1232670 -
Micromachines Sep 2023This study presents a comprehensive literature review that investigates the distinctions between true and false cinnamon. Given the intricate compositions of essential... (Review)
Review
This study presents a comprehensive literature review that investigates the distinctions between true and false cinnamon. Given the intricate compositions of essential oils (EOs), various discrimination approaches were explored to ensure quality, safety, and authenticity, thereby establishing consumer confidence. Through the utilization of physical-chemical and instrumental analyses, the purity of EOs was evaluated via qualitative and quantitative assessments, enabling the identification of constituents or compounds within the oils. Consequently, a diverse array of techniques has been documented, encompassing organoleptic, physical, chemical, and instrumental methodologies, such as spectroscopic and chromatographic methods. Electronic noses (e-noses) exhibit significant potential for identifying cinnamon adulteration, presenting a rapid, non-destructive, and cost-effective approach. Leveraging their capability to detect and analyze volatile organic compound (VOC) profiles, e-noses can contribute to ensuring authenticity and quality in the food and fragrance industries. Continued research and development efforts in this domain will assuredly augment the capacities of this promising avenue, which is the utilization of Artificial Intelligence (AI) and Machine Learning (ML) algorithms in conjunction with spectroscopic data to combat cinnamon adulteration.
PubMed: 37893256
DOI: 10.3390/mi14101819