Search Results (19,413)

Background and Objectives: Pediatric chest pain is usually benign, but identifying children who require admission or have a cardiac diagnosis remains challenging. We aimed to identify factors associated with these outcomes, with a focus on recurrent presentation and the combined value of C-reactive protein (CRP) and predefined red-flag findings. Materials and Methods: This retrospective cohort included children aged 0–18 years presenting with chest pain to a tertiary pediatric emergency department between January 2021 and January 2026; visits with insufficient documentation were excluded. Red-flag findings were predefined as effort-related chest pain, syncope, palpitations, dyspnea, fever, or a family history of sudden cardiac death. Independent predictors were assessed using multivariable Firth logistic regression, and cluster-robust standard errors were used in a sensitivity analysis to account for recurrent visits. Results: Of 1703 visits (1435 patients), 452 (26.5%) were recurrent presentations. Admission occurred in 64 visits (3.8%), and 91 (5.3%) had a cardiac diagnosis. Red-flag findings (OR, 5.15), abnormal ECG (OR, 4.00), CRP > 5 mg/L (OR, 3.71), male sex (OR, 2.13), and recurrent presentation (OR, 2.51) were independently associated with admission (all p ≤ 0.009). Recurrent presentation was not associated with a cardiac or psychogenic diagnosis. Admission and cardiac diagnosis rates increased from 1.4%/2.2% in children with neither elevated CRP nor red-flag findings to 22.2%/24.8% in those with both. Routine ECG, troponin, and chest radiography had low diagnostic yields (2.1–6.0%), whereas echocardiography performed selectively yielded abnormal findings in 79.7%. Conclusions: Red-flag findings, abnormal ECG, elevated CRP, and recurrence were associated with admission, whereas recurrence was not associated with diagnosis. Combining CRP with red flags may improve risk stratification, though prospective validation is needed. Full article

Objectives: This systematic review and meta-analysis aimed to estimate the prevalence of nonconvulsive status epilepticus (NCSE), electrographic status epilepticus (ESE), post-anoxic status epilepticus (PSE), and electrographic seizures in comatose adult survivors of cardiac arrest and to summarize associated diagnostic approaches, treatment strategies, neurological outcomes, and mortality. Methods: PubMed/MEDLINE, MEDLINE Core, Cochrane CENTRAL, Web of Science, and Scopus were searched from inception to May 2026. Eligible studies included adult comatose survivors of cardiac arrest who underwent EEG monitoring and reported NCSE, ESE, PSE, electrographic seizures, or related rhythmic/periodic EEG patterns. Random-effects meta-analysis of proportions was performed, separating strict NCSE/ESE/PSE constructs from broader electrographic seizure/status and epileptiform/IIC constructs. Results: Seventeen studies were included. In the primary analysis of four non-overlapping EEG-monitored cohorts, strict NCSE/ESE/PSE occurred in 112 of 457 patients; the pooled prevalence was 22% (95% CI, 10–41%; 95% prediction interval, 4–64%; I² = 77.8%). The expanded sensitivity analysis including selected, historical, or restricted cohorts yielded a similar pooled prevalence of 22% (95% CI, 16–31%). Broad electrographic seizure/status prevalence was 10% (95% CI, 2–33%; I² = 94.7%), and broad epileptiform/ictal–interictal continuum pattern prevalence was 29% (95% CI, 7–70%; I² = 86.0%); these analyses represented distinct EEG constructs. Poor neurological outcome and mortality were frequent, with pooled estimates of 91% (95% CI, 85–94%) and 87% (95% CI, 78–93%), respectively. Conclusions: The pooled prevalence of NCSE/ESE/PSE was 22% among EEG-monitored comatose survivors of cardiac arrest, although the exact prevalence remains uncertain across settings. Poor neurological outcome and mortality were frequent, but recovery occurred in selected patients with continuous EEG background, later SE onset, fewer multimodal poor-outcome markers, or electrographic cessation. These findings support multimodal, phenotype-guided interpretation rather than reliance on EEG status alone. Future studies should use standardized EEG definitions, consistent denominators, and phenotype-enriched randomized designs. Full article

