Qualitative inquiry is central to nursing and health research, yet many established analytic approaches require substantial time and expertise. These demands can pose challenges for researchers working within constrained timelines and multidisciplinary teams. RRITA, a Rapid, Reflexive, Integrated approach to Thematic Analysis, was developed to address the persistent tension between rigour and feasibility. To introduce RRITA as a reflexive qualitative analysis method, providing a theoretically grounded and practically actionable guide to its implementation. Methodological paper outlining the conceptual foundations, analytic workflow, and applied features of RRITA, illustrated with data from a study on gratitude in palliative care. RRITA comprises seven steps organised around an alternating expand-compress cadence. In steps 1 and 2, researchers formulate a paradigm-aligned research question and define domains to populate the initial structure of the RRITA matrix, the method's central analytic instrument, maintaining a direct line of sight between raw data and analytic output while preserving subtle meanings and complexity. Researchers generate raw data in step 3 and refine them in step 4. Steps 5 and 6 shift to line-by-line inductive coding grounded in participants' accounts, iterative theme construction supported by theme warrants, and active engagement with analytic tensions and discordance. Step 7 culminates in a coherent narrative that integrates thematic articulation and interpretation, illustrative data, the reflexive pivot, and scholarly literature. Throughout the research journey, RRITA supports the systematic scrutiny of researcher subjectivity through the embedded practices of reflexive anchoring and notes. RRITA supports rigorous qualitative analysis through a structured workflow that synthesises key features of reflexive thematic analysis and rapid qualitative approaches. It introduces three integrative analytic innovations: embedded, situated reflexive practices, systematic engagement with discordant data and analytic tensions, and a versioned matrix trail documenting the analytic process from the initial reflexive anchor to final theme construction. RRITA proposes that rigour and accessibility are complementary when supported by thoughtful methodological design. It offers a theoretically grounded approach to qualitative inquiry that fosters interpretive depth while accommodating the practical constraints of nursing and health research. RRITA is particularly suited for clinical inquiry, teaching, and student supervision. Its versioned matrix trail renders analytic reasoning visible and discussable at each step, supporting the development of interpretive competence and the timely generation of high-quality evidence to inform contemporary nursing practice and policy.
Spirometry is the standard physiological test defining airflow obstruction, the key criterion for diagnosing chronic obstructive pulmonary disease. It is underused in high-income settings and often unavailable in low- and middle-income countries, causing underdetection. Deep learning analysis of chest radiographs, which are widely available where spirometry is not, may complement spirometric screening, but its use in North American cohorts and across demographic strata has not been examined. This study aimed to train a deep learning model to estimate the forced expiratory volume in 1 second (FEV₁)/forced vital capacity (FVC) ratio from chest radiographs and classify airflow obstruction (FEV₁/FVC <0.70), evaluate it on a held-out test set, and audit subgroup performance across age, sex, and surname-inferred ethnicity. We conducted a retrospective cohort study of 3537 adults who underwent prebronchodilator spirometry and chest radiography within 30 days at a large hospital network in Ontario, Canada, between October 2020 and May 2023. A ConvNeXt-Base architecture pretrained on ImageNet was trained to predict FEV₁/FVC, with predictions classified using a 0.70 cutoff for binary airflow limitation. At the patient level, the cohort was divided into training (n=2263), validation (n=566), and held-out test (n=708 patients; 3273 examinations) sets. Performance was assessed using regression (mean absolute error [MAE], root mean squared error [RMSE], and Pearson r), classification (sensitivity, specificity, positive and negative predictive value [PPV and NPV], and likelihood ratios [LR+ and LR-]), calibration, and decision curve metrics, with 95% CIs from patient-level cluster bootstrap (1000 resamples). Subgroup analyses used Holm correction and two 1-sided tests. In the held-out test cohort, MAE was 0.08 (95% CI 0.07-0.09) and RMSE was 0.10 (95% CI 0.10-0.11). For binary obstruction, sensitivity was 0.70 (95% CI 0.65-0.74), specificity 0.72 (95% CI 0.67-0.76), PPV 0.71 (95% CI 0.65-0.76), NPV 0.71 (95% CI 0.66-0.76), LR+ 2.46 (95% CI 2.11-2.88), and LR- 0.42 (95% CI 0.36-0.49). Patient-level estimates were similar (sensitivity 0.69, 95% CI 0.66-0.72; specificity 0.74, 95% CI 0.71-0.78). Calibration was excellent for regression (slope=0.97; intercept=0.015) and mildly miscalibrated for the binary task (slope=1.41; intercept=0.04; Brier=0.195). Decision curve analysis showed net benefit at threshold probabilities of approximately 0.27 to 0.86. Sensitivity was meaningfully reduced in Asian patients (0.43, 95% CI 0.29-0.56) compared with White patients (0.75, 95% CI 0.70-0.79; absolute difference -0.32; Holm P<.001), with accompanying differences in specificity, PPV, and LR-, and was lower in younger age groups, peaking at 65-74 years. A deep learning model trained on routine chest radiographs estimated FEV₁/FVC and identified airflow limitation in a North American cohort, with moderate discrimination, well-calibrated regression predictions, and positive net benefit. Performance was not uniform across demographic strata, with reduced sensitivity in Asian patients and younger age groups. Multisite external validation and subgroup-specific verification are important next steps.
