J Int Adv Otol. 2025 Mar 25;21(2):1-9. doi: 10.5152/iao.2025.241693.

ABSTRACT

BACKGROUND: Vestibular schwannoma (VS) management decisions are made within multidisciplinary meetings (MDMs). The improved accuracy of volumetric compared to linear tumor measurements is well-recognized, but current volumetric evaluation methods are too time-intensive. The aim was to determine if the availability of fully automated volumetric tumor measures during MDM preparation resulted in different radiological outcomes compared to a standard approach with linear dimensions, and whether this impacted the clinical management decisions.

METHODS: A prospective cohort study evaluated 50 adult patients (mean age 64.6, SD 12.8; 24 male, 26 female) with unilateral sporadic VS. Two simulated MDMs were convened using different methods to measure tumor size during radiology preparation: MDM-mlm used linear tumor dimensions, while MDM-avm was provided with fully automated deep learning-based volume measurements. Interval changes in VS size from the index to final and penultimate to final magnetic resonance imaging (MRI) studies defined the radiological outcomes. The subsequent clinical MDM outcomes were classified. Wilcoxon signed rank tests compared the radiological classification of VS size change and the management outcomes between the MDM-mlm and the MDM-avm.

RESULTS: The 57 interval MRI comparisons in 33 patients showed a significant difference in the classification of VS size change between the MDM-mlm and MDM-avm for all intervals (z=2.49, P=.01). However, there was no significant difference in the resulting management decisions between the 2 MDMs (z=0.30, P= .76).

CONCLUSION: Provision of fully automated VS volume measurements to "real-world" MDM preparation significantly impacted the radiological classification of VS size change but did not influence management decisions.

PMID:40208025 | DOI:10.5152/iao.2025.241693

J Chem Inf Model. 2025 Apr 10. doi: 10.1021/acs.jcim.5c00238. Online ahead of print.

ABSTRACT

This study focuses on predicting the heat capacity of pure liquid-phase ionic liquids (ILs) using machine learning models from various categories, including support vector machines, instance-based learning, ensemble learning, and neural networks, with linear regression serving as a baseline. A key aim of this work is not only to achieve accurate predictions but also to ensure the interpretability of the results, addressing a gap often overlooked in predictive modeling studies. To accomplish this, we curated and cleaned a comprehensive data set of 13,893 data points covering 322 ILs, using temperature and chemical structure-based features as inputs. We evaluated model performance and conducted a thorough interpretability analysis to reveal the patterns of the top-performing model's predictions, ensuring that they are understandable. All models outperformed the baseline, with XGBoost (eXtreme Gradient Boosting) from the ensemble learning category achieving the best results, with total RMSE, R2, and AARD (%) values of 11.389, 0.997, and 1.212%, respectively. Shallow neural networks also performed competitively, suggesting that complex deep learning architectures may not be necessary. Both 10-fold and leave-one-IL-out (LOILO) cross-validation further validated the robustness of these results. Importantly, the interpretability analysis identified key factors influencing heat capacity predictions, such as anion size (e.g., NTf2 and FAP) and alkyl chain length. These factors were validated by testing the model on previously unseen IL examples. Additionally, a user-friendly web application was developed to make predictions, allowing users to input chemical groups or select compounds from a predefined list of 1633 ILs. This study underscores the importance of combining diverse modeling approaches with robust interpretability techniques to achieve reliable and explainable predictions for IL heat capacity.

PMID:40208008 | DOI:10.1021/acs.jcim.5c00238

Ergonomics. 2025 Apr 10:1-16. doi: 10.1080/00140139.2025.2487558. Online ahead of print.

ABSTRACT

Retained surgical items (RSIs) can lead to severe complications, and infections, with morbidity rates up to 84.32%. Computer-aided detection (CAD) systems offer potential advancement in enhancing the detection of RSIs. This systematic review aims to summarise the characteristics of CAD systems developed for the detection of RSIs, evaluate their development, effectiveness, and limitations, and propose opportunities for enhancement. The systematic review adheres to Preferred Reporting Items for Systematic Reviews and Meta-Analysis 2020 guidelines. Studies that have developed and evaluated CAD systems for identifying RSIs were eligible for inclusion. Five electronic databases were searched from inception to March 2023 and eleven studies were found eligible. The sensitivity of CAD systems ranges from 0.61 to 1 and specificity varied between 0.73 and 1. Most studies utilised synthesised RSI radiographs for developing CAD systems which raises generalisability concerns. Moreover, deep learning-based CAD systems did not incorporate explainable artificial intelligence techniques to ensure decision transparency.

