Towards a seamless product and process development workflow for recombinant proteins produced by plant molecular farming.

Plant molecular farming (PMF) has been promoted as a fast, efficient and cost-effective alternative to bacteria and animal cells for the production of biopharmaceutical proteins. Numerous plant species have been tested to produce a wide range of drug candidates. However, PMF generally lacks a systematic, streamlined and seamless workflow to continuously fill the product pipeline. Therefore, it is currently unable to compete with established platforms in terms of routine, throughput and horizontal integration (the rapid translation of product candidates to preclinical and clinical development). Individual management decisions, limited funding and a lack of qualified production capacity can hinder the execution of such projects, but we also lack suitable technologies for sample handling and data management. This perspectives article will highlight current bottlenecks in PMF and offer potential solutions that combine PMF with existing technologies to build an integrated facility of the future for product development, testing, manufacturing and clinical translation. Ten major bottlenecks have been identified and are discussed in turn: automated cloning and simplified transformation options, reproducibility of bacterial cultivation, bioreactor integration with automated cell handling, options for rapid mid-scale candidate and product manufacturing, interconnection with (group-specific or personalized) clinical trials, diversity of (post-)infiltration conditions, development of downstream processing platforms, continuous process operation, compliance of manufacturing conditions with biosafety regulations, scaling requirements for cascading biomass.

Machine Learning-based Cerebral Venous Thrombosis Diagnosis with Clinical Data.

OBJECTIVES: Cerebral Venous Thrombosis (CVT) poses diagnostic challenges due to the variability in disease course and symptoms. The prognosis of CVT relies on early diagnosis. Our study focuses on developing a machine learning-based screening algorithm using clinical data from a large neurology referral center in southern Iran. METHODS: The Iran Cerebral Venous Thrombosis Registry (ICVTR code: 9001013381) provided data on 382 CVT cases from Namazi Hospital. The control group comprised of adult headache patients without CVT as confirmed by neuroimaging and was retrospectively selected from those admitted to the same hospital. We collected 60 clinical and demographic features for model development and validation. Our modeling pipeline involved imputing missing values and evaluating four machine learning algorithms: generalized linear model, random forest, support vector machine, and extreme gradient boosting. RESULTS: A total of 314 CVT cases and 575 controls were included. The highest AUROC was reached when imputation was used to estimate missing values for all the variables, combined with the support vector machine model (AUROC=0.910, Recall=0.73, Precision=0.88). The best recall was achieved also by the support vector machine model when only variables with less than 50% missing rate were included (AUROC=0.887, Recall=0.77, Precision=0.86). The random forest model yielded the best precision by using variables with less than 50% missing rate (AUROC=0.882, Recall=0.61, Precision=0.94). CONCLUSION: The application of machine learning techniques using clinical data showed promising results in accurately diagnosing CVT within our study population. This approach offers a valuable complementary assistive tool or an alternative to resource-intensive imaging methods.

Streamlining intersectoral provision of real-world health data: a service platform for improved clinical research and patient care.

Introduction: Obtaining real-world data from routine clinical care is of growing interest for scientific research and personalized medicine. Despite the abundance of medical data across various facilities - including hospitals, outpatient clinics, and physician practices - the intersectoral exchange of information remains largely hindered due to differences in data structure, content, and adherence to data protection regulations. In response to this challenge, the Medical Informatics Initiative (MII) was launched in Germany, focusing initially on university hospitals to foster the exchange and utilization of real-world data through the development of standardized methods and tools, including the creation of a common core dataset. Our aim, as part of the Medical Informatics Research Hub in Saxony (MiHUBx), is to extend the MII concepts to non-university healthcare providers in a more seamless manner to enable the exchange of real-world data among intersectoral medical sites. Methods: We investigated what services are needed to facilitate the provision of harmonized real-world data for cross-site research. On this basis, we designed a Service Platform Prototype that hosts services for data harmonization, adhering to the globally recognized Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) international standard communication format and the Observational Medical Outcomes Partnership (OMOP) common data model (CDM). Leveraging these standards, we implemented additional services facilitating data utilization, exchange and analysis. Throughout the development phase, we collaborated with an interdisciplinary team of experts from the fields of system administration, software engineering and technology acceptance to ensure that the solution is sustainable and reusable in the long term. Results: We have developed the pre-built packages "ResearchData-to-FHIR," "FHIR-to-OMOP," and "Addons," which provide the services for data harmonization and provision of project-related real-world data in both the FHIR MII Core dataset format (CDS) and the OMOP CDM format as well as utilization and a Service Platform Prototype to streamline data management and use. Conclusion: Our development shows a possible approach to extend the MII concepts to non-university healthcare providers to enable cross-site research on real-world data. Our Service Platform Prototype can thus pave the way for intersectoral data sharing, federated analysis, and provision of SMART-on-FHIR applications to support clinical decision making.

