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SUMMARY: The vast generation of genetic data poses a significant challenge in efficiently uncovering valuable knowledge. Introducing GENEVIC, an AI-driven chat framework that tackles this challenge by bridging the gap between genetic data generation and biomedical knowledge discovery. Leveraging generative AI, notably ChatGPT, it serves as a biologist's 'copilot'. It automates the analysis, retrieval, and visualization of customized domain-specific genetic information, and integrates functionalities to generate protein interaction networks, enrich gene sets, and search scientific literature from PubMed, Google Scholar, and arXiv, making it a comprehensive tool for biomedical research. In its pilot phase, GENEVIC is assessed using a curated database that ranks genetic variants associated with Alzheimer's disease, schizophrenia, and cognition, based on their effect weights from the Polygenic Score (PGS) Catalog, thus enabling researchers to prioritize genetic variants in complex diseases. GENEVIC's operation is user-friendly, accessible without any specialized training, secured by Azure OpenAI's HIPAA-compliant infrastructure, and evaluated for its efficacy through real-time query testing. As a prototype, GENEVIC is set to advance genetic research, enabling informed biomedical decisions. AVAILABILITY AND IMPLEMENTATION: GENEVIC is publicly accessible at https://genevic- anath2024.streamlit.app. The underlying code is open-source and available via GitHub at https://github.com/bsml320/GENEVIC.git (also at https://github.com/anath2110/GENEVIC.git). SUPPLEMENTARY INFORMATION: Available at Bioinformatics online and at https://github.com/bsml320/GENEVIC_Supplementary.git (also at https://github.com/anath2110/GENEVIC_Supplementary.git).
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The structural connectivity (SC) of the medial temporal lobe and its associated cortical anterior temporal and posterior medial networks (MTL-AT-PM) is linked to pathologies and memory decline in Alzheimer's disease (AD). However, neuroimaging analyses cannot tell us how SC changes occur in AD at the molecular level and do not provide a means of intervening to slow/prevent pathology-related changes in MTL-AT-PM SC. The current study aimed to understand how and where AD-related changes occur within MTL-AT-PM using proteomics. We used a 4-step approach in 101 older adults from a local sample, aiming to understand how proteins and SC in combination at the multivariate level predict AD pathology, and to identify specific proteins related to SC and AD pathology. Separately, we validated the discovered proteins in relation to SC and AD pathology using ADNI sample. We identified 12 latent factors linking proteins and SC; five showed significant relationships with AD pathology and/or episodic memory. Insulin-like growth factor binding proteins and tumor necrosis factor receptors, and hippocampal/parahippocampal edges contributed most to AD-related latent factors. Fast causal inference found protein-protein, protein-SC, and protein-pathology pathways, with seven proteins showing directional links to SC and AD-related neurodegeneration. We validated these results by identifying significant relationships between six available proteins with SC and amyloid-beta and phosphorylated tau in ADNI. We identified multivariate relationships between proteins and MTL-AT-PM networks that add to our understanding of AD pathology and suggest specific non-pathological proteins that warrant further study in relation to brain networks and AD pathology as possible therapeutic targets.