PMID:40208001 | DOI:10.1080/00140139.2025.2487558

Curr Pharm Des. 2025 Apr 8. doi: 10.2174/0113816128369168250311172823. Online ahead of print.

ABSTRACT

Breast cancer poses a significant global health challenge, necessitating improved diagnostic and treatment strategies. This review explores the role of artificial intelligence (AI) in enhancing breast cancer pathology, emphasizing risk assessment, early detection, and analysis of histopathological and mammographic data. AI platforms show promise in predicting breast cancer risks and identifying tumors up to three years before clinical diagnosis. Deep learning techniques, particularly convolutional neural networks (CNNs), effectively classify cancer subtypes and grade tumor risk, achieving accuracy comparable to expert radiologists. Despite these advancements, challenges, such as the need for high-quality datasets and integration into clinical workflows, persist. Continued research on AI technologies is essential for advancing breast cancer detection and improving patient outcomes.

PMID:40207818 | DOI:10.2174/0113816128369168250311172823

Curr Top Med Chem. 2025 Apr 8. doi: 10.2174/0115680266346722250401191232. Online ahead of print.

ABSTRACT

The field of drug design has evolved from conventional approaches relying on empirical evidence to advanced approaches such as Computer-Aided Drug Design (CADD). It aids in intricate phases of drug discovery, such as target discovery, lead optimization, and clinical trials, establishing a safe, rapid, and cost-effective system. Structure based drug design (SBDD), Ligand based drug design (LBDD), and Pharmacophore modelling, being the most utilized techniques of CADD, play a major role in establishing the road map necessary for the discovery. Artificial intelligence (AI) and Machine learning (ML) have improved the field with the incorporation of big data and, thereby, enhancing the efficacy and accuracy of the CADD. Deep Learning (DL), a part of AI helps in processing complex and non-linear data and thereby decreases complexity, increases resource utilization and enhances drug-target interaction prediction. These approaches have revolutionized healthcare by enhancing diagnostic precision and predicting the behavior of drugs. Currently, AI/ML approach has become crucial for rapidly discovering novel insights and transforming healthcare areas lie diagnostics, clinical research, and critical care. In the case of the drug development area, techniques like PBPK modeling and advanced nano-QSAR enhance drug behavior understanding and predict nano material toxicity if any, leading to safe and effective therapeutic predictions and interventions. The advancement of AI/ML techniques will bring accuracy, efficacy, and more patient-tailored responses to the drug development field.

PMID:40207759 | DOI:10.2174/0115680266346722250401191232

Am J Respir Crit Care Med. 2025 Apr 10. doi: 10.1164/rccm.202502-0465ED. Online ahead of print.

NO ABSTRACT

PMID:40208255 | DOI:10.1164/rccm.202502-0465ED

Am J Respir Crit Care Med. 2025 Apr 10. doi: 10.1164/rccm.202407-1506OC. Online ahead of print.

ABSTRACT

RATIONALE: While idiopathic pulmonary fibrosis (IPF) has been widely studied, progressive non-IPF interstitial lung disease (ILD) remains poorly understood.

OBJECTIVE: To identify and validate proteomic biomarkers of non-IPF ILD survival.

METHODS: High-throughput proteomic data were generated using plasma collected as part of prospective registries at the Universities of California and Texas (discovery cohort, n=676) and PRECISIONS multi-omic study (validation cohort, n=616). Proteins associated with three-year transplant-free survival (TFS) were identified using multivariable Cox proportional hazards regression, and those associated with TFS after adjustment for false discovery were advanced for validation cohort testing. Pathway analysis was performed to identify molecular pathways unique to non-IPF ILD and shared with IPF.

MAIN RESULTS: Of 2925 proteins tested in the discovery cohort, 73 were associated with TFS, with 44 showing sustained TFS association in the validation cohort. The top TFS-associated proteins were amphiregulin (HR 2.51, 95% CI 2.07-3.04), integrin subunit beta 6 (HR 2.46; 95% CI 1.95-3.10) and keratin 19 (HR 1.70, 95% CI 1.47-1.98). All but one validated biomarkers showed consistent TFS association across non-IPF ILD subtypes. Pathway analysis identified several molecular pathways shared with IPF, along with three pathways unique to non-IPF ILD.