Reactive oxygen species (ROS), reactive nitrogen species (RNS) and reactive sulphur species (RSS) constitute a coupled molecular language whose marks, the reversible post-translational modifications of cysteine and tyrosine residues, are formed, interconverted and reversed within subcellular microdomains characterised by their own geometry and kinetics. Two decades of broad-spectrum antioxidant clinical trials using vitamin E, beta-carotene, and N-acetylcysteine, amongst other compounds, have produced predominantly neutral and in some settings harmful results, although the outcomes vary appreciably with the compound, the disease, the dose and the trial design, and several of the most studied polyphenols, curcumin, resveratrol and quercetin, display a broadly comparable pattern, compounded in their case by low oral bioavailability of the parent aglycone, which limits the concentration reaching the target tissue. The redox biomarkers available in clinical practice, plasma GSH/GSSG, urinary 8-oxo-dG, and serum MDA, show disappointing individual prognostic value. The present work argues that both failures are consistent with a shared mechanistic explanation: they treat the redox language as undifferentiated noise when it is, in fact, spatially structured information. While other contributing factors, including analytical variability, clinical heterogeneity, and cohort design, are acknowledged, this mechanistic convergence offers a parsimonious interpretive framework. We analyse the failure of global antioxidants from the logic of the microdomain, suggesting that non-specific scavenging suppresses eustress and distress with comparable efficacy, and the failure of conventional redox biomarkers from the loss of spatial and temporal resolution that occurs when signals from multiple compartments are collapsed into a systemic index. Building upon this dual analysis, we propose the principles of an alternative strategy: pharmacological interventions acting on specific microdomain levers (NOS recoupling, restoration of mitochondrial -SSH tone, and Prx/Trx system support) and a precision redox diagnostic framework that replaces global indices with compartment-resolved biomarker panels. The objective is not to silence the redox language, but to learn to read it and, when necessary, to correct its grammar with precision. Full article

Bioelectrical impedance analysis (BIA) provides body composition estimates that may complement conventional assessments of dynamic movement asymmetry. This cross-sectional study examined the association between the two in 50 adults aged 20–65 years. Dynamic variables were obtained from gait analysis, electromyography, and isokinetic strength testing, and left–right asymmetry was expressed as an asymmetry index (AI). Spearman correlations were evaluated with Benjamini–Hochberg correction across a pre-specified family of 42 comparisons. Eight associations survived correction, all positive, linking the AIs of leg total body water (TBW_leg), intracellular water (ICW_leg), and soft lean mass (SLM_leg) to stance time and gait speed during dual-task walking and to ankle and knee joint torque, with correlations ranging from 0.376 to 0.476; whole-body indices showed the opposite pattern. Against a composite score derived from these variables, TBW_leg AI showed the strongest association (ρ = 0.508) and, in an exploratory analysis, separated participants in the highest tertile from the remainder with an area under the curve of 0.786. These findings support further evaluation of BIA-derived leg water and lean-tissue asymmetry indices as candidate markers of dynamic movement asymmetry; the design does not establish predictive or diagnostic validity. Full article

Engineering education for sustainable development plays a critical role in preparing future engineers to address complex sustainability challenges aligned with the United Nations Sustainable Development Goals (SDGs). Higher education institutions contribute to this agenda through both teaching and research, yet the relationship between sustainability-oriented research and curriculum development remains insufficiently explored. This study addresses this gap by developing the Nakad model, a practical approach for examining the alignment between sustainability-oriented course Learning Outcomes (LOs) and faculty members’ research output using SDG mapping. The civil and environmental engineering department and the chemical engineering department were chosen as their research output increasingly contributes to sustainability-oriented knowledge. An exploratory document-based analysis was conducted, combining deductive qualitative content analysis of course syllabi with quantitative descriptive analysis of SDG frequencies of faculty members’ research output. Curriculum LOs were manually mapped to the SDGs and their targets, while faculty research output was mapped using the Scopus SDG classification. The results demonstrated differences in the correspondence between sustainability-oriented research and curriculum LOs across the two departments. In the chemical engineering department, the substantial engagement of faculty members in sustainability-related research was not reflected in the LOs. However, the civil and environmental engineering department demonstrated a more explicit and consistent alignment of sustainability in research and teaching, facilitated by the flexibility of elective courses to incorporate sustainability themes. The analysis also identified a shared gap in the representation of social sustainability across both departments. The Nakad model contributes to the research–teaching nexus by providing a diagnostic approach for identifying strengths, gaps and opportunities to strengthen sustainability integration in engineering education. Full article

