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.

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.

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.

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.

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.

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.

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.

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.

Machine Learning-Based Hospital Discharge Prediction for Patients With Cardiovascular Diseases: Development and Usability Study.

BACKGROUND: Effective resource management in hospitals can improve the quality of medical services by reducing labor-intensive burdens on staff, decreasing inpatient waiting time, and securing the optimal treatment time. The use of hospital processes requires effective bed management; a stay in the hospital that is longer than the optimal treatment time hinders bed management. Therefore, predicting a patient's hospitalization period may support the making of judicious decisions regarding bed management. OBJECTIVE: First, this study aims to develop a machine learning (ML)-based predictive model for predicting the discharge probability of inpatients with cardiovascular diseases (CVDs). Second, we aim to assess the outcome of the predictive model and explain the primary risk factors of inpatients for patient-specific care. Finally, we aim to evaluate whether our ML-based predictive model helps manage bed scheduling efficiently and detects long-term inpatients in advance to improve the use of hospital processes and enhance the quality of medical services. METHODS: We set up the cohort criteria and extracted the data from CardioNet, a manually curated database that specializes in CVDs. We processed the data to create a suitable data set by reindexing the date-index, integrating the present features with past features from the previous 3 years, and imputing missing values. Subsequently, we trained the ML-based predictive models and evaluated them to find an elaborate model. Finally, we predicted the discharge probability within 3 days and explained the outcomes of the model by identifying, quantifying, and visualizing its features. RESULTS: We experimented with 5 ML-based models using 5 cross-validations. Extreme gradient boosting, which was selected as the final model, accomplished an average area under the receiver operating characteristic curve score that was 0.865 higher than that of the other models (ie, logistic regression, random forest, support vector machine, and multilayer perceptron). Furthermore, we performed feature reduction, represented the feature importance, and assessed prediction outcomes. One of the outcomes, the individual explainer, provides a discharge score during hospitalization and a daily feature influence score to the medical team and patients. Finally, we visualized simulated bed management to use the outcomes. CONCLUSIONS: In this study, we propose an individual explainer based on an ML-based predictive model, which provides the discharge probability and relative contributions of individual features. Our model can assist medical teams and patients in identifying individual and common risk factors in CVDs and can support hospital administrators in improving the management of hospital beds and other resources.

Self-Training With Quantile Errors for Multivariate Missing Data Imputation for Regression Problems in Electronic Medical Records: Algorithm Development Study.

BACKGROUND: When using machine learning in the real world, the missing value problem is the first problem encountered. Methods to impute this missing value include statistical methods such as mean, expectation-maximization, and multiple imputations by chained equations (MICE) as well as machine learning methods such as multilayer perceptron, k-nearest neighbor, and decision tree. OBJECTIVE: The objective of this study was to impute numeric medical data such as physical data and laboratory data. We aimed to effectively impute data using a progressive method called self-training in the medical field where training data are scarce. METHODS: In this paper, we propose a self-training method that gradually increases the available data. Models trained with complete data predict the missing values in incomplete data. Among the incomplete data, the data in which the missing value is validly predicted are incorporated into the complete data. Using the predicted value as the actual value is called pseudolabeling. This process is repeated until the condition is satisfied. The most important part of this process is how to evaluate the accuracy of pseudolabels. They can be evaluated by observing the effect of the pseudolabeled data on the performance of the model. RESULTS: In self-training using random forest (RF), mean squared error was up to 12% lower than pure RF, and the Pearson correlation coefficient was 0.1% higher. This difference was confirmed statistically. In the Friedman test performed on MICE and RF, self-training showed a P value between .003 and .02. A Wilcoxon signed-rank test performed on the mean imputation showed the lowest possible P value, 3.05e-5, in all situations. CONCLUSIONS: Self-training showed significant results in comparing the predicted values and actual values, but it needs to be verified in an actual machine learning system. And self-training has the potential to improve performance according to the pseudolabel evaluation method, which will be the main subject of our future research.

Dysfunction of X-linked inhibitor of apoptosis protein (XIAP) triggers neuropathological processes via altered p53 activity in Huntington's disease.

Mitochondrial dysfunction is associated with neuronal damage in Huntington's disease (HD), but the precise mechanism of mitochondria-dependent pathogenesis is not understood yet. Herein, we found that colocalization of XIAP and p53 was prominent in the cytosolic compartments of normal subjects but reduced in HD patients and HD transgenic animal models. Overexpression of mutant Huntingtin (mHTT) reduced XIAP levels and elevated mitochondrial localization of p53 in striatal cells in vitro and in vivo. Interestingly, XIAP interacted directly with the C-terminal domain of p53 and decreased its stability via autophagy. Overexpression of XIAP prevented mitochondrially targeted-p53 (Mito-p53)-induced mitochondrial oxidative stress and striatal cell death, whereas, knockdown of XIAP exacerbated Mito-p53-induced neuronal damage in vitro. In vivo transduction of AAV-shRNA XIAP in the dorsal striatum induced rapid onset of disease and reduced the lifespan of HD transgenic (N171-82Q) mice compared to WT littermate mice. XIAP dysfunction led to ultrastructural changes of the mitochondrial cristae and nucleus morphology in striatal cells. Knockdown of XIAP exacerbated neuropathology and motor dysfunctions in N171-82Q mice. In contrast, XIAP overexpression improved neuropathology and motor behaviors in both AAV-mHTT-transduced mice and N171-82Q mice. Our data provides a molecular and pathological mechanism that deregulation of XIAP triggers mitochondria dysfunction and other neuropathological processes via the neurotoxic effect of p53 in HD. Together, the XIAP-p53 pathway is a novel pathological marker and can be a therapeutic target for improving the symptoms in 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.

