Unipolar Solution Flow in Calcium-Organic Frameworks for Seawater-Evaporation-Induced Electricity Generation.

Seawater-flow- and -evaporation-induced electricity generation holds significant promise in advancing next-generation sustainable energy technologies. This method relies on the electrokinetic effect but faces substantial limitations when operating in a highly ion-concentrated environment, for example, natural seawater. We present herein a novel solution using calcium-based metal-organic frameworks (MOFs, C12H6Ca2O19·2H2O) for seawater-evaporation-induced electricity generation. Remarkably, Ca-MOFs show an open-circuit voltage of 0.4 V and a short-circuit current of 14 µA when immersed in seawater under natural conditions. Our experiments and simulations revealed that sodium (Na) ions selectively transport within sub-nanochannels of these synthetic superhydrophilic MOFs. This selective ion transport engenders a unipolar solution flow, which drives the electricity generation behavior in seawater. This work not only showcases an effective Ca-MOF for electricity generation through seawater flow/evaporation but also contributes significantly to our understanding of water-driven energy harvesting technologies and their potential applications beyond this specific context.

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.

The Lymphoma Epidemiology of Outcomes cohort study: Design, baseline characteristics, and early outcomes.

To address the current and long-term unmet health needs of the growing population of non-Hodgkin lymphoma (NHL) patients, we established the Lymphoma Epidemiology of Outcomes (LEO) cohort study (NCT02736357; https://leocohort.org/). A total of 7735 newly diagnosed patients aged 18 years and older with NHL were prospectively enrolled from 7/1/2015 to 5/31/2020 at 8 academic centers in the United States. The median age at diagnosis was 62 years (range, 18-99). Participants came from 49 US states and included 538 Black/African-Americans (AA), 822 Hispanics (regardless of race), 3386 women, 716 age <40 years, and 1513 rural residents. At study baseline, we abstracted clinical, pathology, and treatment data; banked serum/plasma (N = 5883, 76.0%) and germline DNA (N = 5465, 70.7%); constructed tissue microarrays for four major NHL subtypes (N = 1189); and collected quality of life (N = 5281, 68.3%) and epidemiologic risk factor (N = 4489, 58.0%) data. Through August 2022, there were 1492 deaths. Compared to population-based SEER data (2015-2019), LEO participants had a similar distribution of gender, AA race, Hispanic ethnicity, and NHL subtype, while LEO was underrepresented for patients who were Asian and aged 80 years and above. Observed overall survival rates for LEO at 1 and 2 years were similar to population-based SEER rates for indolent B-cell (follicular and marginal zone) and T-cell lymphomas, but were 10%-15% higher than SEER rates for aggressive B-cell subtypes (diffuse large B-cell and mantle cell). The LEO cohort is a robust and comprehensive national resource to address the role of clinical, tumor, host genetic, epidemiologic, and other biologic factors in NHL prognosis and survivorship.

Neutrophil extracellular traps (NETs) and Th-2 dominant immune responses in chronic granulomatous chromobalstomycosis.

Chromoblastomycosis (CBM), a chronic, granulomatous, suppurative mycosis of the skin and subcutaneous tissue, is caused by several dematiaceous fungi. The formation of granulomas, tissue proliferation, and fibrosis in response to these pathogenic fungi is believed to be intricately linked to host immunity. To understand this complex interaction, we conducted a comprehensive analysis of immune cell infiltrates, neutrophil extracellular traps (NETs) formation, and the fibrosis mechanism in 20 CBM lesion biopsies using immunohistochemical and immunofluorescence staining methods. The results revealed a significant infiltration of mixed inflammatory cells in CBM granulomas, prominently featuring a substantial presence of Th2 cells and M2 macrophages. These cells appeared to contribute to the production of collagen I and III in the late fibrosis mechanism, as well as NETs formation. The abundance of Th2 cytokines may act as a factor promoting the bias of macrophage differentiation toward M2, which hinders efficient fungal clearance while accelerates the proliferation of fibrous tissue. Furthermore, the expression of IL-17 was noted to recruit neutrophils, facilitating subsequent NETs formation within CBM granulomas to impede the spread of sclerotic cells. Understanding of these immune mechanisms holds promise for identifying therapeutic targets for managing chronic granulomatous CBM.

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.

FedFSA: Hybrid and federated framework for functional status ascertainment across institutions.

