Search Results (15,642)

Background: Lactiplantibacillus plantarum is a widely studied probiotic species; however, probiotic characteristics and safety profiles are strain-specific, requiring independent evaluation. This study characterized Lactiplantibacillus plantarum ATA-LPC98052 as a candidate probiotic raw material. Methods: ATA-LPC98052 was evaluated for hemolysis, acid/bile tolerance, Caco-2 adhesion, cytotoxicity, and storage stability. Subacute oral safety was assessed in Wistar rats by gavage for 28 days at 1.2 × 10¹¹ CFU/kg/day; the study design incorporated selected principles of OECD Test Guideline 407. Clinical, hematological, biochemical, organ-weight, and macroscopic endpoints were evaluated. Fecal microbiota was analyzed by 16S rRNA sequencing; WGS was used for taxonomic confirmation and genomic safety screening. Results: ATA-LPC98052 was γ-hemolytic and maintained 60% viability at pH 1.5 and 96% at pH 5.0, while viability ranged from 74% to 82% across 0.1–0.5% bile salt concentrations. Caco-2 cell viability was 99%, adhesion exceeded 90%, and the lyophilized preparation remained stable for 15 months, maintaining 9.6 log₁₀ CFU/g. Repeated oral administration caused no mortality or consistent treatment-related toxicological pattern. Longitudinal microbiota analysis showed no significant treatment × time effects on alpha diversity or Bray–Curtis community structure, and no genus-level MaAsLin2 association was FDR-significant. WGS confirmed L. plantarum identity and no contamination; ResFinder detected no acquired antimicrobial resistance determinants meeting specified thresholds, whereas CARD/RGI identified low-identity qacJ and vanY homologs requiring cautious interpretation. Conclusion: ATA-LPC98052 demonstrated favorable in vitro probiotic characteristics, technological stability, gut microbiota-modulating potential, and a favorable subacute safety profile under the tested conditions; however, microbiota findings were exploratory, and phenotypic MIC testing remains warranted. Full article

Background: NT-proBNP is a mechanistically grounded biomarker of ventricular wall stress with established prognostic value in cardiac surgery, but its perioperative role in lung cancer resection remains incompletely characterised. Pulmonary resection imposes unique haemodynamic stressors—including one-lung ventilation-induced right ventricular afterload increase, permanent pulmonary vascular bed reduction, and ischaemia–reperfusion injury—that create a distinct biological context for natriuretic peptide elevation not represented in general surgical cohorts. Methods: A narrative review of the literature was conducted through systematic searches of PubMed, EMBASE/MEDLINE, and the Cochrane Library (January 2000–June 2026), supplemented by manual reference screening. Approximately 135 articles were included in the final synthesis. Results: Perioperative NT-proBNP elevation predicts postoperative atrial fibrillation, major adverse cardiovascular events, and long-term survival after lung resection, with effect sizes substantially exceeding those reported in general surgical populations. The PRESAGE trial established that NT-proBNP-guided prophylaxis reduces postoperative atrial fibrillation from 40% to 6% in high-risk patients. No thoracic surgery-specific NT-proBNP threshold has been prospectively validated in a multicentre setting, and approximately half the evidence base derives from BNP rather than NT-proBNP assays, precluding direct threshold synthesis. Surgical approach—particularly robot-assisted thoracoscopy—modulates postoperative biomarker elevation through OLV duration rather than inflammatory burden alone. The combination of NT-proBNP with high-sensitivity troponin identifies a dual-elevation subgroup with a MACE rate of 18.4%. Conclusions: NT-proBNP measurement is clinically actionable in thoracic surgery but requires assay-standardised, multicentre validation of population-specific thresholds stratified by resection extent and surgical approach. A dual biomarker strategy combining NT-proBNP with high-sensitivity troponin represents the most evidence-based perioperative risk stratification framework currently available. Full article

