Search Results (71,707)

Background and Objectives: This study aimed to determine the diagnostic yield of familial hypercholesterolemia (FH) genotyping and to describe the clinical phenotype and pharmacogenomic (PGx) implementation readiness of adults referred for clinically suspected FH at a Lithuanian tertiary cardiology center. The study was designed as a descriptive retrospective analysis of existing clinical and genetic test results generated using heterogeneous historical sequencing platforms. Materials and Methods: This single-center descriptive cohort included 48 adults with clinically suspected FH evaluated between February 2022 and June 2023. Patients were classified according to Dutch Lipid Clinic Network criteria before genetic testing. Historical targeted next-generation sequencing (NGS) and whole-exome sequencing (WES) results were reviewed. Genetic findings were interpreted using a three-tier reporting framework: diagnostic FH-associated findings, pharmacogenomic findings relevant to future implementation, and VUS, common polymorphisms, or other non-diagnostic findings. Because harmonized PGx star-allele/diplotype calling was not available across historical platforms, PGx findings were not analysed as a mutually exclusive patient-level category. Results: Pathogenic FH-associated variants were identified in 13 of 48 patients (27.1%), including eight LDLR and five APOB variant carriers. In the remaining 35 patients (72.9%), no pathogenic or likely pathogenic FH-associated variant was identified within the analytical scope of the historical test used. This non-diagnostic category could include VUS, common polymorphisms, exploratory findings, or no reportable variant. Patients carrying pathogenic LDLR variants had higher total cholesterol and LDL-C concentrations than patients without detected pathogenic variants. PGx-related findings were considered observations relevant to reporting and future implementation; they were not evaluated as predictors of statin response, statin intolerance, or LDL-C reduction. Conclusions: In this Lithuanian real-world cohort, pathogenic LDLR or APOB variants were identified in 27.1% of patients referred for suspected FH. Retrospective analyses of heterogeneous historical genetic results can inform diagnostic reporting and implementation planning but cannot establish PGx associations with statin efficacy or intolerance without harmonized diplotype calling and prospectively collected treatment outcomes. Full article

This study aims to quantitatively evaluate bone mineral density (BMD) in women with functional hypothalamic amenorrhea (FHA), determine differences across skeletal sites, and summarise current evidence on the diagnosis and management of impaired bone health in this population. Methods: A systematic search of comparative studies evaluating BMD by dual-energy X-ray absorptiometry (DXA) in women with FHA or anorexia nervosa (AN) and healthy eumenorrhoeic controls was performed. Eleven studies met the eligibility criteria, five of which were included in the meta-analysis. The quantitative synthesis comprised 14 group comparisons, with lumbar spine and hip BMD analysed separately. Effect sizes were pooled using Hedges’ g with 95% confidence intervals (CI) under a random-effects model. Sensitivity analyses using REML estimation with Hartung–Knapp adjustment and leave-one-out analyses were performed to assess the robustness of the pooled estimates. Results: The narrative synthesis of 11 studies generally showed lower BMD in women with FHA or AN compared with healthy controls. In the meta-analysis, among women with FHA, hip BMD was significantly lower than in healthy controls (Hedges’ g = −0.60; 95% CI −1.13 to −0.06; p = 0.030; I² = 63%), while a moderate reduction was observed at the lumbar spine (Hedges’ g = −0.68; 95% CI −1.48 to 0.13; p = 0.101; I² = 83%). In women with AN, substantial reductions were observed at both the lumbar spine (Hedges’ g = −1.66; 95% CI −2.28 to −1.04; p < 0.001; I² = 71%) and the hip (Hedges’ g = −1.37; 95% CI −2.33 to −0.42; p = 0.005; I² = 89%). The lumbar spine reduction in AN remained statistically significant across sensitivity and leave-one-out analyses and was the most robust finding, whereas the statistical significance of hip BMD estimates in both FHA and AN and of lumbar spine BMD in FHA was sensitive to the analytical approach or exclusion of individual studies. The systematic review demonstrated that DXA with interpretation based on Z-scores remains the preferred method for skeletal assessment in premenopausal women with FHA. Bone health assessment is recommended after ≥6 months of amenorrhea or earlier in women with severe energy deficiency or a history of fractures. Restoration of energy availability, weight gain, and recovery of menstrual function remain the cornerstone of treatment. When amenorrhea persists and low BMD is confirmed, physiological transdermal 17β-estradiol combined with cyclic progesterone is the preferred hormonal therapy, whereas combined oral contraceptives, bisphosphonates, and denosumab are not recommended for routine management. Conclusions: Functional hypothalamic amenorrhea is associated with reduced BMD, with substantially greater skeletal deficits observed in the AN subgroup. These findings support early assessment of bone health and timely correction of low energy availability in women with FHA. The considerable between-study heterogeneity and sensitivity of some pooled estimates highlight the need for larger prospective studies using standardized diagnostic criteria and uniform skeletal outcome measures. Full article

