Search Results (599)

Background/Objectives: Breast cancer is the most common malignancy among women, and early detection is critical for improving outcomes. The Breast Imaging Reporting and Data System (BI-RADS) standardizes reporting, but the BI-RADS 4 category presents a major challenge, with malignancy risk ranging from 2% to 95%. Consequently, most women in this category undergo biopsies that ultimately prove unnecessary. This study evaluated whether exhaled breath analysis could distinguish malignant from benign findings in BI-RADS 4 patients. Methods: Participants referred to the McGill University Health Centre Breast Center with BI-RADS 3–5 findings provided multiple breath specimens. Breathprints were captured using an electronic nose (eNose) powered breathalyzer, and diagnoses were confirmed by imaging and pathology. An autoencoder-based model fused the breath data with BI-RADS scores to predict malignancy. Model performance was assessed using repeated cross-validation with ensemble voting, prioritizing sensitivity to minimize false negatives. Results: The breath specimens of eighty-five participants, including sixty-eight patients with biopsy-confirmed benign lesions and seventeen patients with biopsy-confirmed breast cancer within the BI-RADS 4 cohort were analyzed. The model achieved a mean sensitivity of 88%, specificity of 75%, and a negative predictive value (NPV) of 97%. Results were consistent across BI-RADS 4 subcategories, with particularly strong sensitivity in higher-risk groups. Conclusions: This proof-of-concept study shows that exhaled breath analysis can reliably differentiate malignant from benign findings in BI-RADS 4 patients. With its high negative predictive value, this approach may serve as a non-invasive rule-out tool to reduce unnecessary biopsies, lessen patient burden, and improve diagnostic decision-making. Larger, multi-center studies are warranted. Full article

Background: Diagnosing peanut allergy (PA) in children without known exposure remains challenging due to the need to distinguish true clinical allergy from asymptomatic sensitization. This study aimed to evaluate the diagnostic performance of individual and combined in vitro markers, particularly sIgE to Ara h 2, and to develop a multistage decision pathway that may reduce reliance on oral food challenge (OFC). Methods: Eighty children with suspected peanut allergy were prospectively enrolled. All participants, including healthy controls, underwent skin prick testing (SPT), measurement of sIgE to peanut and Ara h 2, and basophil activation testing (BAT). A multistage diagnostic algorithm incorporating these markers was constructed, and its performance was assessed using ROC analysis, predictive values, and likelihood ratios. A secondary analysis evaluated a simplified decision pathway excluding BAT. Results: sIgE to Ara h 2 demonstrated excellent individual performance (AUC 0.889), with 96.6% PPV at the optimal cut-off. The full multistage decision pathway (SPT + sIgE + BAT when interpretable) achieved 100% specificity and avoided OFC in 28.6% of children. However, BAT feasibility was limited; over 25% of results were uninterpretable. The simplified decision pathway (SPT + sIgE to Ara h 2) preserved 100% specificity and enabled the avoidance of OFC in 27.5% of cases, with slightly lower sensitivity. Conclusions: A structured in vitro diagnostic approach using sIgE to Ara h 2 and SPT can reliably identify peanut allergy in selected pediatric patients, particularly those without a reliable peanut exposure history. BAT enhances specificity but should be considered a confirmatory tool due to feasibility limitations. Full article

Background: Although China has eliminated indigenous malaria, imported cases, particularly among young and middle-aged workers returning from Africa, constitute a major challenge for current epidemic prevention and control. In contrast, imported malaria in children is extremely rare and often subject to diagnostic delays in non-endemic areas due to atypical clinical presentations. Case presentation: We report a case of a 2-year-11-month-old boy who returned from Sudan, a malaria-endemic region, presenting with fever and diarrhea as the initial symptoms. The case was identified by the laboratory through the blood routine re-examination rules, crucially informed by the patient’s epidemiological history. The diagnosis was ultimately confirmed as Plasmodium falciparum malaria by rapid diagnostic testing and microscopic examination. Conclusion: This diagnostic pathway exemplifies a closed-loop model of “clinical suspicion → targeted laboratory testing → definitive pathogen identification.” It provides a practical framework for the early detection and diagnosis of pediatric imported malaria with atypical presentations in non-endemic areas. Full article