Machine Learning Identifies Key Proteins in Primary Sclerosing Cholangitis Progression and Links High CCL24 to Cirrhosis.

Primary sclerosing cholangitis (PSC) is a rare, progressive disease, characterized by inflammation and fibrosis of the bile ducts, lacking reliable prognostic biomarkers for disease activity. Machine learning applied to broad proteomic profiling of sera allowed for the discovery of markers of disease presence, severity, and cirrhosis and the exploration of the involvement of CCL24, a chemokine with fibro-inflammatory activity. Sera from 30 healthy controls and 45 PSC patients were profiled with proximity extension assay, quantifying the expression of 2870 proteins, and used to train an elastic net model. Proteins that contributed most to the model were tested for correlation to enhanced liver fibrosis (ELF) score and used to perform pathway analysis. Statistical modeling for the presence of cirrhosis was performed with principal component analysis (PCA), and receiver operating characteristics (ROC) curves were used to assess the useability of potential biomarkers. The model successfully predicted the presence of PSC, where the top-ranked proteins were associated with cell adhesion, immune response, and inflammation, and each had an area under receiver operator characteristic (AUROC) curve greater than 0.9 for disease presence and greater than 0.8 for ELF score. Pathway analysis showed enrichment for functions associated with PSC, overlapping with pathways enriched in patients with high levels of CCL24. Patients with cirrhosis showed higher levels of CCL24. This data-driven approach to characterize PSC and its severity highlights potential serum protein biomarkers and the importance of CCL24 in the disease, implying its therapeutic potential in PSC.

Elucidating the role of liver enzymes as markers and regulators in ovarian cancer: a synergistic approach using Mendelian randomization, single-cell analysis, and clinical evidence.

OBJECTIVE: To investigate the association between liver enzymes and ovarian cancer (OC), and to validate their potential as biomarkers and their mechanisms in OC. Methods Genome-wide association studies for OC and levels of enzymes such as Alkaline phosphatase (ALP), Aspartate aminotransferase (AST), Alanine aminotransferase, and gamma-glutamyltransferase were analyzed. Univariate and multivariate Mendelian randomization (MR), complemented by the Steiger test, identified enzymes with a potential causal relationship to OC. Single-cell transcriptomics from the GSE130000 dataset pinpointed pivotal cellular clusters, enabling further examination of enzyme-encoding gene expression. Transcription factors (TFs) governing these genes were predicted to construct TF-mRNA networks. Additionally, liver enzyme levels were retrospectively analyzed in healthy individuals and OC patients, alongside the evaluation of correlations with cancer antigen 125 (CA125) and Human Epididymis Protein 4 (HE4). RESULTS: A total of 283 single nucleotide polymorphisms (SNPs) and 209 SNPs related to ALP and AST, respectively. Using the inverse-variance weighted method, univariate MR (UVMR) analysis revealed that ALP (P = 0.050, OR = 0.938) and AST (P = 0.017, OR = 0.906) were inversely associated with OC risk, suggesting their roles as protective factors. Multivariate MR (MVMR) confirmed the causal effect of ALP (P = 0.005, OR = 0.938) on OC without reverse causality. Key cellular clusters including T cells, ovarian cells, endothelial cells, macrophages, cancer-associated fibroblasts (CAFs), and epithelial cells were identified, with epithelial cells showing high expression of genes encoding AST and ALP. Notably, TFs such as TCE4 were implicated in the regulation of GOT2 and ALPL genes. OC patient samples exhibited decreased ALP levels in both blood and tumor tissues, with a negative correlation between ALP and CA125 levels observed. CONCLUSION: This study has established a causal link between AST and ALP with OC, identifying them as protective factors. The increased expression of the genes encoding these enzymes in epithelial cells provides a theoretical basis for developing novel disease markers and targeted therapies for OC.