Unregistered and excessive use of restricted anti-dandruff agents and the illegal addition of prohibited antibiotics in hair care products pose growing concerns, but monitoring is complicated by lack of integrated screening workflows, as well as the presence of surfactant-rich matrices and metal-chelating properties of some anti-dandruff agents like zinc pyrithione (ZnPT) and piroctone olamine (PO). This study presents a high-throughput "profile-and-quantify" strategy combining direct analysis in real-time high-resolution mass spectrometry (DART-HRMS) with high-performance liquid chromatography (HPLC) to monitor eight targets in shampoos and conditioners. After acetonitrile-methanol ultrasonic extraction, samples were screened using DART-HRMS under optimized parameters. Samples containing the target analytes were subsequently quantified using high-performance liquid chromatography with diode array detector (HPLC-DAD). Validation of this dual-platform approach demonstrated limits of detection ranging from 0.1 to 4 μg/g for the target analytes. For quantitative analysis, the HPLC-DAD assay exhibited good linearity (r² ≥ 0.9995). Spiked recoveries ranged from 96.1% to 107.2% with precision (RSD) ≤ 3.22%. This integrated strategy was applied to a longitudinal survey of 150 commercial products collected from 2022 to 2024. Screening revealed a widespread occurrence of ZnPT, salicylic acid, climbazole, and PO, while also identifying illicit additions of antifungal drugs including miconazole, elubiol, ketoconazole, and clotrimazole in cosmetic formulations. Featuring a rapid screening step of under 2 min per sample prior to targeted quantification, this integrated workflow offers a highly efficient, sensitive, and eco-friendly solution for large-scale regulatory monitoring of cosmetic safety and compliance.
Standardized motor batteries are widely used to assess motor competence in children with developmental coordination disorder (DCD), although it remains unclear whether they consistently capture the domains most affected by the disorder. This systematic review and meta-analysis aimed to examine the motor competence domains in which children and adolescents with DCD differ from their typically developing (TD) peers, as assessed through standardized motor batteries. A systematic search was conducted in four databases (PubMed, Scopus, Web of Science, and SPORTDiscus) up to November 12, 2025. Methodological quality was evaluated using the Mixed Methods Appraisal Tool. Thirty studies were included, comprising 3934 participants (1663 DCD groups and 2271 TD peers). Most studies used the MABC, with eight using the MABC-1 and 18 the MABC-2; two the TGMD-2, one the BOTMP, and one the KTK. Overall, children with DCD consistently demonstrated lower motor skills across all domains. However, the relative pattern of impairment across the various subdomains was not consistent, particularly in the MABC. Meta-analytic findings indicated that manual dexterity was relatively more affected in the MABC-1, whereas aiming and catching emerged as the relatively more affected domain in the MABC-2, in both DCD and TD groups. These findings suggest that standardized motor batteries are useful for operationalizing Criterion A, but their results should be interpreted cautiously and within a broader clinical and functional framework rather than as sufficient markers in isolation.