Background: Eustachian tube dysfunction (ETD) is associated with several common otologic conditions but lacks standardized diagnostic thresholds in Taiwan. This study aimed to evaluate the diagnostic performance of the GSI TympStar Pro pressure-swallow test and to identify a ROC-derived maximal peak pressure difference (MPD) cutoff for distinguishing clinically diagnosed obstructive Eustachian tube dysfunction (oETD) from healthy controls. Methods: A total of 152 subjects were enrolled, including 100 healthy controls and 52 patients with clinically diagnosed oETD, confirmed by otolaryngologist assessment and supported by ETDQ-7 symptom scoring. Tympanometry and tympanometry-based Eustachian tube function testing using the GSI TympStar Pro ETF–Intact pressure-swallow module, a modified three-tympanogram pressure-swallow protocol, were performed. Group comparisons were conducted using Mann–Whitney U and Chi-square tests. Diagnostic performance was assessed using receiver operating characteristic (ROC) curve analysis. Results: The median MPD was 11 daPa (IQR 6–17) in the control group, compared to 0 daPa (IQR 0–2) in the ETD group (p < 0.001). ROC analysis demonstrated that a cutoff value of ≤4 daPa yielded a sensitivity of 100.0% and specificity of 91.0% in the per-person analysis, and a sensitivity of 97.5% and specificity of 95.5% in the per-ear analysis. In the Type A-only per-ear analysis, MPD retained discriminatory performance, with an AUC of 0.978 (95% CI, 0.961–0.994). Conclusions: When interpreted in conjunction with ETDQ-7 symptom assessment and routine clinical evaluation, pressure-swallow testing may provide complementary objective information for the functional assessment of patients with suspected obstructive Eustachian tube dysfunction, particularly when resting tympanometry is unremarkable. In this single-center Taiwanese cohort, lower MPD values may serve as an adjunctive indicator of impaired Eustachian tube pressure equalization, and the ROC-derived cutoff of ≤4 daPa showed discriminatory value for differentiating symptomatic obstructive ETD from healthy controls. This cutoff should be interpreted as an exploratory, protocol-specific threshold and requires prospective validation in larger multicenter populations before broader clinical application. Full article

QR codes are widely used in digital authentication, mobile payment, access control, and information exchange, making QR code-based covert communication and hidden message delivery relevant to cybersecurity applications. This study proposes a QR code-based information-hiding method via module content modification based on corner-pixel grayscale adjustment. Secret bits are embedded into a QR code image through subtle grayscale adjustments of module corner pixels. To reduce visually noticeable embedding artifacts, embedding in adjacent module corners that share identical original grayscale values is prohibited, thereby preventing noticeable local contrast that could reveal hidden content. Gradient-based smoothing is further applied to suppress contrast artifacts within module interiors after embedding. A theoretical analysis of bilinear interpolation and quarter subregion averages is presented to justify the proposed extraction thresholds. Experiments across multiple QR code versions, error-correction levels, and embedding parameters show that the proposed method achieves an average embedding rate of approximately 1.9 bits per module while generally maintaining QR code readability across the tested commercial scanning devices. Exploratory random-noise experiments further show that hidden-message recovery remains possible up to the observed highest tolerable noise rates in the tested realizations. A comparison with four representative methods indicates that the proposed method achieves substantially higher embedding rates under the adopted comparison framework, demonstrating its potential for high-capacity QR code information-hiding while enabling successful hidden-message recovery under the evaluated conditions. Full article

Underground mine object detection is challenged by low illumination, blur, dust scattering, and repetitive tunnel clutter, which jointly corrupt backbone features, entangle DETR queries, and weaken localization for small objects. Existing enhancement-based and detector-internal methods do not explicitly propagate degradation reliability across features, decoder queries, and box refinement. We propose Spectral-DETR, a detector-internal reliability framework built on RF-DETR. Its central design is a cross-stage reliability pathway that connects Degradation-Aware Frequency Decomposition (DAFD), Degradation-Adaptive Query Contrastive Denoising (DQCD), and Salience-Calibrated Uncertainty with Learned Uncertainty Estimation (SCU+LUE). On Mine-Objects (14 classes, 3081 images), Spectral-DETR achieves an average precision of 0.917 at an intersection-over-union threshold of 0.5 and 0.493 when averaged over thresholds from 0.5 to 0.95, exceeding YOLOv9m by 1.6 and 0.8 percentage points, respectively, under the dataset-specific evaluation protocol. In controlled RF-DETR validation, the three reliability stages improve these two measures from 0.883 to 0.913 and from 0.472 to 0.486, respectively. Spectral-DETR obtains corresponding values of 0.848 and 0.571 on ExDark and 0.973 and 0.495 on ScienceDB. DQCD and SCU remain training-only losses with no inference cost. Full article