With the continuous expansion of modern electrical infrastructure, the scale and deployment of electrical cable networks have rapidly increased, underscoring the critical importance of systematic cable system maintenance. Among various diagnostic technologies, reflectometry has emerged as a highly effective and prominent methodology for precise fault detection and localization. Although the operational reliability of these networks fundamentally relies on precise fault detection, identifying minor or early-stage degradation remains a formidable challenge due to the marginal magnitude of the weak reflected signals. To address this limitation, this paper proposes a concurrent pulse superposition (CPS) method to enhance the detection sensitivity of weak reflection signatures in reflectometry without requiring expensive hardware modifications. The proposed method was evaluated through simulations covering fault resistances from $50\Omega$ to $1000\Omega$ and SNR levels from $15\mathrm{dB}$ to $35\mathrm{dB}$. Across these conditions, CPS increased the reflection peak magnitude by approximately 2.5–3.5 times and maintained identifiable peaks in all tested cases, whereas the conventional method failed under several combinations of high fault resistance and low SNR. Consequently, this approach is expected to significantly empower the diagnostic analysis of extremely weak, early-stage faults, offering a practical solution for predictive maintenance in electrical systems. Full article

Background: Helicobacter pylori colonizes the human gastric mucosa through outer membrane proteins (OMPs), particularly the adhesins BabA and BabB, which recognize host blood group antigens and mediate bacterial attachment and persistent colonization; importantly, structural and genetic studies have confirmed substantial polymorphisms in these adhesins that may affect their functional properties and contribute to differences in the clinical outcomes of infection. Therefore, the present study aimed to investigate the prevalence and genetic diversity of babA/babB variants in H. pylori isolates obtained from Jordanian patients and to evaluate their association with different gastric pathological conditions. Methods: A total of 106 gastric mucosal biopsies were collected from patients with gastric symptoms at two major hospitals in Jordan. Specimens underwent endoscopic evaluation, rapid urease testing (RUT), and histopathological examination according to the Sydney classification system. H. pylori detection was performed using real-time PCR targeting 16S rRNA and universal bab genes using novel primers designed to amplify both babA and babB variants. Conventional PCR was used for specific BabA and BabB gene amplification, followed by sequencing analysis of BabB variants. Results: A total of 106 patients were enrolled, with a mean age of 40.2 ± 15.9 years. H. pylori was detected in 76 (71.7%), 83 (78.3%), and 79 (74.5%) cases by the Rapid Urease Test (RUT), histopathology, and 16S rRNA qPCR, respectively. Using histopathology as the reference method, 16S rRNA qPCR demonstrated a sensitivity of 95.2%, compared with 91.6% for RUT. Among the 83 histopathology-positive cases, the universal bab gene was detected in 72 (86.7%), of which 53/72 (73.6%) were positive according to babB-specific PCR. Among patients with chronic gastritis, universal bab positivity increased progressively from 82.5% in mild to 88.5% in moderate and 93.3% in severe gastritis, whereas babB positivity increased from 50.0% to 76.9% and 80.0%, respectively. Sequencing of 40 babB-positive samples identified 27 distinct babB sequence profiles, with CHI-023 and LIM-008 being the most frequently identified reference sequence matches (10.0% each), highlighting substantial genetic diversity within the analyzed babB region. Conclusions: The findings demonstrate high diagnostic performance of 16S rRNA qPCR and RUT relative to histopathology, together with substantial genetic diversity within the analyzed babB region. Full article