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BACKGROUND: Periodontal disease constitutes a widely prevalent category of non-communicable diseases and ranks among the top 10 causes of disability worldwide. Little however is known about diagnostic errors in dentistry. In this work, by retrospectively deploying an electronic health record (EHR)-based trigger tool, followed by gold standard manual review, we provide epidemiological estimates on the rate of diagnostic misclassification in dentistry through a periodontal use case. METHODS: An EHR-based trigger tool (a retrospective record review instrument that uses a list of triggers (or clues), i.e., data elements within the health record, to alert reviewers to the potential presence of a wrong diagnosis) was developed, tested and run against the EHR at the two participating sites to flag all cases having a potential misdiagnosis. All cases flagged as potentially misdiagnosed underwent extensive manual reviews by two calibrated domain experts. A subset of the non-flagged cases was also manually reviewed. RESULTS: A total of 2,262 patient charts met the study's inclusion criteria. Of these, the algorithm flagged 1,124 cases as potentially misclassified and 1,138 cases as potentially correctly diagnosed. When the algorithm identified a case as potentially misclassified, compared to the diagnosis assigned by the gold standard, the kappa statistic was 0.01. However, for cases the algorithm marked as potentially correctly diagnosed, the review against the gold standard showed a kappa statistic of 0.9, indicating near perfect agreement. The observed proportion of diagnostic misclassification was 32 %. There was no significant difference by clinic or provider characteristics. CONCLUSION: Our work revealed that about a third of periodontal cases are misclassified. Diagnostic errors have been reported to happen more frequently than other types of errors, and to be more preventable. Benchmarking diagnostic quality is a first step. Subsequent research endeavor will delve into comprehending the factors that contribute to diagnostic errors in dentistry and instituting measures to prevent them. CLINICAL SIGNIFICANCE: This study sheds light on the significance of diagnostic excellence in the delivery of dental care, and highlights the potential role of technology in aiding diagnostic decision-making at the point of care.
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Algoritmos , Erros de Diagnóstico , Registros Eletrônicos de Saúde , Doenças Periodontais , Humanos , Erros de Diagnóstico/estatística & dados numéricos , Estudos Retrospectivos , Doenças Periodontais/diagnóstico , Doenças Periodontais/classificação , Doenças Periodontais/epidemiologia , Feminino , Masculino , Pessoa de Meia-Idade , AdultoRESUMO
Increasing genetic and phenotypic data size is critical for understanding the genetic determinants of diseases. Evidently, establishing practical means for collaboration and data sharing among institutions is a fundamental methodological barrier for performing high-powered studies. As the sample sizes become more heterogeneous, complex statistical approaches, such as generalized linear mixed effects models, must be used to correct for the confounders that may bias results. On another front, due to the privacy concerns around Protected Health Information (PHI), genetic information is restrictively protected by sharing according to regulations such as Health Insurance Portability and Accountability Act (HIPAA). This limits data sharing among institutions and hampers efforts around executing high-powered collaborative studies. Federated approaches are promising to alleviate the issues around privacy and performance, since sensitive data never leaves the local sites. Motivated by these, we developed FedGMMAT, a federated genetic association testing tool that utilizes a federated statistical testing approach for efficient association tests that can correct for confounding fixed and additive polygenic random effects among different collaborating sites. Genetic data is never shared among collaborating sites, and the intermediate statistics are protected by encryption. Using simulated and real datasets, we demonstrate FedGMMAT can achieve the virtually same results as pooled analysis under a privacy-preserving framework with practical resource requirements.
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Disseminação de Informação , Humanos , Modelos Lineares , Disseminação de Informação/métodos , Biologia Computacional/métodos , Software , Estudo de Associação Genômica Ampla/métodos , Estudos de Associação GenéticaRESUMO
BACKGROUND: The potential benefits of drug combination synergy in cancer medicine are significant, yet the risks must be carefully managed due to the possibility of increased toxicity. Although artificial intelligence applications have demonstrated notable success in predicting drug combination synergy, several key challenges persist: (1) Existing models often predict average synergy values across a restricted range of testing dosages, neglecting crucial dose amounts and the mechanisms of action of the drugs involved. (2) Many graph-based models rely on static protein-protein interactions, failing to adapt to dynamic and higher-order relationships. These limitations constrain the applicability of current methods. RESULTS: We introduce SAFER, a Sub-hypergraph Attention-based graph model, addressing these issues by incorporating complex relationships among biological knowledge networks and considering dosing effects on subject-specific networks. SAFER outperformed previous models on the benchmark and the independent test set. The analysis of subgraph attention weight for the lung cancer cell line highlighted JAK-STAT signaling pathway, PRDM12, ZNF781, and CDC5L that have been implicated in lung fibrosis. CONCLUSIONS: SAFER presents an interpretable framework designed to identify drug-responsive signals. Tailored for comprehending dose effects on subject-specific molecular contexts, our model uniquely captures dose-level drug combination responses. This capability unlocks previously inaccessible avenues of investigation compared to earlier models. Furthermore, the SAFER framework can be leveraged by future inquiries to investigate molecular networks that uniquely characterize individual patients and can be applied to prioritize personalized effective treatment based on safe dose combinations.