CONCLUSIONS: We identified and validated novel prognostic protein biomarkers in non-IPF ILD, most of which showed consistent association across non-IPF ILD subtypes. While most biomarkers and molecular pathways identified were previously linked to IPF, several were unique to non-IPF ILD, suggesting that unique biology may contribute to progressive non-IPF ILD.

PMID:40208180 | DOI:10.1164/rccm.202407-1506OC

Elife. 2025 Apr 10;13:RP97313. doi: 10.7554/eLife.97313.

ABSTRACT

The spike protein is essential to the SARS-CoV-2 virus life cycle, facilitating virus entry and mediating viral-host membrane fusion. The spike contains a fatty acid (FA) binding site between every two neighbouring receptor-binding domains. This site is coupled to key regions in the protein, but the impact of glycans on these allosteric effects has not been investigated. Using dynamical nonequilibrium molecular dynamics (D-NEMD) simulations, we explore the allosteric effects of the FA site in the fully glycosylated spike of the SARS-CoV-2 ancestral variant. Our results identify the allosteric networks connecting the FA site to functionally important regions in the protein, including the receptor-binding motif, an antigenic supersite in the N-terminal domain, the fusion peptide region, and another allosteric site known to bind heme and biliverdin. The networks identified here highlight the complexity of the allosteric modulation in this protein and reveal a striking and unexpected link between different allosteric sites. Comparison of the FA site connections from D-NEMD in the glycosylated and non-glycosylated spike revealed that glycans do not qualitatively change the internal allosteric pathways but can facilitate the transmission of the structural changes within and between subunits.

PMID:40208235 | DOI:10.7554/eLife.97313

Infect Immun. 2025 Apr 10:e0061824. doi: 10.1128/iai.00618-24. Online ahead of print.

ABSTRACT

Bacteriophages are the dominant members of the human enteric virome and can shape bacterial communities in the gut; however, our understanding of how they directly impact health and disease is limited. Previous studies have shown that specific bacteriophage populations are expanded in patients with Crohn's disease (CD) and ulcerative colitis (UC), suggesting that fluctuations in the enteric virome may contribute to intestinal inflammation. Based on these studies, we hypothesized that a high bacteriophage burden directly induces intestinal epithelial responses. We found that filamentous bacteriophages M13 and Fd induced dose-dependent IL-8 expression in the human intestinal epithelial cell line HT-29 to a greater degree than their lytic counterparts, T4 and ϕX174. We also found that M13, but not Fd, reduced bacterial internalization in HT-29 cells. This led us to investigate the mechanism underlying M13-mediated inhibition of bacterial internalization by examining the antiviral and antimicrobial responses in these cells. M13 upregulated type I and III IFN expressions and augmented short-chain fatty acid (SCFA)-mediated LL-37 expression in HT-29 cells. Taken together, our data establish that filamentous bacteriophages directly affect human intestinal epithelial cells. These results provide new insights into the complex interactions between bacteriophages and the intestinal mucosa, which may underlie disease pathogenesis.

PMID:40208028 | DOI:10.1128/iai.00618-24

Environ Health Perspect. 2025 Apr 10. doi: 10.1289/EHP15648. Online ahead of print.

ABSTRACT

BACKGROUND: Electronic cigarette (e-cigarette) liquids and aerosols contain metals, which can be detrimental to human health. Recently marketed ultrasonic cigarettes (u-cigarettes) claim to be less harmful than e-cigarettes that use heating coils.

OBJECTIVES: We quantified chemical elements/metals in multiple flavors of SURGE u-cigarettes, JUUL e-cigarettes, and "Other Brands" of pod-style e-cigarettes.

METHODS: Elements/metals were identified in atomizers of SURGE using a scanning electron microscope/energy-dispersive X-ray spectrometer. Quantitation of elements/metals in fluids and aerosols from SURGE, JUUL and Other Brands was performed using inductively coupled plasma optical emission spectroscopy.

