N6-methyladenosine in 7SK small nuclear RNA underlies RNA polymerase II transcription regulation.

N6-methyladenosine (m6A) modifications play crucial roles in RNA metabolism. How m6A regulates RNA polymerase II (RNA Pol II) transcription remains unclear. We find that 7SK small nuclear RNA (snRNA), a regulator of RNA Pol II promoter-proximal pausing, is highly m6A-modified in non-small cell lung cancer (NSCLC) cells. In A549 cells, we identified eight m6A sites on 7SK and discovered methyltransferase-like 3 (METTL3) and alkB homolog 5 (ALKBH5) as the responsible writer and eraser. When the m6A-7SK is specifically erased by a dCasRx-ALKBH5 fusion protein, A549 cell growth is attenuated due to reduction of RNA Pol II transcription. Mechanistically, removal of m6A leads to 7SK structural rearrangements that facilitate sequestration of the positive transcription elongation factor b (P-TEFb) complex, which results in reduction of serine 2 phosphorylation (Ser2P) in the RNA Pol II C-terminal domain and accumulation of RNA Pol II in the promoter-proximal region. Taken together, we uncover that m6A modifications of a non-coding RNA regulate RNA Pol II transcription and NSCLC tumorigenesis.

Graph contrastive learning as a versatile foundation for advanced scRNA-seq data analysis.

Single-cell RNA sequencing (scRNA-seq) offers unprecedented insights into transcriptome-wide gene expression at the single-cell level. Cell clustering has been long established in the analysis of scRNA-seq data to identify the groups of cells with similar expression profiles. However, cell clustering is technically challenging, as raw scRNA-seq data have various analytical issues, including high dimensionality and dropout values. Existing research has developed deep learning models, such as graph machine learning models and contrastive learning-based models, for cell clustering using scRNA-seq data and has summarized the unsupervised learning of cell clustering into a human-interpretable format. While advances in cell clustering have been profound, we are no closer to finding a simple yet effective framework for learning high-quality representations necessary for robust clustering. In this study, we propose scSimGCL, a novel framework based on the graph contrastive learning paradigm for self-supervised pretraining of graph neural networks. This framework facilitates the generation of high-quality representations crucial for cell clustering. Our scSimGCL incorporates cell-cell graph structure and contrastive learning to enhance the performance of cell clustering. Extensive experimental results on simulated and real scRNA-seq datasets suggest the superiority of the proposed scSimGCL. Moreover, clustering assignment analysis confirms the general applicability of scSimGCL, including state-of-the-art clustering algorithms. Further, ablation study and hyperparameter analysis suggest the efficacy of our network architecture with the robustness of decisions in the self-supervised learning setting. The proposed scSimGCL can serve as a robust framework for practitioners developing tools for cell clustering. The source code of scSimGCL is publicly available at https://github.com/zhangzh1328/scSimGCL.

Morphological profiling for drug discovery in the era of deep learning.

Morphological profiling is a valuable tool in phenotypic drug discovery. The advent of high-throughput automated imaging has enabled the capturing of a wide range of morphological features of cells or organisms in response to perturbations at the single-cell resolution. Concurrently, significant advances in machine learning and deep learning, especially in computer vision, have led to substantial improvements in analyzing large-scale high-content images at high throughput. These efforts have facilitated understanding of compound mechanism of action, drug repurposing, characterization of cell morphodynamics under perturbation, and ultimately contributing to the development of novel therapeutics. In this review, we provide a comprehensive overview of the recent advances in the field of morphological profiling. We summarize the image profiling analysis workflow, survey a broad spectrum of analysis strategies encompassing feature engineering- and deep learning-based approaches, and introduce publicly available benchmark datasets. We place a particular emphasis on the application of deep learning in this pipeline, covering cell segmentation, image representation learning, and multimodal learning. Additionally, we illuminate the application of morphological profiling in phenotypic drug discovery and highlight potential challenges and opportunities in this field.

Glucagon-Like Peptide-1 Receptor Agonists and Risk for Suicidal Ideation and Behaviors in U.S. Older Adults With Type 2 Diabetes : A Target Trial Emulation Study.

