Prediction of hospitalization and waiting time within 24 hours of emergency department patients with unstructured text data.

Overcrowding of emergency departments is a global concern, leading to numerous negative consequences. This study aimed to develop a useful and inexpensive tool derived from electronic medical records that supports clinical decision-making and can be easily utilized by emergency department physicians. We presented machine learning models that predicted the likelihood of hospitalizations within 24 hours and estimated waiting times. Moreover, we revealed the enhanced performance of these machine learning models compared to existing models by incorporating unstructured text data. Among several evaluated models, the extreme gradient boosting model that incorporated text data yielded the best performance. This model achieved an area under the receiver operating characteristic curve score of 0.922 and an area under the precision-recall curve score of 0.687. The mean absolute error revealed a difference of approximately 3 hours. Using this model, we classified the probability of patients not being admitted within 24 hours as Low, Medium, or High and identified important variables influencing this classification through explainable artificial intelligence. The model results are readily displayed on an electronic dashboard to support the decision-making of emergency department physicians and alleviate overcrowding, thereby resulting in socioeconomic benefits for medical facilities.

Decreased FAK activity and focal adhesion dynamics impair proper neurite formation of medium spiny neurons in Huntington's disease.

Huntington's disease (HD) is a neurodegenerative disorder caused by a polyglutamine expansion in the protein huntingtin (HTT) [55]. While the final pathological consequence of HD is the neuronal cell death in the striatum region of the brain, it is still unclear how mutant HTT (mHTT) causes synaptic dysfunctions at the early stage and during the progression of HD. Here, we discovered that the basal activity of focal adhesion kinase (FAK) is severely reduced in a striatal HD cell line, a mouse model of HD, and the human post-mortem brains of HD patients. In addition, we observed with a FRET-based FAK biosensor [59] that neurotransmitter-induced FAK activation is decreased in HD striatal neurons. Total internal reflection fluorescence (TIRF) imaging revealed that the reduced FAK activity causes the impairment of focal adhesion (FA) dynamics, which further leads to the defect in filopodial dynamics causing the abnormally increased number of immature neurites in HD striatal neurons. Therefore, our results suggest that the decreased FAK and FA dynamics in HD impair the proper formation of neurites, which is crucial for normal synaptic functions [52]. We further investigated the molecular mechanism of FAK inhibition in HD and surprisingly discovered that mHTT strongly associates with phosphatidylinositol 4,5-biphosphate, altering its normal distribution at the plasma membrane, which is crucial for FAK activation [14, 60]. Therefore, our results provide a novel molecular mechanism of FAK inhibition in HD along with its pathological mechanism for synaptic dysfunctions during the progression of HD.

Modulation of SETDB1 activity by APQ ameliorates heterochromatin condensation, motor function, and neuropathology in a Huntington's disease mouse model.

The present study describes evaluation of epigenetic regulation by a small molecule as the therapeutic potential for treatment of Huntington's disease (HD). We identified 5-allyloxy-2-(pyrrolidin-1-yl)quinoline (APQ) as a novel SETDB1/ESET inhibitor using a combined in silico and in vitro cell based screening system. APQ reduced SETDB1 activity and H3K9me3 levels in a HD cell line model. In particular, not only APQ reduced H3K9me3 levels in the striatum but it also improved motor function and neuropathological symptoms such as neuronal size and activity in HD transgenic (YAC128) mice with minimal toxicity. Using H3K9me3-ChIP and genome-wide sequencing, we also confirmed that APQ modulates H3K9me3-landscaped epigenomes in YAC128 mice. These data provide that APQ, a novel small molecule SETDB1 inhibitor, coordinates H3K9me-dependent heterochromatin remodelling and can be an epigenetic drug for treating HD, leading with hope in clinical trials of HD.

CardioNet: a manually curated database for artificial intelligence-based research on cardiovascular diseases.