Creation of Standardized Common Data Elements for Diagnostic Tests in Infectious Disease Studies: Semantic and Syntactic Mapping.

BACKGROUND: It is necessary to harmonize and standardize data variables used in case report forms (CRFs) of clinical studies to facilitate the merging and sharing of the collected patient data across several clinical studies. This is particularly true for clinical studies that focus on infectious diseases. Public health may be highly dependent on the findings of such studies. Hence, there is an elevated urgency to generate meaningful, reliable insights, ideally based on a high sample number and quality data. The implementation of core data elements and the incorporation of interoperability standards can facilitate the creation of harmonized clinical data sets. OBJECTIVE: This study's objective was to compare, harmonize, and standardize variables focused on diagnostic tests used as part of CRFs in 6 international clinical studies of infectious diseases in order to, ultimately, then make available the panstudy common data elements (CDEs) for ongoing and future studies to foster interoperability and comparability of collected data across trials. METHODS: We reviewed and compared the metadata that comprised the CRFs used for data collection in and across all 6 infectious disease studies under consideration in order to identify CDEs. We examined the availability of international semantic standard codes within the Systemized Nomenclature of Medicine - Clinical Terms, the National Cancer Institute Thesaurus, and the Logical Observation Identifiers Names and Codes system for the unambiguous representation of diagnostic testing information that makes up the CDEs. We then proposed 2 data models that incorporate semantic and syntactic standards for the identified CDEs. RESULTS: Of 216 variables that were considered in the scope of the analysis, we identified 11 CDEs to describe diagnostic tests (in particular, serology and sequencing) for infectious diseases: viral lineage/clade; test date, type, performer, and manufacturer; target gene; quantitative and qualitative results; and specimen identifier, type, and collection date. CONCLUSIONS: The identification of CDEs for infectious diseases is the first step in facilitating the exchange and possible merging of a subset of data across clinical studies (and with that, large research projects) for possible shared analysis to increase the power of findings. The path to harmonization and standardization of clinical study data in the interest of interoperability can be paved in 2 ways. First, a map to standard terminologies ensures that each data element's (variable's) definition is unambiguous and that it has a single, unique interpretation across studies. Second, the exchange of these data is assisted by "wrapping" them in a standard exchange format, such as Fast Health care Interoperability Resources or the Clinical Data Interchange Standards Consortium's Clinical Data Acquisition Standards Harmonization Model.

Implementing a Biomedical Data Warehouse From Blueprint to Bedside in a Regional French University Hospital Setting: Unveiling Processes, Overcoming Challenges, and Extracting Clinical Insight.

Background: Biomedical data warehouses (BDWs) have become an essential tool to facilitate the reuse of health data for both research and decisional applications. Beyond technical issues, the implementation of BDWs requires strong institutional data governance and operational knowledge of the European and national legal framework for the management of research data access and use. Objective: In this paper, we describe the compound process of implementation and the contents of a regional university hospital BDW. Methods: We present the actions and challenges regarding organizational changes, technical architecture, and shared governance that took place to develop the Nantes BDW. We describe the process to access clinical contents, give details about patient data protection, and use examples to illustrate merging clinical insights. Unlabelled: More than 68 million textual documents and 543 million pieces of coded information concerning approximately 1.5 million patients admitted to CHUN between 2002 and 2022 can be queried and transformed to be made available to investigators. Since its creation in 2018, 269 projects have benefited from the Nantes BDW. Access to data is organized according to data use and regulatory requirements. Conclusions: Data use is entirely determined by the scientific question posed. It is the vector of legitimacy of data access for secondary use. Enabling access to a BDW is a game changer for research and all operational situations in need of data. Finally, data governance must prevail over technical issues in institution data strategy vis-à-vis care professionals and patients alike.