BACKGROUND: A major concern has recently emerged about a potential link between glucagon-like peptide-1 receptor agonists (GLP-1 RAs) and increased risk for suicidal ideation and behaviors based on International Classification of Diseases codes. OBJECTIVE: To investigate the association between GLP-1 RAs, compared with sodium-glucose cotransporter-2 inhibitors (SGLT2is) or dipeptidyl peptidase-4 inhibitors (DPP4is), and risk for suicidal ideation and behaviors in older adults with type 2 diabetes (T2D). DESIGN: Two target trial emulation studies comparing propensity score (PS)-matched cohorts for GLP-1 RAs versus SGLT2is and GLP-1 RAs versus DPP4is. SETTING: U.S. national Medicare administrative data from January 2017 to December 2020. PATIENTS: Older adults (≥66 years) with T2D; no record of suicidal ideation or behaviors; and a first prescription for a GLP-1 RA, SGLT2i, or DPP4i. MEASUREMENTS: The primary end point was a composite of suicidal ideation and behaviors. New GLP-1 RA users were matched 1:1 on PS to new users of an SGLT2i or DPP4i in each pairwise comparison. A Cox proportional hazards regression was used to estimate the hazard ratio (HR) and 95% CIs within matched groups. RESULTS: This study included 21 807 pairs of patients treated with a GLP-1 RA versus an SGLT2i and 21 402 pairs of patients treated with a GLP-1 RA versus a DPP4i. The HR of suicidal ideation and behaviors associated with GLP-1 RAs relative to SGLT2is was 1.07 (95% CI, 0.80 to 1.45; rate difference, 0.16 [CI, -0.53 to 0.86] per 1000 person-years); the HR relative to DPP4is was 0.94 (CI, 0.71 to 1.24; rate difference, -0.18 [CI, -0.92 to 0.57] per 1000 person-years). LIMITATIONS: Low event rate; imprecise estimates; unmeasured confounders, such as body mass index; and potential misclassification of outcomes. CONCLUSION: Among Medicare beneficiaries with T2D, this study found no clear increased risk for suicidal ideation and behaviors with GLP-1 RAs, although estimates were imprecise and a modest adverse risk could not be ruled out. PRIMARY FUNDING SOURCE: American Foundation for Pharmaceutical Education, Pharmaceutical Research and Manufacturers of America Foundation, National Institute on Aging, and National Institute of Diabetes and Digestive and Kidney Diseases.

Real-World Effectiveness of BNT162b2 Against Infection and Severe Diseases in Children and Adolescents.

BACKGROUND: The efficacy of the BNT162b2 vaccine in pediatrics was assessed by randomized trials before the Omicron variant's emergence. The long-term durability of vaccine protection in this population during the Omicron period remains limited. OBJECTIVE: To assess the effectiveness of BNT162b2 in preventing infection and severe diseases with various strains of the SARS-CoV-2 virus in previously uninfected children and adolescents. DESIGN: Comparative effectiveness research accounting for underreported vaccination in 3 study cohorts: adolescents (12 to 20 years) during the Delta phase and children (5 to 11 years) and adolescents (12 to 20 years) during the Omicron phase. SETTING: A national collaboration of pediatric health systems (PEDSnet). PARTICIPANTS: 77 392 adolescents (45 007 vaccinated) during the Delta phase and 111 539 children (50 398 vaccinated) and 56 080 adolescents (21 180 vaccinated) during the Omicron phase. INTERVENTION: First dose of the BNT162b2 vaccine versus no receipt of COVID-19 vaccine. MEASUREMENTS: Outcomes of interest include documented infection, COVID-19 illness severity, admission to an intensive care unit (ICU), and cardiac complications. The effectiveness was reported as (1-relative risk)*100, with confounders balanced via propensity score stratification. RESULTS: During the Delta period, the estimated effectiveness of the BNT162b2 vaccine was 98.4% (95% CI, 98.1% to 98.7%) against documented infection among adolescents, with no statistically significant waning after receipt of the first dose. An analysis of cardiac complications did not suggest a statistically significant difference between vaccinated and unvaccinated groups. During the Omicron period, the effectiveness against documented infection among children was estimated to be 74.3% (CI, 72.2% to 76.2%). Higher levels of effectiveness were seen against moderate or severe COVID-19 (75.5% [CI, 69.0% to 81.0%]) and ICU admission with COVID-19 (84.9% [CI, 64.8% to 93.5%]). Among adolescents, the effectiveness against documented Omicron infection was 85.5% (CI, 83.8% to 87.1%), with 84.8% (CI, 77.3% to 89.9%) against moderate or severe COVID-19, and 91.5% (CI, 69.5% to 97.6%) against ICU admission with COVID-19. The effectiveness of the BNT162b2 vaccine against the Omicron variant declined 4 months after the first dose and then stabilized. The analysis showed a lower risk for cardiac complications in the vaccinated group during the Omicron variant period. LIMITATION: Observational study design and potentially undocumented infection. CONCLUSION: This study suggests that BNT162b2 was effective for various COVID-19-related outcomes in children and adolescents during the Delta and Omicron periods, and there is some evidence of waning effectiveness over time. PRIMARY FUNDING SOURCE: National Institutes of Health.

