Objective: Candida auris (C. auris) has emerged as a multidrug-resistant nosocomial pathogen associated with outbreaks in healthcare settings and high mortality rates. Its ability to persist in the hospital environment highlights the importance of effective disinfection strategies. This study aimed to evaluate the in vitro susceptibility of clinical C. auris isolates to commonly used antiseptics and disinfectants under conditions reflecting routine clinical practice. Methods: Ten clinical C. auris isolates, along with reference strains of C. krusei (ATCC 6258) and C. parapsilosis (ATCC 22019), were tested. Sodium hypochlorite (5%, 1%, 0.5%), hydrogen peroxide (6%, 3%), povidone-iodine (10%, 5%, 2.5%), a chlorhexidine/n-propanol-containing liquid soap, and an ethanol/isopropyl alcohol-based hand disinfectant were evaluated by a qualitative suspension test at contact times of 1, 2, 5, 10, and 30 minutes. Growth inhibition was assessed after incubation. Results: Povidone-iodine at all concentrations, chlorhexidine/n-propanol-containing liquid soap, alcohol-based hand disinfectant, and sodium hypochlorite at 5% and 1% achieved complete growth inhibition of all isolates within the first minute. In contrast, 0.5% sodium hypochlorite and hydrogen peroxide showed reduced efficacy at early contact times. Growth was observed in some C. auris isolates after 1 minute of exposure to 6% hydrogen peroxide, and in a larger proportion of isolates after 1-2 minutes of exposure to 3% hydrogen peroxide. Similar variability was observed in reference strains, particularly with hydrogen peroxide. Conclusions: Commonly used antiseptics and disinfectants are effective against C. auris when applied at appropriate concentrations and contact times. However, reduced efficacy at lower concentrations and with shorter exposure times underscores the importance of proper disinfectant use in preventing environmental persistence and transmission in healthcare settings.
Objective:This study aimed to evaluate the efficacy of mammography, ultrasound, and magnetic resonance imaging (MRI) features in differentiating pure from mixed mucinous breast carcinoma and in identifying imaging characteristics specific to each subtype. Methods:This retrospective single-center study included 68 patients diagnosed with mucinous breast carcinoma between 2017 and 2024. The tumors were classified as pure or mixed mucinous breast carcinomas based on histopathological findings. All available mammography, ultrasound, and MRI examinations were evaluated by a radiologist blinded to the histopathological subtypes. The features, such as tumor shape, margin, signal intensity, mammographic density, echogenicity, and calcifications, were assessed to identify distinctive patterns associated with pure and mixed mucinous breast tumors. Results:Among the 68 patients, 31 (45.6%) had pure mucinous tumors, while 37 (54.4%) had mixed tumors. On ultrasound, pure mucinous carcinomas more frequently demonstrated an oval shape and a homogeneous internal echo pattern, whereas mixed tumors more frequently exhibited an irregular shape (p<0.05). Mammography showed that non-circumscribed margins were significantly more common in mixed tumors than in pure tumors (p=0.02). On MRI, pure mucinous carcinomas predominantly exhibited oval or lobulated shapes, whereas mixed tumors more frequently exhibited irregular shapes (p=0.02). Pure tumors also showed higher T2 signal intensity (p=0.02) and significantly higher median apparent diffusion coefficient (ADC) values than mixed tumors (1815 [interquartile range (IQR), 1596-2062.5] vs. 1336.5 [IQR, 904.25-1602.5]×10-6 mm²/s, p=0.01). Conclusions:A multimodal imaging approach facilitates differentiation between pure and mixed mucinous breast carcinomas. Among the evaluated modalities, MRI provided the greatest discriminatory value, particularly through T2 signal intensity and ADC measurements, and may complement core needle biopsy when tissue sampling is limited.
