MUSTANG: Multi-sample spatial transcriptomics data analysis with cross-sample transcriptional similarity guidance.

Spatially resolved transcriptomics has revolutionized genome-scale transcriptomic profiling by providing high-resolution characterization of transcriptional patterns. Here, we present our spatial transcriptomics analysis framework, MUSTANG (MUlti-sample Spatial Transcriptomics data ANalysis with cross-sample transcriptional similarity Guidance), which is capable of performing multi-sample spatial transcriptomics spot cellular deconvolution by allowing both cross-sample expression-based similarity information sharing as well as spatial correlation in gene expression patterns within samples. Experiments on a semi-synthetic spatial transcriptomics dataset and three real-world spatial transcriptomics datasets demonstrate the effectiveness of MUSTANG in revealing biological insights inherent in the cellular characterization of tissue samples under study.

Label-free optical imaging for brain cancer assessment.

Advances in label-free optical imaging offer a promising avenue for brain cancer assessment, providing high-resolution, real-time insights without the need for radiation or exogeneous agents. These cost-effective and intricately detailed techniques overcome the limitations inherent in magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET) scans by offering superior resolution and more readily accessible imaging options. This comprehensive review explores a variety of such methods, including photoacoustic imaging (PAI), optical coherence tomography (OCT), Raman imaging, and IR microscopy. It focuses on their roles in the detection, diagnosis, and management of brain tumors. By highlighting recent advances in these imaging techniques, the review aims to underscore the importance of label-free optical imaging in enhancing early detection and refining therapeutic strategies for brain cancer.

Effect of the ROCK inhibitor fasudil on the brain proteomic profile in the tau transgenic mouse model of Alzheimer's disease.

Introduction: The goal of this study is to explore the pharmacological potential of the amyloid-reducing vasodilator fasudil, a selective Ras homolog (Rho)-associated kinases (ROCK) inhibitor, in the P301S tau transgenic mouse model (Line PS19) of neurodegenerative tauopathy and Alzheimer's disease (AD). Methods: We used LC-MS/MS, ELISA and bioinformatic approaches to investigate the effect of treatment with fasudil on the brain proteomic profile in PS19 tau transgenic mice. We also explored the efficacy of fasudil in reducing tau phosphorylation, and the potential beneficial and/or toxic effects of its administration in mice. Results: Proteomic profiling of mice brains exposed to fasudil revealed the activation of the mitochondrial tricarboxylic acid (TCA) cycle and blood-brain barrier (BBB) gap junction metabolic pathways. We also observed a significant negative correlation between the brain levels of phosphorylated tau (pTau) at residue 396 and both fasudil and its metabolite hydroxyfasudil. Conclusions: Our results provide evidence on the activation of proteins and pathways related to mitochondria and BBB functions by fasudil treatment and support its further development and therapeutic potential for AD.

CD4+ T cell immunity is dependent on an intrinsic stem-like program.

CD4+ T cells are central to various immune responses, but the molecular programs that drive and maintain CD4+ T cell immunity are not entirely clear. Here we identify a stem-like program that governs the CD4+ T cell response in transplantation models. Single-cell-transcriptomic analysis revealed that naive alloantigen-specific CD4+ T cells develop into TCF1hi effector precursor (TEP) cells and TCF1-CXCR6+ effectors in transplant recipients. The TCF1-CXCR6+CD4+ effectors lose proliferation capacity and do not reject allografts upon adoptive transfer into secondary hosts. By contrast, the TCF1hiCD4+ TEP cells have dual features of self-renewal and effector differentiation potential, and allograft rejection depends on continuous replenishment of TCF1-CXCR6+ effectors from TCF1hiCD4+ TEP cells. Mechanistically, TCF1 sustains the CD4+ TEP cell population, whereas the transcription factor IRF4 and the glycolytic enzyme LDHA govern the effector differentiation potential of CD4+ TEP cells. Deletion of IRF4 or LDHA in T cells induces transplant acceptance. These findings unravel a stem-like program that controls the self-renewal capacity and effector differentiation potential of CD4+ TEP cells and have implications for T cell-related immunotherapies.

Charting Single Cell Lineage Dynamics and Mutation Networks via Homing CRISPR.