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Redes Neurais de Computação , Humanos , Linhagem Celular Tumoral , Sinergismo Farmacológico , Neoplasias Pulmonares/tratamento farmacológico , Neoplasias Pulmonares/metabolismo , Relação Dose-Resposta a Droga , Transdução de Sinais/efeitos dos fármacos , Antineoplásicos/farmacologiaRESUMO
The highly efficient removal of oils such as oils or dyes from wastewater has aroused wide concern and is of great significance for clean production and environmental remediation. The synthesis of a novel aerogel (designated as HEC/LS) is reported herein, achieved through a sol-gel method followed by freeze-drying utilizing loofa and hydroxyethyl cellulose as the raw materials. The new HEC/LS aerogel exhibits excellent porosity and specific surface area, with a porosity of 88.70 %, a total pore area of 0.607 m2 g-1, and a specific surface area of 230 m2 g-1. The prepared HEC/LS aerogel exhibits exceptional hydrophilicity and self-floatability, facilitating its rapid absorption of water up to 21 times its own weight within a mere 3 s. Additionally, it demonstrates good adsorption performance for methylene blue (MB), with a maximum adsorption capacity of 83.30 mg g-1. Subsequently, a new hydrophobic microorganisms-loaded composite aerogel (namely, Bn-HEC/LS) was obtained by doping microorganisms into the as-prepared HEC/LS in multiple enrichment followed by a hydrophobic and oleophilic surface modification. Based on its rich porous structure and oleophilic wettability, the as-synthesized Bn-HEC/LS exhibits excellent selective adsorption and degradation properties for the oil contamination, the diesel oil could be selectively absorbed in the Bn-HEC/LS and degraded by the loaded microorganisms. Among them, B5-HEC/LS displays the highest removal efficiency of 94.50 % within 180 h, while free microorganisms and HEC/LS aerogels show degradation efficiencies of only 21.70 % and 48.10 %, respectively. The fixation of microorganisms in the aerogel increases their number within the material and enhances the relative microorganisms removal capacity. The hydrophobic and lipophilic modifications improve the selective adsorption performance of the aerogel on diesel oil, resulting in a significantly high removal rate of Bn-HEC/LS for diesel oil. The results indicate that the immobilization of microorganisms into aerogel improves the activity of microorganisms, and the hydrophobic and oleophilic modification enhances the selective adsorption performance of aerogel to diesel oil, thus resulting in a very high removal rate of Bn-HEC/LS for diesel oil. This study is expected to provide a now possibility for the green and efficient bioremediation of oils.
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OBJECTIVES: Heart failure (HF) impacts millions of patients worldwide, yet the variability in treatment responses remains a major challenge for healthcare professionals. The current treatment strategies, largely derived from population based evidence, often fail to consider the unique characteristics of individual patients, resulting in suboptimal outcomes. This study aims to develop computational models that are patient-specific in predicting treatment outcomes, by utilizing a large Electronic Health Records (EHR) database. The goal is to improve drug response predictions by identifying specific HF patient subgroups that are likely to benefit from existing HF medications. MATERIALS AND METHODS: A novel, graph-based model capable of predicting treatment responses, combining Graph Neural Network and Transformer was developed. This method differs from conventional approaches by transforming a patient's EHR data into a graph structure. By defining patient subgroups based on this representation via K-Means Clustering, we were able to enhance the performance of drug response predictions. RESULTS: Leveraging EHR data from 11 627 Mayo Clinic HF patients, our model significantly outperformed traditional models in predicting drug response using NT-proBNP as a HF biomarker across five medication categories (best RMSE of 0.0043). Four distinct patient subgroups were identified with differential characteristics and outcomes, demonstrating superior predictive capabilities over existing HF subtypes (best mean RMSE of 0.0032). DISCUSSION: These results highlight the power of graph-based modeling of EHR in improving HF treatment strategies. The stratification of patients sheds light on particular patient segments that could benefit more significantly from tailored response predictions. CONCLUSIONS: Longitudinal EHR data have the potential to enhance personalized prognostic predictions through the application of graph-based AI techniques.