Background/Objectives: Neurofibromatosis type 1 (NF1) is an autosomal dominant disorder driven by mutations in the NF1 gene, whose pathogenesis centers on the loss of neurofibromin function and subsequent hyperactivation of the RAS/MAPK pathway. Notably, to the best of our knowledge and following a systematic literature search conducted by our research team, no cases of NF1 complicated by severe cardiac structural abnormalities that ultimately lead to cerebral infarction have been reported to date. Thus, it is of paramount importance to avoid missed diagnosis by performing comprehensive cardiac-related examinations in patients with NF1. Case Presentation: A 20-year-old male patient diagnosed with NF1 presented with right-sided limb weakness and was initially identified with cerebral infarction. To clarify the underlying etiology, a comprehensive clinical evaluation was performed, including cardiac imaging assessments (to characterize cardiac structural changes) and whole-exome sequencing (to identify the presence of procoagulant gene mutations). Comprehensive evaluation revealed a spectrum of cardiac structural abnormalities in the patient: aortic valve prolapse with severe regurgitation, non-infective vegetations on the aortic valve leaflets, mild-to-moderate mitral regurgitation, left ventricular hypertrophy and dilation, and left atrial dilation. Whole-exome sequencing detected exclusively a pathogenic variant in the NF1 gene, with no other pathogenic/likely pathogenic variants or thrombophilia-associated polymorphisms being found. Laboratory investigations ruled out infectious etiologies, supporting the notion that NF1-mediated cardiac structural and developmental anomalies are the primary driver of cardiac vegetation formation, given the absence of other identified contributing factors; embolization of one such vegetation ultimately led to both splenic and cerebral infarction. Conclusions: This case emphasizes the necessity of implementing early and proactive cardiac evaluations in patients with NF1. Additionally, for NF1 individuals—particularly those presenting with suggestive vascular or cardiac symptoms—a comprehensive multifactorial assessment of thrombotic risk is critical. Collectively, maintaining clinical vigilance for cardiac abnormalities in NF1 patients and avoiding diagnostic oversight is essential to reduce life-threatening risks. Full article

Micro-fault diagnosis of vehicle driving motor bearings can significantly bring safety and economic benefits in preventing major accidents and extending equipment lifespan. However, under variable operating conditions, effectively capturing and diagnosing fault-related weak current fluctuation or high-frequency noise features, presents substantial technical challenges. Regarding these issues, this paper proposes multi-residual neural networks (multi-ResNets) and an evidential reasoning rule (ER Rule)-based fault diagnosis model. The model employs a benchmark condition generalization mechanism, which selects multiple typical load conditions as diagnostic anchor points based on a multi-residual neural network structure. Furthermore, by integrating a sub-model credibility assessment mechanism to perform diagnostic condition assessment and category assessment based on ER rule. The experimental results indicate that compared to the traditional machine learning algorithms, the proposed multi-ResNets-ER Rule-based model achieves higher diagnostic accuracy and result reliability for micro-faults under variable operating conditions. Full article

Cervical cancer, primarily caused by high-risk Human Papilloma Virus (HPV), remains a global health concern. Prognostic biomarkers reflecting systemic inflammation and immune response—the Systemic Immune-Inflammation Index (SII) and the Systemic Inflammation Response Index (SIRI)—have recently attracted interest for their potential predictive value in cervical cancer. We conducted a retrospective observational study including 344 patients who underwent loop electrosurgical excision of cervical intraepithelial neoplasia at Semmelweis University, Budapest, Hungary, between 2021 and 2024. Demographic, cytologic, histologic, and laboratory data were collected, and SII and SIRI were calculated. Statistical analyses, including Receiver Operating Characteristic (ROC) analyses, were performed. Higher SII and SIRI values were significantly associated with higher-grade lesions and invasive carcinoma. ROC analyses indicated good discriminatory performance, with negative predictive values of 96–100%, suggesting potential utility in ruling out malignant transformation. SII and SIRI are simple, cost-effective, and minimally invasive biomarkers that correlate with lesion severity in cervical disease. Their high negative predictive value supports a potential role as complementary rule-out tools in diagnostic evaluation. Further prospective studies are needed to validate these findings and to define clinically meaningful cut-off values for routine use. Full article