INTRODUCTION: Patients' functional status assesses their independence in performing activities of daily living, including basic ADLs (bADL), and more complex instrumental activities (iADL). Existing studies have discovered that patients' functional status is a strong predictor of health outcomes, particularly in older adults. Depite their usefulness, much of the functional status information is stored in electronic health records (EHRs) in either semi-structured or free text formats. This indicates the pressing need to leverage computational approaches such as natural language processing (NLP) to accelerate the curation of functional status information. In this study, we introduced FedFSA, a hybrid and federated NLP framework designed to extract functional status information from EHRs across multiple healthcare institutions. METHODS: FedFSA consists of four major components: 1) individual sites (clients) with their private local data, 2) a rule-based information extraction (IE) framework for ADL extraction, 3) a BERT model for functional status impairment classification, and 4) a concept normalizer. The framework was implemented using the OHNLP Backbone for rule-based IE and open-source Flower and PyTorch library for federated BERT components. For gold standard data generation, we carried out corpus annotation to identify functional status-related expressions based on ICF definitions. Four healthcare institutions were included in the study. To assess FedFSA, we evaluated the performance of category- and institution-specific ADL extraction across different experimental designs. RESULTS: ADL extraction performance ranges from an F1-score of 0.907 to 0.986 for bADL and 0.825 to 0.951 for iADL across the four healthcare sites. The performance for ADL extraction with impairment ranges from an F1-score of 0.722 to 0.954 for bADL and 0.674 to 0.813 for iADL across four healthcare sites. For category-specific ADL extraction, laundry and transferring yielded relatively high performance, while dressing, medication, bathing, and continence achieved moderate-high performance. Conversely, food preparation and toileting showed low performance. CONCLUSION: NLP performance varied across ADL categories and healthcare sites. Federated learning using a FedFSA framework performed higher than non-federated learning for impaired ADL extraction at all healthcare sites. Our study demonstrated the potential of the federated learning framework in functional status extraction and impairment classification in EHRs, exemplifying the importance of a large-scale, multi-institutional collaborative development effort.

The long-term effects of blood urea nitrogen levels on cardiovascular disease and all-cause mortality in diabetes: a prospective cohort study.

BACKGROUND: The long-term effects of blood urea nitrogen(BUN) in patients with diabetes remain unknown. Current studies reporting the target BUN level in patients with diabetes are also limited. Hence, this prospective study aimed to explore the relationship of BUN with all-cause and cardiovascular mortalities in patients with diabetes. METHODS: In total, 10,507 participants with diabetes from the National Health and Nutrition Examination Survey (1999-2018) were enrolled. The causes and numbers of deaths were determined based on the National Death Index mortality data from the date of NHANES interview until follow-up (December 31, 2019). Multivariate Cox proportional hazard regression models were used to calculate the hazard ratios (HRs) and 95% confidence interval (CIs) of mortality. RESULTS: Of the adult participants with diabetes, 4963 (47.2%) were female. The median (interquartile range) BUN level of participants was 5 (3.93-6.43) mmol/L. After 86,601 person-years of follow-up, 2,441 deaths were documented. After adjusting for variables, the HRs of cardiovascular disease (CVD) and all-cause mortality in the highest BUN level group were 1.52 and 1.35, respectively, compared with those in the lowest BUN level group. With a one-unit increment in BUN levels, the HRs of all-cause and CVD mortality rates were 1.07 and 1.08, respectively. The results remained robust when several sensitivity and stratified analyses were performed. Moreover, BUN showed a nonlinear association with all-cause and CVD mortality. Their curves all showed that the inflection points were close to the BUN level of 5 mmol/L. CONCLUSION: BUN had a nonlinear association with all-cause and CVD mortality in patients with diabetes. The inflection point was at 5 mmol/L.

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.

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.

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.

Two-Component System Genes in Brassica napus: Identification, Analysis, and Expression Patterns in Response to Abiotic and Biotic Stresses.