Cutaneous verrucae (warts) are benign epidermal proliferations caused by human papillomavirus (HPV) infection. The host immune response particularly T helper 17 (Th17) related cytokines such as interleukin-17A (IL-17A) and interleukin-23 (IL-23) plays a critical role in the regulation of viral control and inflammatory responses. However, the systemic levels of these cytokines in patients with verrucae have not been sufficiently characterized. To investigate serum IL-17A and IL-23 levels in patients with cutaneous verrucae, to compare them with those of healthy controls, and to evaluate their associations with age, sex, number of lesions, disease duration, and lesion localization. In this case-control study, 49 patients with palmoplantar or anogenital verrucae and 41 age- and sex-matched healthy controls were included. Serum IL-17A and IL-23 levels were measured using enzyme-linked immunosorbent assay (ELISA). Continuous variables were analyzed using the Mann-Whitney U test, and categorical variables using the chi-square test. Correlations between variables were assessed using Spearman correlation analysis. The median serum IL-23 level was 11.8 pg/mL (interquartile range, IQR: 7.8-21.3) in patients with cutaneous verrucae and 8.8 pg/mL (IQR: 7.2-12.0) in controls, with a statistically significant difference (p = 0.018). The median serum IL-17A level was 16.6 pg/mL (IQR: 8.7-37.9) in patients and 16.4 pg/mL (IQR: 10.9-24.5) in controls, with no significant difference between the groups (p = 0.755). A strong positive correlation was observed between IL-17A and IL-23 levels in patients (rs = 0.880, p < 0.001). No significant associations were found between cytokine levels and age, sex, number of lesions, disease duration, or localization. Elevated IL-23 levels and its strong positive correlation with IL-17A suggest that the IL-23/IL-17Axis may play a role in immune activation associated with HPV-related cutaneous verrucae. IL-23 may represent a potential indicator of immune activation associated with cutaneous verrucae; however, its standalone diagnostic utility appears limited. Future studies with larger cohorts and tissue-level analyses are needed to further elucidate the role of Th17-related cytokines in the pathogenesis of verrucae.
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Lipoxygenase proteins (LOXs) play a crucial role in plant growth, development, and defense notably through their involvement in jasmonic acid (JA) biosynthesis. Here, we aimed to identify and characterize genes encoding lipoxygenases in three coffee species, Coffea arabica, Coffea canephora, and Coffea eugenioides, and to evaluate whether LOX genes are differentially expressed following hexanoic acid application in C. arabica. We found 18 LOX genes in C. arabica and 9 genes each in C. eugenioides and C. canephora. Chromosomal localization analyses revealed strong correspondence between the LOX genes of tetraploid C. arabica and those of its putative diploid progenitors, C. eugenioides and C. canephora. Transcriptomic and enzymatic analyses showed that hexanoic acid application modulates the expression of specific LOX genes and alters lipoxygenase activity in leaves and roots of C. arabica cvs. Catuaí Vermelho and Obatã. Notably, three LOX genes displayed strong correlations between transcript abundance and enzymatic activity. Together, these results indicate that a subset of LOX genes in C. arabica represents promising candidates for detailed functional analyses, as they likely contribute substantially to lipoxygenase activity and elicitor-induced defense responses in Coffea species.
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Patients with chronic neurological diseases (CND) are at increased risk of pulmonary complications that often require ICU admission. This study aimed to identify clinical factors associated with ICU mortality and long-term survival in patients with CND who developed acute respiratory failure (ARF). This retrospective cohort study was conducted in a level III respiratory ICU. Patients with pre-existing CND admitted to the ICU with ARF were included. ICU mortality was analyzed using multivariable logistic regression. Long-term survival after ICU discharge was evaluated using Kaplan-Meier survival analysis and Cox proportional hazards models. Mortality timing was further characterized using hazard function analysis. A total of 220 patients were included; the most common neurological diagnoses were dementia (37.3%), stroke (22.7%), and amyotrophic lateral sclerosis (14.1%). ICU mortality was 33.6%. Higher APACHE II scores were independently associated with increased ICU mortality (OR 1.076 per point increase; 95% CI 1.029-1.126; p < 0.001). Long-term survival differed significantly by post-discharge respiratory support strategy, with Kaplan-Meier analysis demonstrating more favorable survival patterns among patients receiving home non-invasive mechanical ventilation (NIMV) (p = 0.003). In Cox regression analysis, age, home NIMV, and feeding modality at discharge were independently associated with long-term outcomes. Survival analyses revealed an early clustering of deaths within the first months after ICU discharge, particularly among patients with dementia. In patients with CND, acute physiological severity was the main determinant of ICU mortality, whereas long-term survival after ICU discharge was poor, with deaths clustering within the first months thereafter. Post-discharge respiratory support and nutritional management should be individualized according to the expected clinical trajectory and patient values.
