A Case Demonstration of the Open Health Natural Language Processing Toolkit From the National COVID-19 Cohort Collaborative and the Researching COVID to Enhance Recovery Programs for a Natural Language Processing System for COVID-19 or Postacute Sequelae of SARS CoV-2 Infection: Algorithm Development and Validation.

BACKGROUND: A wealth of clinically relevant information is only obtainable within unstructured clinical narratives, leading to great interest in clinical natural language processing (NLP). While a multitude of approaches to NLP exist, current algorithm development approaches have limitations that can slow the development process. These limitations are exacerbated when the task is emergent, as is the case currently for NLP extraction of signs and symptoms of COVID-19 and postacute sequelae of SARS-CoV-2 infection (PASC). OBJECTIVE: This study aims to highlight the current limitations of existing NLP algorithm development approaches that are exacerbated by NLP tasks surrounding emergent clinical concepts and to illustrate our approach to addressing these issues through the use case of developing an NLP system for the signs and symptoms of COVID-19 and PASC. METHODS: We used 2 preexisting studies on PASC as a baseline to determine a set of concepts that should be extracted by NLP. This concept list was then used in conjunction with the Unified Medical Language System to autonomously generate an expanded lexicon to weakly annotate a training set, which was then reviewed by a human expert to generate a fine-tuned NLP algorithm. The annotations from a fully human-annotated test set were then compared with NLP results from the fine-tuned algorithm. The NLP algorithm was then deployed to 10 additional sites that were also running our NLP infrastructure. Of these 10 sites, 5 were used to conduct a federated evaluation of the NLP algorithm. RESULTS: An NLP algorithm consisting of 12,234 unique normalized text strings corresponding to 2366 unique concepts was developed to extract COVID-19 or PASC signs and symptoms. An unweighted mean dictionary coverage of 77.8% was found for the 5 sites. CONCLUSIONS: The evolutionary and time-critical nature of the PASC NLP task significantly complicates existing approaches to NLP algorithm development. In this work, we present a hybrid approach using the Open Health Natural Language Processing Toolkit aimed at addressing these needs with a dictionary-based weak labeling step that minimizes the need for additional expert annotation while still preserving the fine-tuning capabilities of expert involvement.

Adoption of the OMOP CDM for Cancer Research using Real-world Data: Current Status and Opportunities.

Background: The Observational Medical Outcomes Partnership (OMOP) common data model (CDM) that is developed and maintained by the Observational Health Data Sciences and Informatics (OHDSI) community supports large scale cancer research by enabling distributed network analysis. As the number of studies using the OMOP CDM for cancer research increases, there is a growing need for an overview of the scope of cancer research that relies on the OMOP CDM ecosystem. Objectives: In this study, we present a comprehensive review of the adoption of the OMOP CDM for cancer research and offer some insights on opportunities in leveraging the OMOP CDM ecosystem for advancing cancer research. Materials and Methods: Published literature databases were searched to retrieve OMOP CDM and cancer-related English language articles published between January 2010 and December 2023. A charting form was developed for two main themes, i.e., clinically focused data analysis studies and infrastructure development studies in the cancer domain. Results: In total, 50 unique articles were included, with 30 for the data analysis theme and 23 for the infrastructure theme, with 3 articles belonging to both themes. The topics covered by the existing body of research was depicted. Conclusion: Through depicting the status quo of research efforts to improve or leverage the potential of the OMOP CDM ecosystem for advancing cancer research, we identify challenges and opportunities surrounding data analysis and infrastructure including data quality, advanced analytics methodology adoption, in-depth phenotypic data inclusion through NLP, and multisite evaluation.

Chinese expert consensus on management of antifungal-resistant dermatophytoses (2024 edition).

Antifungal-resistant dermatophytes (ARD) infection is a hotspot issue in clinical microbiology and the dermatology field. Trichophyton indotineae as the dominant species of dermatophyte with terbinafine-resistance or multidrug resistance, is easy to be missed detection clinically, which brings severe challenges to diagnosis and treatment. ARD infection cases have emerged in China, and it predicts a risk of transmission among human. Based on the existing medical evidence and research data, the Mycology Group of Combination of Traditional and Western Medicine Dermatology and Chinese Antifungal⁃Resistant Dermatophytoses Expert Consensus Group organized experts to make consensus on the management of the infection. Here, the consensus formulated diagnosis and treatment recommendations, to raise attention to dermatophytes drug resistance problem, and expect to provide reference information for the clinical diagnosis, treatment, prevention and control.