The two-component system (TCS), consisting of histidine kinases (HKs), histidine phosphotransfer proteins (HPs) and response regulators (RRs) in eukaryotes, plays pivotal roles in regulating plant growth, development, and responses to environment stimuli. However, the TCS genes were poorly characterized in rapeseed, which is an important tetraploid crop in Brassicaceae. In this work, a total of 182 BnaTCS genes were identified, including 43 HKs, 16 HPs, and 123 RRs, which was more than that in other crops due to segmental duplications during the process of polyploidization. It was significantly different in genetic diversity between the three subfamilies, and some members showed substantial genetic differentiation among the three rapeseed ecotypes. Several hormone- and stress-responsive cis-elements were identified in the putative promoter regions of BnaTCS genes. Furthermore, the expression of BnaTCS genes under abiotic stresses, exogenous phytohormone, and biotic stresses was analyzed, and numerous candidate stress-responsive genes were screened out. Meanwhile, using a natural population with 505 B. napus accessions, we explored the genetic effects of BnaTCS genes on salt tolerance by association mapping analysis and detected some significant association SNPs/genes. The result will help to further understand the functions of TCS genes in the developmental and stress tolerance improvement in B. napus.

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.

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.

Proteome-wide identification of methylglyoxalated proteins in rapeseed (Brassica napus L.).

Methylglyoxal (MG), a highly reactive cellular metabolite, is crucial for plant growth and environmental responses. MG may function by modifying its target proteins, but little is known about MG-modified proteins in plants. Here, MG-modified proteins were pulled down by an antibody against methylglyoxalated proteins and detected using liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. We identified 543 candidate proteins which are involved in multiple enzymatic activities and metabolic processes. A great number of candidate proteins were predicted to localize to cytoplasm, chloroplast, and nucleus, consistent with the known subcellular compartmentalization of MG. By further analyzing the raw LC-MS/MS data, we obtained 42 methylglyoxalated peptides in 35 proteins and identified 10 methylglyoxalated lysine residues in a myrosinase-binding protein (BnaC06G0061400ZS). In addition, we demonstrated that MG modifies the glycolate oxidase and ß-glucosidase to enhance and inhibit the enzymatic activity, respectively. Together, our study contributes to the investigation of the MG-modified proteins and their potential roles in rapeseed.

Real-world Trends, Rural-urban Differences, and Socioeconomic Disparities in Utilization of Narrow versus Broad Next-generation Sequencing Panels.

Advances in genetic technology have led to the increasing use of genomic panels in precision oncology practice, with panels ranging from a couple to hundreds of genes. However, the clinical utilization and utility of oncology genomic panels, especially among vulnerable populations, is unclear. We examined the association of panel size with socioeconomic status and clinical trial matching. We retrospectively identified 9,886 eligible adult subjects in the Mayo Clinic Health System who underwent genomic testing between January 1, 2016 and June 30, 2020. Patient data were retrieved from structured and unstructured data sources of institutional collections, including cancer registries, clinical data warehouses, and clinical notes. Socioeconomic surrogates were approximated using the Area Deprivation Index (ADI) corresponding to primary residence addresses. Logistic regression was performed to analyze relationships between ADI or rural/urban status and (i) use of genomic test by panel size; (ii) clinical trial matching status. Compared with patients from the most affluent areas, patients had a lower odds of receiving a panel test (vs. a single-gene test) if from areas of higher socioeconomic deprivation [OR (95% confidence interval (CI): 0.71 (0.61-0.83), P < 0.01] or a rural area [OR (95% CI): 0.85 (0.76-0.96), P < 0.01]. Patients in areas of higher socioeconomic deprivation were less likely to be matched to clinical trials if receiving medium panel tests [(OR) (95% CI): 0.69 (0.49-0.97), P = 0.03]; however, there was no difference among patients receiving large panel tests (P > 0.05) and rural patients were almost 2x greater odds of being matched if receiving a large panel test [(OR) (95% CI): 1.76 (1.21-2.55), P < 0.01]. SIGNIFICANCE: We identified socioeconomic and rurality disparities in the use of genomic tests and trial matching by panel size, which may have implications for equal access to targeted therapies. The lack of association between large panel tests and clinical trial matching by socioeconomic status, suggests a potential health equity impact, while removing barriers in access to large panels for rural patients may improve access to trials. However, further research is needed.

An Integrated Voice Recognition and Natural Language Processing Platform to Automatically Extract Thoracolumbar Injury Classification Score Features From Radiology Reports.