Integrating Deep Learning with Electronic Health Records for Early Glaucoma Detection: A Multi-Dimensional Machine Learning Approach.

BACKGROUND: Recent advancements in deep learning have significantly impacted ophthalmology, especially in glaucoma, a leading cause of irreversible blindness worldwide. In this study, we developed a reliable predictive model for glaucoma detection using deep learning models based on clinical data, social and behavior risk factor, and demographic data from 1652 participants, split evenly between 826 control subjects and 826 glaucoma patients. METHODS: We extracted structural data from control and glaucoma patients' electronic health records (EHR). Three distinct machine learning classifiers, the Random Forest and Gradient Boosting algorithms, as well as the Sequential model from the Keras library of TensorFlow, were employed to conduct predictive analyses across our dataset. Key performance metrics such as accuracy, F1 score, precision, recall, and the area under the receiver operating characteristics curve (AUC) were computed to both train and optimize these models. RESULTS: The Random Forest model achieved an accuracy of 67.5%, with a ROC AUC of 0.67, outperforming the Gradient Boosting and Sequential models, which registered accuracies of 66.3% and 64.5%, respectively. Our results highlighted key predictive factors such as intraocular pressure, family history, and body mass index, substantiating their roles in glaucoma risk assessment. CONCLUSIONS: This study demonstrates the potential of utilizing readily available clinical, lifestyle, and demographic data from EHRs for glaucoma detection through deep learning models. While our model, using EHR data alone, has a lower accuracy compared to those incorporating imaging data, it still offers a promising avenue for early glaucoma risk assessment in primary care settings. The observed disparities in model performance and feature significance show the importance of tailoring detection strategies to individual patient characteristics, potentially leading to more effective and personalized glaucoma screening and intervention.

Machine learning techniques to predict the risk of developing diabetic nephropathy: a literature review.

Purpose: Diabetes is a major public health challenge with widespread prevalence, often leading to complications such as Diabetic Nephropathy (DN)-a chronic condition that progressively impairs kidney function. In this context, it is important to evaluate if Machine learning models can exploit the inherent temporal factor in clinical data to predict the risk of developing DN faster and more accurately than current clinical models. Methods: Three different databases were used for this literature review: Scopus, Web of Science, and PubMed. Only articles written in English and published between January 2015 and December 2022 were included. Results: We included 11 studies, from which we discuss a number of algorithms capable of extracting knowledge from clinical data, incorporating dynamic aspects in patient assessment, and exploring their evolution over time. We also present a comparison of the different approaches, their performance, advantages, disadvantages, interpretation, and the value that the time factor can bring to a more successful prediction of diabetic nephropathy. Conclusion: Our analysis showed that some studies ignored the temporal factor, while others partially exploited it. Greater use of the temporal aspect inherent in Electronic Health Records (EHR) data, together with the integration of omics data, could lead to the development of more reliable and powerful predictive models.

Drug repurposing for fungal infections.