Au@Pd nanozyme-mediated catalytic therapy: a novel strategy for targeting tumor microenvironment in cancer treatment.

BACKGROUND: Breast cancer, with its high morbidity and mortality rates, is a significant global health burden. Traditional treatments-surgery, chemotherapy, and radiotherapy-are widely used but come with drawbacks such as recurrence, metastasis, and significant side effects, including damage to healthy tissues. To address these limitations, new therapeutic strategies are being developed. Peroxidases (POD) can catalyze excess H2O2 in the tumor microenvironment to generate reactive oxygen species (ROS), which induce cancer cell apoptosis by disrupting redox homeostasis and modulating apoptosis-related proteins. However, natural enzymes face challenges like poor stability, high cost, and sensitivity to environmental conditions, limiting their application in breast cancer treatment. Nanozymes, nanomaterials with enzyme-like activity, offer a promising alternative by overcoming these limitations. METHODS: In this study, we successfully prepared Au@Pd nanozymes with peroxidase activity by depositing metallic Pd on Au nanoparticles (Au NPs) synthesized using a trisodium citrate reduction method and ascorbic acid reduction. The in vitro validation was conducted through a series of experiments, including ROS detection, flow cytometry, CCK-8 assay, DNA damage assessment, live/dead cell staining, Western blot (WB), and qPCR. Tumor treatment was performed via tail vein injection of the drug, followed by HE staining of the treated tissues and biochemical analysis of the blood. RESULTS: Au@Pd nanozymes can effectively accumulate at the tumor site through the EPR effect and exert peroxidase-like activity, catalyzing the excess H2O2 in the tumor microenvironment to produce ROS. This triggers apoptosis pathways and DNA damage, leading to the downregulation of the anti-apoptotic protein Bcl-2, upregulation of the pro-apoptotic protein Bax, and induction of apoptosis-related genes, demonstrating strong anti-tumor effects. CONCLUSIONS: This study developed an efficient nanozyme-mediated catalytic therapy strategy targeting the tumor microenvironment for the treatment of breast cancer cells.

Glucagon-Like Peptide-1 Receptor Agonists and Risk of Parkinson's Disease in Patients with Type 2 Diabetes: A Population-Based Cohort Study.

BACKGROUND: Previous studies have suggested that glucagon-like peptide-1 receptor agonists (GLP-1RAs) may have a disease-modifying effect in the development of Parkinson's disease (PD), but population studies yielded inconsistent results. OBJECTIVE: The aim was to compare the risk of PD associated with GLP-1RAs compared to dipeptidyl peptidase 4 inhibitors (DPP4i) among older adults with type 2 diabetes (T2D). METHODS: Using U.S. Medicare administrative data from 2016 to 2020, we conducted a population-based cohort study comparing the new use of GLP-1RA with the new use of DPP4i among adults aged ≥66 years with T2D. The primary endpoint was a new diagnosis of PD. A stabilized inverse probability of treatment weighting (sIPTW)-adjusted Cox proportional hazards regression model was employed to estimate the hazard ratio (HR) and 95% confidence intervals (CI) for PD between GLP-1RA and DPP4i users. RESULTS: This study included 89,074 Medicare beneficiaries who initiated either GLP-1RA (n = 30,091) or DPP4i (n = 58,983). The crude incidence rate of PD was lower among GLP-1RA users than DPP4i users (2.85 vs. 3.92 patients per 1000 person-years). An sIPTW-adjusted Cox model showed that GLP-1RA users were associated with a 23% lower risk of PD than DPP4i users (HR, 0.77; 95% CI, 0.63-0.95). Our findings were largely consistent across different subgroup analyses such as sex, race, and molecular structure of GLP-1RA. CONCLUSION: Among Medicare beneficiaries with T2D, the new use of GLP-1RAs was significantly associated with a decreased risk of PD compared to the new use of DPP4i. © 2024 International Parkinson and Movement Disorder Society.