To screen for independent risk factors for unplanned readmission in elderly patients with mild ischemic stroke, and to construct and validate an online risk prediction calculator based on an interpretable machine learning model, thereby providing a promising practical tool for accurate clinical assessment of 30‑day all‑cause unplanned readmission risk in this population. A prospective cohort study was conducted, including 1050 patients aged ≥ 60 years with mild ischemic stroke admitted between August 2023 and September 2024. Participants were randomly divided into a training set (840 cases) and a test set (210 cases) at a ratio of 8:2. Risk factors were screened by univariate analysis and multivariable Logistic regression. Four machine learning models, namely LightGBM, XGBoost, Random Forest, and K‑Nearest Neighbors (KNN), were developed and their performance was evaluated using AUC, accuracy, sensitivity, and specificity as metrics. The SHAP framework was used for interpretability analysis, and an online calculator was subsequently developed based on the optimal model. Univariate analysis showed significant differences (P < 0.05) in 13 factors including age, smoking, AIP, TyG index, HALP score, etc. Multivariable Logistic regression identified age (OR = 9.752), smoking (OR = 5.171), AIP (OR = 6.691), TyG index (OR = 4.393), HALP score (OR = 2.831), and ≥ 2 comorbidities (OR = 3.664) as independent risk factors. All four machine learning models demonstrated good predictive performance. Based on a comprehensive evaluation of multiple metrics and computational efficiency, the LightGBM model exhibited the best predictive performance (AUC = 0.884, accuracy = 0.829, sensitivity = 0.812, specificity = 0.875). SHAP analysis showed that age, AIP, TyG index, smoking, and HALP score were key predictors. An online calculator developed based on this model enables individualized risk predictions. Key risk factors associated with 30‑day unplanned readmission in elderly patients with mild ischemic stroke were identified. The LightGBM model demonstrated high predictive accuracy, and together with the interpretability analysis and online calculator, offers a practical tool to support clinical risk assessment. However, this tool requires future external validation.
Second-neighbor interactions play a fundamental role in determining the higher-order organization of molecular graphs, yet they are not explicitly represented by conventional degree, distance or adjacency-based graph descriptors. This work introduces the Leap Architecture Matrix (LAM), a novel matrix representation that characterizes the distribution of graph edges between distinct leap-degree classes, thereby preserving the structural organization of second-neighbor connectivity within molecular networks. Based on this representation, three complementary descriptors are developed: the Transition Occupancy Ratio (TOR) which measures the occupancy of leap-transition classes; the Architecture Energy (AE) defined as the sum of the absolute eigenvalues of the Leap Architecture Matrix and the corresponding Spectral Radius (ρ) which quantifies the dominant architectural connectivity. The proposed framework is applied to a dataset of biologically important regulatory amino acids exhibiting diverse structural and physicochemical characteristics. Its discriminative capability is evaluated through comparisons with established molecular descriptors including the Wiener, Randic and first Zagreb indices, together with Pearson correlation analysis, principal component analysis and sensitivity analysis. The results demonstrate that the proposed descriptors capture complementary structural information associated with higher-order molecular organization while maintaining low computational complexity. In particular, the spectral descriptors derived from the Leap Architecture Matrix effectively distinguish subtle variations in molecular architecture that are not fully characterized by conventional topological indices. The proposed matrix framework provides an interpretable and computationally efficient approach for molecular structural characterization and offers a new family of graph-theoretical descriptors with potential applications in molecular similarity analysis, structural classification, spectral graph theory, QSAR/QSPR modeling and computational molecular informatics.