BACKGROUND: Cardiovascular diseases (CVDs) are difficult to diagnose early and have risk factors that are easy to overlook. Early prediction and personalization of treatment through the use of artificial intelligence (AI) may help clinicians and patients manage CVDs more effectively. However, to apply AI approaches to CVDs data, it is necessary to establish and curate a specialized database based on electronic health records (EHRs) and include pre-processed unstructured data. METHODS: To build a suitable database (CardioNet) for CVDs that can utilize AI technology, contributing to the overall care of patients with CVDs. First, we collected the anonymized records of 748,474 patients who had visited the Asan Medical Center (AMC) or Ulsan University Hospital (UUH) because of CVDs. Second, we set clinically plausible criteria to remove errors and duplication. Third, we integrated unstructured data such as readings of medical examinations with structured data sourced from EHRs to create the CardioNet. We subsequently performed natural language processing to structuralize the significant variables associated with CVDs because most results of the principal CVD-related medical examinations are free-text readings. Additionally, to ensure interoperability for convergent multi-center research, we standardized the data using several codes that correspond to the common data model. Finally, we created the descriptive table (i.e., dictionary of the CardioNet) to simplify access and utilization of data for clinicians and engineers and continuously validated the data to ensure reliability. RESULTS: CardioNet is a comprehensive database that can serve as a training set for AI models and assist in all aspects of clinical management of CVDs. It comprises information extracted from EHRs and results of readings of CVD-related digital tests. It consists of 27 tables, a code-master table, and a descriptive table. CONCLUSIONS: CardioNet database specialized in CVDs was established, with continuing data collection. We are actively supporting multi-center research, which may require further data processing, depending on the subject of the study. CardioNet will serve as the fundamental database for future CVD-related research projects.

Free energy measurements by the generalized fluctuation theorems: Theory and numerical study of a model filament.

We measure the free energy of a model filament, which undergoes deformations and structural transitions, as a function of its extension, in silico. We perform Brownian Dynamics (BD) simulations of pulling experiments at various speeds, following a protocol close to experimental ones. The results from the fluctuation theorems are compared with the estimates from Monte Carlo (MC) simulation, where the rugged free energy landscape is produced by the density of states method. The fluctuation theorems (FT) give accurate estimates of the free energy up to moderate pulling speeds. At higher pulling speeds, the work distributions do not efficiently sample the domain of small work and FT slightly overestimates free energy. In order to comprehend the differences, we analyze the work distributions from the BD simulations in the framework of trajectory thermodynamics and propose the generalized fluctuation theorems that take into account the information (relative entropy) evaluated in the expanded phase space. The measured work - free energy relation is consistent with the results obtained from the generalized fluctuation theorems. We discuss operational methods to improve the estimates at high pulling speed.

Use and abuse of fecal occult blood tests: a community hospital experience.

BACKGROUND: The Fecal Occult Blood Test (FOBT) is one of the diagnostic modalities indicated for screening patients for Colorectal Cancer (CRC). Despite being approved only for screening for CRC, numerous studies in the past have illustrated misuse of the FOBT. We examined utilization of the FOBT for patients admitted to a community teaching hospital. METHODS: The study was conducted at Saint Joseph Hospital, Chicago USA. A retrospective review of Electronic Medical Records (EMRs) of patients admitted from January 2016 to December 2017 was performed. RESULTS: We reviewed the EMRs of 729 patients who received the stool testing for occult blood (FOBT). All tests (100%) were carried out for purposes other than CRC screening. Anemia (38%) was the most common reason documented for carrying out the FOBT. Further, 88% of the tests were ordered on patients who either did not fulfill CRC screening criteria or had other contraindications for testing. Usage of contraindicated medication was the most important factor (58% of patients) that made the candidates ineligible for testing. A total 73 Colonoscopies were ordered for patients who received the test inappropriately with a resulting low yield (0.47%) of CRC diagnosis. CONCLUSION: The stool occult blood test continues to be utilized for reasons other than CRC screening. Majority of patients who underwent the test were not suitable candidates due to the presence of contraindications for testing. Unsuitable FOBT testing led to further unnecessary investigations.

