Diagnostics最新文献

Background/Objectives: Current musculoskeletal health assessment expanded beyond bone mineral density (BMD) at central DXA to include, for instance, trabecular bone score (TBS) and emergent biomarkers, such as adipokines and myokines (e.g., irisin) assays. A current gap in their application is reflected in limited research regarding adrenal tumors, especially non-functional adrenal tumors/mild autonomous cortisol secretion (NFATs/MACS). To assess this current gap, we aimed to explore beyond BMD, specifically, TBS and circulating irisin, in relation to the adrenal status in NFATs/MACS. Methods: This is a prospective, cross-sectional, single-center, exploratory study, conducted between October 2024 and December 2025. Results: A total of 81 menopausal women were included (mean age of 63.26 ± 8.82 years, 15.86 ± 9.5 years since menopause, average BMI of 30.69 ± 5.76 kg/sqcm. Out of them, 33.33% had NFATs/MCAS (group AI) and 66.67% were controls (group C), with similar age, years since menopause, and BMI. The prevalence of type 2 diabetes was 66.67% versus 68.52% (p = 0.865). TBS correlated with lumbar BMD/T-score (N = 33), while age and lumbar BMD were independent TBS predictors (N = 81), but not type 2 diabetes nor NFAs/MCAS. TBS correlated with the five-year age groups (r = -0.273, p = 0.003). Irisin correlated with osteocalcin (r = -0.252, p = 0.007), P1NP (r = -0.187, p = 0.049) and CrossLaps (r = -0.209, p = 0.026) in tumor-free controls. In the AI group, a higher irisin was associated with a higher second-day cortisol after 1 mg DST (r = 0.11, p = 0.584) and a lower ACTH (r = -0.716, p < 0.001). The rate of low TBS (based on 1.350 cutoffs) was 48.15% versus 38.89% in group AI versus C. In the AI group, patients with low TBS had lower osteocalcin, P1NP, and CrossLaps than those with normal TBS, with a similar rate of type 2 diabetes (which might reduce the bone turnover markers) and MACS-positive prevalence (between 25 and 28%). Conclusions: The median glycated hemoglobin A1c (5.78% versus 5.93%, p = 0.94) and median HOMA-IR (1.53 versus 1.42, p = 0.948) suggest a certain level of glucose control, which might not be reflected in severely damaged bone microarchitecture, as shown by TBS. Irisin may be one of the additional factors in these tumors reflecting the hormonal burden. Irisin was statistically significantly elevated with the increase in BMI groups. To our best awareness, this is the first synchronous analysis of TBS and irisin levels in this type of tumor to address the bone status in relation to the glucose profile and adrenal panel. Noting this is an exploratory, hypothesis-generating study, further research will highlight the true value of TBS and irisin for practitioners in the adrenal field, including multi-layered models of bone status prediction.

Background/Objectives: Lung cancer (LC) is the leading cause of cancer-related mortality worldwide, making early and accurate diagnosis crucial for improving patient outcomes. Although chest computed tomography (CT) enables detailed assessment of lung abnormalities, manual interpretation is time-consuming, requires expert expertise, and is prone to diagnostic variability. To address these challenges, this study proposes DE-SAMNet, a hybrid deep learning framework for automated multi-class LC classification from CT scans. Methods: The model integrates two pre-trained convolutional neural networks-DenseNet121 and EfficientNetB0-operating in parallel to extract complementary multi-scale features. A Spatial Attention Module (SAM) is applied to each feature stream to emphasize clinically important regions. Final classification is performed through a compact fusion mechanism involving global average pooling, batch normalization, and a fully connected layer. DE-SAMNet was evaluated on two datasets: a public dataset (IQ-OTH/NCCD) with benign, malignant, and normal cases, and a private clinical dataset including benign, malignant, cystic, and healthy cases. Results: On the public dataset, the model achieved a 99.00% F1-score, 98.41% recall, 99.64% precision, and 99.54% accuracy. On the private dataset, it obtained 95.96% accuracy, 95.99% precision, 96.04% F1-score, and 96.21% recall, outperforming existing approaches. To enhance reliability, explainable AI (XAI) techniques such as Grad-CAM were used to visualize the model's decision rationale. The resulting heatmaps effectively highlight lesion-specific regions, offering transparency and supporting clinical interpretability. Conclusions: This explainability strengthens trust in automated predictions and demonstrates the clinical potential of the proposed system. Overall, DE-SAMNet delivers a highly accurate and interpretable solution for early LC detection.