Infant feeding practices, including breastfeeding, are known to benefit maternal and child health outcomes. Therefore, parent access to evidence-based infant feeding advice is critical. In recent years, there has been increased use of digital health technologies to support infant feeding. Despite its potential, using AI to complement existing health care and connect families to timely infant feeding support remains relatively unexplored. This study aims to explore women's perceptions of using AI-enabled infant feeding support within mobile health (mHealth) interventions. The study investigates (A) openness to AI-enabled support, (B) experiences with existing AI-enabled support, (C) preferences for SMS text messages generated by AI versus "child and family health" nurses, and (D) opinions on infant feeding topics suitable for AI. Two data collection activities were undertaken with women (primary caregivers) of infants aged 6-14 months, residing in the Hunter New England Local Health District (HNELHD) of New South Wales, Australia. Different women who received antenatal care in HNELHD were recruited for quantitative and qualitative data collection. Quantitative surveys assessed women's openness to receiving AI-enabled support (objective A). Descriptive and logistic regression analyses were conducted to explore associations between participant characteristics and openness to AI. Qualitative data collection involved focus groups to explore women's perceptions and preferences on infant feeding topics suitable for AI (objectives B, C, and D). Thematic analysis was used to analyze focus group transcripts. A total of 164 women completed the quantitative survey. Approximately 53% (87/164) of participants were open to receiving AI recommendations to see a health professional for infant feeding support, 34% (56/164) were open to AI assessing their breastfeeding experiences, and 41% (67/163) were open to AI providing advice to prevent or address breastfeeding challenges. Fewer Aboriginal and Torres Strait Islander participants were open to receiving AI-generated support (adjusted odds ratio 0.41, 95% CI 0.19-0.92) or advice to see a health professional (adjusted odds ratio 0.29, 95% CI 0.13-0.64). Twelve women participated in 3 online focus groups. Thematic analysis resulted in three overarching themes: (1) opportunities to fill gaps in support, (2) variable confidence engaging with AI for information and advice, and (3) potential convenience of AI and mHealth to offer timely support. The study highlights the potential of AI and barriers to women's acceptability and engagement. While women recognized the potential for AI to fill health care gaps in infant feeding support, including after business hours, there was less interest in AI replacing "in-person" support or information easily located via online search. Women's concerns regarding the credibility and trustworthiness of AI-enabled support should be addressed to maximize their use of emerging AI-enabled tools, embedded within digital technologies and mHealth. There is potential for AI to complement rather than replace usual care.
Irinotecan (CPT-11) is widely used for colorectal cancer treatment, with delayed-onset diarrhea as its primary side effect. Xiao-Chai-Hu-Tang (XCHT) has been clinically observed to alleviate chemotherapy-induced diarrhea, but its underlying mechanisms remain unclear. This study aimed to elucidate the regulatory mechanisms through which XCHT alleviated CPT-11-induced diarrhea using an integrated multi-omics approach. The chemical components of XCHT were detected using UHPLC-QE-Orbitrap-MS, and 12 active components were quantified via UHPLC-QQQ-MS/MS to ensure quality stability. CPT-11-induced diarrheal mice were established to evaluate the therapeutic effects of XCHT. Differential metabolites in liver and intestinal tissues among control, CPT-11, and XCHT mice were analyzed by untargeted metabolomics, followed by molecular network analysis using IPA. Bile acids were quantified using targeted UHPLC-QQQ-MS/MS method. Proteomics of colon tissues identified differentially expressed proteins, with functional enrichment conducted via GO and KEGG. Western blot and flow cytometry were used to validate the potential biopathway. Total 1108 chemical components were identified in XCHT, mainly including flavonoids, terpenoids and phenylpropanoids. XCHT significantly improved diarrhea symptoms and reduced intestinal inflammation. Metabolomics revealed 117 and 53 differential metabolites in the liver and intestine, respectively, with both tissues showing alterations in bile acid metabolism. Targeted bile acid analysis showed that CPT-11 inhibited the synthesis and uptake of bile acids in the liver and enhanced bile excretion, resulting in an increase in bile acids in the gallbladder. Meanwhile, CPT-11 impaired the reabsorption of bile acids in the intestine, leading to a decrease in total bile acid levels, reduction of circulating bile acids, and accumulation of conjugated bile acids in the colon. XCHT reversed these abnormal