The rise of multidrug-resistant bacteria is a well-recognized threat to world health, necessitating the implementation of effective treatments. This issue has been identified as a top priority on the global agenda by the World Health Organization. Certain strains, such as Candida glabrata, Candida krusei, Candida lusitaniae, Candida auris, select cryptococcal species, and opportunistic Aspergillus or Fusarium species, have significant intrinsic resistance to numerous antifungal medicines. This inherent resistance and subsequent suboptimal clinical outcomes underscore the critical imperative for enhanced therapeutic alternatives and management protocols. The challenge of effectively treating fungal infections, compounded by the protracted timelines involved in developing novel drugs, underscores the pressing need to explore alternative therapeutic avenues. Among these, drug repurposing emerges as a particularly promising and expeditious solution, providing cost-effective solutions and safety benefits. In the fight against life-threatening resistant fungal infections, the idea of repurposing existing medications has encouraged research into both established and new compounds as a last-resort therapy. This chapter seeks to provide a comprehensive overview of contemporary antifungal drugs, as well as their key resistance mechanisms. Additionally, it seeks to provide insight into the antimicrobial properties of non-traditional drugs, thereby offering a holistic perspective on the evolving landscape of antifungal therapeutics.

Assessment of Bone Age Based on Hand Radiographs Using Regression-Based Multi-Modal Deep Learning.

(1) Objective: In this study, a regression-based multi-modal deep learning model was developed for use in bone age assessment (BAA) utilizing hand radiographic images and clinical data, including patient gender and chronological age, as input data. (2) Methods: A dataset of hand radiographic images from 2974 pediatric patients was used to develop a regression-based multi-modal BAA model. This model integrates hand radiographs using EfficientNetV2S convolutional neural networks (CNNs) and clinical data (gender and chronological age) processed by a simple deep neural network (DNN). This approach enhances the model's robustness and diagnostic precision, addressing challenges related to imbalanced data distribution and limited sample sizes. (3) Results: The model exhibited good performance on BAA, with an overall mean absolute error (MAE) of 0.410, root mean square error (RMSE) of 0.637, and accuracy of 91.1%. Subgroup analysis revealed higher accuracy in females ≤ 11 years (MAE: 0.267, RMSE: 0.453, accuracy: 95.0%) and >11 years (MAE: 0.402, RMSE: 0.634, accuracy 92.4%) compared to males ≤ 13 years (MAE: 0.665, RMSE: 0.912, accuracy: 79.7%) and >13 years (MAE: 0.647, RMSE: 1.302, accuracy: 84.6%). (4) Conclusion: This model showed a generally good performance on BAA, showing a better performance in female pediatrics compared to male pediatrics and an especially robust performance in female pediatrics ≤ 11 years.

"Reality is frequently inaccurate" A case study examining the whens and whys of post-live database changes in a UK clinical trials unit *Douglas Adams.

INTRODUCTION: Accurately estimating the costs of clinical trials is challenging. There is currently no reference class data to allow researchers to understand the potential costs associated with database change management in clinical trials. METHODS: We used a case-based approach, summarising post-live changes in eleven clinical trial databases managed by Sheffield Clinical Trials Research Unit. We reviewed the database specifications for each trial and summarised the number of changes, change type, change category, and timing of changes. We pooled our experiences and made observations in relation to key themes. RESULTS: Median total number of changes across the eleven trials was 71 (range 40-155) and median number of changes per study week was 0.48 (range 0.32-1.34). The most common change type was modification (median 39, range 20-90), followed by additions (median 32, range 18-55), then deletions (median 7, range 1-12). In our sample, changes were more common in the first half of the trial's lifespan, regardless of its overall duration. Trials which saw continuous changes seemed more likely to be external pilots or trials in areas where the trial team was either less experienced overall or within the particular therapeutic area. CONCLUSIONS: Researchers should plan trials with the expectation that clinical trial databases will require changes within the life of the trial, particularly in the early stages or with a less experienced trial team. More research is required to understand potential differences between clinical trial units and database types.

Control compounds for preclinical drug-induced liver injury assessment: Consensus-driven systematic review by the ProEuroDILI network.