Single cell lineage tracing, essential for unraveling cellular dynamics in disease evolution is critical for developing targeted therapies. CRISPR-Cas9, known for inducing permanent and cumulative mutations, is a cornerstone in lineage tracing. The novel homing guide RNA (hgRNA) technology enhances this by enabling dynamic retargeting and facilitating ongoing genetic modifications. Charting these mutations, especially through successive hgRNA edits, poses a significant challenge. Our solution, LINEMAP, is a computational framework designed to trace and map these mutations with precision. LINEMAP meticulously discerns mutation alleles at single-cell resolution and maps their complex interrelationships through a mutation evolution network. By utilizing a Markov Process model, we can predict mutation transition probabilities, revealing potential mutational routes and pathways. Our reconstruction algorithm, anchored in the Markov model's attributes, reconstructs cellular lineage pathways, shedding light on the cell's evolutionary journey to the minutiae of single-cell division. Our findings reveal an intricate network of mutation evolution paired with a predictive Markov model, advancing our capability to reconstruct single-cell lineage via hgRNA. This has substantial implications for advancing our understanding of biological mechanisms and propelling medical research forward.

Automated catheter segmentation and tip detection in cerebral angiography with topology-aware geometric deep learning.

BACKGROUND: Visual perception of catheters and guidewires on x-ray fluoroscopy is essential for neurointervention. Endovascular robots with teleoperation capabilities are being developed, but they cannot 'see' intravascular devices, which precludes artificial intelligence (AI) augmentation that could improve precision and autonomy. Deep learning has not been explored for neurointervention and prior works in cardiovascular scenarios are inadequate as they only segment device tips, while neurointervention requires segmentation of the entire structure due to coaxial devices. Therefore, this study develops an automatic and accurate image-based catheter segmentation method in cerebral angiography using deep learning. METHODS: Catheters and guidewires were manually annotated on 3831 fluoroscopy frames collected prospectively from 40 patients undergoing cerebral angiography. We proposed a topology-aware geometric deep learning method (TAG-DL) and compared it with the state-of-the-art deep learning segmentation models, UNet, nnUNet and TransUNet. All models were trained on frontal view sequences and tested on both frontal and lateral view sequences from unseen patients. Results were assessed with centerline Dice score and tip-distance error. RESULTS: The TAG-DL and nnUNet models outperformed TransUNet and UNet. The best performing model was nnUNet, achieving a mean centerline-Dice score of 0.98 ±0.01 and a median tip-distance error of 0.43 (IQR 0.88) mm. Incorporating digital subtraction masks, with or without contrast, significantly improved performance on unseen patients, further enabling exceptional performance on lateral view fluoroscopy despite not being trained on this view. CONCLUSIONS: These results are the first step towards AI augmentation for robotic neurointervention that could amplify the reach, productivity, and safety of a limited neurointerventional workforce.

TP-0184 inhibits FLT3/ACVR1 to overcome FLT3 inhibitor resistance and hinder AML growth synergistically with venetoclax.

We identified activin A receptor type I (ACVR1), a member of the TGF-ß superfamily, as a factor favoring acute myeloid leukemia (AML) growth and a new potential therapeutic target. ACVR1 is overexpressed in FLT3-mutated AML and inhibition of ACVR1 expression sensitized AML cells to FLT3 inhibitors. We developed a novel ACVR1 inhibitor, TP-0184, which selectively caused growth arrest in FLT3-mutated AML cell lines. Molecular docking and in vitro kinase assays revealed that TP-0184 binds to both ACVR1 and FLT3 with high affinity and inhibits FLT3/ACVR1 downstream signaling. Treatment with TP-0184 or in combination with BCL2 inhibitor, venetoclax dramatically inhibited leukemia growth in FLT3-mutated AML cell lines and patient-derived xenograft models in a dose-dependent manner. These findings suggest that ACVR1 is a novel biomarker and plays a role in AML resistance to FLT3 inhibitors and that FLT3/ACVR1 dual inhibitor TP-0184 is a novel potential therapeutic tool for AML with FLT3 mutations.

A Deep Learning Decision Support Tool to Improve Risk Stratification and Reduce Unnecessary Biopsies in BI-RADS 4 Mammograms.