Coffee is deeply embedded in Saudi culture and is among the most consumed beverages worldwide. However, contamination by toxic heavy metals may represent an underrecognized public health concern, particularly in regions already burdened by diabetes mellitus and cardiovascular disease. This study evaluated concentrations of lead (Pb), cadmium (Cd), arsenic (As), chromium (Cr), and nickel (Ni) in 21 commercial coffee samples representing seven product categories marketed in Saudi Arabia. Samples were prepared using EPA Method 3050B acid digestion and analyzed by inductively coupled plasma mass spectrometry (ICP-MS). Health risks were assessed through estimated daily intake (EDI), hazard quotient (HQ), hazard index (HI), and incremental lifetime cancer risk (ILCR) using USEPA exposure parameters and Saudi-specific coffee consumption data. All metals were detected in every sample. Mean concentrations (mg/kg dry weight) were 1.638 for Pb, 0.560 for Cd, 0.622 for Cr, 0.536 for Ni, and 0.034 for As. Cadmium exceeded the European Union maximum limit for roasted coffee in all samples, while elevated Pb levels were observed in dark roast and Yemeni coffees. Although the overall non-carcinogenic risk remained below the accepted threshold (HI = 0.438), the combined ILCR for As, Cd, and Pb was 5.84 × 10⁻⁵. Inclusion of a conservative screening-level chromium contribution, calculated by assuming that all measured total Cr was present as Cr(VI), increased the upper-bound total ILCR to 9.38 × 10⁻⁵. This value remained within, but approached the upper boundary of, the risk-management range applied in this study. These findings highlight the need for strengthened monitoring and regulatory control of coffee products in Saudi Arabia. Full article

Dockless bike-sharing systems are increasingly important for sustainable urban mobility, yet they frequently suffer from spatial misallocation between supply and demand. Conventional demand forecasting relies primarily on historical trip records and therefore systematically underestimates true demand when users abandon bike-finding attempts. To address this limitation, we propose a user-behaviour-based dynamic clustering optimisation algorithm that integrates observed riding behaviour with latent unmet demand through the concept of Golden Distance—a district-adaptive service-radius threshold estimated heuristically from operational logs of successful rides and inferred unmet-demand events, serving as a behaviourally motivated proxy for aggregate walking tolerance. Building on a prior AIoT-enabled demand-prediction framework, the method first applies HDBSCAN density-based clustering to discover intrinsic demand topology, then selectively refines only those clusters that violate Golden Distance coverage constraints via an elongation-aware adaptive k-medoids formulation. District-level true demand is predicted using an XGBoost regression model and subsequently downscaled to the cluster level based on historical activity shares. Experiments on one full year of operational data from three Hong Kong districts (Tseung Kwan O, Sha Tin, and Tuen Mun) show that the proposed pipeline produces Golden-Distance-compliant service zones at a substantially finer operational resolution than the DBSCAN baseline: 86.5% to 91.6% of clustered demand points lie within the Golden Distance district of their assigned medoid, at cluster coverages of 68.0% to 78.5%, against 60.0% to 71.1% at coverages of 49.7% to 88.1% for the baseline. A resolution-fair evaluation shows that the cluster-level RMSE advantage reported previously largely reflects the finer reporting unit rather than better prediction: the same fixed district-level forecast spans a five- to seven-fold range of RMSE when scored on progressively coarser spatial units, and at a matched clustering resolution the DBSCAN baseline equals or exceeds the proposed pipeline on cluster-level metrics. Accuracy is therefore evaluated with scale-free metrics, on which the proposed method is not the more accurate of the two, and the contribution of this work is positioned on operationally deployable, behaviourally constrained spatial zoning rather than on per-cluster forecasting accuracy. Full article

With extremely low odor thresholds and potent flavor activity, sulfur-containing aroma compounds (SACs) constitute the molecular cornerstone of characteristic flavors in meat, coffee, and fermented foods. Research has advanced from early component identification to the elucidation of structure–activity relationships, olfactory receptor recognition mechanisms, and food-flavor improvement. This review first summarizes the detection and quantification methods for SACs, their distribution in foods, key odor contributions, and major formation pathways. It then highlights progress in understanding molecular structural parameters, olfactory receptor recognition, and computational simulations that decode flavor perception mechanisms. From a translational perspective, we further discuss flavor retention in real food matrices, off-flavor regulation, cross-modal perceptual enhancement, and functional applications. Current challenges include food matrix complexity, high compound reactivity, and nonlinear olfactory combinatorial coding. Future directions involve constructing a multiscale predictive framework integrating neuroscience, developing explainable artificial intelligence to decode olfactory coding, and advancing closed-loop green biomanufacturing for the precise design and sustainable production of SACs. Full article