Development of a 4-aminopyrazolo[3,4-d]pyrimidine-based dual IGF1R/Src inhibitor as a novel anticancer agent with minimal toxicity.

BACKGROUND: Both the type I insulin-like growth factor receptor (IGF1R) and Src pathways are associated with the development and progression of numerous types of human cancer, and Src activation confers resistance to anti-IGF1R therapies. Hence, targeting both IGF1R and Src concurrently is one of the main challenges in combating resistance to the currently available anti-IGF1R-based anticancer therapies. However, the enhanced toxicity from this combinatorial treatment could be one of the main hurdles for this strategy, suggesting the necessity of developing a novel strategy for co-targeting IGF1R and Src to meet an urgent clinical need. METHODS: We synthesized a series of 4-aminopyrazolo[3,4-d]pyrimidine-based dual IGF1R/Src inhibitors, selected LL28 as an active compound and evaluated its potential antitumor effects in vitro and in vivo using the MTT assay, colony formation assays, flow cytometric analysis, a tumor xenograft model, and the Kras G12D/+ -driven spontaneous lung tumorigenesis model. RESULTS: LL28 markedly suppressed the activation of IGF1R and Src and significantly inhibited the viability of several NSCLC cell lines in vitro by inducing apoptosis. Administration of mice with LL28 significantly suppressed the growth of H1299 NSCLC xenograft tumors without overt toxicity and substantially reduced the multiplicity, volume, and load of lung tumors in the Kras G12D/+ -driven lung tumorigenesis model. CONCLUSIONS: The present results suggest the potential of LL28 as a novel anticancer drug candidate targeting both IGF1R and Src, providing a new avenue to efficient anticancer therapies. Further investigation is warranted in advanced preclinical and clinical settings.

Super-helical filaments at surfaces: dynamics and elastic responses.

Bio-filaments often behave in a way unexpected from the standard semi-flexible polymer chain model (WLC), when squeezed to a surface, confined in microfluidic channels or clamped by their end. This calls for the super-helical filament model, going beyond WLC, where the filament forms a helix much wider than its diameter. We study this model using Brownian dynamics simulations, focusing on filaments confined to a surface by a strong potential. We analyze shapes and shape fluctuations under tension where excited states comprising a number of inflection points (twist-kink) can be stabilized. Pulling/releasing experiments during a cycle of increasing/decreasing tension show hysteresis. We find that the excited state, once established, is long-lived and the life time grows with the filament length cubed. Twist-kink diffusion involves position (filament shape) dependent friction for which we provide analytical expression. Dynamic responses to tension are investigated via numerical simulations and several mechanisms of shape relaxation are found and rationalized.

The alteration of serine transporter activity in a cell line model of amyotrophic lateral sclerosis (ALS).