Purpose: To evaluate the performance of a biopsy decision support algorithmic model, the intelligent-augmented breast cancer risk calculator (iBRISK), on a multicenter patient dataset. Materials and Methods: iBRISK was previously developed by applying deep learning to clinical risk factors and mammographic descriptors from 9700 patient records at the primary institution and validated using another 1078 patients. All patients were seen from March 2006 to December 2016. In this multicenter study, iBRISK was further assessed on an independent, retrospective dataset (January 2015-June 2019) from three major health care institutions in Texas, with Breast Imaging Reporting and Data System (BI-RADS) category 4 lesions. Data were dichotomized and trichotomized to measure precision in risk stratification and probability of malignancy (POM) estimation. iBRISK score was also evaluated as a continuous predictor of malignancy, and cost savings analysis was performed. Results: The iBRISK model's accuracy was 89.5%, area under the receiver operating characteristic curve (AUC) was 0.93 (95% CI: 0.92, 0.95), sensitivity was 100%, and specificity was 81%. A total of 4209 women (median age, 56 years [IQR, 45-65 years]) were included in the multicenter dataset. Only two of 1228 patients (0.16%) in the "low" POM group had malignant lesions, while in the "high" POM group, the malignancy rate was 85.9%. iBRISK score as a continuous predictor of malignancy yielded an AUC of 0.97 (95% CI: 0.97, 0.98). Estimated potential cost savings were more than $420 million. Conclusion: iBRISK demonstrated high sensitivity in the malignancy prediction of BI-RADS 4 lesions. iBRISK may safely obviate biopsies in up to 50% of patients in low or moderate POM groups and reduce biopsy-associated costs.Keywords: Mammography, Breast, Oncology, Biopsy/Needle Aspiration, Radiomics, Precision Mammography, AI-augmented Biopsy Decision Support Tool, Breast Cancer Risk Calculator, BI-RADS 4 Mammography Risk Stratification, Overbiopsy Reduction, Probability of Malignancy (POM) Assessment, Biopsy-based Positive Predictive Value (PPV3) Supplemental material is available for this article. Published under a CC BY 4.0 license.See also the commentary by McDonald and Conant in this issue.

Life-Course Brain Health as a Determinant of Late-Life Mental Health: American Association for Geriatric Psychiatry Expert Panel Recommendations.

This position statement of the Expert Panel on Brain Health of the American Association for Geriatric Psychiatry (AAGP) emphasizes the critical role of life course brain health in shaping mental well-being during the later stages of life. Evidence posits that maintaining optimal brain health earlier in life is crucial for preventing and managing brain aging-related disorders such as dementia/cognitive decline, depression, stroke, and anxiety. We advocate for a holistic approach that integrates medical, psychological, and social frameworks with culturally tailored interventions across the lifespan to promote brain health and overall mental well-being in aging adults across all communities. Furthermore, our statement underscores the significance of prevention, early detection, and intervention in identifying cognitive decline, mood changes, and related mental illness. Action should also be taken to understand and address the needs of communities that traditionally have unequal access to preventive health information and services. By implementing culturally relevant and tailored evidence-based practices and advancing research in geriatric psychiatry, behavioral neurology, and geroscience, we can enhance the quality of life for older adults facing the unique challenges of aging. This position statement emphasizes the intrinsic link between brain health and mental health in aging, urging healthcare professionals, policymakers, and a broader society to prioritize comprehensive strategies that safeguard and promote brain health from birth through later years across all communities. The AAGP Expert Panel has the goal of launching further activities in the coming months and years.

Targeting Ribosome Biogenesis as a Novel Therapeutic Approach to Overcome EMT-related Chemoresistance in Breast Cancer.

Epithelial-to-mesenchymal transition (EMT) contributes significantly to chemotherapy resistance and remains a critical challenge in treating advanced breast cancer. The complexity of EMT, involving redundant pro-EMT signaling pathways and its paradox reversal process, mesenchymal-to-epithelial transition (MET), has hindered the development of effective treatments. In this study, we utilized a Tri-PyMT EMT lineage-tracing model and single-cell RNA sequencing (scRNA-seq) to comprehensively analyze the EMT status of tumor cells. Our findings revealed elevated ribosome biogenesis (RiBi) during the transitioning phases of both EMT and MET processes. RiBi and its subsequent nascent protein synthesis mediated by ERK and mTOR signalings are essential for EMT/MET completion. Importantly, inhibiting excessive RiBi genetically or pharmacologically impaired the EMT/MET capability of tumor cells. Combining RiBi inhibition with chemotherapy drugs synergistically reduced metastatic outgrowth of epithelial and mesenchymal tumor cells under chemotherapies. Our study suggests that targeting the RiBi pathway presents a promising strategy for treating patients with advanced breast cancer. Significance: This study uncovers the crucial involvement of ribosome biogenesis (RiBi) in the regulation of epithelial and mesenchymal state oscillations in breast cancer cells, which plays a major role in the development of chemoresistant metastasis. By proposing a novel therapeutic strategy targeting the RiBi pathway, the study offers significant potential to enhance treatment efficacy and outcomes for patients with advanced breast cancer. This approach could help overcome the limitations of current chemotherapy options and address the complex challenges posed by EMT-mediated chemoresistance.