RESULTS: U-cigarettes contained a sonicator, unlike e-cigarettes which had heated coils. Sixteen elements were identified in at least one fluid or aerosol sample. Generally, u-cigarette fluids and aerosols had more elements/metals at higher concentrations than aerosols from 4th generation e-cigarettes. Element concentrations generally increased in fluids after vaping. All products, including SURGE, had silicon in their fluids and aerosols. Nickel, which was present in low concentrations in all fluids except KWIT Stick (up to 66,050 μg/mL), transferred to the aerosols with low efficiency. SURGE, but not e-cigarettes, also had copper and zinc in their fluids, but little transferred to their aerosols. SURGE fluids and aerosols, unlike e-cigarettes, had relatively high concentrations of arsenic and selenium. Arsenic and selenium, which are on the FDA's Harmful and Potentially Harmful List, likely came from poor quality solvents used to produce the e-liquids in SURGE pods and possibly from the sonicator, which heats during use.

DISCUSSION: SURGE u-cigarettes produce aerosols with metals equivalent to heated coil-style e-cigarettes and had high levels of arsenic and selenium, which are a health concern. Regulations limiting arsenic and selenium in these products are needed, and routine surveillance to identify rogue products, such as Kwit Stick, that have abnormally high levels of nickel or other metals could protect human health. https://doi.org/10.1289/EHP15648.

PMID:40207990 | DOI:10.1289/EHP15648

J Med Virol. 2025 Apr;97(4):e70203. doi: 10.1002/jmv.70203.

ABSTRACT

After the coronavirus disease 2019 (COVID-19) pandemic, the postacute effects of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection have gradually attracted attention. To precisely evaluate the health status of convalescent patients with COVID-19, we analyzed symptom and proteome data of 442 plasma samples from healthy controls, hospitalized patients, and convalescent patients 6 or 12 months after SARS-CoV-2 infection. Symptoms analysis revealed distinct relationships in convalescent patients. Results of plasma protein expression levels showed that C1QA, C1QB, C2, CFH, CFHR1, and F10, which regulate the complement system and coagulation, remained highly expressed even at the 12-month follow-up compared with their levels in healthy individuals. By combining symptom and proteome data, 442 plasma samples were categorized into three subtypes: S1 (metabolism-healthy), S2 (COVID-19 retention), and S3 (long COVID). We speculated that convalescent patients reporting hair loss could have a better health status than those experiencing headaches and dyspnea. Compared to other convalescent patients, those reporting sleep disorders, appetite decrease, and muscle weakness may need more attention because they were classified into the S2 subtype, which had the most samples from hospitalized patients with COVID-19. Subtyping convalescent patients with COVID-19 may enable personalized treatments tailored to individual needs. This study provides valuable plasma proteomic datasets for further studies associated with long COVID.

PMID:40207927 | DOI:10.1002/jmv.70203

Nucleic Acids Res. 2025 Apr 10;53(7):gkaf273. doi: 10.1093/nar/gkaf273.

ABSTRACT

Proteins of the BCL7 family (BCL7A, BCL7B, and BCL7C) are among the most recently identified subunits of the mammalian SWI/SNF chromatin remodeler complex and are absent from the unicellular version of this complex. Their function in the complex is unknown, and very limited structural information is available, despite the fact that they are mutated in several cancer types, most notably blood malignancies and hence medically relevant. Here, using cryo-electron microscopy in combination with biophysical and biochemical approaches, we show that BCL7A forms a stable, high-affinity complex with the nucleosome core particle (NCP) through binding of BCL7A with the acidic patch of the nucleosome via an arginine anchor motif. This interaction is impaired by BCL7A mutations found in cancer. Further, we determined that BCL7A contributes to the remodeling activity of the mSWI/SNF complex and we examined its function at the genomic level. Our findings reveal how BCL7 proteins interact with the NCP and help rationalize the impact of cancer-associated mutations. By providing structural information on the positioning of BCL7 on the NCP, our results broaden the understanding of the mechanism by which SWI/SNF recognizes the chromatin fiber.

PMID:40207634 | DOI:10.1093/nar/gkaf273

Int J Immunopathol Pharmacol. 2025 Jan-Dec;39:3946320251327618. doi: 10.1177/03946320251327618. Epub 2025 Apr 10.