Sodium-glucose cotransporter 2 inhibitors and the risk of Parkinson disease in real-world patients with type 2 diabetes.

IMPORTANCE: Diabetes increases the risk of Parkinson disease (PD). Sodium-glucose cotransporter 2 (SGLT2) inhibitors, a new glucose-lowering therapeutic class, have shown neuroprotective effects in mechanistic studies. However, the association between SGLT2 inhibitors and PD risk in real-world populations with type 2 diabetes (T2D) remains unclear. OBJECTIVE: The aim was to assess the association between SGLT2 inhibitors and the risk of PD in older populations with T2D. DESIGN, SETTING AND PARTICIPANTS: This retrospective cohort analysis used Medicare claims data from 2016 to 2020 to identify fee-for-service beneficiaries ≥65 years diagnosed with T2D and without pre-existing PD. EXPOSURES: The initiation of an SGLT2 inhibitor was compared with that of a dipeptidyl peptidase-4 (DPP4) inhibitor. MAIN OUTCOMES AND MEASURES: The outcome was the first incident PD ever since the date initiating either an SGLT2 inhibitor or a DPP4 inhibitor. We employed a 1:1 propensity score matching to balance the baseline covariates between treatment groups, including sociodemographics, comorbidities and co-medications. We applied Cox regression models to assess the effect of SGLT2 inhibitors versus DPP4 inhibitors on incident PD. RESULTS: Of 89 330 eligible Medicare beneficiaries (mean age: 75 ± 7 years, 52% women), 0.6% (n = 537) had incident PD over the follow-up. After 1:1 propensity matching, the PD incidence was 2.5 and 3.5 events per 1000 person-years in the SGLT2 inhibitor group and DPP4 inhibitor group, respectively. The SGLT2 inhibitor group was associated with a significantly lower risk of incident PD than the DPP4 inhibitor group (hazard ratio: 0.70 [95% confidence interval: 0.55-0.89]). There is a potential trend that the risk reduction in incident PD was profound in non-Hispanic Black individuals and insulin users. CONCLUSION AND RELEVANCE: Compared to DPP4 inhibitors, SGLT2 inhibitors were associated with a significantly lower risk of incident PD in older populations with T2D.

Developing an automated algorithm for identification of children and adolescents with diabetes using electronic health records from the OneFlorida+ clinical research network.

AIM: To develop an automated computable phenotype (CP) algorithm for identifying diabetes cases in children and adolescents using electronic health records (EHRs) from the UF Health System. MATERIALS AND METHODS: The CP algorithm was iteratively derived based on structured data from EHRs (UF Health System 2012-2020). We randomly selected 536 presumed cases among individuals aged <18 years who had (1) glycated haemoglobin levels ≥ 6.5%; or (2) fasting glucose levels ≥126 mg/dL; or (3) random plasma glucose levels ≥200 mg/dL; or (4) a diabetes-related diagnosis code from an inpatient or outpatient encounter; or (5) prescribed, administered, or dispensed diabetes-related medication. Four reviewers independently reviewed the patient charts to determine diabetes status and type. RESULTS: Presumed cases without type 1 (T1D) or type 2 diabetes (T2D) diagnosis codes were categorized as non-diabetes/other types of diabetes. The rest were categorized as T1D if the most recent diagnosis was T1D, or otherwise categorized as T2D if the most recent diagnosis was T2D. Next, we applied a list of diagnoses and procedures that can determine diabetes type (e.g., steroid use suggests induced diabetes) to correct misclassifications from Step 1. Among the 536 reviewed cases, 159 and 64 had T1D and T2D, respectively. The sensitivity, specificity, and positive predictive values of the CP algorithm were 94%, 98% and 96%, respectively, for T1D and 95%, 95% and 73% for T2D. CONCLUSION: We developed a highly accurate EHR-based CP for diabetes in youth based on EHR data from UF Health. Consistent with prior studies, T2D was more difficult to identify using these methods.