Background/Objectives: Temporomandibular disorders (TMDs) and sleep-related bruxism affect up to 40% and 31% of adults, respectively, and their management relies on accurate diagnostic assessment of the dental arch—including tooth identification, arch-form analysis, and occlusal-plane evaluation—followed by the fabrication of full-coverage hard occlusal stabilization splints. Current digital workflows still depend on manual CAD delineation for both diagnostic segmentation and splint design, introducing inter-operator variability. This study presents a computational framework that automates the diagnostic segmentation of 3D intraoral scans and the subsequent generation of patient-specific stabilization splints. Methods: Using the Teeth3DS+ dataset, 3D meshes are rendered into 32 views via a Fibonacci Hemisphere camera-placement strategy, and seven deep learning architectures are trained for pixel-level diagnostic classification of 34 dental structures. Predictions are back-projected onto the mesh through probability-aggregation voting and iterative graph diffusion; the occlusal plane is estimated by singular value decomposition of tooth centroids; and a volumetric dual-shell splint is synthesized by topological dilation and normal-based displacement. Results: UNet++ achieves the strongest performance (MPA 98.75%, mIoU 71.76%, Dice 75.20%), with its superiority over every competing architecture confirmed by both paired t-test and Wilcoxon signed-rank tests at the image and scan levels (all p < 0.001). On 60 held-out scans, back-projection and graph diffusion raise segmentation quality from a per-view mIoU of 62.0% to a mesh-level mIoU of 87.4% (Dice from 65.2% to 91.3%), with every held-out scan improving. Per-class analysis reveals near-ceiling accuracy on well-represented teeth; performance on the rarest and most posterior teeth (third molars) was markedly lower in preliminary experiments but improved substantially once the learning rate was selected via a validation-based sweep—a finding with direct implications for the diagnostic reliability of AI-assisted dental arch assessment and for the sensitivity of rare-class segmentation to training hyperparameters. Conclusions: The pipeline converts a raw intraoral scan into a diagnostically segmented model and a 3D-printable Michigan-type splint geometry without manual intervention, providing a foundation for accelerating the diagnostic-to-treatment cycle in TMD and bruxism management. Full article

Background and Objective: Obstructive sleep apnea (OSA) is a fatal widespread sleep-related breathing disorder and a major risk factor for cardiovascular and cerebrovascular diseases, significantly impacting older adults’ health worldwide. Due to the risk of such complications, timely and accurate identification of OSA is crucial. Polysomnography is considered the most accurate technique for detecting OSA; however, it is limited by its complexity and multi-channel requirements. A promising alternative is electrocardiogram (ECG)-based diagnosis, which continuously monitors heart rhythm and captures subtle cardiac changes associated with OSA. Nevertheless, existing ECG-based approaches still face challenges related to complex feature engineering, limited capture of complementary temporal–spectral information and global dependencies, along with inadequate feature recalibration and fusion, which can restrict OSA detection. Thus, further improvements are still required to achieve clinically reliable performance. Methods: To address these challenges, this study proposes SApneaNet, a novel advanced deep learning method for detecting OSA events using ECG signals. The proposed approach employs the continuous wavelet transform (CWT) to convert ECG signals into RGB log-scalograms, enabling the simultaneous analysis of temporal and frequency-domain features. The generated RGB log-scalograms are then fed into a deep CNN encoder with adaptive squeeze-and-excitation (ASE), followed by a transformer and an adaptive gated feature fusion (AGFF) architecture. In this framework, to improve OSA detection performance, the CNN extracts rich local features, the ASE module performs channel-wise recalibration to enhance feature representations, the transformer performs data-parallel processing and captures global contextual dependencies, and the AGFF mechanism adaptively emphasizes informative features while suppressing less relevant ones. Results: The experimental results on the Apnea-ECG dataset showed that the model achieved a sensitivity of 94.7%, specificity of 95.2%, F1-score of 93.5%, accuracy of 95.1%, Cohen’s kappa of 89.4%, and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.989 for per-segment classification. Furthermore, for per-recording classification, the model achieved an accuracy of 100.0%, a mean absolute error (MAE) of 2.025, and a Pearson correlation coefficient (PCC) of 0.992. Overall, the experimental results demonstrated that the proposed model achieved excellent and competitive performance compared with other advanced state-of-the-art methods for OSA classification. Conclusions: The proposed model demonstrates strong efficacy in OSA detection, providing a novel and robust alternative to conventional diagnostic methods. The model’s reliable and consistent diagnostic performance highlights its potential for integration into practical OSA diagnostic systems, including home-based health monitoring devices and clinical decision-support tools. Full article