Background/Objectives: The cervicovaginal microbiome has emerged as a critical determinant of cervical health. In this study, we aimed to characterize the cervicovaginal microbiome across a spectrum of cervical health states and to identify community-level features that distinguish invasive disease from precursor states. Methods: We analyzed cervicovaginal samples of 86 patients with normal epithelium, low-grade (LSIL) and high-grade (HSIL) intraepithelial lesions, and cervical carcinoma (CCU) and available HPV genotyping. Vaginal samples were subjected to full-length 16S rRNA gene sequencing and genus-level taxonomic profiles were generated using ONT-supported workflows. Microbiome diversity and composition were assessed using Aitchison-based beta-diversity, non-parametric testing, and PERMANOVA. Differential abundance was evaluated using ANCOM-BC2 with false discovery rate correction. Disease-associated community shifts were quantified using log-ratio indices and co-occurrence network analysis. Results: Microbial diversity increased with disease severity, with cervical cancer showing the highest alpha diversity and distinct community composition. Normal samples were uniformly dominated by Lactobacillus, whereas LSIL and HSIL exhibited transitional communities with partial loss of lactobacillar dominance and increasing representation of anaerobic taxa. Cervical cancer was associated with depletion of Lactobacillus and expansion of anaerobic consortia. A Lactobacillus-to-anaerobe log-ratio declined monotonically with disease severity and robustly discriminated invasive cancer from precursor states. Microbial co-occurrence networks became progressively more structured with disease severity, transitioning to dense anaerobic networks in cervical cancer. Conclusions: Cervicovaginal microbiome signatures reflect cervical disease stage and may complement existing screening and risk stratification strategies.

Background: Recent studies have demonstrated the feasibility and potential of using ECG-synchronized computed tomography (CT) to assess the elastic and deformation properties of the aorta. However, to date, there is insufficient evidence to support the practical use of this approach. We aimed to study the association of CT-derived indices, characterizing ascending aorta elasticity, with the biomechanical properties of intraoperative ascending aorta (AsAo) samples, and to assess its predictive potential in non-surgical patients with ascending aorta dilatation. Methods: In total, 71 patients with AsAo dilatation (>45 mm) and 29 control patients (AsAo diameter < 40 mm) underwent ECG-synchronized CT-aortography. In 42 surgical patients, CT-derived parameters (circumferential strain, compliance, stiffness) were compared with the tensile strength and relative strain of intraoperative aortic samples. In 29 non-surgical patients (diameter 45-50 mm), the predictive potential of CT-derived elasticity indices was determined over 36 months of follow-up. Results: A moderate correlation was found between CT-derived strain/distensibility and ex vivo relative strain. CT data confirmed that dilated aortas are stiffer and less elastic than those in controls. In 29 non-surgical patients, CT elasticity parameters did not demonstrate the ability to predict adverse aneurysm progression. Conclusions: While CT can assess aortic elasticity correlated with ex vivo aortic properties, these parameters lacked prognostic value for the growth in small aneurysms.

Background: Local bone quality of the proximal humerus is a key determinant of fracture risk and implant stability in osteoporotic bone. Beyond established HU-based calibration, CT-osteoabsorptiometry (CT-OAM)-derived indices and microarchitecture-oriented workflows warrant systematic cross-modality evaluation. Methods: Twelve proximal humeral heads from six body donors (age 65-86 years; bilateral specimens) were analyzed using paired clinical CT and high-resolution micro-CT. Bone quality was quantified by (i) a HU-calibrated cancellous vBMD method (Krappinger et al.), (ii) a CT-OAM-inspired workflow reporting an ROI-averaged mean-intensity index in arbitrary units (a.u.), and (iii) a calibrated Bone Microarchitecture Analysis (BMA) workflow in Analyze 15.0. Paired tests, linear regression, and repeated-measures ANOVA after z-standardization were applied. Results: HU calibration yielded a mean trabecular vBMD of 114.37 ± 35.15 mg/cm³ on clinical CT. The BMA workflow produced higher CT-based values (207.37 ± 23.78 mg/cm³, p < 0.001) and markedly higher micro-CT values (469.34 ± 30.99 a.u.), indicating a systematic level shift between calibration frameworks. The CT-OAM index averaged 166.94 ± 40.12 a.u. on clinical CT and 455.89 ± 132.63 a.u. on micro-CT. Cross-modality agreement was very strong for CT-OAM (R² = 0.888) and moderate for BMA (R² = 0.502). After z-standardization, no significant differences were detected between the three CT-based approaches. Conclusions: A CT-OAM-inspired ROI-mean index and a BMA-based workflow provide complementary, transferable readouts of proximal humeral bone quality across clinical CT and micro-CT, with stronger cross-modality rank consistency for CT-OAM. Absolute density values differ systematically between calibration frameworks and should not be interpreted as directly interchangeable. These approaches support opportunistic, site-specific bone quality assessment from routine CT, but require prospective validation against fixation-related outcomes and robust scanner-independent standardization.