The alteration of d-serine levels is associated with the pathogenesis of sporadic ALS and mutant SOD1 (G93A) animal model of ALS. However, the exact mechanism of d-serine transport is not known in ALS. To better understand the distribution of d-serine in ALS, we determined the activity and the expression of serine transporter in a motor neuronal cell line model of ALS (NSC-34/hSOD1G93A cells). The uptake of [3H]d-serine was significantly lower in NSC-34/hSOD1G93A cells than in control NSC-34 and NSC-34/hSOD1wt cells. In contrast, the uptake of [3H]l-serine, precursor of d-serine, was markedly increased in NSC-34/hSOD1G93A cells compared to control NSC-34 and NSC-34/hSOD1wt cells. Both [3H]d-serine and [3H]l-serine uptake were saturable in these cells. The estimated Michaelis-Menten constant, Km, for d-serine uptakes was higher in NSC-34/hSOD1G93A cells than in NSC-34/hSOD1wt cells while the Km for l-serine uptake was 2 fold lower in NSC-34/hSOD1G93A cells than in control cells. [3H]d-serine and [3H]l-serine uptakes took place in a Na+-dependent manner, and both uptakes were significantly inhibited by system ASC (alanine-serine-cysteine) substrates. As a result of small interfering RNA experiments, we found that ASCT2 (SLC1A5) and ASCT1 (SLC1A4) are involved in [3H]d-serine and [3H]l-serine uptake in NSC-34/hSOD1G93A cells, respectively. The level of SLC1A4 mRNA was significantly increased in NSC-34/hSOD1G93A compared to NSC-34 and NSC-34/hSOD1wt cells. In contrast, the level of SLC7A10 mRNA was relatively lower in NSC-34/hSOD1G93A cells than the control cells. Together, these data suggest that the pathological alteration of d- and l-serine uptakes in ALS is driven by the affinity change of d-and l-serine uptake system.

Remodeling of heterochromatin structure slows neuropathological progression and prolongs survival in an animal model of Huntington's disease.

Huntington's disease (HD) is an autosomal-dominant inherited neurological disorder caused by expanded CAG repeats in exon 1 of the Huntingtin (HTT) gene. Altered histone modifications and epigenetic mechanisms are closely associated with HD suggesting that transcriptional repression may play a pathogenic role. Epigenetic compounds have significant therapeutic effects in cellular and animal models of HD, but they have not been successful in clinical trials. Herein, we report that dSETDB1/ESET, a histone methyltransferase (HMT), is a mediator of mutant HTT-induced degeneration in a fly HD model. We found that nogalamycin, an anthracycline antibiotic and a chromatin remodeling drug, reduces trimethylated histone H3K9 (H3K9me3) levels and pericentromeric heterochromatin condensation by reducing the expression of Setdb1/Eset. H3K9me3-specific ChIP-on-ChIP analysis identified that the H3K9me3-enriched epigenome signatures of multiple neuronal pathways including Egr1, Fos, Ezh1, and Arc are deregulated in HD transgenic (R6/2) mice. Nogalamycin modulated the expression of the H3K9me3-landscaped epigenome in medium spiny neurons and reduced mutant HTT nuclear inclusion formation. Moreover, nogalamycin slowed neuropathological progression, preserved motor function, and extended the life span of R6/2 mice. Together, our results indicate that modulation of SETDB1/ESET and H3K9me3-dependent heterochromatin plasticity is responsible for the neuroprotective effects of nogalamycin in HD and that small compounds targeting dysfunctional histone modification and epigenetic modification by SETDB1/ESET may be a rational therapeutic strategy in HD.

LDP-GAN : Generative adversarial networks with local differential privacy for patient medical records synthesis.

Electronic medical records(EMR) have considerable potential to advance healthcare technologies, including medical AI. Nevertheless, due to the privacy issues associated with the sharing of patient's personal information, it is difficult to sufficiently utilize them. Generative models based on deep learning can solve this problem by creating synthetic data similar to real patient data. However, the data used for training these deep learning models run into the risk of getting leaked because of malicious attacks. This means that traditional deep learning-based generative models cannot completely solve the privacy issues. Therefore, we suggested a method to prevent the leakage of training data by protecting the model from malicious attacks using local differential privacy(LDP). Our method was evaluated in terms of utility and privacy. Experimental results demonstrated that the proposed method can generate medical data with reasonable performance while protecting training data from malicious attacks.

Are polypharmacy side effects predicted by public data still valid in real-world data?