Advancing Interpretable Regression Analysis for Binary Data: A Novel Distributed Algorithm Approach.

Sparse data bias, where there is a lack of sufficient cases, is a common problem in data analysis, particularly when studying rare binary outcomes. Although a two-step meta-analysis approach may be used to lessen the bias by combining the summary statistics to increase the number of cases from multiple studies, this method does not completely eliminate bias in effect estimation. In this paper, we propose a one-shot distributed algorithm for estimating relative risk using a modified Poisson regression for binary data, named ODAP-B. We evaluate the performance of our method through both simulation studies and real-world case analyses of postacute sequelae of SARS-CoV-2 infection in children using data from 184 501 children across eight national academic medical centers. Compared with the meta-analysis method, our method provides closer estimates of the relative risk for all outcomes considered including syndromic and systemic outcomes. Our method is communication-efficient and privacy-preserving, requiring only aggregated data to obtain relatively unbiased effect estimates compared with two-step meta-analysis methods. Overall, ODAP-B is an effective distributed learning algorithm for Poisson regression to study rare binary outcomes. The method provides inference on adjusted relative risk with a robust variance estimator.

CLCAP: Contrastive learning improves antigenicity prediction for influenza A virus using convolutional neural networks.

Influenza viruses are detected year-round over the world and the viruses will usually circulate during fall and winter, causing the seasonal flu. The growing novel variants of influenza viruses pose a significant concern to public health annually. However, the rapid mutation of the influenza viruses makes it challenging to timely track their evolution. Therefore, a fast, low-cost, and precise method to predict the antigenic variant of influenza viruses could help vaccine development and prevent viral transmission. In this study, we propose a multi-channel convolutional neural network using contrastive learning to predict the antigenicity of influenza A viruses. An integrated dataset containing antigenic data and protein sequences was collected from various public resources and literature. The experimental results on three different influenza subtypes indicate our proposed model outperforms other traditional machine learning classifiers for antigenicity prediction. In addition, it also demonstrates superior performance over several state-of-the-art approaches, with 5.18 %, 7.03 % and 7.82 % increase in accuracy compared to the best results for H1N1, H3N2 and H5N1, respectively. The proposed framework is timely and effective in influenza antigenicity prediction and can be adapted to the study of other viruses.

Model tuning or prompt Tuning? a study of large language models for clinical concept and relation extraction.

OBJECTIVE: To develop soft prompt-based learning architecture for large language models (LLMs), examine prompt-tuning using frozen/unfrozen LLMs, and assess their abilities in transfer learning and few-shot learning. METHODS: We developed a soft prompt-based learning architecture and compared 4 strategies including (1) fine-tuning without prompts; (2) hard-prompting with unfrozen LLMs; (3) soft-prompting with unfrozen LLMs; and (4) soft-prompting with frozen LLMs. We evaluated GatorTron, a clinical LLM with up to 8.9 billion parameters, and compared GatorTron with 4 existing transformer models for clinical concept and relation extraction on 2 benchmark datasets for adverse drug events and social determinants of health (SDoH). We evaluated the few-shot learning ability and generalizability for cross-institution applications. RESULTS AND CONCLUSION: When LLMs are unfrozen, GatorTron-3.9B with soft prompting achieves the best strict F1-scores of 0.9118 and 0.8604 for concept extraction, outperforming the traditional fine-tuning and hard prompt-based models by 0.6 âˆ¼ 3.1 % and 1.2 âˆ¼ 2.9 %, respectively; GatorTron-345 M with soft prompting achieves the best F1-scores of 0.8332 and 0.7488 for end-to-end relation extraction, outperforming other two models by 0.2 âˆ¼ 2 % and 0.6 âˆ¼ 11.7 %, respectively. When LLMs are frozen, small LLMs have a big gap to be competitive with unfrozen models; scaling LLMs up to billions of parameters makes frozen LLMs competitive with unfrozen models. Soft prompting with a frozen GatorTron-8.9B model achieved the best performance for cross-institution evaluation. We demonstrate that (1) machines can learn soft prompts better than hard prompts composed by human, (2) frozen LLMs have good few-shot learning ability and generalizability for cross-institution applications, (3) frozen LLMs reduce computing cost to 2.5 âˆ¼ 6 % of previous methods using unfrozen LLMs, and (4) frozen LLMs require large models (e.g., over several billions of parameters) for good performance.