Background/Objectives: Mild Cognitive Impairment (MCI) represents a critical transition stage between normal aging and dementia, with executive dysfunction playing a key prognostic role. Traditional neuropsychological tests show limited ecological validity and may fail to detect early executive deficits. Virtual Reality (VR) offers an innovative alternative by reproducing everyday situations in realistic environments. This study investigated whether the Picture Interpretation Test 360° (PIT 360°), a VR-based assessment, can (1) discriminate between MCI patients and healthy controls (HCs); (2) identify executive dysfunction within the MCI group; and (3) correlate with standard neuropsychological measures. Methods: One hundred and one participants aged ≥65 years (53 MCI, 48 HCs) underwent a comprehensive neuropsychological assessment and PIT 360° evaluation. The PIT 360° requires interpreting a complex scene in a 360-degree virtual environment. Hierarchical linear regression, Receiver operating characteristic (ROC) curve analysis, and binary logistic regression were performed to examine group differences and diagnostic accuracy. MCI patients were stratified based on their performance on the Modified Five Point Test to identify visuospatial dysexecutive deficits. Results: MCI patients showed significantly longer PIT 360° completion times than HCs (92.6 vs. 65.3 s, p = 0.006), independent of age. MCI patients with visuospatial dysexecutive deficits exhibited the most severe deficits (median = 105 s, p = 0.017 vs. HCs). ROC analysis revealed adequate discriminative ability (AUC = 0.64, 95% CI [0.53, 0.75]) with a preliminary, sample-derived cut-off at ≥22 s, yielding high sensitivity (86.5%) but low specificity (42.6%). This threshold requires validation in independent samples. PIT 360° completion time correlated significantly with visuospatial executive functions, visual memory, and verbal fluency. Conclusions: The PIT 360° effectively screens for visuospatial executive dysfunction in MCI with high sensitivity, making it suitable for ruling out clinically significant impairment. Its ecological validity, brief administration, and correlations with traditional measures support integration into routine clinical practice for early detection and rehabilitation planning. Full article

This paper proposes an integrated, stress-based framework for fatigue diagnosis of wind turbine blades that is tailored to field deployments where detailed structural design information is unavailable. The approach combines a data-driven reduced-order model (ROM) for directional damage equivalent loads (DELs) with a physics-based Soderberg index and a one-class support vector machine (SVM) anomaly detector. The framework is implemented and evaluated using measurements from a 2 MW onshore turbine equipped with blade-root strain gauges and standard SCADA monitoring. Ten-minute operating windows are formed by synchronizing SCADA records with high-frequency strain data, converting strain to stress, and computing DELs via Rainflow counting for flapwise, edgewise, and torsional blade root directions. SCADA inputs are summarized by their 10 min statistics and augmented with yaw misalignment features; these are used to train LightGBM-based ROMs that map operating conditions to directional DELs. On an independent test set, the DEL-ROM achieves coefficients of determination of approximately 0.87, 0.99, and 0.99 for flapwise, edgewise, and torsional directions, respectively, with small absolute errors relative to the measured DELs. The Soderberg index is then used to define conservative Normal/Alert/Alarm classes based on representative material parameters, while a one-class SVM is trained on DEL- and stress-based fatigue features to learn the distribution of normal operation. A simple AND-normal/OR-abnormal rule combines the Soderberg class and SVM label into a hybrid diagnostic decision. Application to the field dataset shows that the proposed framework provides interpretable fatigue-safety margins and reliably highlights operating periods with elevated flapwise fatigue usage, demonstrating its suitability as a scalable building block for digital-twin-enabled condition monitoring and life-extension assessment of wind turbine blades. Full article

Anomalous IP packet fragmentation, whether caused by evasion attacks, misconfigurations, or network policy interference, presents a measurable threat to network integrity and intrusion detection systems. This paper introduces a lightweight, rule-based framework for detecting and classifying fragmented IP traffic. Unlike complex machine learning models that operate as “black boxes,” our model leverages the deterministic semantics of RFC 791 to inspect structural packet characteristics—such as offset alignment, Time-to-Live (TTL) consistency, and payload regularity—and classifies traffic into three transparent categories: normal (NONE), misconfigured (MISCONFIG), and adversarial (ATTACK). We generate an open-source and synthetic dataset of 10,000 packets, meticulously engineered to simulate a wide spectrum of benign and malicious fragmentation scenarios. Evaluation demonstrates high accuracy (99.23% overall) on this controlled dataset. Crucially, validation on the CIC-IDS-2017 real-world dataset confirms the model’s practical utility, showing a low false-positive rate (0.8%) on normal traffic and a significant increase in detectable anomalies during attack periods. This work provides a reproducible, interpretable, and efficient tool for forensic analysis and intrusion detection, enabling the precise diagnostics of packet-level fragmentation anomalies in operational networks. Full article