Severe Microbial Corrosion of L245 Transportation Pipeline Triggered by Wild Sulfate Reducing Bacteria in Shale Gas Produced Water.

The development of pitting corrosion on L245 carbon steel in a culture medium solution containing sulfate-reducing bacteria (SRB) was investigated. The results showed that the occurrence of corrosion in L245 carbon steel is closely linked to the evolution of biofilm and product film. As the test duration extended, overall corrosion was inhibited. Simultaneously, bacteria beneath the film layer promoted the generation and development of pitting corrosion, and the aggregation of bacteria (colonies) led to the aggregation of pitting corrosion.

Notch1 signaling pathway promotes growth and metastasis of gastric cancer via modulating CDH5.

OBJECTIVE: To explore the underlying molecular mechanism of Notch1/cadherin 5 (CDH5) pathway in modulating in cell malignant behaviors of gastric cancer (GC). METHODS: We performed bioinformatic analyses to screen the potential target genes of Notch1 from cadherins in GC. Western blot and RT-PCR were conducted to detect CDH5 expression in GC tissues and cells. We utilized chromatin immunoprecipitation (CHIP) assays to assess the interaction of Notch1 with CDH5 gene. The effects of Notch1/CDH5 axis on the proliferation, invasion, migration and vasculogenic mimicry in GC cells were evaluated by EdU, wound healing, transwell, and tubule formation assays. RESULTS: Significantly increased CDH5 expression was found in GC tissues compared with paracancerous tissues and associated to clinical stage and poor overall survival (OS) in patients with GC. Notch1 positively regulate the expression of CDH5 in GC cells. CHIP assays validated that CDH5 was a direct target of Notch1. In addition, Notch1 upregulation enhanced the proliferation, migration, invasion and vasculogenic mimicry capacity of GC cells, which could be attenuated by CDH5 silencing. CONCLUSIONS: These results indicated Notch1 upregulation enhanced GC malignant behaviors by triggering CDH5, suggesting that targeting Notch1/CDH5 axis could be a potential therapeutic strategy for GC progression.

Efficacy and safety of low-dose interleukin 2 in the treatment of moderate-to-severe bullous pemphigoid: A single center perspective-controlled trial.

BACKGROUND: Regulatory T cells (Tregs) are reduced in the peripheral blood and skin lesions of patients with bullous pemphigoid (BP). Low-dose interleukin 2 (IL-2) therapy can stimulate Tregs specifically, suggesting potential for the treatment of BP. OBJECTIVE: To evaluate the response to low-dose IL-2 therapy in the treatment of moderate-to-severe BP. METHODS: Forty-three patients with moderate-to-severe BP were included. The therapy included systemic corticosteroids with an initial dose of 0.5 mg/kg/d for moderate and 1.0 mg/kg/d for severe disease, respectively, combined with allowed immunosuppressants for the control group, whereas in addition to the same corticosteroid therapy, IL-2 (half million IU) was administered subcutaneously every other day for the treatment group for 8 weeks. The primary outcome was the number of days required to achieve disease control. Secondary outcomes included other clinical responses. RESULTS: The number of days required to achieve disease control with the treatment group was (7.60 ± 3.00), which was shorter than in the control group (10.43 ± 3.06) (P = .008). The total amount of systemic corticosteroids was less, and no serious infections were detected in the treatment group. LIMITATIONS: Single center, open-label study with short duration and small size. CONCLUSION: Our trial supports the potential of low-dose IL-2 therapy for patients with moderate-to-severe BP, which showed earlier treatment responses.

A generalist vision-language foundation model for diverse biomedical tasks.

Traditional biomedical artificial intelligence (AI) models, designed for specific tasks or modalities, often exhibit limited flexibility in real-world deployment and struggle to utilize holistic information. Generalist AI holds the potential to address these limitations due to its versatility in interpreting different data types and generating tailored outputs for diverse needs. However, existing biomedical generalist AI solutions are typically heavyweight and closed source to researchers, practitioners and patients. Here, we describe BiomedGPT, the first open-source and lightweight vision-language foundation model, designed as a generalist capable of performing various biomedical tasks. BiomedGPT achieved state-of-the-art results in 16 out of 25 experiments while maintaining a computing-friendly model scale. We also conducted human evaluations to assess the capabilities of BiomedGPT in radiology visual question answering, report generation and summarization. BiomedGPT exhibits robust prediction ability with a low error rate of 3.8% in question answering, satisfactory performance with an error rate of 8.3% in writing complex radiology reports, and competitive summarization ability with a nearly equivalent preference score to human experts. Our method demonstrates that effective training with diverse data can lead to more practical biomedical AI for improving diagnosis and workflow efficiency.