A scoping review of fair machine learning techniques when using real-world data.

OBJECTIVE: The integration of artificial intelligence (AI) and machine learning (ML) in health care to aid clinical decisions is widespread. However, as AI and ML take important roles in health care, there are concerns about AI and ML associated fairness and bias. That is, an AI tool may have a disparate impact, with its benefits and drawbacks unevenly distributed across societal strata and subpopulations, potentially exacerbating existing health inequities. Thus, the objectives of this scoping review were to summarize existing literature and identify gaps in the topic of tackling algorithmic bias and optimizing fairness in AI/ML models using real-world data (RWD) in health care domains. METHODS: We conducted a thorough review of techniques for assessing and optimizing AI/ML model fairness in health care when using RWD in health care domains. The focus lies on appraising different quantification metrics for accessing fairness, publicly accessible datasets for ML fairness research, and bias mitigation approaches. RESULTS: We identified 11 papers that are focused on optimizing model fairness in health care applications. The current research on mitigating bias issues in RWD is limited, both in terms of disease variety and health care applications, as well as the accessibility of public datasets for ML fairness research. Existing studies often indicate positive outcomes when using pre-processing techniques to address algorithmic bias. There remain unresolved questions within the field that require further research, which includes pinpointing the root causes of bias in ML models, broadening fairness research in AI/ML with the use of RWD and exploring its implications in healthcare settings, and evaluating and addressing bias in multi-modal data. CONCLUSION: This paper provides useful reference material and insights to researchers regarding AI/ML fairness in real-world health care data and reveals the gaps in the field. Fair AI/ML in health care is a burgeoning field that requires a heightened research focus to cover diverse applications and different types of RWD.

Identifying social determinants of health from clinical narratives: A study of performance, documentation ratio, and potential bias.

OBJECTIVE: To develop a natural language processing (NLP) package to extract social determinants of health (SDoH) from clinical narratives, examine the bias among race and gender groups, test the generalizability of extracting SDoH for different disease groups, and examine population-level extraction ratio. METHODS: We developed SDoH corpora using clinical notes identified at the University of Florida (UF) Health. We systematically compared 7 transformer-based large language models (LLMs) and developed an open-source package - SODA (i.e., SOcial DeterminAnts) to facilitate SDoH extraction from clinical narratives. We examined the performance and potential bias of SODA for different race and gender groups, tested the generalizability of SODA using two disease domains including cancer and opioid use, and explored strategies for improvement. We applied SODA to extract 19 categories of SDoH from the breast (n = 7,971), lung (n = 11,804), and colorectal cancer (n = 6,240) cohorts to assess patient-level extraction ratio and examine the differences among race and gender groups. RESULTS: We developed an SDoH corpus using 629 clinical notes of cancer patients with annotations of 13,193 SDoH concepts/attributes from 19 categories of SDoH, and another cross-disease validation corpus using 200 notes from opioid use patients with 4,342 SDoH concepts/attributes. We compared 7 transformer models and the GatorTron model achieved the best mean average strict/lenient F1 scores of 0.9122 and 0.9367 for SDoH concept extraction and 0.9584 and 0.9593 for linking attributes to SDoH concepts. There is a small performance gap (∼4%) between Males and Females, but a large performance gap (>16 %) among race groups. The performance dropped when we applied the cancer SDoH model to the opioid cohort; fine-tuning using a smaller opioid SDoH corpus improved the performance. The extraction ratio varied in the three cancer cohorts, in which 10 SDoH could be extracted from over 70 % of cancer patients, but 9 SDoH could be extracted from less than 70 % of cancer patients. Individuals from the White and Black groups have a higher extraction ratio than other minority race groups. CONCLUSIONS: Our SODA package achieved good performance in extracting 19 categories of SDoH from clinical narratives. The SODA package with pre-trained transformer models is available at https://github.com/uf-hobi-informatics-lab/SODA_Docker.