Background and Objective: Although interest in predicting drug-drug interactions is growing, many predictions are not verified by real-world data. This study aimed to confirm whether predicted polypharmacy side effects using public data also occur in data from actual patients. Methods: We utilized a deep learning-based polypharmacy side effects prediction model to identify cefpodoxime-chlorpheniramine-lung edema combination with a high prediction score and a significant patient population. The retrospective study analyzed patients over 18 years old who were admitted to the Asan medical center between January 2000 and December 2020 and took cefpodoxime or chlorpheniramine orally. The three groups, cefpodoxime-treated, chlorpheniramine-treated, and cefpodoxime & chlorpheniramine-treated were compared using inverse probability of treatment weighting (IPTW) to balance them. Differences between the three groups were analyzed using the Kaplan-Meier method and Cox proportional hazards model. Results: The study population comprised 54,043 patients with a history of taking cefpodoxime, 203,897 patients with a history of taking chlorpheniramine, and 1,628 patients with a history of taking cefpodoxime and chlorpheniramine simultaneously. After adjustment, the 1-year cumulative incidence of lung edema in the patient group that took cefpodoxime and chlorpheniramine simultaneously was significantly higher than in the patient groups that took cefpodoxime or chlorpheniramine only (p=0.001). Patients taking cefpodoxime and chlorpheniramine together had an increased risk of lung edema compared to those taking cefpodoxime alone [hazard ratio (HR) 2.10, 95% CI 1.26-3.52, p<0.005] and those taking chlorpheniramine alone, which also increased the risk of lung edema (HR 1.64, 95% CI 0.99-2.69, p=0.05). Conclusions: Validation of polypharmacy side effect predictions with real-world data can aid patient and clinician decision-making before conducting randomized controlled trials. Simultaneous use of cefpodoxime and chlorpheniramine was associated with a higher long-term risk of lung edema compared to the use of cefpodoxime or chlorpheniramine alone.

Forecasting Hospital Room and Ward Occupancy Using Static and Dynamic Information Concurrently: Retrospective Single-Center Cohort Study.

BACKGROUND: Predicting the bed occupancy rate (BOR) is essential for efficient hospital resource management, long-term budget planning, and patient care planning. Although macro-level BOR prediction for the entire hospital is crucial, predicting occupancy at a detailed level, such as specific wards and rooms, is more practical and useful for hospital scheduling. OBJECTIVE: The aim of this study was to develop a web-based support tool that allows hospital administrators to grasp the BOR for each ward and room according to different time periods. METHODS: We trained time-series models based on long short-term memory (LSTM) using individual bed data aggregated hourly each day to predict the BOR for each ward and room in the hospital. Ward training involved 2 models with 7- and 30-day time windows, and room training involved models with 3- and 7-day time windows for shorter-term planning. To further improve prediction performance, we added 2 models trained by concatenating dynamic data with static data representing room-specific details. RESULTS: We confirmed the results of a total of 12 models using bidirectional long short-term memory (Bi-LSTM) and LSTM, and the model based on Bi-LSTM showed better performance. The ward-level prediction model had a mean absolute error (MAE) of 0.067, mean square error (MSE) of 0.009, root mean square error (RMSE) of 0.094, and R2 score of 0.544. Among the room-level prediction models, the model that combined static data exhibited superior performance, with a MAE of 0.129, MSE of 0.050, RMSE of 0.227, and R2 score of 0.600. Model results can be displayed on an electronic dashboard for easy access via the web. CONCLUSIONS: We have proposed predictive BOR models for individual wards and rooms that demonstrate high performance. The results can be visualized through a web-based dashboard, aiding hospital administrators in bed operation planning. This contributes to resource optimization and the reduction of hospital resource use.

Activation of lipopolysaccharide-TLR4 signaling accelerates the ototoxic potential of cisplatin in mice.