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Registros Eletrônicos de Saúde , Insuficiência Cardíaca , Redes Neurais de Computação , Humanos , Insuficiência Cardíaca/tratamento farmacológico , Masculino , Feminino , Idoso , Resultado do Tratamento , Pessoa de Meia-Idade , Peptídeo Natriurético Encefálico/sangue , Fármacos Cardiovasculares/uso terapêuticoRESUMO
Liver transplantation often faces fairness challenges across subgroups defined by sensitive attributes such as age group, gender, and race/ethnicity. Machine learning models for outcome prediction can introduce additional biases. Therefore, we introduce Fairness through the Equitable Rate of Improvement in Multitask Learning (FERI) algorithm for fair predictions of graft failure risk in liver transplant patients. FERI constrains subgroup loss by balancing learning rates and preventing subgroup dominance in the training process. Our results show that FERI maintained high predictive accuracy with AUROC and AUPRC comparable to baseline models. More importantly, FERI demonstrated an ability to improve fairness without sacrificing accuracy. Specifically, for the gender, FERI reduced the demographic parity disparity by 71.74%, and for the age group, it decreased the equalized odds disparity by 40.46%. Therefore, the FERI algorithm advanced fairness-aware predictive modeling in healthcare and provides an invaluable tool for equitable healthcare systems.
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In this research, with an aim to develop novel pyrazole oxime ether derivatives possessing potential biological activity, thirty-two pyrazole oxime ethers, including a substituted pyridine ring, have been synthesized and structurally identified through 1H NMR, 13C NMR, and HRMS. Bioassay data indicated that most of these compounds owned strong insecticidal properties against Mythimna separata, Tetranychus cinnabarinus, Plutella xylostella, and Aphis medicaginis at a dosage of 500 µg/mL, and some title compounds were active towards Nilaparvata lugens at 500 µg/mL. Furthermore, some of the designed compounds had potent insecticidal effects against M. separata, T. cinnabarinus, or A. medicaginis at 100 µg/mL, with the mortalities of compounds 8a, 8c, 8d, 8e, 8f, 8g, 8o, 8s, 8v, 8x, and 8z against A. medicaginis, in particular, all reaching 100%. Even when the dosage was lowered to 20 µg/mL, compound 8s also expressed 50% insecticidal activity against M. separata, and compounds 8a, 8e, 8f, 8o, 8v, and 8x displayed more than 60% inhibition rates against A. medicaginis. The current results provided a significant basis for the rational design of biologically active pyrazole oxime ethers in future.
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Desenho de Fármacos , Inseticidas , Oximas , Pirazóis , Pirazóis/química , Pirazóis/farmacologia , Pirazóis/síntese química , Oximas/química , Oximas/farmacologia , Oximas/síntese química , Inseticidas/química , Inseticidas/síntese química , Inseticidas/farmacologia , Animais , Relação Estrutura-Atividade , Éteres/química , Estrutura Molecular , Piridinas/química , Piridinas/farmacologia , Piridinas/síntese química , Mariposas/efeitos dos fármacosRESUMO
Randomized Clinical trials (RCT) suffer from a high failure rate which could be caused by heterogeneous responses to treatment. Despite many models being developed to estimate heterogeneous treatment effects (HTE), there remains a lack of interpretable methods to identify responsive subgroups. This work aims to develop a framework to identify subgroups based on treatment effects that prioritize model interpretability. The proposed framework leverages an ensemble uplift tree method to generate descriptive decision rules that separate samples given estimated responses to the treatment. Subsequently, we select a complementary set of these decision rules and rank them using a sparse linear model. To address the trial's limited sample size problem, we proposed a data augmentation strategy by borrowing control patients from external studies and generating synthetic data. We apply the proposed framework to a failed randomized clinical trial for investigating an intracerebral hemorrhage therapy plan. The Qini-scores show that the proposed data augmentation strategy plan can boost the model's performance and the framework achieves greater interpretability by selecting complementary descriptive rules without compromising estimation quality. Our model derives clinically meaningful subgroups. Specifically, we find those patients with Diastolic Blood Pressure≥70 mm hg and Systolic Blood Pressure<215 mm hg benefit more from intensive blood pressure reduction therapy. The proposed interpretable HTE analysis framework offers a promising potential for extracting meaningful insight from RCTs with neutral treatment effects. By identifying responsive subgroups, our framework can contribute to developing personalized treatment strategies for patients more efficiently.