Interferon-gamma is quintessential for NOS2 and COX2 expression in ER- breast tumors that lead to poor outcome.

A strong correlation between NOS2 and COX2 tumor expression and poor clinical outcomes in ER breast cancer has been established. However, the mechanisms of tumor induction of these enzymes are unclear. Analysis of The Cancer Genome Atlas (TCGA) revealed correlations between NOS2 and COX2 expression and Th1 cytokines. Herein, single-cell RNAseq analysis of TNBC cells shows potent NOS2 and COX2 induction by IFNγ combined with IL1ß or TNFα. Given that IFNγ is secreted by cytolytic lymphocytes, which improve clinical outcomes, this role of IFNγ presents a dichotomy. To explore this conundrum, tumor NOS2, COX2, and CD8+ T cells were spatially analyzed in aggressive ER-, TNBC, and HER2 + breast tumors. High expression and clustering of NOS2-expressing tumor cells occurred at the tumor/stroma interface in the presence of stroma-restricted CD8+ T cells. High expression and clustering of COX2-expressing tumor cells extended into immune desert regions in the tumor core where CD8+ T cell penetration was limited or absent. Moreover, high NOS2-expressing tumor cells were proximal to areas with increased satellitosis, suggestive of cell clusters with a higher metastatic potential. Further in vitro experiments revealed that IFNγ + IL1ß/TNFα increased the elongation and migration of treated tumor cells. This spatial analysis of the tumor microenvironment provides important insight into distinct neighborhoods where stroma-restricted CD8+ T cells exist proximal to NOS2-expressing tumor niches that could have increased metastatic potential.

BBox-Guided Segmentor: Leveraging expert knowledge for accurate stroke lesion segmentation using weakly supervised bounding box prior.

Stroke is one of the leading causes of death and disability in the world. Despite intensive research on automatic stroke lesion segmentation from non-invasive imaging modalities including diffusion-weighted imaging (DWI), challenges remain such as a lack of sufficient labeled data for training deep learning models and failure in detecting small lesions. In this paper, we propose BBox-Guided Segmentor, a method that significantly improves the accuracy of stroke lesion segmentation by leveraging expert knowledge. Specifically, our model uses a very coarse bounding box label provided by the expert and then performs accurate segmentation automatically. The small overhead of having the expert provide a rough bounding box leads to large performance improvement in segmentation, which is paramount to accurate stroke diagnosis. To train our model, we employ a weakly-supervised approach that uses a large number of weakly-labeled images with only bounding boxes and a small number of fully labeled images. The scarce fully labeled images are used to train a generator segmentation network, while adversarial training is used to leverage the large number of weakly-labeled images to provide additional learning signals. We evaluate our method extensively using a unique clinical dataset of 99 fully labeled cases (i.e., with full segmentation map labels) and 831 weakly labeled cases (i.e., with only bounding box labels), and the results demonstrate the superior performance of our approach over state-of-the-art stroke lesion segmentation models. We also achieve competitive performance as a SOTA fully supervised method using less than one-tenth of the complete labels. Our proposed approach has the potential to improve stroke diagnosis and treatment planning, which may lead to better patient outcomes.

Interferon-gamma is Quintessential for NOS2 and COX2 Expression in ER - Breast Tumors that Lead to Poor Outcome.