Dysfunction in immune surveillance during anticancer chemotherapy of patients often causes weakness of the host defense system and a subsequent increase in microbial infections. However, the deterioration of organ-specific function related to microbial challenges in cisplatin-treated patients has not yet been elucidated. In this study, we investigated cisplatin-induced TLR4 expression and its binding to LPS in mouse cochlear tissues and the effect of this interaction on hearing function. Cisplatin increased the transcriptional and translational expression of TLR4 in the cochlear tissues, organ of Corti explants, and HEI-OC1 cells. Furthermore, cisplatin increased the interaction between TLR4 and its microbial ligand LPS, thereby upregulating the production of proinflammatory cytokines, such as TNF-α, IL-1ß, and IL-6, via NF-κB activation. In C57BL/6 mice, the combined injection of cisplatin and LPS caused severe hearing impairment compared with that in the control, cisplatin-alone, or LPS-alone groups, whereas this hearing dysfunction was completely suppressed in both TLR4 mutant and knockout mice. These results suggest that hearing function can be easily damaged by increased TLR expression and microbial infections due to the weakened host defense systems of cancer patients receiving therapy comprising three to six cycles of cisplatin alone or cisplatin combined with other chemotherapeutic agents. Moreover, such damage can occur even though patients may not experience ototoxic levels of cumulative cisplatin concentration.

Machine learning models to predict the warfarin discharge dosage using clinical information of inpatients from South Korea.

As warfarin has a narrow therapeutic window and obvious response variability among individuals, it is difficult to rapidly determine personalized warfarin dosage. Adverse drug events(ADE) resulting from warfarin overdose can be critical, so that typically physicians adjust the warfarin dosage through the INR monitoring twice a week when starting warfarin. Our study aimed to develop machine learning (ML) models that predicts the discharge dosage of warfarin as the initial warfarin dosage using clinical data derived from electronic medical records within 2 days of hospitalization. During this retrospective study, adult patients who were prescribed warfarin at Asan Medical Center (AMC) between January 1, 2018, and October 31, 2020, were recruited as a model development cohort (n = 3168). Additionally, we created an external validation dataset (n = 891) from a Medical Information Mart for Intensive Care III (MIMIC-III). Variables for a model prediction were selected based on the clinical rationale that turned out to be associated with warfarin dosage, such as bleeding. The discharge dosage of warfarin was used the study outcome, because we assumed that patients achieved target INR at discharge. In this study, four ML models that predicted the warfarin discharge dosage were developed. We evaluated the model performance using the mean absolute error (MAE) and prediction accuracy. Finally, we compared the accuracy of the predictions of our models and the predictions of physicians for 40 data point to verify a clinical relevance of the models. The MAEs obtained using the internal validation set were as follows: XGBoost, 0.9; artificial neural network, 0.9; random forest, 1.0; linear regression, 1.0; and physicians, 1.3. As a result, our models had better prediction accuracy than the physicians, who have difficulty determining the warfarin discharge dosage using clinical information obtained within 2 days of hospitalization. We not only conducted the internal validation but also external validation. In conclusion, our ML model could help physicians predict the warfarin discharge dosage as the initial warfarin dosage from Korean population. However, conducting a successfully external validation in a further work is required for the application of the models.

RIDAB: Electronic medical record-integrated real world data platform for predicting and summarizing interactions in biomedical research from heterogeneous data resources.

BACKGROUND AND OBJECTIVE: With the advent of bioinformatics, biological databases have been constructed to computerize data. Biological systems can be described as interactions and relationships between elements constituting the systems, and they are organized in various biomedical open databases. These open databases have been used in approaches to predict functional interactions such as protein-protein interactions (PPI), drug-drug interactions (DDI) and disease-disease relationships (DDR). However, just combining interaction data has limited effectiveness in predicting the complex relationships occurring in a whole context. Each contributing source contains information on each element in a specific field of knowledge but there is a lack of inter-disciplinary insight in combining them. METHODS: In this study, we propose the RWD Integrated platform for Discovering Associations in Biomedical research (RIDAB) to predict interactions between biomedical entities. RIDAB is established as a graph network to construct a platform that predicts the interactions of target entities. Biomedical open database is combined with EMRs each representing a biomedical network and a real-world data. To integrate databases from different domains to build the platform, mapping of the vocabularies was required. In addition, the appropriate structure of the network and the graph embedding method to be used were needed to be selected to fit the tasks. RESULTS: The feasibility of the platform was evaluated using node similarity and link prediction for drug repositioning task, a commonly used task for biomedical network. In addition, we compared the US Food and Drug Administration (FDA)-approved repositioned drugs with the predicted result. By integrating EMR database with biomedical networks, the platform showed increased f1 score in predicting repositioned drugs, from 45.62% to 57.26%, compared to platforms based on biomedical networks alone. CONCLUSIONS: This study demonstrates that the elements of biomedical research findings can be reflected by integrating EMR data with open-source biomedical networks. In addition, showed the feasibility of using the established platform to represent the integration of biomedical networks and reflected the relationship between real world networks.