This paper investigates waveform-level voltage difference features for the early indication of cell-to-cell voltage reversal in two series-connected lithium-ion cells for electric-vehicle battery management systems (BMSs). The key observation is that a local sign reversal of the cell-to-cell voltage difference can appear inside the constant-current (CC) discharge waveform before the cycle-mean voltage difference changes sign. A positive-fraction feature is introduced to quantify this local waveform reversal and is interpreted as a candidate BMS-oriented indication layer rather than as a universal alarm criterion. The analysis combines a Kirchhoff decomposition of the cycle-mean voltage difference with waveform matrix analysis and physical consistency checks based on principal component analysis (PCA), voltage slope behavior, equivalent-circuit-model sensitivity, and auxiliary temperature signals. In the present dataset, the first 5% crossing of the introduced positive-fraction feature occurs 44 cycles before the cycle-mean voltage reversal, and the five-cycle persistent onset precedes it by 39 cycles; this lead stays between 29 and 46 cycles across the persistence and detection threshold settings examined. The onset location is consistent with the low-slope graphite-staging region of the loaded-voltage curve, where the open-circuit voltage (OCV) slope compensation is weak and the resistance-related offset can dominate locally. The reference event used to quantify this lead is the cycle-mean voltage reversal itself, which is an observable benchmark defined on the scalar that a BMS already computes rather than a ground-truth onset of imbalance; the two cells are measurably unequal from the first cycle. The onset is shown to be insensitive to the resampling grid, the interpolation rule, the detection threshold and realistic voltage noise and channel offsets, and to produce no false onset anywhere in the 998-cycle record. The result is a waveform-level diagnostic observable quantity that converts a hidden local sign reversal into an interpretable cycle-level indication for BMS-oriented screening and monitoring. Because this study rests on a single two-cell series connection, it is presented as a proof-of-concept case study, and replication on independent cell pairs is required before the behavior can be regarded as general. Full article

Background/Objectives: Immune checkpoint inhibitor-related hepatitis (ICI-H) can resemble classical autoimmune hepatitis (AIH), but comparative multidomain data remain limited. We compared their clinical, biochemical, and serological profiles and descriptively summarized histopathological findings. Methods: This single-centre retrospective cohort screened 100 records from January 2018 to October 2023; 79 patients met predefined criteria (40 ICI-H; 39 AIH). ICI-H required an updated RUCAM score of ≥6, whereas AIH required a simplified AIH score of ≥6 and compatible histology. Continuous and categorical variables were assessed using Mann–Whitney U and Fisher’s exact tests with Benjamini–Hochberg correction where applicable; histopathological findings were summarized descriptively because biopsy ascertainment was markedly unequal between groups. Results: Compared with AIH, ICI-H occurred more often in men (65.0% vs. 20.5%) and showed higher AST, ALT, ALP, GGT, INR, CRP, and neutrophil-to-lymphocyte ratio (all q < 0.001), but lower ANA positivity (12.5% vs. 74.4%; q < 0.001). Histopathological observations were limited to the biopsy-assessed subgroup (10 ICI-H; 39 AIH) and are reported descriptively because biopsy ascertainment was markedly unequal and non-random. Conclusions: ICI-H and classical AIH showed different observed clinical, biochemical, and serological profiles; however, these differences should not be interpreted as independent diagnostic discriminators because disease-specific criteria contributed to group assignment. Diagnosis after ICI exposure should integrate exposure timing, exclusion of competing causes, structured causality assessment, serology, and selective histology. Outcome comparisons require standardized longitudinal follow-up. Full article