A strong correlation between NOS2 and COX2 tumor expression and poor clinical outcomes in ER-breast cancer has been established. However, mechanisms of tumor induction of these enzymes are unclear. Analysis of The Cancer Genome Atlas (TCGA) revealed correlations between NOS2 and COX2 expression and Th1 cytokines. Herein, single cell RNAseq analysis of TNBC cells shows potent NOS2 and COX2 induction by IFNγ combined with IL1ß or TNFα. Given that IFNγ is secreted by cytolytic lymphocytes, which improve clinical outcomes, this role of IFNγpresents a dichotomy. To explore this conundrum, tumor NOS2, COX2, and CD8 + T cells were spatially analyzed in aggressive ER-, TNBC, and HER2+ breast tumors. High expression and clustering of NOS2-expressing tumor cells occurred at the tumor/stroma interface in the presence of stroma-restricted CD8 + T cells. High expression and clustering of COX2-expressing tumor cells extended into immune desert regions in the tumor core where CD8 + T cell penetration was limited or absent. Moreover, high NOS2-expressing tumor cells were proximal to areas with increased satellitosis suggestive of cell clusters with a higher metastatic potential. Further in vitro experiments revealed that IFNγ+IL1ß/TNFα increased elongation and migration of treated tumor cells. This spatial analysis of the tumor microenvironment provides important insight of distinct neighborhoods where stroma-restricted CD8 + T cells exist proximal to NOS2-expressing tumor niches that could have increased metastatic potential.

Spatial Transcriptomics Depict Ligand-Receptor Cross-talk Heterogeneity at the Tumor-Stroma Interface in Long-Term Ovarian Cancer Survivors.

Advanced high-grade serous ovarian cancer (HGSC) is an aggressive disease that accounts for 70% of all ovarian cancer deaths. Nevertheless, 15% of patients diagnosed with advanced HGSC survive more than 10 years. The elucidation of predictive markers of these long-term survivors (LTS) could help identify therapeutic targets for the disease, and thus improve patient survival rates. To investigate the stromal heterogeneity of the tumor microenvironment (TME) in ovarian cancer, we used spatial transcriptomics to generate spatially resolved transcript profiles in treatment-naïve advanced HGSC from LTS and short-term survivors (STS) and determined the association between cancer-associated fibroblasts (CAF) heterogeneity and survival in patients with advanced HGSC. Spatial transcriptomics and single-cell RNA-sequencing data were integrated to distinguish tumor and stroma regions, and a computational method was developed to investigate spatially resolved ligand-receptor interactions between various tumor and CAF subtypes in the TME. A specific subtype of CAFs and its spatial location relative to a particular ovarian cancer cell subtype in the TME correlated with long-term survival in patients with advanced HGSC. Also, increased APOE-LRP5 cross-talk occurred at the stroma-tumor interface in tumor tissues from STS compared with LTS. These findings were validated using multiplex IHC. Overall, this spatial transcriptomics analysis revealed spatially resolved CAF-tumor cross-talk signaling networks in the ovarian TME that are associated with long-term survival of patients with HGSC. Further studies to confirm whether such cross-talk plays a role in modulating the malignant phenotype of HGSC and could serve as a predictive biomarker of patient survival are warranted. SIGNIFICANCE: Generation of spatially resolved gene expression patterns in tumors from patients with ovarian cancer surviving more than 10 years allows the identification of novel predictive biomarkers and therapeutic targets for better patient management. See related commentary by Kelliher and Lengyel, p. 1383.

Addressing the genetic/nongenetic duality in cancer with systems biology.

The dogma that cancer is a genetic disease is being questioned. Recent findings suggest that genetic/nongenetic duality is necessary for cancer progression. A think tank organized by the Shraman Foundation's Institute for Theoretical Biology compiled key challenges and opportunities that theoreticians, experimentalists, and clinicians can explore from a systems biology perspective to provide a better understanding of the disease as well as help discover new treatment options and therapeutic strategies.

Bone Metastasis Initiation Is Coupled with Bone Remodeling through Osteogenic Differentiation of NG2+ Cells.