Elevators are critical vertical transportation systems in modern urban infrastructure; however, their intricate mechanical and electrical configurations render them highly susceptible to safety-critical failures. Although various automated diagnostic techniques have been proposed, many data-driven approaches exhibit limited generalizability due to their insufficient consideration of physical fault mechanisms and strong dependence on facility-specific training data. To overcome these limitations, this study presents a rule-based automated diagnostic framework for elevator state recognition that prioritizes reliability, real-time performance, and interpretability. The proposed approach explicitly integrates physically meaningful fault characteristics and dominant frequency components into the diagnostic process, and employs predefined expert rules derived from established standards to classify fault states in an automated manner. The effectiveness of the proposed method is verified using real operational data collected from an in-service elevator, demonstrating improved diagnostic accuracy and computational efficiency compared to conventional manual inspection procedures. The proposed framework provides a practical and scalable solution for intelligent elevator condition monitoring and is expected to serve as a foundational technology for future smart maintenance and preventive safety systems. Full article

Background and Clinical Significance: Statins are widely prescribed for cardiovascular risk reduction and generally demonstrate a favorable safety profile. While myalgia and elevations in liver enzymes are well-recognized adverse effects, headaches are less commonly reported and often underrecognized in clinical practice. This may result in unnecessary diagnostic evaluations, increased healthcare costs, and delayed identification of the underlying cause. Case Presentation: We describe an adult patient who developed intractable headaches that emerged after many years of statin therapy. The headaches persisted despite conventional analgesic treatment and resolved completely following discontinuation of the statin. Secondary causes were excluded, and comorbid conditions were systematically ruled out. Statin-associated headache is uncommon but clinically relevant. Proposed mechanisms include nitric-oxide-mediated vasodilation, central effects of lipophilic statins, and mitochondrial involvement. In this case, the patient was taking metoprolol succinate, lisinopril, simvastatin, clopidogrel, and tamsulosin. Except for lisinopril, none of the other comedications are strongly linked to new-onset headaches. Holding it did not resolve his headache, making simvastatin the most plausible contributor. This was confirmed by resolution of headache through its discontinuation. Because such headaches may be overlooked, clinicians should consider a statin-related cause when symptoms begin after initiation and may manage this by switching to a hydrophilic statin or using alternative lipid-lowering therapy. Conclusions: Clinicians should remain vigilant about the possibility of statin-induced headache, even in long-term users. Early recognition can prevent unnecessary diagnostic investigations, expedite symptom resolution, and support optimal management of both cardiovascular risk and treatment-related adverse effects. Full article

Artificial Intelligence in Education is expanding rapidly, yet the adaptation of chatbots to specific reading-comprehension levels remains underexplored. This mixed-methods study presents Lectobot, a conversational agent designed to provide personalized scaffolding across three levels of reading comprehension (literal, inferential, and critical). First, we conducted a diagnostic assessment with first-year undergraduates (N = 58) using validated instruments: COMPLECsec (reading comprehension), EMA (Academic Motivation Scale), and MARSI (Metacognitive Strategies). Non-parametric analyses (Kolmogorov–Smirnov; Mann–Whitney U with Benjamini–Hochberg adjustment) indicated wide heterogeneity in comprehension (median global accuracy ≈ 55%) and a predominance of extrinsic motivation, with selective use of problem-solving strategies. These findings informed design rules for Lectobot (text selection, adaptive task difficulty, and strategy prompts). In a five-week implementation with a focus group (n = 8), semi-structured interviews were transcribed and coded in MAXQDA, guided by the Technology Acceptance Model (perceived usefulness and ease of use). Students perceived Lectobot as useful for text understanding and synthesis and moderately easy to use; reported difficulties were mainly technical (access and session continuity), leading to actionable design improvements. We discuss ethical and practical implications for personalized scaffolding in higher education and outline avenues for larger-scale evaluations and broader grade levels. Full article