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Background: Synoptic reporting, the documenting of clinical information in a structured manner, is known to improve patient care by reducing errors, increasing readability, interoperability, and report completeness. Despite its advantages, manually synthesizing synoptic reports from narrative reports is expensive and error prone when the number of structured fields are many. While the recent revolutionary developments in Large Language Models (LLMs) have significantly advanced natural language processing, their potential for innovations in medicine is yet to be fully evaluated. Objectives: In this study, we explore the strengths and challenges of utilizing the state-of-the-art language models in the automatic synthesis of synoptic reports. Materials and Methods: We use a corpus of 7,774 cancer related, narrative pathology reports, which have annotated reference synoptic reports from Mayo Clinic EHR. Using these annotations as a reference, we reconfigure the state-of-the-art large language models, such as LLAMA-2, to generate the synoptic reports. Our annotated reference synoptic reports contain 22 unique data elements. To evaluate the accuracy of the reports generated by the LLMs, we use several metrics including the BERT F1 Score and verify our results by manual validation. Results: We show that using fine-tuned LLAMA-2 models, we can obtain BERT Score F1 of 0.86 or higher across all data elements and BERT F1 scores of 0.94 or higher on over 50% (11 of 22) of the questions. The BERT F1 scores translate to average accuracies of 76% and as high as 81% for short clinical reports. Conclusions: We demonstrate successful automatic synoptic report generation by fine-tuning large language models.
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BACKGROUND: Securing adequate data privacy is critical for the productive utilization of data. De-identification, involving masking or replacing specific values in a dataset, could damage the dataset's utility. However, finding a reasonable balance between data privacy and utility is not straightforward. Nonetheless, few studies investigated how data de-identification efforts affect data analysis results. This study aimed to demonstrate the effect of different de-identification methods on a dataset's utility with a clinical analytic use case and assess the feasibility of finding a workable tradeoff between data privacy and utility. METHODS: Predictive modeling of emergency department length of stay was used as a data analysis use case. A logistic regression model was developed with 1155 patient cases extracted from a clinical data warehouse of an academic medical center located in Seoul, South Korea. Nineteen de-identified datasets were generated based on various de-identification configurations using ARX, an open-source software for anonymizing sensitive personal data. The variable distributions and prediction results were compared between the de-identified datasets and the original dataset. We examined the association between data privacy and utility to determine whether it is feasible to identify a viable tradeoff between the two. RESULTS: All 19 de-identification scenarios significantly decreased re-identification risk. Nevertheless, the de-identification processes resulted in record suppression and complete masking of variables used as predictors, thereby compromising dataset utility. A significant correlation was observed only between the re-identification reduction rates and the ARX utility scores. CONCLUSIONS: As the importance of health data analysis increases, so does the need for effective privacy protection methods. While existing guidelines provide a basis for de-identifying datasets, achieving a balance between high privacy and utility is a complex task that requires understanding the data's intended use and involving input from data users. This approach could help find a suitable compromise between data privacy and utility.