phenomena, restoring the intestinal bile acid homeostasis. Proteomics identified 86 differentially expressed proteins in colon tissues, with significant enrichment in 'Focal adhesion' pathway. Further verification indicated that CPT-11 abnormally activated the focal adhesion kinase (FAK) and its downstream RhoA/ROCK pathways, then reduced tight junction proteins ZO-1 and Occludin, damaging the intestinal barrier. XCHT inhibited this pathway, restored the expression of tight junction proteins, balanced Th1/Th2 differentiation, and alleviated intestinal inflammation. Cholestyramine, a bile acid chelator, also alleviated CPT-11-induced diarrhea by reducing the accumulation of bile acids in the intestine, confirming the key role of bile acids in the treatment of CPT-11-induced diarrhea. This study revealed that the combined administration of XCHT could restore bile acid reabsorption, reduced the accumulation of conjugated bile acids in the colon, inhibited the abnormally activated FAK-RhoA/ROCK pathway, improved intestinal epithelial barrier function and alleviate diarrhea. These findings provide a new entry point for the treatment of chemotherapy-induced diarrhea (CID) and offer data support for the combined medication regimen of XCHT and CPT-11.
The MLO gene family plays a critical role in plant-pathogen interactions, regulating susceptibility and resistance to fungal diseases. In this study, we performed a genome-wide characterization of the MLO genes in soybean (Glycine max), integrating structural analysis, phylogenetic classification, expression profiling, functional validation by virus-induced gene silencing (VIGS), and SNP mining. We identified 40 MLO genes, including a novel member (GmMLO25) with an unusually small MLO domain (93 aa). Protein length varied from 130 to 600 aa, with differences in the number of transmembrane domains and conserved motifs, including calmodulin-binding domains (CaMBDs). Analysis of SNPs in coding sequences revealed missense substitutions affecting conserved motifs and transmembrane regions, particularly in clade V genes. RNA-seq and RT-qPCR analyses revealed differential regulation of clade V members between resistant (Rpp5) and susceptible (BRS 184) genotypes during Phakopsora pachyrhizi infection. Functional validation by VIGS demonstrated that silencing multiple clade V genes did not reduce the number of uredinia but drastically reduced fungal sporulation. This study identified GmMLO38 as a functional susceptibility gene in soybean and as a prime target for gene editing or RNAi-based strategies to enhance resistance to Asian soybean rust (ASR).
Brain glucose metabolism measured by [18F]fluorodeoxyglucose (FDG) PET is a marker of residual activity in disorders of consciousness (DoC). Brain amino acid metabolism remains poorly characterized. We examined cross-sectional and longitudinal glucose and methionine uptake and associations with improvement of consciousness. This is a single-center, prospective, observational study. We consecutively enrolled patients older than 15 years admitted to our Rehabilitation Center between 2017 and 2022 with DoC after severe brain injury, who had adequate glucose control. Within this cohort, we performed 1) an exploratory cross-sectional analysis based on PET data from baseline evaluation (mean 20.9 ± 42.1 months after injury) and 2) a prospective longitudinal study. Participants underwent serial Coma Recovery Scale-Revised (CRS-R) assessments and both FDG and [11C]methionine (MET) PET during routine care. The primary outcome was improvement of consciousness, defined as a ≥2-point increase in CRS-R. Associations between metabolic measures and ΔCRS-R were assessed using Spearman correlation with 95% CIs, with exploratory logistic regression and receiver operating characteristic analyses. We included 60 patients in the cross-sectional study and 43 in the longitudinal analysis. In cross-sectional analyses vs 28 non-DoC controls, patients showed reduced whole-brain glucose metabolism (SUVmax mean difference -5.99 [95% CI -7.63 to -4.35]; p < 0.0001) and higher brainstem MET uptake (SUVmean mean difference 0.15 [95% CI 0.01-0.29]; p = 0.03). During the study, 21 (49%) patients demonstrated clinical improvement, with a median 4-point CRS-R increase (mean age 49.6 ± 19.4 years; 33% female; median CRS-R 11 [interquartile range 6-19]). In longitudinal analyses, improvement was associated with increased glucose SUVmax and higher brainstem MET uptake. CRS-R changes correlated with glucose SUVmax (ρ = 0.43 [95% CI 0.15-0.65]; p = 0.004) and brainstem MET SUVmean (ρ = 0.35 [95% CI 0.06-0.59]; p = 0.021). Brainstem MET SUVmean ≥2.28 discriminated improvement (area under the curve 0.686 [95% CI 0.527-0.846]). Increased brainstem MET uptake was associated with improvement of consciousness and may complement FDG-PET findings in DoC, although interpretation is limited by the single-center design, exploratory analyses, and sample size. Japan Registry of Clinical Trials: jrct.mhlw.go.jp; identifier: jRCTs031180091. Registration, January 21, 2019.