The bone microenvironment is dynamic and undergoes remodeling in normal and pathologic conditions. Whether such remodeling affects disseminated tumor cells (DTC) and bone metastasis remains poorly understood. Here, we demonstrated that pathologic fractures increase metastatic colonization around the injury. NG2+ cells are a common participant in bone metastasis initiation and bone remodeling in both homeostatic and fractured conditions. NG2+ bone mesenchymal stem/stromal cells (BMSC) often colocalize with DTCs in the perivascular niche. Both DTCs and NG2+ BMSCs are recruited to remodeling sites. Ablation of NG2+ lineage impaired bone remodeling and concurrently diminished metastatic colonization. In cocultures, NG2+ BMSCs, especially when undergoing osteodifferentiation, enhanced cancer cell proliferation and migration. Knockout of N-cadherin in NG2+ cells abolished these effects in vitro and phenocopied NG2+ lineage depletion in vivo. These findings uncover dual roles of NG2+ cells in metastasis and remodeling and indicate that osteodifferentiation of BMSCs promotes metastasis initiation via N-cadherin-mediated cell-cell interaction. SIGNIFICANCE: The bone colonization of cancer cells occurs in an environment that undergoes constant remodeling. Our study provides mechanistic insights into how bone homeostasis and pathologic repair lead to the outgrowth of disseminated cancer cells, thereby opening new directions for further etiologic and epidemiologic studies of tumor recurrences. This article is highlighted in the In This Issue feature, p. 247.

Spatial analysis of NOS2 and COX2 interaction with T-effector cells reveals immunosuppressive landscapes associated with poor outcome in ER- breast cancer patients.

Multiple immunosuppressive mechanisms exist in the tumor microenvironment that drive poor outcomes and decrease treatment efficacy. The co-expression of NOS2 and COX2 is a strong predictor of poor prognosis in ER- breast cancer and other malignancies. Together, they generate pro-oncogenic signals that drive metastasis, drug resistance, cancer stemness, and immune suppression. Using an ER- breast cancer patient cohort, we found that the spatial expression patterns of NOS2 and COX2 with CD3+CD8+PD1- T effector (Teff) cells formed a tumor immune landscape that correlated with poor outcome. NOS2 was primarily associated with the tumor-immune interface, whereas COX2 was associated with immune desert regions of the tumor lacking Teff cells. A higher ratio of NOS2 or COX2 to Teff was highly correlated with poor outcomes. Spatial analysis revealed that regional clustering of NOS2 and COX2 was associated with stromal-restricted Teff, while only COX2 was predominant in immune deserts. Examination of other immunosuppressive elements, such as PDL1/PD1, Treg, B7H4, and IDO1, revealed that PDL1/PD1, Treg, and IDO1 were primarily associated with restricted Teff, whereas B7H4 and COX2 were found in tumor immune deserts. Regardless of the survival outcome, other leukocytes, such as CD4 T cells and macrophages, were primarily in stromal lymphoid aggregates. Finally, in a 4T1 model, COX2 inhibition led to a massive cell infiltration, thus validating the hypothesis that COX2 is an essential component of the Teff exclusion process and, thus, tumor evasion. Our study indicates that NOS2/COX2 expression plays a central role in tumor immunosuppression. Our findings indicate that new strategies combining clinically available NOS2/COX2 inhibitors with various forms of immune therapy may open a new avenue for the treatment of aggressive ER-breast cancers.

An AI-driven clinical care pathway to reduce 30-day readmission for chronic obstructive pulmonary disease (COPD) patients.

Healthcare regulatory agencies have mandated a reduction in 30-day hospital readmission rates and have targeted COPD as a major contributor to 30-day readmissions. We aimed to develop and validate a simple tool deploying an artificial neural network (ANN) for early identification of COPD patients with high readmission risk. Using COPD patient data from eight hospitals within a large urban hospital system, four variables were identified, weighted and validated. These included the number of in-patient admissions in the previous 6 months, the number of medications administered on the first day, insurance status, and the Rothman Index on hospital day one. An ANN model was trained to provide a predictive algorithm and validated on an additional dataset from a separate time period. The model was implemented in a smartphone app (Re-Admit) incorporating four input risk factors, and a clinical care plan focused on high-risk readmission candidates was then implemented. Subsequent readmission data was analyzed to assess impact. The areas under the curve of receiver operating characteristics predicting readmission with ANN is 0.77, with sensitivity 0.75 and specificity 0.67 on the separate validation data. Readmission rates in the COPD high-risk subgroup after app and clinical intervention implementation saw a significant 48% decline. Our studies show the efficacy of ANN model on predicting readmission risks for COPD patients. The AI enabled Re-Admit smartphone app predicts readmission risk on day one of the patient's admission, allowing for early implementation of medical, hospital, and community resources to optimize and improve clinical care pathways.

Risk factors and machine learning model for predicting hospitalization outcomes in geriatric patients with dementia.