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Confidencialidade , Anonimização de Dados , Humanos , Confidencialidade/normas , Serviço Hospitalar de Emergência , Tempo de Internação , República da Coreia , MasculinoRESUMO
Background: The potential benefits of drug combination synergy in cancer medicine are significant, yet the risks must be carefully managed due to the possibility of increased toxicity. Although artificial intelligence applications have demonstrated notable success in predicting drug combination synergy, several key challenges persist: (1) Existing models often predict average synergy values across a restricted range of testing dosages, neglecting crucial dose amounts and the mechanisms of action of the drugs involved. (2) Many graph-based models rely on static protein-protein interactions, failing to adapt to dynamic and context-dependent networks. This limitation constrains the applicability of current methods. Results: We introduced SAFER, a Sub-hypergraph Attention-based graph model, addressing these issues by incorporating complex relationships among biological knowledge networks and considering dosing effects on subject-specific networks. SAFER outperformed previous models on the benchmark and the independent test set. The analysis of subgraph attention weight for the lung cancer cell line highlighted JAK-STAT signaling pathway, PRDM12, ZNF781, and CDC5L that have been implicated in lung fibrosis. Conclusions: SAFER presents an interpretable framework designed to identify drug-responsive signals. Tailored for comprehending dose effects on subject-specific molecular contexts, our model uniquely captures dose-level drug combination responses. This capability unlocks previously inaccessible avenues of investigation compared to earlier models. Finally, the SAFER framework can be leveraged by future inquiries to investigate molecular networks that uniquely characterize individual patients.
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Orofacial clefts (OFCs) are among the most common human congenital birth defects. Previous multiethnic studies have identified dozens of associated loci for both cleft lip with or without cleft palate (CL/P) and cleft palate alone (CP). Although several nearby genes have been highlighted, the "casual" variants are largely unknown. Here, we developed DeepFace, a convolutional neural network model, to assess the functional impact of variants by SNP activity difference (SAD) scores. The DeepFace model is trained with 204 epigenomic assays from crucial human embryonic craniofacial developmental stages of post-conception week (pcw) 4 to pcw 10. The Pearson correlation coefficient between the predicted and actual values for 12 epigenetic features achieved a median range of 0.50-0.83. Specifically, our model revealed that SNPs significantly associated with OFCs tended to exhibit higher SAD scores across various variant categories compared to less related groups, indicating a context-specific impact of OFC-related SNPs. Notably, we identified six SNPs with a significant linear relationship to SAD scores throughout developmental progression, suggesting that these SNPs could play a temporal regulatory role. Furthermore, our cell-type specificity analysis pinpointed the trophoblast cell as having the highest enrichment of risk signals associated with OFCs. Overall, DeepFace can harness distal regulatory signals from extensive epigenomic assays, offering new perspectives for prioritizing OFC variants using contextualized functional genomic features. We expect DeepFace to be instrumental in accessing and predicting the regulatory roles of variants associated with OFCs, and the model can be extended to study other complex diseases or traits.
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Fenda Labial , Fissura Palatina , Aprendizado Profundo , Polimorfismo de Nucleotídeo Único , Humanos , Fissura Palatina/genética , Fissura Palatina/embriologia , Fenda Labial/genética , Fenda Labial/embriologia , Redes Neurais de Computação , Epigenômica/métodos , Desenvolvimento Embrionário/genéticaRESUMO
The rate of progression of Alzheimer's disease (AD) differs dramatically between patients. Identifying the most is critical because when their numbers differ between treated and control groups, it distorts the outcome, making it impossible to tell whether the treatment was beneficial. Much recent effort, then, has gone into identifying RPs. We pooled de-identified placebo-arm data of three randomized controlled trials (RCTs), EXPEDITION, EXPEDITION 2, and EXPEDITION 3, provided by Eli Lilly and Company. After processing, the data included 1603 mild-to-moderate AD patients with 80 weeks of longitudinal observations on neurocognitive health, brain volumes, and amyloid-beta (Aß) levels. RPs were defined by changes in four neurocognitive/functional health measures. We built deep learning models using recurrent neural networks with attention mechanisms to predict RPs by week 80 based on varying observation periods from baseline (e.g., 12, 28 weeks). Feature importance scores for RP prediction were computed and temporal feature trajectories were compared between RPs and non-RPs. Our evaluation and analysis focused on models trained with 28 weeks of observation. The models achieved robust internal validation area under the receiver operating characteristic (AUROCs) ranging from 0.80 (95% CI 0.79-0.82) to 0.82 (0.81-0.83), and the area under the precision-recall curve (AUPRCs) from 0.34 (0.32-0.36) to 0.46 (0.44-0.49). External validation AUROCs ranged from 0.75 (0.70-0.81) to 0.83 (0.82-0.84) and AUPRCs from 0.27 (0.25-0.29) to 0.45 (0.43-0.48). Aß plasma levels, regional brain volumetry, and neurocognitive health emerged as important factors for the model prediction. In addition, the trajectories were stratified between predicted RPs and non-RPs based on factors such as ventricular volumes and neurocognitive domains. Our findings will greatly aid clinical trialists in designing tests for new medications, representing a key step toward identifying effective new AD therapies.