Background: Schizophrenia is frequently underdiagnosed or diagnosed late due to the lack of objective diagnostic markers. This study aimed to develop and validate a machine learning model using routine hematological parameters for the auxiliary diagnosis of schizophrenia. Methods: A total of 150 first-episode drug-naïve schizophrenia patients and 150 healthy controls were enrolled. Study parameters included routine hematological parameters and its derived inflammatory markers. Feature selection was performed using Elastic Net regression, followed by the construction of an L2-regularized logistic regression model. Model discriminative performance, calibration, and clinical utility were assessed through internal validation, area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis. An independent cohort of 50 schizophrenia patients and 50 patients with major depressive disorder (MDD) was used for exploratory differential diagnostic evaluation. Results: The final model incorporated 12 features: neutrophil count (NEUT), eosinophil count (EO), mean platelet volume (MPV), hematocrit (HCT), hemoglobin (HGB), neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), neutrophil-to-platelet ratio (NPR), along with age and sex. The model achieved an AUC of 0.859 (95% CI: 0.780–0.937) on the test set, with an accuracy of 0.767, sensitivity of 0.711, and specificity of 0.822. Calibration curves confirmed good calibration, and decision curve analysis further verified its clinical utility. In the exploratory differential diagnostic analysis, the model showed limited performance in distinguishing schizophrenia from MDD. Conclusions: The model based on routine hematological parameters and L2-regularized logistic regression can effectively differentiate first-episode drug-naïve schizophrenia patients from healthy controls, providing a low-cost and easily accessible auxiliary diagnostic tool for clinical practice. Full article

Background: In the 1960s and 1970s, several authors reported that manipulation of diet by excluding certain additives and natural chemical substances led to apparent clinical benefit for patients with adverse food reactions involving the skin, gastrointestinal tract, respiratory tract, and central nervous system. Methods: Over the past 48 years, patients of all ages presenting to the Royal Prince Alfred Hospital Allergy Unit with adverse food reactions were offered an elimination diet low in natural salicylates, biogenic amines, and glutamate, and free of additives such as preservatives and colors, for two to six weeks. Those who became asymptomatic were offered testing with a “N-of-1” double-blind, placebo-controlled (DBPC) challenge protocol. Results: There were 14,119 patients who elected to undertake either the strict elimination diet (10,074) (71.4%), the moderate approach (2760) (19.5%), or the simple approach (1285) (9.1%). Of these, 6645 (47.1%) became asymptomatic within two to six weeks; 628 (4.4%) reported no improvement; and 6846 (48.5%) were lost to follow-up. Of those who improved, 4963 (35.2%) returned for interpretation of their challenges, and 2429 had undertaken open food challenges; 3326 had completed the DBPC capsule challenge protocol, and 1830 completed both DBPC capsule and food challenges. Although individual reactivity was idiosyncratic, a highly reproducible pattern was evident across the entire group. The proportion reacting to each of the active chemical challenges versus placebo was highly significant (p < 0.001). Conclusions: In many patients with food intolerance, idiosyncratic dietary triggers can be reliably identified with a standardized elimination diet and DBPC challenge protocol. Long-term symptom control can be achieved by appropriate dietary modification. Full article

Electroencephalography (EEG) is widely used to study cognitive states and clinical biomarkers, yet the contribution of stable between-subject differences to common EEG feature representations is rarely quantified directly or incorporated into evaluation design. Here, we examine subject-linked structure in spectral EEG features using a longitudinal four-session dataset spanning 199 days and replicate key findings across four public datasets covering 39 to 395 participants, multiple paradigms, and recording systems ranging from low-channel consumer devices to research-grade EEG. In spectral band-power representations, subject identity accounted for substantially more variance than session-related drift and supported extremely strong individual discriminability under subject-wise held-out protocols, with high verification performance and robust cross-session recognition over months. However, this identity structure was not fully invariant across paradigms, showing stronger transfer in richer, higher-channel recordings than in low-channel consumer EEG under larger task shifts. We further show that the same subject-linked structure can inflate downstream clinical classification when evaluation is performed with naive segment-level splits, demonstrating that apparent diagnostic performance can partly reflect identity leakage rather than biomarker learning. These findings highlight subject identity as a measurable and persistent source of structured variance in spectral EEG features and support routine use of subject-wise evaluation and explicit confound diagnostics in EEG machine-learning studies. Full article