Introduction: Geriatric patients with dementia incur higher healthcare costs and longer hospital stays than other geriatric patients. We aimed to identify risk factors for hospitalization outcomes that could be mitigated early to improve outcomes and impact overall quality of life. Methods: We identified risk factors, that is, demographics, hospital complications, pre-admission, and post-admission risk factors including medical history and comorbidities, affecting hospitalization outcomes determined by hospital stays and discharge dispositions. Over 150 clinical and demographic factors of 15,678 encounters (8407 patients) were retrieved from our institution's data warehouse. We further narrowed them down to twenty factors through feature selection engineering by using analysis of variance (ANOVA) and Glmnet. We developed an explainable machine-learning model to predict hospitalization outcomes among geriatric patients with dementia. Results: Our model is based on stacking ensemble learning and achieved accuracy of 95.6% and area under the curve (AUC) of 0.757. It outperformed prevalent methods of risk assessment for encounters of patients with Alzheimer's disease dementia (ADD) (4993), vascular dementia (VD) (4173), Parkinson's disease with dementia (PDD) (3735), and other unspecified dementias (OUD) (2777). Top identified hospitalization outcome risk factors, mostly from medical history, include encephalopathy, number of medical problems at admission, pressure ulcers, urinary tract infections, falls, admission source, age, race, anemia, etc., with several overlaps in multi-dementia groups. Discussion: Our model identified several predictive factors that can be modified or intervened so that efforts can be made to prevent recurrence or mitigate their adverse effects. Knowledge of the modifiable risk factors would help guide early interventions for patients at high risk for poor hospitalization outcome as defined by hospital stays longer than seven days, undesirable discharge disposition, or both. The interventions include starting specific protocols on modifiable risk factors like encephalopathy, falls, and infections, where non-existent or not routine, to improve hospitalization outcomes of geriatric patients with dementia. Highlights: A total 15,678 encounters of Geriatrics with dementia with a final 20 risk factors.Developed a predictive model for hospitalization outcomes for multi-dementia types.Risk factors for each type were identified including those amenable to interventions.Top factors are encephalopathy, pressure ulcers, urinary tract infection (UTI), falls, and admission source.With accuracy of 95.6%, our ensemble predictive model outperforms other models.

Ex vivo expanded human regulatory T cells modify neuroinflammation in a preclinical model of Alzheimer's disease.

BACKGROUND: Regulatory T cells (Tregs) play a neuroprotective role by suppressing microglia and macrophage-mediated inflammation and modulating adaptive immune reactions. We previously documented that Treg immunomodulatory mechanisms are compromised in Alzheimer's disease (AD). Ex vivo expansion of Tregs restores and amplifies their immunosuppressive functions in vitro. A key question is whether adoptive transfer of ex vivo expanded human Tregs can suppress neuroinflammation and amyloid pathology in a preclinical mouse model. METHODS: An immunodeficient mouse model of AD was generated by backcrossing the 5xFAD onto Rag2 knockout mice (5xFAD-Rag2KO). Human Tregs were expanded ex vivo for 24 days and administered to 5xFAD-Rag2KO. Changes in amyloid burden, microglia characteristics and reactive astrocytes were evaluated using ELISA and confocal microscopy. NanoString Mouse AD multiplex gene expression analysis was applied to explore the impact of ex vivo expanded Tregs on the neuroinflammation transcriptome. RESULTS: Elimination of mature B and T lymphocytes and natural killer cells in 5xFAD-Rag2KO mice was associated with upregulation of 95 inflammation genes and amplified number of reactive microglia within the dentate gyrus. Administration of ex vivo expanded Tregs reduced amyloid burden and reactive glial cells in the dentate gyrus and frontal cortex of 5xFAD-Rag2KO mice. Interrogation of inflammation gene expression documented down-regulation of pro-inflammatory cytokines (IL1A&B, IL6), complement cascade (C1qa, C1qb, C1qc, C4a/b), toll-like receptors (Tlr3, Tlr4 and Tlr7) and microglial activations markers (CD14, Tyrobp,Trem2) following Treg administration. CONCLUSIONS: Ex vivo expanded Tregs with amplified immunomodulatory function, suppressed neuroinflammation and alleviated AD pathology in vivo. Our results provide preclinical evidences for Treg cell therapy as a potential treatment strategy in AD.