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BACKGROUND: Lumbar disc herniation is a common spinal disease that causes low back pain; surgery is required when conservative treatment is ineffective. There is a growing demand for minimally invasive surgery in younger patient populations due to their fear of significant damage and a long recovery period following standard open discectomy. The development history of minimally invasive surgery is relatively short, and no gold standard has been established. OBJECTIVES: We aimed to find, via a network meta-analysis, the best treatment for low back pain in younger patient populations. STUDY DESIGN: Network meta-analysis. METHODS: The PubMed, Embase, Cochrane Library, and Web of Science databases were searched. Data quality was evaluated using RevMan 5.3 (The Nordic Cochrane Centre for The Cochrane Collaboration), while STATA 14.0 (StataCorp LLC) was used for the network meta-analysis and to merge data on the Visual Analog Scale (VAS) score, Oswestry Disability Index (ODI) score, complication, blood loss, reoperation rate, and function score. RESULTS: We included 50 randomized controlled trials, involving 7 interventions; heterogeneity and inconsistency were acceptable. Comparatively, microendoscopic discectomy and percutaneous endoscopic lumbar discectomy were the best surgical procedures from the aspects of VAS score and ODI score, while standard open discectomy was the worst one from the aspect of ODI score. Regarding complications, tubular discectomy was preferred with the fewest complications. Additionally, microendoscopic discectomy outperformed other surgical procedures in reducing blood loss and reoperation rate. LIMITATIONS: First, follow-up data were not reported in all included studies, and the follow-up time varied from several months to 8 years, which affected the results accuracy of our study to some extent. Second, there were some nonsurgical factors that also affected the self-reported outcomes, such as rehabilitation and pain management, which also brought a certain bias in our study results. CONCLUSIONS: Compared to standard open discectomy, minimally invasive surgical procedures not only achieve satisfactory efficacy, but also microendoscopic discectomy and percutaneous endoscopic lumbar discectomy can obtain a more satisfactory short-term VAS score and ODI score. Microendoscopic discectomy has significant advantages in blood loss and reoperation rate, and tubular discectomy has fewer postoperative complications.
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Discotomia , Deslocamento do Disco Intervertebral , Vértebras Lombares , Procedimentos Cirúrgicos Minimamente Invasivos , Humanos , Deslocamento do Disco Intervertebral/cirurgia , Procedimentos Cirúrgicos Minimamente Invasivos/métodos , Discotomia/métodos , Vértebras Lombares/cirurgia , Metanálise em Rede , Dor Lombar/cirurgia , Dor Lombar/etiologiaRESUMO
OBJECTIVES: This study aimed to identify predictors associated with the tooth loss phenotype in a large periodontitis patient cohort in the university setting. METHODS: Information on periodontitis patients and nineteen factors identified at the initial visit was extracted from electronic health records. The primary outcome is tooth loss phenotype (presence or absence of tooth loss). Prediction models were built on significant factors (single or combinatory) selected by the RuleFit algorithm, and these factors were further adopted by regression models. Model performance was evaluated by Area Under the Receiver Operating Characteristic Curve (AUROC) and Area Under the Precision-Recall Curve (AUPRC). Associations between predictors and the tooth loss phenotype were also evaluated by classical statistical approaches to validate the performance of machine learning models. RESULTS: In total, 7840 patients were included. The machine learning model predicting the tooth loss phenotype achieved AUROC of 0.71 and AUPRC of 0.66. Age, periodontal diagnosis, number of missing teeth at baseline, furcation involvement, and tooth mobility were associated with the tooth loss phenotype in both machine learning and classical statistical models. CONCLUSIONS: The rule-based machine learning approach improves model explainability compared to classical statistical methods. However, the model's generalizability needs to be further validated by external datasets. CLINICAL SIGNIFICANCE: Predictors identified by the current machine learning approach using the RuleFit algorithm had clinically relevant thresholds in predicting the tooth loss phenotype in a large and diverse periodontitis patient cohort. The results of this study will assist clinicians in performing risk assessment for periodontitis at the initial visit.