Objective:We set out to examine how completely artificial intelligence (AI)-based orthopedic studies report their methods. Methods:A PubMed search covering 2023-2025 was run with a predefined strategy. Screening of titles, abstracts and full texts left 280 eligible papers, and 200 of these were drawn at random for scoring. Each paper was rated against a six-item framework built from the TRIPOD-AI, STARD-AI and CONSORT-AI guidelines. Results:Mean score across the 200 studies was 4.8±0.9. Twenty-eight percent reached 6/6, 37% scored 5/6 and 23% scored 4/6; the remaining 12% scored 3 or below. Model description, dataset characteristics and performance metrics appeared in every paper (100%), and training and validation procedures in 88%. External validation was far less common, reported by only 32%, while 61% included some form of clinical comparison. Conclusions:Reporting in this literature is moderate to high overall, yet two weaknesses persist: external validation and clinical integration. Work in this area would benefit from closer attention to generalizability, clinical relevance and the existing reporting guidelines.
Objective:The neutrophil-to-high-density lipoprotein cholesterol ratio (NHR) is an emerging inflammatory-lipid biomarker. Whether NHR is associated with anatomically significant multivessel disease (MVD) across different acute coronary syndrome (ACS) phenotypes remains unclear. Methods:We analyzed 421 consecutive ACS patients [268 with non-ST-segment elevation myocardial infarction (NSTEMI) and 153 with ST-segment elevation myocardial infarction (STEMI)] who underwent coronary angiography. Anatomically significant MVD was defined as ≥70% stenosis in at least two major epicardial coronary arteries, or ≥70% stenosis in one epicardial coronary artery in addition to ≥50% stenosis of the left main coronary artery. Receiver operating characteristic analysis and multivariable logistic regression were performed. Results:Among NSTEMI patients, NHR predicted anatomically significant MVD with an area under the curve (AUC) of 0.694 [95% confidence inteval (CI): 0.630-0.757, p<0.001], using an optimal cut-off value of 0.1365 (78.0% sensitivity, 59.4% specificity). After multivariable adjustment, NHR remained independently associated with MVD (odds ratio: 5.870, 95% CI: 1.402-11.859, p<0.001). Among STEMI patients, NHR did not demonstrate discriminatory ability (AUC=0.481, p=0.690). In the overall ACS population, the NHR demonstrated modest discriminatory performance (AUC=0.615, p<0.001). Conclusions:NHR was associated with anatomically significant MVD mainly in patients with NSTEMI, whereas no meaningful discriminatory value was observed in patients with STEMI. This phenotype-specific finding suggests that NHR may better reflect the presence of MVD in NSTEMI than serve as a uniform marker across all ACS presentations.
Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by persistent impairments in social communication, restricted interests, and repetitive behaviors. This narrative review synthesizes advances in machine learning applications to ASD genomic research through May 2026, spanning gene expression analysis, whole-exome sequencing (WES), non-coding variant interpretation, multi-omics integration, single-cell transcriptomics, epigenetic profiling, and gut microbiome analysis. A purposive, thematic literature synthesis approach was employed, allowing broad coverage of emerging methodological innovations and biological insights. We critically evaluate state-of-the-art deep learning architectures-including the Separate Translated Autism Research Neural Network and SHapley Additive exPlanations-based explainable artificial intelligence frameworks. Reported discrimination across the field varies widely, from receiver operating characteristic-area under the curve (ROC-AUC) values near 0.66 to implausibly perfect values of 1.00; the best-validated specialized genomic architecture achieves only modest discrimination (ROC-AUC≈0.73). We emphasize that interpretability and predictive performance are orthogonal properties: specialized architectures yield biologically interpretable feature attributions despite modest discriminative power; and several extreme AUC values in the literature are, in our assessment, more consistent with overfitting or data leakage than with genuine signal, although the primary reports did not always provide the information needed to definitively attribute them. Key themes include: (1) identification of differentially expressed genes through meta-analysis of transcriptomic data; (2) validation of predictive gene features from large-scale WES; (3) detection of non-coding regulatory mutations affecting synaptic transmission pathways; (4) discovery of gut microbiome signatures associated with ASD classification; and (5) discovery of data-driven subtypes enabling precision medicine stratification. Critical challenges include population bias toward European ancestry, socioeconomic ascertainment bias, modest predictive effect sizes, conflation of association with causation, and gaps between computational prediction and clinical utility. Future directions emphasize multi-modal data integration, diverse cohort expansion, engagement with neurodiversity perspectives, and regulatory science development.