BACKGROUND & AIMS: Idiosyncratic drug-induced liver injury (DILI) is a complex and unpredictable event caused by drugs, and herbal or dietary supplements. Early identification of human hepatotoxicity at preclinical stages remains a major challenge, in which the selection of validated in vitro systems and test drugs has a significant impact. In this systematic review, we analyzed the compounds used in hepatotoxicity assays and established a list of DILI-positive and -negative control drugs for validation of in vitro models of DILI, supported by literature and clinical evidence and endorsed by an expert committee from the COST Action ProEuroDILI Network (CA17112). METHODS: Following 2020 PRISMA guidelines, original research articles focusing on DILI which used in vitro human models and performed at least one hepatotoxicity assay with positive and negative control compounds, were included. Bias of the studies was assessed by a modified 'Toxicological Data Reliability Assessment Tool'. RESULTS: A total of 51 studies (out of 2,936) met the inclusion criteria, with 30 categorized as reliable without restrictions. Although there was a broad consensus on positive compounds, the selection of negative compounds lacked clarity. 2D monoculture, short exposure times and cytotoxicity endpoints were the most tested, although there was no consensus on drug concentrations. CONCLUSIONS: Extensive analysis highlighted the lack of agreement on control compounds for in vitro DILI assessment. Following comprehensive in vitro and clinical data analysis together with input from the expert committee, an evidence-based consensus-driven list of 10 positive and negative control drugs for validation of in vitro models of DILI is proposed. IMPACT AND IMPLICATIONS: Prediction of human toxicity early in the drug development process remains a major challenge, necessitating the development of more physiologically relevant liver models and careful selection of drug-induced liver injury (DILI)-positive and -negative control drugs to better predict the risk of DILI associated with new drug candidates. Thus, this systematic study has crucial implications for standardizing the validation of new in vitro models of DILI. By establishing a consensus-driven list of positive and negative control drugs, the study provides a scientifically justified framework for enhancing the consistency of preclinical testing, thereby addressing a significant challenge in early hepatotoxicity identification. Practically, these findings can guide researchers in evaluating safety profiles of new drugs, refining in vitro models, and informing regulatory agencies on potential improvements to regulatory guidelines, ensuring a more systematic and efficient approach to drug safety assessment.

TriClip G4: A game-changer for tricuspid valve regurgitation treatment.

Tricuspid valve regurgitation, or TR, is a difficult-to-manage condition. In addition to EVOQUE, percutaneous annuloplasty, and surgical repair, the TriClip G4 system has been added to the interventional therapeutic choices for TR. Recently, the Food and Drug Administration (FDA) approved the use of the TriClip G4 device to treat patients with symptomatic, severe TR who have received optimal medication therapy but are at intermediate or higher risk of surgery. This review attempts to offer a thorough examination of the procedural features, learning curves, results of the device and compares the TriClip G4 system to other interventional therapies for TR. TriClip G4 has shown to have promising results in pivotal clinical trials, be cost-effective, and improve the quality of life of patients. Furthermore, it has its unique advantages against other conventional techniques and devices.

Corrigendum: Intelligent diagnosis of the severity of disease conditions in COVID-19 patients based on the LASSO method.

[This corrects the article DOI: 10.3389/fpubh.2024.1302256.].

Exploring the tradeoff between data privacy and utility with a clinical data analysis use case.

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.

Differential Effect of Positive End-Expiratory Pressure Strategies in Patients With ARDS: A Bayesian Analysis of Clinical Subphenotypes.