AI is increasingly discussed and deployed in health care, yet safe and effective implementation depends on the preparedness, trust, and training of the professionals who are expected to use these tools. This study aimed to assess current AI use, perceived benefits and concerns, confidence, and training needs among French health care professionals and students. We conducted a national web-based cross-sectional survey distributed through the PulseLife professional community between December 4, 2024, and March 5, 2025. The survey instrument was administered in French and included respondent characteristic items together with 12 substantive closed-ended questions covering current AI use, confidence, perceived benefits, and concerns, and interest in AI-related training. Access was restricted to authenticated individual PulseLife accounts, and multiple submissions from the same account were not allowed. Questions were not mandatory; incomplete questionnaires were retained for item-level analyses, and percentages were calculated using item-specific denominators. Because the exact invitation denominator was not retained by the platform, view, participation, and completion rates could not be calculated. Descriptive statistics and Pearson chi-square tests were performed using R. Internal consistency and exploratory psychometric properties were assessed using the Cronbach α, exploratory factor analysis, and confirmatory factor analysis. A total of 1625 respondents participated, including 1212 (74.6%) health professionals and 413 (25.4%) students. Among professionals, physicians represented the largest group (642/1212, 53%), followed by nurses (232/1212, 19.1%) and pharmacists (92/1212, 7.6%). Only 6.6% (90/1366) of the respondents reported prior AI-specific training, whereas 78.3% (920/1175) wished to receive such training. Confidence in AI for diagnosis and patient management remained limited: only 9.2% (120/1301) of the respondents reported being very confident. Nearly half (673/1455, 46.3%) of the respondents who answered this item reported no current AI use in professional activity, whereas 10.5% (153/1455) reported frequent use. Physicians and younger respondents reported more frequent AI use, and prior AI training was associated with greater confidence (P<.001 in all cases). Commonly perceived benefits included improved diagnosis (774/1625, 47.6%), time savings (685/1625, 42.2%), reduced medical errors (634/1625, 39%), and improved patient follow-up (593/1625, 36.5%). Frequently reported concerns included algorithmic bias (785/1625, 48.3%), limited transparency (666/1625, 41%), deterioration of the patient-health care professional relationship (628/1625, 38.6%), and data confidentiality (557/1625, 34.3%). In this national French sample, formal AI training was uncommon despite high interest in receiving it. These findings support the need for more structured educational initiatives in AI literacy across undergraduate, postgraduate, and continuing professional education. Because this study relied on a convenience sample recruited through a digital platform, the findings should be interpreted as descriptive and exploratory rather than nationally representative.