Background/Objectives: CKD affects over 10% of adults and is often silent, delaying diagnosis. Opportunistic primary care screening supported by clinical decision support systems (CDSSs) may improve detection with minimal burden. We evaluated the feasibility, diagnostic yield, clinical actions, and reagent costs of a CDSS-enabled, albuminuria-first program using eGFR. Methods: This one-year cross-sectional intervention screened all patients receiving routine laboratory tests at a primary care center using a CDSS integrating prior labs, medical records, and guideline rules. Eligibility required patients age 60–85 (Group 1) or 18–59 with hypertension, diabetes, or cardiovascular disease (Group 2). Eligible patients received urine albumin and eGFR testing with standard phlebotomy; abnormal findings triggered confirmatory tests. Outcomes were diagnostic yield, KDIGO risk stratification, referral patterns, and reagent costs. The CDSS surfaced prompts and pre-populated orders in the laboratory interface. Results: Of 7722 targets, 1892 (24.5%) were flagged (34.2% of Group 2, 7.9% of Group 1), and 1774 (93.8%) completed screening. We identified 104 new CKD cases (5.9%): 75% KDIGO moderate risk, 19% high, and 6% very high. Twenty patients (1.1%) met criteria for nephrology referral. Guideline-directed therapy was started or optimized in 90%, and 62.5% received a new CKD diagnosis code. Reagent costs averaged EUR 0.51 per person screened and EUR 11.14 per CKD case detected. Most cases were early-stage and manageable in primary care. Conclusions: CDSS-enabled opportunistic screening in primary care is feasible, acceptable, and low-cost. It identifies previously unrecognized CKD at modest expense, enabling early interventions that may slow progression and reduce cardiovascular events. Scaling with follow-up should assess long-term outcomes. Full article

Distinguishing subcutaneous adipose tissue (SAT) from intermuscular adipose tissue (IMAT) is clinically important because IMAT infiltration is strongly associated with age-related functional decline, sarcopenia, diabetes, cardiovascular disease, and obesity. Current assessments rely on MRI or CT, which are stationary, costly, and labor-intensive. Portable ultrasound-based solutions could enable broader, proactive screening. This model study investigated the feasibility of differentially assessing SAT and IMAT using features extracted from propagating ultrasound signals. Twenty-five phantoms were constructed using gelatin as a muscle-mimicking matrix and oil as the SAT and IMAT compartments, arranged to provide gradual variations in fat fractions ranging from 0% to 50%. Ultrasound measurements were collected at 0.8 MHz and 2.2 MHz, and multiple evaluation criteria were computed, including ultrasound velocity and parameters derived from the signal intensity. Classification domains were then generated from intersecting decision rules associated with these criteria. In parallel, artificial neural networks (ANN/LSTM) were trained and tested on identical phantom subsets to evaluate data-driven classification performance. Both the rule-based and ANN/LSTM approaches achieved diagnostically meaningful separation of SAT and IMAT. The aim of this work was to perform an experimental proof-of-concept study on idealized tissue models to demonstrate that ultrasound measurements can reliably differentiate SAT and IMAT, supporting the development of future screening devices. Full article

A five-month-old, intact, female Miniature Schnauzer Terrier presented with persistent severe hemolytic anemia following an initial infection with Babesia gibsoni and B. vogeli. Despite treatment, severe regenerative anemia persisted, and the patient was unresponsive to antibiotics, as well as antiprotozoal and immunosuppressive agents. Subsequent laboratory tests and diagnostic imaging ruled out persistent hemiparasitic infections, immune-mediated diseases, or neoplasia. Genomic DNA and cDNA sequencing identified a point mutation in exon 8 (g.4978G>T) that introduced a premature termination codon, leading to exon 8 skipping and a single-nucleotide deletion at the exon 7–intron 7 boundary (c.966delG) during splicing. A 151 bp deletion in the coding region of the patient’s PKLR cDNA was subsequently detected, which ultimately resulted in pyruvate kinase deficiency. This missplicing results in a premature stop codon and disrupts PKLR tetramer formation owing to the partial loss of domain A and complete loss of domain C. Enzyme activity assays confirmed a complete loss of function in the mutant PKLR protein compared to the wild-type, supporting the causal role of this deletion in non-spherocytic hemolytic anemia. This is the first report as per our knowledge documenting truncated PKLR variant in a dog, and notably, the first such case in a Miniature Schnauzer breed. Full article