Large-scale spiking neural network simulation requires numerical integration that preserves membrane dynamics and spike timing without making fine-resolution updates prohibitively expensive. This balance is difficult for conductance-based leaky integrate-and-fire (LIF) networks because synaptic decay, threshold crossings, resets, and refractory periods form a hybrid dynamical system. To address this difficulty, we introduce a spike-aware propagation (SAP) approximation method that combines exact receptor-trace updates, analytic homogeneous membrane propagation, Gauss–Legendre quadrature, and spike localization, improving the accuracy–efficiency Pareto frontier. We establish an error bound and conditional convergence under consistent refinement for the proposed SAP. At h = 1 ms, the single-realization T = 1000 ms comparison showed a lower voltage RMSE for SAP than for Euler at the same width in the two high-activity regimes. The five-seed T = 200 ms robustness experiment likewise showed lower voltage RMSE for SAP than for NEST at the same width. At the highest drive, the paired mean reduction was 3.91 mV (95% CI, 3.85–3.97 mV). This quantified gain supports SAP as a practical route to an improved accuracy–efficiency balance in large-scale conductance-based LIF simulation while underscoring the method’s configuration-dependent and regime-dependent scope. Full article

Background: Early identification of patients at risk of severe acute pancreatitis (AP) remains a clinical priority. We evaluated whether the admission Systemic Immune-Inflammation Index (SII, ×10⁹/L) adds prognostic value to the BISAP score, alone and in combination. SII integrates neutrophil, lymphocyte, and platelet counts from the routine complete blood count. Methods: In this retrospective single-center cohort, 523 adults with AP (January 2020–December 2025) were classified by the Revised Atlanta Classification. Admission SII and BISAP were related to severe AP, intensive care unit (ICU) admission, and in-hospital mortality using logistic regression, ROC analysis with DeLong comparison, 1000-sample bootstrap validation, and decision curve analysis. Results: Severe AP occurred in 48 patients (9.2%), ICU admission in 61 (11.7%), and death in 26 (5.0%). Admission SII was markedly higher in severe than non-severe disease (4085 vs. 1499; p < 0.001). Both SII and BISAP independently predicted all three outcomes after mutual adjustment (severe AP: SII odds ratio 2.12 and BISAP odds ratio 4.92 per standard deviation). For severe AP, the combined BISAP + SII model outperformed either marker (AUC 0.954 vs. 0.911 and 0.917), as it did for mortality (0.957); for ICU admission SII was the strongest single marker (0.869) and the combined model did not significantly improve discrimination compared with SII alone (DeLong p = 0.287). Internal validation showed adequate calibration with modest optimism and suggested potential net clinical benefit across the evaluated threshold ranges, pending external validation. Conclusions: Combining the admission SII with the BISAP score provides incremental prognostic information beyond BISAP alone for the early risk stratification of severe acute pancreatitis and in-hospital mortality using data already available at presentation. External, prospective validation is warranted before routine adoption. Full article