ABSTRACT

The aim of this study was to evaluate the country-specific reporting status profile of immunomodulatory drugs (IMiDs)-related adverse events (ImrAEs) in real-world clinical practice, using data from the Japanese Adverse Drug Event Report (JADER) and Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) databases. Immunomodulatory drugs, including thalidomide and its derivatives, are a new class of anticancer and anti-inflammatory drugs. IMiD risk management programs have instituted sufficient measures to prevent fetal effects but do not address adverse effects experienced by patients themselves. To date, no study has compared ImrAE profiles across countries. Adverse events were defined using the preferred terms in the Medical Dictionary for Regulatory Activities. The number of reported adverse events related to IMiDs in each country (the United States and Japan) was investigated. In both Japan and the United States, myelosuppression, pneumonia, and neuropathy peripheral have been reported as adverse events suspected to be associated with IMiDs. Adverse event profiles differed between the countries. The number of adverse event reports for thalidomide increased transiently in the United States in 2008 following the multiple myeloma indication, and then exhibited a downward trend. The number of adverse event reports for lenalidomide and pomalidomide has increased in the United States since their launch. The number of transient reports increased in Japan in 2015, when pomalidomide was launched. In this study, the profile of ImrAEs was revealed using the FAERS and JADER databases. Our comparative safety study indicated the importance of comparing the safety profiles of IMiDs using post-marketing real-world data. It is important to focus on the adverse events experienced by patients taking IMiDs, as well as the effects of IMiDs on fetuses.

PMID:40207612 | DOI:10.1177/03946320251327618

Biochem Biophys Rep. 2025 Mar 19;42:101957. doi: 10.1016/j.bbrep.2025.101957. eCollection 2025 Jun.

ABSTRACT

Colorectal cancer (CRC) is a multifaceted disease characterized by abnormal cell proliferation in the colon and rectum. The BCL-2 family proteins are implicated in CRC pathogenesis, yet the impacts of genetic variations within these proteins remains elusive. This in-silico study employs diverse sequence- and structure-based bioinformatics tools to identify potentially pathogenic nonsynonymous single nucleotide polymorphisms (nsSNPs) in BCL-2 family proteins. Leveraging computational tools including SIFT, PolyPhen-2, SNPs&GO, PhD-SNP, PANTHER, and Condel, 94 nsSNPs were predicted as deleterious, damaging, and disease-associated by at least five tools. Stability analysis with I-Mutant2.0, MutPred, and PredictSNP further identified 31 nsSNPs that reduce protein stability. Conservation analysis highlighted highly functional, exposed variants (rs960653284, rs758817904, rs1466732626, rs569276903, rs746711568, rs764437421, rs779690846, and rs2038330314) and structural, buried variants (rs376149674, rs1375767408, rs1582066443, rs367558446, rs367558446, rs1319541919, and rs1370070128). To explore the functional effects of these mutations, molecular docking and molecular dynamics simulations were conducted. G233D (rs376149674) and R12G (rs960653284) mutations in the BCL2 protein exhibited the greatest differences in docking scores with d-α-Tocopherol and Tocotrienol, suggesting enhanced protein-ligand interactions. The simulations revealed that d-α-Tocopherol and Tocotrienol (strong binders) contributed to greater stability of BCL-2 family proteins, while Fluorouracil, though weaker, still demonstrated selective binding stability. This work represents the first comprehensive computational analysis of functional nsSNPs in BCL-2 family proteins, providing insights into their roles in CRC pathogenesis. While these findings demand experimental validation, they hold great promise for guiding future large-scale population studies, facilitating drug repurposing efforts, and advancing the development of targeted diagnostic and therapeutic modalities for CRC.

PMID:40207085 | PMC:PMC11979393 | DOI:10.1016/j.bbrep.2025.101957

Front Cell Dev Biol. 2025 Mar 26;13:1505697. doi: 10.3389/fcell.2025.1505697. eCollection 2025.

ABSTRACT

BACKGROUND: Understanding the cell functionality during disease progression or drugs' mechanism are major challenges for precision medicine. Predictive models describing biological phenotypes can be challenging to obtain, particularly in scenarios where sample availability is limited, such as in the case of rare diseases. Here we propose a new method that reproduces the fibroadipogenic expansion that occurs in muscle wasting.