Enhancing Clinical Relevance of Pretrained Language Models Through Integration of External Knowledge: Case Study on Cardiovascular Diagnosis From Electronic Health Records.

BACKGROUND: Despite their growing use in health care, pretrained language models (PLMs) often lack clinical relevance due to insufficient domain expertise and poor interpretability. A key strategy to overcome these challenges is integrating external knowledge into PLMs, enhancing their adaptability and clinical usefulness. Current biomedical knowledge graphs like UMLS (Unified Medical Language System), SNOMED CT (Systematized Medical Nomenclature for Medicine-Clinical Terminology), and HPO (Human Phenotype Ontology), while comprehensive, fail to effectively connect general biomedical knowledge with physician insights. There is an equally important need for a model that integrates diverse knowledge in a way that is both unified and compartmentalized. This approach not only addresses the heterogeneous nature of domain knowledge but also recognizes the unique data and knowledge repositories of individual health care institutions, necessitating careful and respectful management of proprietary information. OBJECTIVE: This study aimed to enhance the clinical relevance and interpretability of PLMs by integrating external knowledge in a manner that respects the diversity and proprietary nature of health care data. We hypothesize that domain knowledge, when captured and distributed as stand-alone modules, can be effectively reintegrated into PLMs to significantly improve their adaptability and utility in clinical settings. METHODS: We demonstrate that through adapters, small and lightweight neural networks that enable the integration of extra information without full model fine-tuning, we can inject diverse sources of external domain knowledge into language models and improve the overall performance with an increased level of interpretability. As a practical application of this methodology, we introduce a novel task, structured as a case study, that endeavors to capture physician knowledge in assigning cardiovascular diagnoses from clinical narratives, where we extract diagnosis-comment pairs from electronic health records (EHRs) and cast the problem as text classification. RESULTS: The study demonstrates that integrating domain knowledge into PLMs significantly improves their performance. While improvements with ClinicalBERT are more modest, likely due to its pretraining on clinical texts, BERT (bidirectional encoder representations from transformer) equipped with knowledge adapters surprisingly matches or exceeds ClinicalBERT in several metrics. This underscores the effectiveness of knowledge adapters and highlights their potential in settings with strict data privacy constraints. This approach also increases the level of interpretability of these models in a clinical context, which enhances our ability to precisely identify and apply the most relevant domain knowledge for specific tasks, thereby optimizing the model's performance and tailoring it to meet specific clinical needs. CONCLUSIONS: This research provides a basis for creating health knowledge graphs infused with physician knowledge, marking a significant step forward for PLMs in health care. Notably, the model balances integrating knowledge both comprehensively and selectively, addressing the heterogeneous nature of medical knowledge and the privacy needs of health care institutions.

Sex differences in sociodemographic, clinical, and laboratory variables in childhood asthma: A birth cohort study.

BACKGROUND: There are marked sex differences in the prevalence and severity of asthma, both during childhood and adulthood. There is a relative lack of comprehensive studies exploring sexdifferences in pediatric asthma cohorts. OBJECTIVE: To identify the most relevant sex differences in sociodemographic, clinical, and laboratory variables in a well-characterized large pediatric asthma cohort. METHODS: We performed a cross-sectional analysis of the Mayo Clinic Olmsted County Birth Cohort. In the full birth cohort, we used a natural language-processing algorithm based on the Predetermined Asthma Criteria for asthma ascertainment. In a stratified random sample of 300 children, we obtained additional pulmonary function tests and laboratory data. We identified the significant sex differences among available sociodemographic, clinical, and laboratory variables. RESULTS: Boys were more frequently diagnosed with having asthma than girls and were younger at the time of asthma diagnosis. There were no sex differences in relation to socioeconomic status. We identified a male predominance in the presence of a tympanostomy tube and a female predominance in the history of pneumonia. A higher percentage of boys had a forced expiratory volume in 1 second/forced vital capacity ratio less than 0.85. Blood eosinophilia and atopic sensitization were also more common in boys. Finally, boys had higher levels of serum periostin than girls. CONCLUSION: This study described significant sex differences in a large pediatric asthma cohort. Overall, boys had earlier and more severe asthma than girls. Differences in blood eosinophilia and serum periostin provide insights into possible mechanisms of the sex bias in childhood asthma.