Background/Objectives: The surgical treatment of basal cell carcinoma (BCC) remains challenging due to the time-consuming, expensive and invasive nature of Mohs micrographic surgery. The objective is to develop a standardized protocol for managing diagnosis, surgery, and margin control within a single patient visit. Methods: Several protocols were tested to establish a "BCC-One-Stop-Shop", combining in vivo and ex vivo margin mapping of BCC, pre- and postoperatively using Line-field confocal optical coherence tomography (LC-OCT). We introduce an algorithm enabling real-time localization of LC-OCT acquisitions on a previously acquired dermoscopy image. Additionally, an artificial intelligence model provides a BCC probability score based on LC-OCT images. Together, the co-localization algorithm and AI BCC model generate a color-coded visualization of the tumor within the dermoscopy image, allowing precise pre-operative in vivo margin assessment. Results: We found our protocol, the implementation of the co-localization tool and the AI model, to be quick to apply, easy to learn and helpful regarding the initial determination of BCC tumor margins. Patients responded positively to the recognizable visualization of the disease. Conclusions: Pre- and postoperative margin mapping using LC-OCT imaging appears to be effective and feasible and could reduce time, costs, resources, excision sizes and patient burden by sparing additional excision steps in micrographic surgery. The integration of real-time co-localization and the AI-calculated probability score represent meaningful and practical enhancements for routine clinical use. To further evaluate the efficacy and safety of the BCC-One-Stop-Shop-Method and the newly introduced device features, larger-scale studies are warranted and are currently being conducted.

This narrative review presents an updated overview of the etiology, pathophysiology, diagnostic approaches, and management strategies for Placenta Accreta Spectrum (PAS), with emphasis on clinical implications and current gaps in evidence. PAS is associated with substantial maternal morbidity and mortality, with reported maternal mortality rates approaching 7%. Affected patients often experience prolonged hospitalization, repeated surgical interventions, and long-term psychological and emotional consequences. The development of PAS is primarily attributed to impaired decidualization in areas of uterine scarring, resulting in abnormal adherence or invasion of chorionic villi into the myometrium. Optimal outcomes in high-risk pregnancies depend on early antenatal identification using characteristic pathological and imaging findings. Current evidence supports planned cesarean hysterectomy as the safest and most definitive treatment for most patients, whereas conservative and uterus-preserving approaches should be reserved for carefully selected cases managed in specialized centers. Further progress in PAS management requires standardized diagnostic criteria, prospective evaluation of conservative strategies, and improved access to multidisciplinary expertise.

Background/Objectives: Isolated methylmalonic acidemia (iMMA) is a rare autosomal recessive metabolic disorder caused by defects in methylmalonyl-CoA mutase (MCM) activity or in the biosynthesis of its cofactor, adenosylcobalamin. Mutations in five genes-MMUT, MMAA, MMAB, MMADHC, and MCEE-are known to underlie this condition. This study aimed to characterize the clinical features and molecular spectrum of iMMA in Malaysian patients of diverse ethnic backgrounds. Material and Methods: Patients with biochemical evidence suggestive of iMMA, including elevated propionylcarnitine (C3), increased C3/C2 ratio, and raised urine methylmalonic acid levels in the absence of hyperhomocysteinemia, were selected for genetic testing. Sanger sequencing was performed to identify pathogenic variants in the MMUT, MMAA, MMAB, MMADHC, or MCEE genes. Results: The cohort consisted predominantly of Iban patients (n = 5), with the remaining cases comprising one Malay and one Thai-Malay individual. Age at diagnosis ranged from Day 1 of life to 6 years. All 7 patients were confirmed to have iMMA through molecular analysis. A total of seven pathogenic or likely pathogenic variants were identified, including two novel MMUT variants (c.246_250delinsGA and c.1358G>C), four known MMUT variants (c.560C>G, c.693C>G, c.982C>T, c.1106G>A), and one known MMAB variant (c.644+1G>A). Clinical presentation and disease severity varied across cases, reflecting underlying genotypic heterogeneity. Conclusions: This study highlights the molecular diversity and clinical variability of iMMA in Malaysia. Our findings reinforce the importance of integrating metabolic screening with molecular diagnostics to identify disease-causing variants and guide patient management strategies effectively.