Background: Effective hypertension management depends partly on medication adherence. Mental health disorders, such as anxiety and depression, may influence adherence, but evidence in older populations is limited. This secondary analysis of a cohort study identifies adherence trajectories to antihypertensive medications among older adults and assesses whether anxiety and depression are associated with trajectory membership. Patients and Methods: A cohort of 986 older adults (aged 65 and above) using antihypertensive treatments was analyzed. Adherence was measured using prescription claims over a 12-month period. Adherence patterns over time were characterized using Group-Based Trajectory Modeling (GBTM). Self-reported symptoms and diagnostic codes for anxiety and depression were used to assess for mental health disorders. Associations between depression or anxiety and adherence trajectories were investigated using logistic regression models adjusting for potential confounders. Results: We identified two stable adherence trajectories: a high-adherence group (83.6%) and a low-adherence group (16.4%). No evidence of an association was observed between the presence of anxiety (adjusted odds ratio (OR) of 1.2, 95% confidence interval (CI): 0.8–1.9) or depression (adjusted OR of 1.0, 95% CI: 0.6–1.6) and adherence trajectories. Conclusions: While most older adults in the study maintained high adherence to antihypertensive medications, a notable minority consistently demonstrated low adherence. These findings suggest that additional determinants of adherence trajectory, beyond mental health, should be investigated. Full article

Urban resilience is fundamental to sustainable city development, yet its governance faces two intertwined challenges: assessing whether resilience targets can be achieved under uncertainty represented by historical fluctuations and interdimensional covariance, and determining where limited resources should be prioritized. Taking Ningbo, China, as a case study, this paper constructs a four-dimensional resilience evaluation system based on 22 indicators from 2000 to 2023, covering economic and fiscal conditions, social well-being, ecological environment and resources, and spatial–infrastructural capacity. This paper develops a probabilistic forecasting framework that preserves the correlation structure among dimensions to assess the attainability of urban resilience targets (2024–2030) and combines obstacle-degree analysis with model-based diagnostic elasticity analysis to identify key weaknesses and priority directions for resilience enhancement. The results show that Ningbo’s comprehensive resilience has steadily improved from 0.291 in 2000 to 0.794 in 2023, while future uncertainty continues to expand. By 2030, the probabilities of achieving high-level resilience targets remain insufficient under existing development inertia alone. A dual “spatial–economic” bottleneck is identified. The study concludes that resilience governance should implement differentiated interventions based on the combined logic of “weakness severity–marginal driving capacity.” This framework can provide a methodology reference that has been locally calibrated for coastal cities with similar port economic dependencies and complex disaster exposures. However, the specific indicator rankings in Ningbo do not have direct replicability. Full article

Research on news framing of sexual violence is constrained by a persistent conceptual problem: analytically heterogeneous categories have often been treated as equivalent frames even though they designate different objects and levels of interpretation. This slippage reduces conceptual and diagnostic precision. Drawing on a critical narrative review supported by structured searches of Scopus and the Web of Science Core Collection and theoretically guided purposive selection, this conceptual article develops a multidimensional model of news framing. Theoretically, rather than adding substantive frame labels, it distinguishes three dimensions of framing—event, actor, and societal-response—according to the primary object upon which interpretative operations act. Methodologically, it provides a proposition-level procedure for distinguishing framing objects, interpretative operations, discursive devices, and attributed voices, and for reconstructing their relations as broader interpretative configurations. This makes visible how propositions across dimensions may co-occur, reinforce, qualify, or contradict one another. Its analytical value is illustrated through post-#MeToo news reporting, where structural definitions of sexual violence may coexist with selective distributions of credibility or the delegitimation of institutional reform. Without reducing multidimensional discourse to a predominant frame, the model supports transparent qualitative, quantitative, and comparative analysis while clarifying how journalism constructs recognition, credibility, responsibility, and legitimate responses. Full article