Background/Objectives: Sodium-glucose cotransporter-2 inhibitors (SGLT2i) are foundational therapy for heart failure with reduced ejection fraction (HFrEF), irrespective of diabetes. We evaluated recorded SGLT2i adoption and diabetes-related differences among adults with an electronic health record (EHR)-defined HFrEF phenotype at a Jordanian public hospital. Methods: This retrospective Hakeem cohort included 1750 adults. The frozen HFrEF phenotype was internally validated in an independent non-overlapping sample, and SGLT2i ascertainment was verified by complete-cohort medication review. Comparable annual analyses covered 2022–2025, when medication-source completeness exceeded 90%. Secondary analyses examined first recorded use after a 180-day lookback, concurrent four-class guideline-directed medical therapy (GDMT) in 2025, measured-eGFR threshold cohorts, and prespecified Firth regression among patients with type 2 diabetes (T2DM). Additional sensitivity analyses included a stable four-year cohort, a time-to-first-record analysis with explicit censoring, expanded model diagnostics with a time-to-event sensitivity analysis, and a strict directly documented-coverage GDMT analysis. Results: Sixty-four patients had verified recorded SGLT2i use (3.66%; 95% CI 2.83–4.65). Use was 11.41% (64/561) with T2DM and 0% (0/1189; exact 95% CI 0–0.31) without diabetes. Annual prevalence increased from 0.15% in 2022 to 5.13% in 2025 (absolute increase 4.98 percentage points), while remaining 0% without diabetes. In the stable four-year cohort sensitivity analysis, prevalence similarly increased from 0.21% to 5.34%. In the verified 12-month fixed-horizon cohort, 27/1268 patients (2.13%) had a first recorded use within one year; the 322-day median was conditional on the 50 patients who eventually had a first post-index record and was not interpreted as a cohort-wide waiting time. Only 24/936 patients (2.56%) had all four foundational classes concurrently active in the primary 2025 analysis. The inverse nondialysis-CKD estimate was treated as exploratory because only four treated patients had nondialysis CKD. Conclusions: Recorded adoption improved but remained low and strongly concentrated among patients with T2DM. Multicenter linkage of prescribing, eligibility, dispensing, persistence, and reimbursement data is needed to identify implementation mechanisms. Full article

This paper presents an investigation of the viability of Fiber Bragg Grating (FBG) accelerometers and load sensors for two diagnostic tasks in robotics: vibration analysis of industrial manipulators and slip detection in robotic grippers. The study synthesizes experimental results from two independent experiments, focusing on the use of optical sensors in place of traditional electromechanical devices such as MEMS accelerometers and resistive strain gauges. The experiments were performed on an ABB industrial robot with a parallel robotic gripper equipped with embedded optical fibers. Under the conditions tested, the optical chains provided good sensitivity and detected dynamic events such as slip onset and contact or movement-induced vibrations. Compared with the conventional chains recorded alongside them, the fiber-optic accelerometers delivered cleaner vibration spectra (SNR 36.7 dB against 13.7 dB), while the FBG load sensors measured gripping forces with a lower noise floor and less mains-band interference, although the force lost before a slip alarm favored the FBG chain only in the experiment carried out with brass. Obtained with a single specimen of each sensor type and a limited set of grasped objects, these findings indicate that optical sensing is a viable alternative for robotic diagnostics, but it comes at the cost of higher price and complexity. Full article