Identification of immune-associated biomarkers of diabetes nephropathy tubulointerstitial injury based on machine learning: a bioinformatics multi-chip integrated analysis.

BACKGROUND: Diabetic nephropathy (DN) is a major microvascular complication of diabetes and has become the leading cause of end-stage renal disease worldwide. A considerable number of DN patients have experienced irreversible end-stage renal disease progression due to the inability to diagnose the disease early. Therefore, reliable biomarkers that are helpful for early diagnosis and treatment are identified. The migration of immune cells to the kidney is considered to be a key step in the progression of DN-related vascular injury. Therefore, finding markers in this process may be more helpful for the early diagnosis and progression prediction of DN. METHODS: The gene chip data were retrieved from the GEO database using the search term ' diabetic nephropathy '. The ' limma ' software package was used to identify differentially expressed genes (DEGs) between DN and control samples. Gene set enrichment analysis (GSEA) was performed on genes obtained from the molecular characteristic database (MSigDB. The R package 'WGCNA' was used to identify gene modules associated with tubulointerstitial injury in DN, and it was crossed with immune-related DEGs to identify target genes. Gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed on differentially expressed genes using the 'ClusterProfiler' software package in R. Three methods, least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE) and random forest (RF), were used to select immune-related biomarkers for diagnosis. We retrieved the tubulointerstitial dataset from the Nephroseq database to construct an external validation dataset. Unsupervised clustering analysis of the expression levels of immune-related biomarkers was performed using the 'ConsensusClusterPlus 'R software package. The urine of patients who visited Dongzhimen Hospital of Beijing University of Chinese Medicine from September 2021 to March 2023 was collected, and Elisa was used to detect the mRNA expression level of immune-related biomarkers in urine. Pearson correlation analysis was used to detect the effect of immune-related biomarker expression on renal function in DN patients. RESULTS: Four microarray datasets from the GEO database are included in the analysis : GSE30122, GSE47185, GSE99340 and GSE104954. These datasets included 63 DN patients and 55 healthy controls. A total of 9415 genes were detected in the data set. We found 153 differentially expressed immune-related genes, of which 112 genes were up-regulated, 41 genes were down-regulated, and 119 overlapping genes were identified. GO analysis showed that they were involved in various biological processes including leukocyte-mediated immunity. KEGG analysis showed that these target genes were mainly involved in the formation of phagosomes in Staphylococcus aureus infection. Among these 119 overlapping genes, machine learning results identified AGR2, CCR2, CEBPD, CISH, CX3CR1, DEFB1 and FSTL1 as potential tubulointerstitial immune-related biomarkers. External validation suggested that the above markers showed diagnostic efficacy in distinguishing DN patients from healthy controls. Clinical studies have shown that the expression of AGR2, CX3CR1 and FSTL1 in urine samples of DN patients is negatively correlated with GFR, the expression of CX3CR1 and FSTL1 in urine samples of DN is positively correlated with serum creatinine, while the expression of DEFB1 in urine samples of DN is negatively correlated with serum creatinine. In addition, the expression of CX3CR1 in DN urine samples was positively correlated with proteinuria, while the expression of DEFB1 in DN urine samples was negatively correlated with proteinuria. Finally, according to the level of proteinuria, DN patients were divided into nephrotic proteinuria group (n = 24) and subrenal proteinuria group. There were significant differences in urinary AGR2, CCR2 and DEFB1 between the two groups by unpaired t test (P < 0.05). CONCLUSIONS: Our study provides new insights into the role of immune-related biomarkers in DN tubulointerstitial injury and provides potential targets for early diagnosis and treatment of DN patients. Seven different genes ( AGR2, CCR2, CEBPD, CISH, CX3CR1, DEFB1, FSTL1 ), as promising sensitive biomarkers, may affect the progression of DN by regulating immune inflammatory response. However, further comprehensive studies are needed to fully understand their exact molecular mechanisms and functional pathways in DN.

Considerations for Quality Control Monitoring of Machine Learning Models in Clinical Practice.