Spontaneous intracerebral hemorrhage (ICH) is associated with substantial mortality and morbidity. Current management paradigms rely heavily on the rapid interpretation of neuroimaging and clinical data, yet are frequently constrained by limitations in processing speed, diagnostic accuracy, and prognostic precision. Artificial intelligence (AI), specifically machine learning (ML) and deep learning (DL), offers transformative potential to circumvent these challenges across the entire continuum of ICH care. This comprehensive review synthesizes the rapidly evolving landscape of AI applications in ICH management. Through a systematic evaluation of recent literature, we examine studies focused on the development, validation, or critical appraisal of AI-driven technologies for ICH care. Our analysis encompasses automated neuroimaging, computer-assisted surgical navigation, brain-computer interfaces (BCIs), prognostic modeling, and fundamental research into disease mechanisms. AI has demonstrated performance comparable to that of clinical experts in automating hematoma segmentation, predicting complications such as hematoma expansion, and refining surgical planning via augmented reality. Furthermore, BCIs present innovative therapeutic avenues for motor rehabilitation. However, the translation of these technological advances into routine clinical practice is impeded by substantial challenges, including data heterogeneity, model opacity ("black-box" issues), workflow integration barriers, regulatory ambiguities, and ethical concerns surrounding accountability and algorithmic bias. The integration of AI into ICH care signifies a paradigm shift from standardized treatment protocols toward dynamic, precision medicine. Realizing this vision necessitates interdisciplinary collaboration to engineer robust, generalizable, and interpretable AI systems. Key priorities include the establishment of large-scale multimodal data repositories, the advancement of explainable AI (XAI) frameworks, the execution of rigorous prospective clinical trials to validate efficacy, and the implementation of adaptive regulatory and ethical guidelines. By systematically addressing these barriers, AI can evolve from a mere analytical tool into an indispensable clinical partner, ultimately optimizing patient outcomes.

Background/Objectives: Intensive care unit-acquired weakness (ICUAW) is a frequent complication characterized by symmetrical and proximal limb muscle weakness. Its diagnosis is primarily based on clinical symptoms; however, ICUAW assessment can often be uncertain. Blood biomarkers have not yet been widely investigated for this purpose. Serum gelsolin (GSN) is synthesized by skeletal muscle cells. It plays a crucial role in binding extracellular actin filaments and pro-inflammatory cytokines. In sepsis-associated ICUAW, GSN levels might massively decrease due to their buffering activity and muscle wasting. We elucidated the predictive capacity of GSN regarding ICUAW and its additional diagnostic/prognostic potential in sepsis compared to classical parameters. Methods: We recruited septic and non-septic ICU patients for our follow-up study. Patients were retrospectively categorized into ICUAW positive (n = 26) and negative (n = 47) groups based on their clinical characteristics. Sera were collected on the 1st, 2nd and 3rd days of ICU stay. Ambulatory patients (n = 34) served as controls. GSN levels were measured by our previously developed automated immunoturbidimetric assay. Clinical and laboratory parameters were collected from our hospital information system. Results: Admission GSN levels were significantly reduced in ICU patients compared to controls (median: 11.60 vs. 75.99 mg/L). ICUAW positive patients had significantly lower admission GSN levels than ICUAW negative patients (median: 8.10 vs. 14.30 mg/L), and a similar tendency was observed during follow-up. GSN showed predictive capacity regarding ICUAW (ROC AUC: 0.711, p < 0.01), especially when combined with albumin (ROC AUC: 0.750, p < 0.01). The combination of admission GSN, albumin, and procalcitonin demonstrated significant diagnostic performance (ROC AUC: 0.803) regarding the requirement for invasive ventilation, and GSN had prognostic value for 28-day mortality as well. Conclusions: GSN might serve as an intriguing marker in the prediction of ICUAW, especially when combined with albumin. The parallel decline of GSN and albumin could reflect the combined effects of systemic inflammation and muscle wasting seen in ICUAW.