BACKGROUND: ARDS is a heterogeneous condition with two subphenotypes identified by different methodologies. Our group similarly identified two ARDS subphenotypes using nine routinely available clinical variables. However, whether these are associated with differential response to treatment has yet to be explored. RESEARCH QUESTION: Are there differential responses to positive end-expiratory pressure (PEEP) strategies on 28-day mortality according to subphenotypes in adult patients with ARDS? STUDY DESIGN AND METHODS: We evaluated data from two prior ARDS trials (Higher vs Lower Positive End-Expiratory Pressures in Patients With the ARDS [ALVEOLI] and ARDS Trial [ART]) that compared different PEEP strategies. We classified patients into one of two subphenotypes as described previously. We assessed the differential effect of PEEP with a Bayesian hierarchical logistic model for the primary outcome of 28-day mortality. RESULTS: We analyzed data from 1,559 patients with ARDS. Compared with lower PEEP, a higher PEEP strategy resulted in higher 28-day mortality in patients with subphenotype A disease in the ALVEOLI study (OR, 1.61; 95% credible interval [CrI], 0.90-2.94) and ART (OR, 1.73; 95% CrI, 1.01-2.98), with a probability of harm resulting from higher PEEP in this subphenotype of 94.3% and 97.7% in the ALVEOLI and ART studies, respectively. Higher PEEP was not associated with mortality in patients with subphenotype B disease in each trial (OR, 0.95 [95% CrI, 0.51-1.73] and 1.00 [95% CrI, 0.63-1.55], respectively), with probability of benefit of 56.4% and 50.7% in the ALVEOLI and ART studies, respectively. These effects were not modified by Pao2 to Fio2 ratio, driving pressure, or the severity of illness for the cohorts. INTERPRETATION: We found evidence of differential response to PEEP strategies across two ARDS subphenotypes, suggesting possible harm with a higher PEEP strategy in one subphenotype. These observations may assist with predictive enrichment in future clinical trials.

Blockchain en salud: transformando la seguridad y la gestión de datos clínicos / Blockchain in health: Transforming security and clinical data management

Introducción Los avances tecnológicos continúan transformando la sociedad, incluyendo el sector de la salud. La naturaleza descentralizada y verificable de la tecnología blockchain presenta un gran potencial para abordar desafíos actuales en la gestión de datos sanitarios. Discusión Este artículo indaga sobre cómo la adopción generalizada de blockchain se enfrenta a importantes desafíos y barreras que deben abordarse, como la falta de regulación, la complejidad técnica, la salvaguarda de la privacidad y los costos tanto económicos como tecnológicos. La colaboración entre profesionales médicos, tecnólogos y legisladores es esencial para establecer un marco normativo sólido y una capacitación adecuada. Conclusión La tecnología blockchain tiene potencial de revolucionar la gestión de datos en el sector de la salud, mejorando la calidad de la atención médica, empoderando a los usuarios y fomentando la compartición segura de datos. Es necesario un cambio cultural y regulatorio, junto a más evidencia, para concluir sus ventajas frente a las alternativas tecnológicas existentes. (AU)

Introduction Technological advances continue to transform society, including the health sector. The decentralized and verifiable nature of blockchain technology presents great potential for addressing current challenges in healthcare data management. Discussion This article reports on how the generalized adoption of blockchain faces important challenges and barriers that must be addressed, such as the lack of regulation, technical complexity, safeguarding privacy, and economic and technological costs. Collaboration between medical professionals, technologists and legislators is essential to establish a solid regulatory framework and adequate training. Conclusion Blockchain technology has the potential to revolutionize data management in the healthcare sector, improving the quality of medical care, empowering users, and promoting the secure sharing of data, but an important cultural change is needed, along with more evidence, to reveal its advantages in front of the existing technological alternative. (AU)

[Clinical Characteristics Analysis of 1051 Cases of Cleft Lip With/Without Cleft Palate in the Ethnic Tibetan Population]. / 1051ä¾‹è—æ—å”‡è ­è£‚æ‚£è€ ä¸´åºŠç‰¹å¾åˆ†æž.