Integrating machine learning (ML) models into clinical practice presents a challenge of maintaining their efficacy over time. While existing literature offers valuable strategies for detecting declining model performance, there is a need to document the broader challenges and solutions associated with the real-world development and integration of model monitoring solutions. This work details the development and use of a platform for monitoring the performance of a production-level ML model operating in Mayo Clinic. In this paper, we aimed to provide a series of considerations and guidelines necessary for integrating such a platform into a team's technical infrastructure and workflow. We have documented our experiences with this integration process, discussed the broader challenges encountered with real-world implementation and maintenance, and included the source code for the platform. Our monitoring platform was built as an R shiny application, developed and implemented over the course of 6 months. The platform has been used and maintained for 2 years and is still in use as of July 2023. The considerations necessary for the implementation of the monitoring platform center around 4 pillars: feasibility (what resources can be used for platform development?); design (through what statistics or models will the model be monitored, and how will these results be efficiently displayed to the end user?); implementation (how will this platform be built, and where will it exist within the IT ecosystem?); and policy (based on monitoring feedback, when and what actions will be taken to fix problems, and how will these problems be translated to clinical staff?). While much of the literature surrounding ML performance monitoring emphasizes methodological approaches for capturing changes in performance, there remains a battery of other challenges and considerations that must be addressed for successful real-world implementation.

Automatic uncovering of patient primary concerns in portal messages using a fusion framework of pretrained language models.

OBJECTIVES: The surge in patient portal messages (PPMs) with increasing needs and workloads for efficient PPM triage in healthcare settings has spurred the exploration of AI-driven solutions to streamline the healthcare workflow processes, ensuring timely responses to patients to satisfy their healthcare needs. However, there has been less focus on isolating and understanding patient primary concerns in PPMs-a practice which holds the potential to yield more nuanced insights and enhances the quality of healthcare delivery and patient-centered care. MATERIALS AND METHODS: We propose a fusion framework to leverage pretrained language models (LMs) with different language advantages via a Convolution Neural Network for precise identification of patient primary concerns via multi-class classification. We examined 3 traditional machine learning models, 9 BERT-based language models, 6 fusion models, and 2 ensemble models. RESULTS: The outcomes of our experimentation underscore the superior performance achieved by BERT-based models in comparison to traditional machine learning models. Remarkably, our fusion model emerges as the top-performing solution, delivering a notably improved accuracy score of 77.67 ± 2.74% and an F1 score of 74.37 ± 3.70% in macro-average. DISCUSSION: This study highlights the feasibility and effectiveness of multi-class classification for patient primary concern detection and the proposed fusion framework for enhancing primary concern detection. CONCLUSIONS: The use of multi-class classification enhanced by a fusion of multiple pretrained LMs not only improves the accuracy and efficiency of patient primary concern identification in PPMs but also aids in managing the rising volume of PPMs in healthcare, ensuring critical patient communications are addressed promptly and accurately.

Successful management of chromoblastomycosis utilizing conventional antifungal agents and imiquimod therapy.

Chromoblastomycosis (CBM), a chronic fungal infection affecting the skin and subcutaneous tissues, is predominantly caused by dematiaceous fungi in tropical and subtropical areas. Characteristically, CBM presents as plaques and nodules, often leading to scarring post-healing. Besides traditional diagnostic methods such as fungal microscopy, culture, and histopathology, dermatoscopy and reflectance confocal microscopy can aid in diagnosis. The treatment of CBM is an extended and protracted process. Imiquimod, acting as an immune response modifier, boosts the host's immune response against CBM, and controls scar hyperplasia, thereby reducing the treatment duration. We present a case of CBM in Guangdong with characteristic reflectance confocal microscopy manifestations, effectively managed through a combination of itraconazole, terbinafine, and imiquimod, shedding light on novel strategies for managing this challenging condition.

Potassium iodide enhances the killing effect of methylene blue mediated photodynamic therapy against F. monophora.