Heterogeneous graph construction and HinSAGE learning from electronic medical records.

Graph representation learning is a method for introducing how to effectively construct and learn patient embeddings using electronic medical records. Adapting the integration will support and advance the previous methods to predict the prognosis of patients in network models. This study aims to address the challenge of implementing a complex and highly heterogeneous dataset, including the following: (1) demonstrating how to build a multi-attributed and multi-relational graph model (2) and applying a downstream disease prediction task of a patient's prognosis using the HinSAGE algorithm. We present a bipartite graph schema and a graph database construction in detail. The first constructed graph database illustrates a query of a predictive network that provides analytical insights using a graph representation of a patient's journey. Moreover, we demonstrate an alternative bipartite model where we apply the model to the HinSAGE to perform the link prediction task for predicting the event occurrence. Consequently, the performance evaluation indicated that our heterogeneous graph model was successfully predicted as a baseline model. Overall, our graph database successfully demonstrated efficient real-time query performance and showed HinSAGE implementation to predict cardiovascular disease event outcomes on supervised link prediction learning.

Clioquinol induces autophagy in cultured astrocytes and neurons by acting as a zinc ionophore.

Recent studies have demonstrated that clioquinol, an antibiotic with an anti-amyloid effect, acts as a zinc ionophore under physiological conditions. Because increases in labile zinc may induce autophagy, we examined whether clioquinol induces autophagy in cultured astrocytes in a zinc-dependent manner. Within 1h of exposure to 0.1-10 µM clioquinol, the levels of microtubule-associated protein 1 light chain 3 (LC3)-II, a marker of autophagy, began to increase in astrocytes. Confocal live-cell imaging of GFP-LC3-transfected astrocytes showed the formation of LC3(+) autophagic vacuoles (AVs), providing a further indication that clioquinol induced autophagy. Addition of 3-methyladenine or small-interfering RNA against autophagy-related gene 6 (ATG6/Beclin-1) blocked clioquinol-induced increases in LC3-II. FluoZin-3 fluorescence microscopy showed that, like the zinc ionophore pyrithione, clioquinol increased intracellular zinc levels in the cytosol and AVs in an extracellular zinc-dependent manner. Zinc chelation with N,N,N',N'-tetrakis-(2-pyridylmethyl) ethylenediamine (TPEN) reduced, and addition of zinc increased the levels of LC3-II and LC3(+) puncta, indicating that zinc influx plays a key role therein. Moreover, astrocytes and SH-SY5Y cells expressing mutant huntingtin (mHttQ74) accumulated less aggregates when treated with clioquinol, and this effect was reversed by TPEN. These results indicate that clioquinol-induced autophagy is likely to be physiologically functional. The present study demonstrates that clioquinol induces autophagy in a zinc-dependent manner and contributes to clearance of aggregated proteins in astrocytes and neurons. Hence, in addition to its metal-chelating effect in and around amyloid beta (Aß) plaques, clioquinol may contribute to the reduction of Aß loads by activating autophagy by increasing or normalizing intracellular zinc levels in brain cells.