Three-dimensional (3D) and four-dimensional (4D) printing expand the design space for nano-enabled soft robots intended for cancer diagnosis and therapy. This structured narrative review separates three evidence classes: core systems satisfying all scope criteria, adjacent technologies satisfying only part of the definition, and enabling technologies not yet integrated as oncology soft robots. The revised evidence set contains 12 core reports, 7 adjacent comparators and 68 enabling methods, standards or background reports. Study-level analysis using independent manufacturing, biological, workflow and safety (M/B/W/S) axes shows that the core literature is dominated by single-platform, in vitro therapeutic demonstrations with absent or short-term safety evidence (S0-S1). No core study established cancer-specific diagnostic sensitivity or specificity, no human validation was identified, and only two systems were classified as genuine or candidate 4D platforms under a strict programmed-transformation definition. Hydrogel, magnetic, photothermal, conductive, imaging-active and melanin-based materials are evaluated together with printability, dose, localization, sterilization, retrieval, degradation and nanoparticle fate. Translation requires reproducible manufacture, quantitative application endpoints, exposure-matched safety testing and comparison with established oncology workflows. The principal gap is not the availability of additional functions, but the lack of integrated evidence connecting controlled manufacture to clinically meaningful diagnosis or therapy. These findings apply only to the studies identified within the predefined search and eligibility framework and do not establish the absence of relevant evidence elsewhere. Full article

Assessment of generative artificial intelligence (GenAI) literacy in higher education draws on both GenAI-specific instruments and broader artificial intelligence (AI) literacy measures, but these evidence sources are not interchangeable. This systematic review examined the GenAI-literacy assessment landscape while distinguishing direct GenAI-specific evidence from general AI-literacy evidence. Following PRISMA 2020 and a registered INPLASY protocol, four electronic information sources were searched for peer-reviewed English-language studies published from 2022 through 26 July 2026. Thirty-one studies met the eligibility criteria: 23 (74.2%) assessed general AI literacy, whereas eight (25.8%) used GenAI-specific instruments. Self-report Likert-type scales predominated (n = 28; 90.3%); performance-based and hybrid approaches were uncommon. Psychometric evidence centered on internal consistency and factor structure, while temporal stability, measurement invariance, external validity, and item-level functioning were rarely examined. Potential implications were mapped to digital competencies, quality education, and responsible AI use, but inclusive learning appeared in only 11 studies (35.5%). Conclusions about GenAI-specific assessment are therefore limited to eight studies. The evidence indicates a need for GenAI-specific hybrid instruments, broader validation, and accessible performance tasks. Such assessment can inform education but constitutes a diagnostic contribution to SDG 4 rather than evidence of its achievement. Full article

Acute gastrointestinal bleeding is a serious emergency condition which, without prompt and accurate diagnosis, can lead to severe hemorrhagic shock and death. The initial diagnosis relies on a clinical assessment of the bleeding site. If symptoms such as hematemesis and melena are present, an esophagogastroduodenoscopy (EGD) is typically the first diagnostic procedure. In cases of massive rectal bleeding, a computed tomography scan with angiography (CTA) is often used to identify the source and cause of the bleeding. Multiphasic CTA is the definitive first-line diagnostic standard, enabling prompt diagnosis and crucial pre-procedural planning for endovascular management in massive cystojejunal bleeding. Full article

Background/Objectives: Multimodal artificial intelligence (AI) is increasingly used in radiology to combine medical images with radiology reports, clinical narratives, structured health records, laboratory measurements, and other patient data. These systems may support more context-aware interpretation, reporting, and clinical decision-making than unimodal approaches. This narrative review examines recent technical and clinical advances in multimodal radiology AI and identifies barriers to responsible implementation. Methods: Relevant biomedical and technical literature was identified through targeted searches of PubMed, IEEE Xplore, ACM Digital Library, Web of Science, Scopus, arXiv, ScienceDirect, SpringerLink, and Google Scholar. A documented screening process identified 111 reviewed publications from 1154 records. Study selection and data extraction were conducted by one reviewer without independent verification. Original research, reviews, and commentaries, including selected preprints, were synthesized thematically. Results: The field has progressed from early and late feature fusion toward cross-modal attention, contrastive image–text pretraining, vision–language models, multimodal large language models, and general-purpose foundation models. Applications include diagnostic classification, prognostic modelling, image–text retrieval, visual question answering, clinical decision support, and automated radiology report generation. Despite promising technical results, comparison across studies remains difficult because of heterogeneous datasets, tasks, metrics, and validation designs. Clinical translation is further limited by scarce external and prospective validation, uncertain interpretability, hallucination and omission risks, privacy and fairness concerns, and limited real-world workflow evaluation. Conclusions: Multimodal AI may enable more clinically informed radiological interpretation and reporting, but progress in benchmark performance has outpaced evidence of safety, generalizability, and clinical utility. Future research should prioritize multicentre evaluation, clinically meaningful metrics, transparent reporting, robust safety assessment, and workflow-centred implementation. Full article