METHODS: We used immortalized fibroadipogenic progenitor cells (FAPs) and differentiated them into fibroblasts or adipocytes. The method successfully identified FAPs cell differentiation fate using accurate measurements of changes in specific proteins, which ultimately constitute a valid cellular in vitro platform for drug screening. Results were confirmed using primary FAPs differentiation as well as comparison with omics data from proteomics and genomic studies.

RESULTS: Our method allowed us to screen 508 different drugs from 2 compounds libraries. Out of these 508, we identified 4 compounds that reduced fibrogenesis and adipogenesis of ≥30% of fibrogenesis and adipogenesis using immortalized cells. After selecting the optimal dose of each compound, the inhibitory effect on FAP differentiation was confirmed by using primary FAPs from healthy subjects (n = 3) and DMD patients (n = 3). The final 4 selected hits reduced fibrogenic differentiation in healthy and DMD samples. The inhibition of adipogenesis was more evident in DMD samples than healthy samples. After creating an inhibitory map of the tested drugs, we validated the signalling pathways more involved in FAPs differentiation analysing data from proteomic and genomic studies.

CONCLUSION: We present a map of molecular targets of approved drugs that helps in predicting which therapeutic option may affect FAP differentiation. This method allows to study the potential effect of signalling circuits on FAP differentiation after drug treatment providing insights into molecular mechanism of action of muscle degeneration. The accuracy of the method is demonstrated by comparing the signal pathway activity obtained after drug treatment with proteomic and genomic data from patient-derived cells.

PMID:40206397 | PMC:PMC11979640 | DOI:10.3389/fcell.2025.1505697

OMICS. 2025 Apr 10. doi: 10.1089/omi.2024.0205. Online ahead of print.

ABSTRACT

The COVID-19 pandemic phase caused by the SARS-CoV-2 has ended, but the emergence of new variants continues to threaten public health. The public health toolbox for COVID-19 is in need of not only vaccines but also drug discovery against the SARS-CoV-2 virus, the causative agent for the ongoing COVID-19 infections. We report here an in silico molecular docking and dynamics study that uncovered the interactions of 26 flexible heteroarotinoids (FHT18), which are a class of anti-cancer compounds, as potential inhibitors against all 24 SARS-CoV-2 proteins. Of the 624 docked complexes, 69 displayed binding energies between -9.0 and -11.6 kcal/mol, indicating good to strong binding affinities. At least five of these compounds displayed excellent binding affinities against the nonstructural protein 2, papain-like protease, nonstructural protein 4 (Nsp4), proof-reading exoribonuclease, membrane protein, and nucleocapsid protein. Structure-activity relationship (SAR) analyses of these results revealed that a urea linker in place of a thiourea linker, enhanced the hydrophobic side chains attached to the chromane unit, and a CF3 or OCF3 functional group attached to the benzene ring contributed to increased binding affinities. Further, the molecular dynamics simulation study of the best-docked complex FHT18-6c with Nsp4 remained stable for at least 200 ns, leading to decreased structural fluctuations and increased compactness of the binding site. In conclusion, FHT18-6c deserves further translational research to explore its potential for repurposing as a potent drug candidate to combat COVID-19. We also call for continued drug discovery efforts to enrich the public health toolbox for COVID-19.

PMID:40205995 | DOI:10.1089/omi.2024.0205

J Clin Transl Hepatol. 2025 Apr 28;13(4):339-357. doi: 10.14218/JCTH.2024.00402. Epub 2025 Jan 17.