BACKGROUND: Chromoblastomycosis (CMB) is a chronic granulomatous fungal infection that affect the skin and subcutaneous tissues. It is clinically problematic due to limited treatment options, low cure rates, and high rates of relapse. This underscores the necessity for innovative treatment approaches. In this study, potassium iodide (KI) combined with Methylene Blue (MB) mediated antimicrobial photodynamic therapy (PDT) were assessed in the treatment of Fonsecaea monophora (F. monophora) both in vitro and in vivo. And the underlying mechanism that contributes to the efficacy of this treatment approach was investigated. METHODS: In vitro experiments were conducted using different combinations and concentrations of MB, KI, and 660 nm light (60 mW/cm2) to inhibit F. monophora. The study was carried out using colony-forming unit (CFU) counts and scanning electron microscopy (SEM). The production of singlet oxygen (1O2), free iodine (I2), hydrogen peroxide (H2O2), and superoxide anion during the KI combined MB-mediated antimicrobial PDT process was also detected. In vivo experiments were developed using a Balb/c mouse paw infection model with F. monophora and treated with PBS, 10 mM KI, 2 mM MB +100 J/cm² and 10 mM KI+2 mM MB +100 J/cm² respectively. Inflammatory swelling, fungal load and histopathological analyses of the mouse footpads were assessed. RESULTS: KI enhanced the killing effect of MB-mediated antimicrobial PDT on the conidial spores of F. monophora at the cell and infected animal model level. During the process, the main antimicrobial agents in KI combined with MB- mediated antimicrobial PDT could produce stronger toxic active species including free I2 and H2O2. CONCLUSION: KI combined with MB-mediated antimicrobial PDT could be an effective adjunct therapy for treating CBM.

Epidemiologic features and therapeutic strategies of kerion: A nationwide multicentre study.

BACKGROUND: Kerion is a severe type of tinea capitis that is difficult to treat and remains a public health problem. OBJECTIVES: To evaluate the epidemiologic features and efficacy of different treatment schemes from real-world experience. METHODS: From 2019 to 2021, 316 patients diagnosed with kerion at 32 tertiary Chinese hospitals were enrolled. We analysed the data of each patient, including clinical characteristics, causative pathogens, treatments and outcomes. RESULTS: Preschool children were predominantly affected and were more likely to have zoophilic infection. The most common pathogen in China was Microsporum canis. Atopic dermatitis (AD), animal contact, endothrix infection and geophilic pathogens were linked with kerion occurrence. In terms of treatment, itraconazole was the most applied antifungal agent and reduced the time to mycological cure. A total of 22.5% of patients received systemic glucocorticoids simultaneously, which reduced the time to complete symptom relief. Furthermore, glucocorticoids combined with itraconazole had better treatment efficacy, with a higher rate and shorter time to achieving mycological cure. CONCLUSIONS: Kerion often affects preschoolers and leads to serious sequelae, with AD, animal contact, and endothrix infection as potential risk factors. Glucocorticoids, especially those combined with itraconazole, had better treatment efficacy.

Modulation of copper sites in porphyrin metal-organic frameworks for electrochemical ascorbic acid sensing.

Two metal-organic frameworks (MOFs) with different Cu-centered coordination structures were synthesized. By introducing 4,4-bipyridine as a linker in the Cu-MOFs, we have discovered that Cu-O, instead of Cu-N, is the active site with higher electrocatalytical activity towards ascorbic acid, which is essential to understand and develop Cu-based ascorbic acid sensors.

Large Language Models for Social Determinants of Health Information Extraction from Clinical Notes - A Generalizable Approach across Institutions.

The consistent and persuasive evidence illustrating the influence of social determinants on health has prompted a growing realization throughout the health care sector that enhancing health and health equity will likely depend, at least to some extent, on addressing detrimental social determinants. However, detailed social determinants of health (SDoH) information is often buried within clinical narrative text in electronic health records (EHRs), necessitating natural language processing (NLP) methods to automatically extract these details. Most current NLP efforts for SDoH extraction have been limited, investigating on limited types of SDoH elements, deriving data from a single institution, focusing on specific patient cohorts or note types, with reduced focus on generalizability. This study aims to address these issues by creating cross-institutional corpora spanning different note types and healthcare systems, and developing and evaluating the generalizability of classification models, including novel large language models (LLMs), for detecting SDoH factors from diverse types of notes from four institutions: Harris County Psychiatric Center, University of Texas Physician Practice, Beth Israel Deaconess Medical Center, and Mayo Clinic. Four corpora of deidentified clinical notes were annotated with 21 SDoH factors at two levels: level 1 with SDoH factor types only and level 2 with SDoH factors along with associated values. Three traditional classification algorithms (XGBoost, TextCNN, Sentence BERT) and an instruction tuned LLM-based approach (LLaMA) were developed to identify multiple SDoH factors. Substantial variation was noted in SDoH documentation practices and label distributions based on patient cohorts, note types, and hospitals. The LLM achieved top performance with micro-averaged F1 scores over 0.9 on level 1 annotated corpora and an F1 over 0.84 on level 2 annotated corpora. While models performed well when trained and tested on individual datasets, cross-dataset generalization highlighted remaining obstacles. To foster collaboration, access to partial annotated corpora and models trained by merging all annotated datasets will be made available on the PhysioNet repository.