Introduction: Computer-based simulation technologies, including virtual reality (VR), augmented reality (AR), mixed reality (MR), and three-dimensional (3D) modeling, are increasingly used in pediatric cardiovascular care. Despite growing adoption of these technologies, no comprehensive synthesis across VR, AR, MR, and 3D modeling modalities exists for pediatric cardiovascular care. Objective: This review aimed to synthesize evidence regarding the applications and outcomes of computer-based simulation technologies in pediatric cardiovascular diseases. Methods: A systematic review was conducted by searching nine databases (PubMed, Web of Science, Scopus, Ovid, the Cochrane Library, IEEE Xplore, ProQuest, CINAHL, and EBSCO Host) for eligible studies published up to 1 September 2025. Thirty-six studies were included and appraised using the Mixed Methods Appraisal Tool (MMAT), the Critical Appraisal Skills Programme (CASP) checklist, and the ROBINS-I tool. Substantial clinical and methodological heterogeneity precluded meta-analysis. Results: VR was the most frequently used modality (n = 14), followed by 3D modeling (n = 8), multimodal approaches (n = 8), MR (n = 6), and AR (n = 3). This technology was mainly used for congenital heart defects (n = 18). MR holography improved diagnostic accuracy for complex anomalies (95.5% vs. 89.7%); VR-based surgical plans aligned better with the real surgery than did 2D imaging plans (80% vs. 66%); VR was preferred over 3D-printed models by 87% of participants (8.5/10 vs. 6.3/10 for anatomical understanding); and VR curricula (Stanford Virtual Heart) significantly increased CHD knowledge scores among students and residents (p < 0.05). However, this evidence was predominantly derived from small, single-center observational studies, with barriers including hardware limitations and limited long-term outcome data. Conclusions: Computer-based simulation technologies show considerable potential for surgical planning, diagnostic assessment, and education in pediatric cardiovascular diseases. They can be used as complementary tools rather than replacements for standard imaging or clinical judgment. While VR and 3D modeling show promise for surgical planning and education, multicenter comparative studies with standardized outcomes and cost-effectiveness analyses are essential before routine clinical implementation. Full article