ABSTRACT

Liver injury in Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) is a multifaceted disorder, lacking cohort homogeneity due to a variety of potential causes, including drugs, arsenic and other heavy metals, glyphosate, infections, and ultraviolet radiation. The goals of this review were (1) to analyze the role of diagnostic algorithms in assessing causality for potential culprits involved in the development of liver injury associated with immune-mediated SJS and TEN, which represent immune-based variant disorders within a continuous spectrum. Milder forms are classified as SJS or SJS/TEN overlap, while TEN is known as the most serious form; and (2) to interpret the findings that allow for the characterization of the different types of these disorders. The manuscript is based on an extensive literature search for single case reports, case cohorts, and review articles. Search terms included: Stevens-Johnson Syndrome, Toxic Epidermal Necrolysis, and specific diagnostic algorithms such as the Roussel Uclaf Causality Assessment Method (RUCAM) and the Algorithm of Drug Causality for Epidermal Necrolysis (ALDEN). For the purpose of basic feature description, the uniform term SJS/TEN is used in the current analysis. SJS/TEN presents with five different cohort types: SJS/TEN type (1), which refers to a cohort of SJS/TEN caused by drugs, as assessed by both ALDEN and RUCAM; type (2), representing SJS/TEN due to drugs and assessed by ALDEN only, but not by RUCAM; type (3), which includes a cohort of SJS/TEN caused by drugs, assessed by non-ALDEN and non-RUCAM tools; type (4), which focuses on a cohort of SJS/TEN caused by non-drug culprits, assessed by various tools; and type (5), which considers a cohort of SJS/TEN caused by unknown culprits. Using this new SJS/TEN typology will help better characterize individual features, personalize treatment, and clarify pathogenetic specifics for each of the five disease types. This new SJS/TEN typology provides clarity by replacing issues of inhomogeneity with cohort homogeneity.

PMID:40206276 | PMC:PMC11976437 | DOI:10.14218/JCTH.2024.00402

Pharm Stat. 2025 May-Jun;24(3):e70011. doi: 10.1002/pst.70011.

ABSTRACT

Patient enrollment can be a substantial burden in rare disease trials. One potential approach is to incorporate external control (EC) into concurrent randomized trials, or EC borrowing, to reduce such burden. Extensive research has been conducted to explore statistical methodologies. As in all designs, type I error control is essential. Conditional type I error rate has been used in the literature as the de facto metrics for type I error rate. However, research has shown that controlling the conditional type I error rate at the alpha level will disallow EC borrowing. Therefore, EC borrowing is practically at an impasse. Kopp-Schneider et al. concluded that a more appropriate metrics for type I error is necessary. We show that a trial with EC borrowing can be considered as a two-stage adaptive design. With this perspective, we propose to define type I error as the weighted averages of conditional type I error rate in trials with EC borrowing. Dynamic borrowing methods for controlling type I error are proposed.

PMID:40205746 | DOI:10.1002/pst.70011

Front Artif Intell. 2025 Mar 26;8:1451865. doi: 10.3389/frai.2025.1451865. eCollection 2025.

ABSTRACT

As we become increasingly dependent on technology in our daily lives, the usability of HCIs is a key driver of individual empowerment for us all. A primary focus of AI systems has been to make HCIs easier to use by identifying what users need and agentively taking over some of the cognitive work users would have otherwise performed, as such, they are becoming our delegates. To become effective and reliable delegates, AI agents need to understand all relevant situational semantic context surrounding a user's need and how the tools of the HCI can be leveraged. Current ML systems have fundamental semantic gaps in bespoke human context, real-time world knowledge, and how those relate to HCI tooling. These challenges are difficult to close due factors such as privacy, continual learning, access to real-time context, and how deeply integrated the semantics are with in-context learning. As such, we need to research and explore new ways to safely capture, compactly model, and incrementally evolve semantics in ways that can efficiently integrate into how AI systems act on our behalf. This article presents a thought experiment called the Game of Delegation as a lens to view the effectiveness of delegation and the semantic efficiency with which the delegation was achieved.

PMID:40206708 | PMC:PMC11979188 | DOI:10.3389/frai.2025.1451865

Med J Aust. 2025 Apr 10. doi: 10.5694/mja2.52650. Online ahead of print.

ABSTRACT

Paediatric cancer is the leading cause of disease-related death in Australian children. Limited research focuses on cancer in Aboriginal and Torres Strait Islander children. Although there appears to be a lower incidence of cancer overall in Aboriginal and Torres Strait Islander children compared with non-Indigenous children, a high proportion of Aboriginal and Torres Strait Islander children are diagnosed with acute myeloid leukaemia. Five-year overall survival is lower for many cancer types in Aboriginal and Torres Strait Islander children. There is a need for Indigenous-specific research focused on molecular and genetic profiles, pharmacogenomics and survivorship, both within Australia and globally. Future research in this space should be co-designed and led by Aboriginal and Torres Strait Islander communities; alongside clinicians, researchers and services to ensure that the priorities of Aboriginal and Torres Strait Islander people are met.

PMID:40207417 | DOI:10.5694/mja2.52650