Prioritizing Clinically Significant Lung Cancer Somatic Mutations for Targeted Therapy Through Efficient NGS Data Filtering System.

In the realm of lung cancer treatment, where genetic heterogeneity presents formidable challenges, precision oncology demands an exacting approach to identify and hierarchically sort clinically significant somatic mutations. Current Next-Generation Sequencing (NGS) data filtering pipelines, while utilizing various external databases for mutation screening, often fall short in comprehensive integration and flexibility needed to keep pace with the evolving landscape of clinical data. Our study introduces a sophisticated NGS data filtering system, which not only aggregates but effectively synergizes diverse data sources, encompassing genetic variants, gene functions, clinical evidence, and an extensive body of literature. This system is distinguished by a unique algorithm that facilitates a rigorous, multi-tiered filtration process. This allows for the efficient prioritization of 420 genes and 1,193 variants from large datasets, with a particular focus on 80 variants demonstrating high clinical actionability. These variants have been aligned with FDA approvals, NCCN guidelines, and thoroughly reviewed literature, thereby equipping oncologists with a refined arsenal for targeted therapy decisions. The innovation of our system lies in its dynamic integration framework and its algorithm, tailored to emphasize clinical utility and actionability-a nuanced approach often lacking in existing methodologies. Our validation on real-world lung adenocarcinoma NGS datasets has shown not only an enhanced efficiency in identifying genetic targets but also the potential to streamline clinical workflows, thus propelling the advancement of precision oncology. Planned future enhancements include expanding the range of integrated data types and developing a user-friendly interface, aiming to facilitate easier access to data and promote collaborative efforts in tailoring cancer treatments.

Bacterial adhesion and corrosion behavior of different pure metals induced by sulfate reducing bacteria.

The corrosion behaviors of four pure metals (Fe, Ni, Mo and Cr) in the presence of sulfate reducing bacteria (SRB) were investigated in enriched artificial seawater (EASW) after 14-day incubation. Metal Fe and metal Ni experienced weight losses of 1.96 mg cm-2 and 1.26 mg cm-2, respectively. In contrast, metal Mo and metal Cr exhibited minimal weight losses, with values of only 0.05 mg cm-2 and 0.03 mg cm-2, respectively. In comparison to Mo (2.2 × 106 cells cm-2) or Cr (1.4 × 106 cells cm-2) surface, the sessile cell counts on Fe (4.0 × 107 cells cm-2) or Ni (3.1 × 107 cells cm-2) surface was higher.

Identification of pancreatic cancer risk factors from clinical notes using natural language processing.

OBJECTIVES: Screening for pancreatic ductal adenocarcinoma (PDAC) is considered in high-risk individuals (HRIs) with established PDAC risk factors, such as family history and germline mutations in PDAC susceptibility genes. Accurate assessment of risk factor status is provider knowledge-dependent and requires extensive manual chart review by experts. Natural Language Processing (NLP) has shown promise in automated data extraction from the electronic health record (EHR). We aimed to use NLP for automated extraction of PDAC risk factors from unstructured clinical notes in the EHR. METHODS: We first developed rule-based NLP algorithms to extract PDAC risk factors at the document-level, using an annotated corpus of 2091 clinical notes. Next, we further improved the NLP algorithms using a cohort of 1138 patients through patient-level training, validation, and testing, with comparison against a pre-specified reference standard. To minimize false-negative results we prioritized algorithm recall. RESULTS: In the test set (n = 807), the NLP algorithms achieved a recall of 0.933, precision of 0.790, and F1-score of 0.856 for family history of PDAC. For germline genetic mutations, the algorithm had a high recall of 0.851, while precision and F1-score were lower at 0.350 and 0.496 respectively. Most false positives for germline mutations resulted from erroneous recognition of tissue mutations. CONCLUSIONS: Rule-based NLP algorithms applied to unstructured clinical notes are highly sensitive for automated identification of PDAC risk factors. Further validation in a large primary-care patient population is warranted to assess real-world utility in identifying HRIs for pancreatic cancer screening.