Sequencing of Argonaute-bound microRNA/mRNA hybrids reveals regulation of the unfolded protein response by microRNA-320a.

MicroRNAs (miRNA) are short non-coding RNAs widely implicated in gene regulation. Most metazoan miRNAs utilize the RNase III enzymes Drosha and Dicer for biogenesis. One notable exception is the RNA polymerase II transcription start sites (TSS) miRNAs whose biogenesis does not require Drosha. The functional importance of the TSS-miRNA biogenesis is uncertain. To better understand the function of TSS-miRNAs, we applied a modified Crosslinking, Ligation, and Sequencing of Hybrids on Argonaute (AGO-qCLASH) to identify the targets for TSS-miRNAs in HCT116 colorectal cancer cells with or without DROSHA knockout. We observed that miR-320a hybrids dominate in TSS-miRNA hybrids identified by AGO-qCLASH. Targets for miR-320a are enriched for the eIF2 signaling pathway, a downstream component of the unfolded protein response. Consistently, in miR-320a mimic- and antagomir- transfected cells, differentially expressed gene products are associated with eIF2 signaling. Within the AGO-qCLASH data, we identified the endoplasmic reticulum (ER) chaperone calnexin as a direct miR-320a down-regulated target, thus connecting miR-320a to the unfolded protein response. During ER stress, but not amino acid deprivation, miR-320a up-regulates ATF4, a critical transcription factor for resolving ER stress. In summary, our study investigates the targetome of the TSS-miRNAs in colorectal cancer cells and establishes miR-320a as a regulator of unfolded protein response.

A Combined Method of Seismic Monitoring and Transient Electromagnetic Detection for the Evaluation of Hydraulic Fracturing Effect in Coal Burst Prevention.

In order to mitigate the risk of roof-dominated coal burst in underground coal mining, horizontal long borehole staged hydraulic fracturing technology has been prevailingly employed to facilitate the weakening treatment of the hard roof in advance. Such weakening effect, however, can hardly be evaluated, which leads to a lack of a basis in which to design the schemes and parameters of hydraulic fracturing. In this study, a combined underground-ground integrated microseismic monitoring and transient electromagnetic detection method was utilized to carry out simultaneous evaluations of the seismic responses to each staged fracturing and the apparent resistivity changes before and after all finished fracturing. On this basis, the comparable and applicable fracturing effects on coal burst prevention were evaluated and validated by the distribution of microseismic events and their energy magnitude during the mining process. Results show that the observed mining-induced seismic events are consistent with the evaluation results obtained from the combined seismic-electromagnetic detection method. However, there is a limited reduction effect on resistivity near the fractured section that induces far-field seismic events. Mining-induced seismic events are concentrated primarily within specific areas, while microseismic events in the fractured area exhibit high frequency but low energy overall. This study validates the rationality of combined seismic-electromagnetic detection results and provides valuable insights for optimizing fracturing construction schemes as well as comprehensively evaluating outcomes associated with underground directional long borehole staged hydraulic fracturing.

Heterogeneous treatment effects of metformin on risk of dementia in patients with type 2 diabetes: A longitudinal observational study.

INTRODUCTION: Little is known about the heterogeneous treatment effects of metformin on dementia risk in people with type 2 diabetes (T2D). METHODS: Participants (≥ 50 years) with T2D and normal cognition at baseline were identified from the National Alzheimer's Coordinating Center database (2005-2021). We applied a doubly robust learning approach to estimate risk differences (RD) with a 95% confidence interval (CI) for dementia risk between metformin use and no use in the overall population and subgroups identified through a decision tree model. RESULTS: Among 1393 participants, 104 developed dementia over a 4-year median follow-up. Metformin was significantly associated with a lower risk of dementia in the overall population (RD, -3.2%; 95% CI, -6.2% to -0.2%). We identified four subgroups with varied risks for dementia, defined by neuropsychiatric disorders, non-steroidal anti-inflammatory drugs, and antidepressant use. DISCUSSION: Metformin use was significantly associated with a lower risk of dementia in individuals with T2D, with significant variability among subgroups.