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Aprendizado de Máquina , Periodontite , Fenótipo , Perda de Dente , Humanos , Masculino , Feminino , Periodontite/complicações , Pessoa de Meia-Idade , Adulto , Curva ROC , Mobilidade Dentária , Fatores de Risco , Algoritmos , Registros Eletrônicos de Saúde , Estudos de Coortes , Área Sob a Curva , Defeitos da Furca , IdosoRESUMO
OBJECTIVES: The rapid expansion of biomedical literature necessitates automated techniques to discern relationships between biomedical concepts from extensive free text. Such techniques facilitate the development of detailed knowledge bases and highlight research deficiencies. The LitCoin Natural Language Processing (NLP) challenge, organized by the National Center for Advancing Translational Science, aims to evaluate such potential and provides a manually annotated corpus for methodology development and benchmarking. MATERIALS AND METHODS: For the named entity recognition (NER) task, we utilized ensemble learning to merge predictions from three domain-specific models, namely BioBERT, PubMedBERT, and BioM-ELECTRA, devised a rule-driven detection method for cell line and taxonomy names and annotated 70 more abstracts as additional corpus. We further finetuned the T0pp model, with 11 billion parameters, to boost the performance on relation extraction and leveraged entites' location information (eg, title, background) to enhance novelty prediction performance in relation extraction (RE). RESULTS: Our pioneering NLP system designed for this challenge secured first place in Phase I-NER and second place in Phase II-relation extraction and novelty prediction, outpacing over 200 teams. We tested OpenAI ChatGPT 3.5 and ChatGPT 4 in a Zero-Shot setting using the same test set, revealing that our finetuned model considerably surpasses these broad-spectrum large language models. DISCUSSION AND CONCLUSION: Our outcomes depict a robust NLP system excelling in NER and RE across various biomedical entities, emphasizing that task-specific models remain superior to generic large ones. Such insights are valuable for endeavors like knowledge graph development and hypothesis formulation in biomedical research.
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Background: The progressive cognitive decline, an integral component of Alzheimer's disease (AD), unfolds in tandem with the natural aging process. Neuroimaging features have demonstrated the capacity to distinguish cognitive decline changes stemming from typical brain aging and AD between different chronological points. Objective: To disentangle the normal aging effect from the AD-related accelerated cognitive decline and unravel its genetic components using a neuroimaging-based deep learning approach. Methods: We developed a deep-learning framework based on a dual-loss Siamese ResNet network to extract fine-grained information from the longitudinal structural magnetic resonance imaging (MRI) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. We then conducted genome-wide association studies (GWAS) and post-GWAS analyses to reveal the genetic basis of AD-related accelerated cognitive decline. Results: We used our model to process data from 1,313 individuals, training it on 414 cognitively normal people and predicting cognitive assessment for all participants. In our analysis of accelerated cognitive decline GWAS, we identified two genome-wide significant loci: APOE locus (chromosome 19 p13.32) and rs144614292 (chromosome 11 p15.1). Variant rs144614292 (Gâ>âT) has not been reported in previous AD GWA studies. It is within the intronic region of NELL1, which is expressed in neurons and plays a role in controlling cell growth and differentiation. The cell-type-specific enrichment analysis and functional enrichment of GWAS signals highlighted the microglia and immune-response pathways. Conclusions: Our deep learning model effectively extracted relevant neuroimaging features and predicted individual cognitive decline. We reported a novel variant (rs144614292) within the NELL1 gene.