Salinity is a major constraint on maize establishment, yet the extent of early-stage variation among native Mexican maize accessions remains insufficiently characterized. This study evaluated the in vitro response of 130 native maize accessions (Zea mays L.) to saline stress in order to identify promising materials for early-stage salinity tolerance. Seeds were germinated under control and saline conditions (150 mM NaCl), and early seedling traits were recorded, including germination, coleoptile length, mesocotyl length, radicle length, seminal root number, and seedling biomass. Univariate analyses were used to identify contrasting accession responses, whereas principal component analysis, Ward's hierarchical clustering, and optimized stratification were applied to classify phenotypic patterns under saline stress. Salinity reduced germination and early seedling growth overall, but the magnitude of these effects varied markedly among accessions, revealing substantial phenotypic variation at the earliest developmental stages. Several accessions maintained favourable performance under saline stress. In particular, CIMMYT 433 combined stable germination with superior radicle length and seminal root number, whereas CIMMYT 658 showed favourable coleoptile and mesocotyl elongation. Multivariate analysis based on accession-specific NaCl-response differences identified four contrasting response groups. Cluster 1 showed the lowest overall NaCl-induced reduction and therefore grouped accessions with greater early-stage stability, whereas Cluster 2 showed the greatest reductions and represented the most affected response group. Tuxpeño and Olotillo related materials were prominent among the accessions with favourable early-stage responses. Native Mexican maize harbours substantial early-stage variation in response to salinity, and this variation can be effectively captured through integrated seedling traits and multivariate classification. The accessions identified here represent promising germplasm for further physiological, genetic, and breeding studies aimed at improving salinity tolerance.
Length of hospital stay (LOS) after surgery for oral cavity squamous cell carcinoma (OCSCC) reflects both surgical complexity and host status. Whether routine nutritional and inflammatory biomarkers can identify patients at risk of prolonged stay has not been clearly established. Single-centre retrospective cohort study of 168 patients who underwent primary curative-intent surgery for OCSCC between 2015 and 2021. Length of stay was dichotomised at the 75th percentile (≥23 days). Inflammatory and nutritional parameters (NLR, LMR, PLR, Prognostic Nutritional Index [PNI], serum albumin, fibrinogen) were measured at three perioperative timepoints. Associations were evaluated using ROC analysis with Youden's index, multivariable logistic regression, and Kaplan-Meier time-to-discharge. Median LOS was 16.5 days (IQR 11-23); prolonged stay occurred in 46 patients (27.4%). Optimal cutoffs were PNI <35.8 (AUC 0.654) and postoperative fibrinogen ≥553 mg/dL (AUC 0.620). In multivariable analysis adjusted for age, sex, T and N stage, reconstructive modality, smoking, diabetes and cardiovascular comorbidity, low PNI on the day of surgery was the only independent predictor of prolonged stay (OR 2.87, 95% CI 1.19-6.91; p = 0.019; model AUC 0.772). Kaplan-Meier curves confirmed significantly longer admissions in patients with low PNI or elevated postoperative fibrinogen (log-rank p < 0.001 for both). The discriminatory capacity of biomarkers was concentrated in patients managed without free flap reconstruction (AUC 0.71-0.74), whereas in microsurgical patients hospital stay was governed by post-flap monitoring protocols. Day-of-surgery PNI and postoperative fibrinogen identify patients with OCSCC at increased risk of prolonged hospital stay. Their predictive value is most relevant in non-microsurgical patients, in whom proactive nutritional optimisation could shorten admission.
We report a case of a uterine mesenchymal tumor with myogenic differentiation and SRF::RELA fusion occurring in a 23-year-old woman manifesting as an endometrial polyp. The tumor consisted of bland spindle cells with a predominantly fascicular growth pattern and entrapment of the endometrial glands and vessels. The tumor was positive for smooth muscle markers and CD10. NGS RNA analysis revealed SRF::RELA fusion. NGS DNA analysis revealed no pathogenic variants or copy-number alterations. On follow-up 12 months after curettage, the patient showed no signs of the disease. To the best of our knowledge, only 3 cases of uterine tumors with SRF::RELA fusion arising in the gynecologic tract have been described previously, and all show benign behavior so far. Differential diagnosis includes both benign and potentially aggressive entities, such as leiomyoma, inflammatory myofibroblastic tumor, perivascular epithelioid cell tumor, uterine adenosarcoma, low-grade endometrial stromal sarcoma, and uterine sarcoma with KAT6B/A::KANSL1 fusion.