Emerging and novel ovine viruses pose increasing threats to animal health, livestock productivity, trade, food security, and public health preparedness. Their emergence is driven by complex interactions among animal movement, mixed-species production systems, wildlife–livestock interfaces, arthropod vectors, environmental changes, and viral evolution. This review critically compares current strategies for identifying major, emerging, re-emerging, zoonotic, and newly recognized ovine viruses, with emphasis on their analytical sensitivity, turnaround time, throughput, operational cost, accessibility, field applicability, validation status, and capacity for novel-virus detection. Conventional diagnostic approaches, including virus isolation, serology, antigen detection, histopathology, and immunohistochemistry, remain essential for confirmation and flock-level surveillance but may be limited by slow turnaround, dependence on specialized facilities, reduced sensitivity at low viral loads, and an inability to identify highly divergent or unknown viruses. Targeted molecular assays, including PCR, RT-PCR, qPCR, multiplex assays, digital PCR, isothermal amplification, and CRISPR-based diagnostics, have improved detection speed and sensitivity but generally require prior knowledge of viral genomic targets. Genomic and metagenomic approaches, including whole-genome sequencing, next-generation sequencing, nanopore sequencing, viral metagenomics, bioinformatics, and phylogenetic analysis, provide broader detection capabilities by enabling characterization of viral diversity, outbreak tracing, co-infection identification, and discovery of previously unrecognized viruses. However, their interpretation remains challenging due to low viral abundance, poor sample quality, host nucleic acid background, contamination, incomplete reference databases, limited computational capacity, and the inability of sequence detection alone to confirm disease causality. Therefore, future ovine virus surveillance requires integration of molecular diagnostics with active, passive, outbreak-based, risk-based, vector, wildlife, and animal-movement surveillance within a One Health framework. Linking genomic information with ecological, epidemiological, and environmental data will be essential for transforming ovine virus surveillance from reactive diagnosis toward proactive preparedness. Advances in standardized sampling, validated field diagnostics, affordable sequencing, curated databases, bioinformatics capacity, and cross-sector data sharing will strengthen early recognition, risk assessment, and preparedness against emerging viral threats in sheep. Full article

Parkinson’s disease (PD) causes motor control deficiencies, resulting in gait irregularities such as shorter strides, slower walking speed, and irregular step timing. This study presents a video-based deep learning approach for PD classification that extracts skeletal keypoints from Timed Up and Go (TUG) test videos using AlphaPose and the COCO-17 representation. A total of 24 features were generated, comprising 23 conventional gait features and one Freezing of Gait (FoG) feature derived from frequency-domain analysis of ankle velocity signals. This FoG feature was not validated against clinician-confirmed FoG episodes and should be interpreted as a frequency-domain proxy rather than a diagnostic measure. Three feature selection procedures and four LSTM-based architectures were evaluated across full, walking, and turning segments. Experimental results on a self-collected TUG dataset showed that the Standalone FI configuration achieved the numerically highest test accuracy of 77.78% on the turning segment among the evaluated LSTM configurations, while conventional features achieved 66.67% on both the full and walking segments. These test-set metrics provide descriptive estimates derived from a static subject-level test division involving six held-out participants (excluded from training and validation) and should not be viewed as statistically dependable indicators of clinical performance at the population level; in addition, gait-cycle boundaries were not independently validated and fallback usage was not quantified. Turning segments demonstrated higher discriminative power than straight-walking segments. Zero-shot cross-dataset evaluation on Turn-REMAP and PD-Walk revealed a substantial generalization gap, with accuracy falling to 55.12% and 49.53%, respectively, indicating that the present model is not yet suitable for cross-site clinical deployment without adaptation or calibration. The proposed framework provides systematic insights into the comparative role of conventional and FoG-derived gait parameters for non-invasive video-based PD screening. Full article