Objective: To examine the characteristics of the prevalence of congenital cleft lip with/without cleft palate in the ethnic Tibetan population and to provide support for the precise prevention and treatment of cleft lip with/without cleft palate in the Tibetan population. Methods: The clinical data of Tibetan patients with cleft lip with/without cleft palate were collected and the clinical characteristics of the patients were analyzed. The patients' age ranged from 2 months to 51 years old. All the subjects were admitted to West China Stomatology Hospital, Sichuan University for the treatment of cleft lip with/without cleft palate between January 2016 and August 2023. Most of the subjects came from Sichuan Province and the Tibet Autonomous Region. Results: A total of 1051 patients were enrolled and children aged under 12 months (460 cases) accounted for the largest proportion. Among the subjects, 383 had cleft lip only (36.44%), 140 had cleft palate only (13.32%), and 528 had cleft lip with cleft palate (50.24%). The male-to-female ratios of patients with cleft lip only (0.99∶1), cleft palate only (0.54∶1), and cleft lip with cleft palate (1.67∶1) exhibited significant differences (P<0.001). However, there was no significant difference in the male-to-female ratio in patients with cleft lip only or those with cleft lip with cleft palate when the subjects were divided into two groups according to whether they had unilateral or bilateral cleft lip with/without cleft palate. Most of the patients with bilateral cleft lip were female, while most of the patients with unilateral cleft lip and unilateral or bilateral cleft lip with cleft palate were male. The unilateral cleft lip with/without cleft palate was located predominantly on the left side. Syndromic cleft lip with/without cleft palate accounted for 3.43% of all the cases and the most common concomitant deformity was congenital heart disease. 3.81% (40 cases) of the patients had a family history. In the patients with cleft lip only and those with cleft palate only, the proportion of patients having parents with corresponding phenotypes was higher than those of other phenotypes of cleft lip with/without cleft palate. Regarding the birth time distribution of the children with cleft lip with/without cleft palate, Spring saw the highest number of births of these children (311 cases, 29.59%), while Winter saw the lowest number of births (231 cases, 21.98%). Conclusion: The cases of cleft lip with/without cleft palate in the ethnic Tibetan population are predominantly cleft lip and palate. Unilateral cleft lip only or cleft lip with palate is predominantly located on the left side. Lip disease phenotypes may be more heritable.

An Empirical Evaluation of Prompting Strategies for Large Language Models in Zero-Shot Clinical Natural Language Processing: Algorithm Development and Validation Study.

BACKGROUND: Large language models (LLMs) have shown remarkable capabilities in natural language processing (NLP), especially in domains where labeled data are scarce or expensive, such as the clinical domain. However, to unlock the clinical knowledge hidden in these LLMs, we need to design effective prompts that can guide them to perform specific clinical NLP tasks without any task-specific training data. This is known as in-context learning, which is an art and science that requires understanding the strengths and weaknesses of different LLMs and prompt engineering approaches. OBJECTIVE: The objective of this study is to assess the effectiveness of various prompt engineering techniques, including 2 newly introduced types-heuristic and ensemble prompts, for zero-shot and few-shot clinical information extraction using pretrained language models. METHODS: This comprehensive experimental study evaluated different prompt types (simple prefix, simple cloze, chain of thought, anticipatory, heuristic, and ensemble) across 5 clinical NLP tasks: clinical sense disambiguation, biomedical evidence extraction, coreference resolution, medication status extraction, and medication attribute extraction. The performance of these prompts was assessed using 3 state-of-the-art language models: GPT-3.5 (OpenAI), Gemini (Google), and LLaMA-2 (Meta). The study contrasted zero-shot with few-shot prompting and explored the effectiveness of ensemble approaches. RESULTS: The study revealed that task-specific prompt tailoring is vital for the high performance of LLMs for zero-shot clinical NLP. In clinical sense disambiguation, GPT-3.5 achieved an accuracy of 0.96 with heuristic prompts and 0.94 in biomedical evidence extraction. Heuristic prompts, alongside chain of thought prompts, were highly effective across tasks. Few-shot prompting improved performance in complex scenarios, and ensemble approaches capitalized on multiple prompt strengths. GPT-3.5 consistently outperformed Gemini and LLaMA-2 across tasks and prompt types. CONCLUSIONS: This study provides a rigorous evaluation of prompt engineering methodologies and introduces innovative techniques for clinical information extraction, demonstrating the potential of in-context learning in the clinical domain. These findings offer clear guidelines for future prompt-based clinical NLP research, facilitating engagement by non-NLP experts in clinical NLP advancements. To the best of our knowledge, this is one of the first works on the empirical evaluation of different prompt engineering approaches for clinical NLP in this era of generative artificial intelligence, and we hope that it will inspire and inform future research in this area.