Background: Whether routinely available laboratory measures are associated with longitudinal bone mineral density (BMD) change in clinical practice remains uncertain, particularly when treatment exposure and other major skeletal determinants are incompletely recorded. This study examined BMD trajectories and incident osteoporosis in a Saudi dual-energy X-ray absorptiometry (DXA) cohort. Anti-osteoporosis therapy, glucocorticoid exposure and menopausal status were unavailable, limiting causal interpretation. Methods: This retrospective longitudinal cohort included DXA examinations performed at King Saud University Medical City (KSUMC), Riyadh, from 2016 to 2021. The trajectory analysis comprised 2147 patients with at least two scans and a minimum one-year interval. Diagnostic category was based on the lowest T-score across the lumbar spine, bilateral femoral necks and distal radius. Twenty linear mixed-effects (LME) models evaluated time-by-biomarker interactions with Benjamini–Hochberg correction. Cox proportional hazards (PH) regression was the primary analysis of incident osteoporosis among 789 patients without osteoporosis at baseline. Results: During 2045 person-years of observation, 107 patients developed osteoporosis (5.2 events per 100 person-years). No event occurred among 124 patients with normal baseline BMD, compared with 107 events among 665 patients with baseline osteopenia (log-rank p < 0.001). In the complete-case Cox model (n = 384; 53 events; C-index = 0.679), baseline osteopenia had a hazard ratio (HR) of 3.12 (95% Confidence interval (CI) 0.89–10.97; p = 0.076); no modelled covariate reached statistical significance. None of the 20 time-by-biomarker interactions remained significant after multiplicity correction (smallest q = 0.214). Four nominal terms with raw p < 0.10 were below the measurement-based clinical threshold and were compatible with chance variation. Conclusions: No routinely measured biomarker demonstrated clinical utility for identifying BMD trajectory in this cohort. Baseline DXA category separated the observed progression pattern, although this partly reflects the distance from the diagnostic threshold and should not be interpreted as a validated prediction model. The principal contribution is a carefully characterised null result. Prospective studies with explicit treatment tracking and repeated bone-turnover measurements are required for confirmation. Full article

Background/Objectives: Deep learning models for cephalometric landmark detection report near-ceiling accuracy on single benchmarks, yet most are trained and tested on the same dataset. Whether the best in-domain architecture remains best out-of-domain has not been systematically quantified. Methods: Four architecture families (heatmap CNN, two-stage cascade, coordinate-regression Vision Transformer, pretrained ResNet-50) were each trained on two independently sourced datasets—ISBI 2015 (400 images, one device) and Aariz (1000 images, seven devices)—and evaluated on both, over their 19 shared landmarks in millimeters. All axes used three seeds (mean ± SD): the cross-dataset matrix, leave-one-device-out shift, balanced joint training, a pretrained-versus-scratch ablation, and a landmark-level breakdown. Results: In-domain mean radial error (MRE) was 2.47–3.04 mm (Aariz) and 4.12–5.55 mm (ISBI); cross-dataset error rose steeply (Generalization Drop—the relative increase in error out-of-domain—155–426%). The most accurate model in-domain (a from-scratch heatmap CNN, 2.47 mm) showed the largest drop, and every pretrained estimate fell below every from-scratch estimate (family means 248% vs. 380%): in-domain ranking did not predict cross-domain ranking. Leave-one-device-out revealed reproducible device-specific shift (held-out MRE 1.89–7.94 mm). Inter-observer variability was 0.53 mm, so cross-domain errors were 15–44× the human band. Balanced joint training reduced the cross-domain gap for all four architectures (both domains ≈ 2.1–3.5 mm) without harming in-domain accuracy. Pretraining more than halved cross-domain error from Aariz to ISBI (7.73 vs. 16.36 mm) but not in reverse, supporting the mechanism in one direction rather than uniformly. A-point, B-point, Nasion, and Menton all exceeded the 2 mm clinical threshold out-of-domain, including points localized to sub-millimeter accuracy in-domain. Conclusions: Single-dataset accuracy substantially overstates clinical generalizability, and the best in-domain architecture is not the most transferable, so in-domain leaderboards are an unreliable basis for selecting a model for clinical deployment; balanced multi-source training recovers most of the loss across the architectures tested. Full article