Development of the phenylpyrazolo[3,4-d]pyrimidine-based, insulin-like growth factor receptor/Src/AXL-targeting small molecule kinase inhibitor.

Rationale: The type I insulin-like growth factor receptor (IGF-1R) signaling pathway plays key roles in the development and progression of numerous types of human cancers, and Src and AXL have been found to confer resistance to anti-IGF-1R therapies. Hence, co-targeting Src and AXL may be an effective strategy to overcome resistance to anti-IGF-1R therapies. However, pharmacologic targeting of these three kinases may result in enhanced toxicity. Therefore, the development of novel multitarget anticancer drugs that block IGF-1R, Src, and AXL is urgently needed. Methods: We synthesized a series of phenylpyrazolo[3,4-d]pyrimidine (PP)-based compounds, wherein the PP module was conjugated with 2,4-bis-arylamino-1,3-pyrimidines (I2) via a copper(I)-catalyzed alkyne-azide cycloaddition reaction. To develop IGF-1R/Src/AXL-targeting small molecule kinase inhibitors, we selected LL6 as an active compound and evaluated its antitumor and antimetastatic effects in vitro and in vivo using the MTT assay, colony formation assays, migration assay, flow cytometric analysis, a tumor xenograft model, the KrasG12D/+ -driven spontaneous lung tumorigenesis model, and a spontaneous metastasis model using Lewis lung carcinoma (LLC) allografts. We also determined the toxicity of LL6 in vitro and in vivo. Results: LL6 induced apoptosis and suppressed viability and colony-forming capacities of various non-small cell lung cancer (NSCLC) cell lines and their sublines with drug resistance. LL6 also suppressed the migration of NSCLC cells at nontoxic doses. Administration of LL6 in mice significantly suppressed the growth of NSCLC xenograft tumors and metastasis of LLC allograft tumors with outstanding toxicity profiles. Furthermore, the multiplicity, volume, and load of lung tumors in KrasG12D/+ transgenic mice were substantially reduced by the LL6 treatment. Conclusions: Our results show the potential of LL6 as a novel IGF-1R/Src/AXL-targeting small molecule kinase inhibitor, providing a new avenue for anticancer therapies.

Severe reactive astrocytes precipitate pathological hallmarks of Alzheimer's disease via H2O2- production.

Although the pathological contributions of reactive astrocytes have been implicated in Alzheimer's disease (AD), their in vivo functions remain elusive due to the lack of appropriate experimental models and precise molecular mechanisms. Here, we show the importance of astrocytic reactivity on the pathogenesis of AD using GiD, a newly developed animal model of reactive astrocytes, where the reactivity of astrocytes can be manipulated as mild (GiDm) or severe (GiDs). Mechanistically, excessive hydrogen peroxide (H2O2) originated from monoamine oxidase B in severe reactive astrocytes causes glial activation, tauopathy, neuronal death, brain atrophy, cognitive impairment and eventual death, which are significantly prevented by AAD-2004, a potent H2O2 scavenger. These H2O2--induced pathological features of AD in GiDs are consistently recapitulated in a three-dimensional culture AD model, virus-infected APP/PS1 mice and the brains of patients with AD. Our study identifies H2O2 from severe but not mild reactive astrocytes as a key determinant of neurodegeneration in AD.

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.

A Versatile Adeno-Associated Viral Vector Cross-Linking Platform Capable of Tuning Cellular Tropisms and Simultaneously Inducing Solid-Phase Gene Delivery.

Developing gene carriers with improved affinities for target cells and the simultaneous diversification of their delivery modes will be pivotal for upgrading gene therapy technologies. In this study, a simple and versatile adeno-associated virus (AAV) conjugation platform using the cross-linker 3,3'-dithiobis(sulfosuccinimidyl propionate) (DTSSP) is proposed. Depending on the quantity of the DTSSP molecules, the AAV-DTSSP complexes could either be linked with the relevant biomolecules for altering cellular tropisms or further form a self-assembled AAV-DTSSP pellet capable of mimicking a polymeric gene delivery system. At lower quantities of DTSSP, the AAV-DTSSP complexes were conjugated with aminated l-fucose molecules, whose levels are typically upregulated in pancreatic cancer cells, resulting in enhanced gene delivery efficiencies in pancreatic cancer cells. At higher concentrations of DTSSP, visible solid forms of the AAV-DTSSP pellets were formed, and the AAV pellets demonstrated the capability to induce a localized, sustained gene expression pattern comparable to that of conventional biomaterial-based approaches. Thus, a multipurpose AAV cross-linking platform, which can enable AAV vector systems that are capable of altering cellular tropisms and simultaneously inducing solid-phase delivery, will provide crucial insights into vector design for further upgrading of gene delivery technologies.