Heterogeneous treatment effects of sodium-glucose cotransporter 2 inhibitors on risk of dementia in people with type 2 diabetes: A population-based cohort study.

INTRODUCTION: Sodium-glucose cotransporter 2 (SGLT2) inhibitors exhibit potential benefits in reducing dementia risk, yet the optimal beneficiary subgroups remain uncertain. METHODS: Individuals with type 2 diabetes (T2D) initiating either SGLT2 inhibitor or sulfonylurea were identified from OneFlorida+ Clinical Research Network (2016-2022). A doubly robust learning was deployed to estimate risk difference (RD) and 95% confidence interval (CI) of all-cause dementia. RESULTS: Among 35,458 individuals with T2D, 1.8% in the SGLT2 inhibitor group and 4.7% in the sulfonylurea group developed all-cause dementia over a 3.2-year follow-up, yielding a lower risk for SGLT2 inhibitors (RD, -2.5%; 95% CI, -3.0% to -2.1%). Hispanic ethnicity and chronic kidney disease were identified as the two important variables to define four subgroups in which RD ranged from -4.3% (-5.5 to -3.2) to -0.9% (-1.9 to 0.2). DISCUSSION: Compared to sulfonylureas, SGLT2 inhibitors were associated with a reduced risk of all-cause dementia, but the association varied among different subgroups. HIGHLIGHTS: New users of sodium-glucose cotransporter 2 (SGLT2) inhibitors were significantly associated with a lower risk of all-cause dementia as compared to those of sulfonylureas. The association varied among different subgroups defined by Hispanic ethnicity and chronic kidney disease. A significantly lower risk of Alzheimer's disease and vascular dementia was observed among new users of SGLT2 inhibitors compared to those of sulfonylureas.

Immune tolerance induced in the anterior chamber ameliorates corneal transplant rejection.

The relevance of regulatory T cells (Tregs) in induction of tolerance against corneal allografts has been well established. However, whether Tregs can be induced in the anterior chamber and suppress local alloimmune response after corneal transplantation is largely unknown. In the current study we report that not only can alloantigen specific Tregs be generated in the anterior chamber during corneal transplantation, they also play important roles in suppressing allograft rejection. Allograft rejected mice exhibit reduced Treg induction in the anterior chamber and the ability of aqueous humor and corneal endothelial cells from allograft rejected mice to induce Tregs is compromised. Further analysis revealed that the expression of immune-tolerance-related molecules is significantly decreased. Finally, we demonstrate that increasing Treg cells specifically in the anterior chamber can effectively suppress allograft rejection and exhibits better efficacy in promoting corneal allograft survival than systemic administration of Treg cells. Our current study may provide new ideas for the prevention and treatment of corneal transplant rejection.

Contextualized medication information extraction using Transformer-based deep learning architectures.

OBJECTIVE: To develop a natural language processing (NLP) system to extract medications and contextual information that help understand drug changes. This project is part of the 2022 n2c2 challenge. MATERIALS AND METHODS: We developed NLP systems for medication mention extraction, event classification (indicating medication changes discussed or not), and context classification to classify medication changes context into 5 orthogonal dimensions related to drug changes. We explored 6 state-of-the-art pretrained transformer models for the three subtasks, including GatorTron, a large language model pretrained using > 90 billion words of text (including > 80 billion words from > 290 million clinical notes identified at the University of Florida Health). We evaluated our NLP systems using annotated data and evaluation scripts provided by the 2022 n2c2 organizers. RESULTS: Our GatorTron models achieved the best F1-scores of 0.9828 for medication extraction (ranked 3rd), 0.9379 for event classification (ranked 2nd), and the best micro-average accuracy of 0.9126 for context classification. GatorTron outperformed existing transformer models pretrained using smaller general English text and clinical text corpora, indicating the advantage of large language models. CONCLUSION: This study demonstrated the advantage of using large transformer models for contextual medication information extraction from clinical narratives.