Background: Cancer pain remains highly prevalent and undertreated despite established guidelines. In Italy, Law 38/2010 mandates systematic pain assessment, yet only 26% of clinicians routinely evaluate pain at each clinical visit, and fewer than one-quarter have received formal training in pain medicine or palliative care. A national multidisciplinary roundtable, convened in Rome in March 2025, formally identified four systemic gaps—insufficient education, fragmented care pathways, unclear professional roles, and challenges in implementing shared diagnostic and therapeutic pathways—and planned the development of a structured Delphi consensus. Methods: A Delphi consensus process was undertaken in accordance with CREDES guidelines. The Steering Committee, comprising representatives of six Italian scientific societies (AIRO, AIOM, AISD, Federdolore-SICD, SICP, ACD-SIAARTI) and a patient advocacy group (Fondazione Nora e Alberto Gentili), developed 15 clinical statements addressing pain assessment, management, referral criteria, monitoring, and documentation over five online meetings held between March and September 2025. Sixty-six Italian clinicians from various specialties were invited to participate; the survey was open from 1 October to 31 December 2025, with reminders every 10 days. Consensus was defined as ≥75% agreement (scoring 4 or 5 on a 5-point Likert scale). Results: Fifty-six clinicians completed the survey (response rate: 84.8%), representing medical oncology, radiation oncology, pain therapy, and palliative care specialties; individual statements were rated by 53–54 panelists, as skipping single items was permitted. All statements reached consensus in the first round (77.8–100%), precluding the need for a second voting round. Panelists’ qualitative comments informed minor wording refinements; substantial content was unchanged. Conclusions: The Delphi process produced a validated, multidisciplinary clinical pathway for cancer pain management in the Italian National Health System (NHS). The pathway establishes structured roles for the clinical reference physician and specialist consultants, objective decision thresholds for analgesic titration and referral, and minimum requirements for standardized pain documentation. These consensus-based statements provide actionable clinical guidance that may help address analgesic undertreatment and support the implementation of Law 38/2010 across Italian oncology centers. Full article

Asthma is one of the most prevalent chronic respiratory diseases worldwide, and environmental pollutants such as desert dust play a significant role in triggering respiratory sensitization and aggravating asthma symptoms. In this study, a mathematical model is proposed to investigate the dynamics of dust-induced asthma progression. The population is divided into unaware susceptible individuals, aware susceptible individuals, dust-sensitized individuals, and asthmatic individuals, while two additional variables describe dust concentration in the human population and the environment. Fundamental qualitative properties of the model, including positivity, boundedness, existence of equilibria, and stability conditions, are established. It is shown that the asthma-free equilibrium is locally and globally asymptotically stable whenever the dust-induced asthma threshold is below unity, whereas a unique asthma-persistent equilibrium exists when the dust-induced asthma threshold exceeds unity. To reduce the burden of asthma, an optimal control problem is formulated by incorporating three time-dependent interventions: awareness campaigns, medical intervention for dust-sensitized individuals, and environmental dust reduction. Pontryagin’s Maximum Principle is employed to characterize the optimal controls and derive the corresponding adjoint system. Numerical simulations, performed using the forward–backward sweep method, demonstrate that the proposed interventions significantly reduce the number of asthmatic individuals compared with the uncontrolled case. Furthermore, a cost-effectiveness analysis based on the Average Cost-Effectiveness Ratio (ACER) and Incremental Cost-Effectiveness Ratio (ICER) is conducted to compare seven intervention strategies. The results indicate that the medical intervention strategy is the most economically attractive option, while the combined strategy involving all controls achieves the largest reduction in asthma burden. Full article

Multi-infeed LCC-HVDC siting requires a static strength test, a finite-fault propagation assessment, and an auditable rule for selecting the subset of candidates that receives detailed electromagnetic-transient (EMT) evaluation. This paper develops a directional fault-response matrix whose row denotes the responding converter bus and whose column denotes the fault-source terminal. A two-infeed derivation, stated for scalar or common-angle impedances and checked numerically for unequal $X/R$ ratios, shows that a stronger inter-bus tie can raise the weaker terminal’s simplified MSCR while increasing the mutual impedance and the cross-terminal fault response. Structural properties of the nonnegative matrix are derived, and a closed-form first-order surrogate calibrated by one pooled least-squares gain screens all 15 four-infeed combinations of six synthetic sites and routes seven candidates (28 single-source switching-EMT runs) to detailed verification. The complete 60-run library is additionally simulated offline and used only as ground truth: the reduced and EMT composite scores have Spearman coefficients of 0.750 over the routed set and 0.911 over all 15 candidates (0.911 under leave-one-candidate-out calibration), the reduced hard screen produces zero false retentions and zero false rejections, and the matrix score is compared with MSCR-only and impedance-ratio MIIF baselines. ABCE is the best verified candidate, with a minimum simplified MSCR of 2.008 and a maximum EMT cross-response of 0.0395 p.u. A 0.05 ohm stress profile activates the transient limit for 9 of the 15 candidates. Element-level error, weight and threshold sensitivity, measured simulation times, and model limitations are reported. The method remains a screening and diagnostic framework rather than a stability law or a proof of global EMT optimality. Full article