BACKGROUND: Many predictive models for estimating clinical outcomes after spine surgery have been reported in the literature. However, implementation of predictive scores in practice is limited by the time-intensive nature of manually abstracting relevant predictors. In this study, we designed natural language processing (NLP) algorithms to automate data abstraction for the thoracolumbar injury classification score (TLICS). METHODS: We retrieved the radiology reports of all Mayo Clinic patients with an International Classification of Diseases, 9th or 10th revision, code corresponding to a fracture of the thoracolumbar spine between January 2005 and October 2020. Annotated data were used to train an N-gram NLP model using machine learning methods, including random forest, stepwise linear discriminant analysis, k-nearest neighbors, and penalized logistic regression models. RESULTS: A total of 1085 spine radiology reports were included in our analysis. Our dataset included 483 compression, 401 burst, 103 translational/rotational, and 98 distraction fractures. A total of 103 reports had documented an injury of the posterior ligamentous complex. The overall accuracy of the random forest model for fracture morphology feature detection was 76.96% versus 65.90% in the stepwise linear discriminant analysis, 50.69% in the k-nearest neighbors, and 62.67% in the penalized logistic regression. The overall accuracy to detect posterior ligamentous complex integrity was highest in the random forest model at 83.41%. Our random forest model was implemented in the backend of a web application in which users can dictate reports and have TLICS features automatically extracted. CONCLUSIONS: We have developed a machine learning NLP model for extracting TLICS features from radiology reports, which we deployed in a web application that can be integrated into clinical practice.

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.

Plasmon-Switched Kinetics for Formic Acid Dehydrogenation: Selective Adsorption Driven by Local Field and Hot Carriers.

Understanding illumination-mediated kinetics is essential for catalyst design in plasmon catalysis. Here we prepare Pd-based plasmonic catalysts with tunable electronic structures to reveal the underlying illumination-enhanced kinetic mechanisms for formic acid (HCOOH) dehydrogenation. We demonstrate a kinetic switch from a competitive Langmuir-Hinshelwood adsorption mode in dark to a non-competitive type under irradiation triggered by local field and hot carriers. Specifically, the electromagnetic field induces a spatial-temporal separation of dehydrogenation-favorable configurations of reactant molecule HCOOH and HCOO- due to their natural different polarities. Meanwhile, the generated energetic carriers can serve as active sites for selective molecular adsorption. The hot electrons act as adsorption sites for HCOOH, while holes prefer to adsorb HCOO-. Such unique non-competitive adsorption kinetics induced by plasmon effects serves as another typical characteristic of plasmonic catalysis that remarkably differs from thermocatalysis. This work unravels unique adsorption transformations and a kinetic switching driven by plasmon nonthermal effects.

QiDiTangShen granules alleviates diabetic nephropathy podocyte injury: A network pharmacology study and experimental validation in vivo and vitro.

Background: QiDiTangShen granules (QDTS), a traditional Chinese medicine (TCM) compound prescription, have remarkable efficacy in diabetic nephropathy (DN) patients, and their pharmacological mechanism needs further exploration. Methods: According to the active ingredients and targets of the QDTS in the TCMSP database, the network pharmacology of QDTS was investigated. The potential active ingredients were chosen based on the oral bioavailability and the drug similarity index. At the same time, targets for DN-related disease were obtained from GeneCards, OMIM, PharmGKB, TTD, and DrugBank. The TCM-component-target network and the protein-protein interaction (PPI) network were constructed with the Cytoscape and STRING platforms, respectively, and then the core targets of DN were selected with CytoNCA. GO and KEGG enrichment analysis using R software. Molecular docking to identify the core targets of QDTS for DN. In vivo, db/db mice were treated as DN models, and the urine microalbuminuria, the pathological changes in the kidney and the protein expression levels of p-PI3K, p-Akt, JUN, nephrin and synaptopodin were detected by immunohistochemistry, immunofluorescence method and Western blotting. After QDTS was used in vitro, the protein expression of mouse podocyte clone-5 (MPC5) cells was detected by immunohistochemistry, immunofluorescence and Western blot. Results: Through network pharmacology analysis, 153 potential targets for DN in QDTS were identified, 19 of which were significant. The KEGG enrichment analysis indicated that QDTS might have therapeutic effects on IL-17, TNF, AGE-RAGE, PI3K-Akt, HIF-1, and EGFR through interfering with Akt1 and JUN. The main active ingredients in QDTS are quercetin, ß-sitosterol, stigmasterol and kaempferol. Both in vivo and in vitro studies showed that QDTS could decrease the urine microalbuminuria and renal pathology of db/db mice, and alleviate podocyte injuries through the PI3K/Akt signaling pathway. Conclusion: Through network pharmacology, in vivo and in vitro experiments, QDTS has been shown to improve the urine microalbuminuria and renal pathology in DN, and to reduce podocyte damage via the PI3K/Akt pathway.

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.