Background: Invasive filamentous fungal infections carry substantial mortality among immunocompromised hosts, and diagnostic delay associated with morphology-based mould identification is a recognised contributor to poor outcomes. The Mycelium Transfer (MyT) method, implemented on the Bruker MALDI Biotyper^® platform, allows direct mass spectrometric profiling of leading-edge mycelium sampled from routine solid agar without solvent extraction. We conducted a single-centre analytical validation of an optimised, triplicate-spot MyT workflow as a standalone identification platform at a Canadian tertiary care academic centre. Methods: Fifty-six archived, pre-characterised clinical isolates spanning five fungal categories—Aspergillus species (n = 21), other hyaline non-Aspergillus moulds (n = 14), Mucorales (n = 8), dermatophytes (n = 8), and dematiaceous fungi (n = 5). These isolates were selected to represent our local isolate population and tested by MyT on a Bruker Microflex LT/SH system, with acquisition and scoring performed prospectively by technologists blinded to the archived reference identification. Spectra were interrogated against the MBT Myco 4.1 Filamentous Fungi Library. Each isolate was spotted in triplicate; a final identification required ≥2/3 concordant spots at a score ≥ 1.8, consistent with CLSI M58 guidance. A parallel formic acid–ethanol extraction was performed for a subset of 11 isolates as an exploratory, within-study comparator. Primary culture media were Brain Heart Infusion (BHI) agar and Sabouraud Dextrose Agar with chloramphenicol and gentamicin (SAB-CG). Agreement was assessed against CLSI M52 criteria, with an a priori target of ≥90% overall concordance. Results: Applying the triplicate-spot consensus rule at the ≥1.8 threshold, species- or complex-level identification was achieved for 43/56 isolates (76.8%; 95% CI 64.2–85.9%), and overall acceptable identification (species/complex plus acceptable genus-level calls) for 50/56 isolates (89.3%; 95% CI 78.5–95.0%). No misidentifications were observed. Aspergillus spp. and dermatophytes each achieved 100% overall identification; performance was lowest for dematiaceous fungi (2/5, 40.0%). In a post hoc analysis restricted to the categories in which discordant single-spot results occurred (other hyaline non-Aspergillus moulds and Mucorales, combined n = 22), triplicate consensus corrected three isolates in which a single high-scoring spot would have produced a false identification, and enabled identification of four further isolates that a single spot would have missed, corresponding to an estimated single-spot equivalent identification rate of 12/22 (54.5%) versus 19/22 (86.4%) with triplicate consensus, which is an approximately 32 percentage point absolute difference restricted to this 22-isolate subgroup and not an effect demonstrated across the complete 56-isolate panel. Growth sufficient for MyT sampling at 72 h was numerically higher on BHI agar than SAB-CG for hyaline moulds at 72 h (88.6% vs. 77.1%), although this difference was not statistically significant (McNemar’s test, p = 0.29). In a smaller exploratory subset of 11 isolates tested in parallel by both methods, MyT showed similar performance to formic acid–ethanol extraction (equivalent or higher scores in 8/11 isolates tested in parallel); this was not a formally powered non-inferiority comparison. Conclusions: A triplicate-spot MyT workflow at a ≥1.8 score threshold achieved 89.3% overall concordance, with reference identification and zero misidentifications across a 56-isolate panel, approaching the CLSI M52 ≥ 90% benchmark overall and meeting it for Aspergillus species and dermatophytes specifically. To our knowledge, this is among the first Canadian evaluations of the MyT method for routine clinical mould identification. Dematiaceous fungi and several individual species within other categories remained underpowered and of limited performance, reflecting both small subgroup sizes and known gaps in current spectral library coverage. These findings support MyT as a practical frontline identification method for Aspergillus, Mucorales, and dermatophytes within existing laboratory infrastructure, with chemical extraction or molecular methods retained as adjuncts for isolates that do not achieve threshold identification; broader implementation and clinical outcome benefits remain to be evaluated prospectively. Full article

Male infertility represents a major clinical challenge. Despite standard genetic testing (karyotyping, Y-chromosome azoospermia factor (AZF) microdeletion testing, and CFTR variant analysis), the molecular cause remains unidentified in a substantial proportion of patients. These patients are consequently diagnosed with idiopathic male infertility. This review provides a comprehensive overview of the current evidence regarding the application of whole-exome sequencing (WES) in the molecular diagnosis and clinical management of idiopathic male infertility. WES can identify pathogenic variants associated with quantitative spermatogenic defects, qualitative abnormalities of sperm motility and morphology, and pre-testicular causes related to hypogonadotropic hypogonadism. When defining the cohort strictly as patients with true idiopathic non-obstructive azoospermia (NOA) who remain undiagnosed after standard testing, the pooled diagnostic yield of WES is approximately 10–15%. Estimates vary due to differences in cohort selection, variant interpretation, and the range of genes analysed. Establishing a precise molecular diagnosis improves genetic counselling, helps predict the likelihood of successful sperm retrieval via testicular sperm extraction (TESE) or microdissection TESE (micro-TESE), and informs treatment planning for assisted reproductive technologies. However, routine clinical implementation remains limited by the high frequency of variants of uncertain significance, the absence of standardised diagnostic pipelines, unequal access to testing, and ethical concerns. Emerging “all-in-one” diagnostic strategies, multi-omics integration, and whole-genome or long-read sequencing hold promise for improving genomic diagnostics. However, broader adoption will ultimately require ongoing standardisation and functional validation of identified variants. Full article