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This study presents the application of the World Health Organization's "Workload Indicators of Staffing Need" (WISN) methodology to the Prevention Departments of Local Health Units (LHUs) in Italy, with a focus on the Campania Region. These departments are responsible for essential public health activities, particularly food safety and animal health, which differ significantly from traditional healthcare settings because of their decentralized and inspection-based nature. The Campania Region developed a performance-based staffing model using WISN principles, introducing two key units, the unit of individual performance (U.I.P.) and the unit of structural performance (U.P.S.), to quantify the actual workload and staffing needs. The U.I.P. was defined as the time required for an operator to perform a simple inspection (4 h, including ancillary activities), while the U.P.S. was calculated by aggregating U.I.P.s at structure level and applying correction factors (such as the legal requirement to perform inspections in pairs), representing an original adaptation of the WISN approach to the Italian veterinary and food safety prevention setting. The model accounts for institutional duties, ancillary tasks, territorial logistics, and regulatory constraints such as the need for paired inspections. Applied to 2022 data, the system identified a substantial staffing shortfall, with an estimated overall deficit of approximately 76 full-time equivalents (FTEs) across the Campania LHUs, ranging from about 18 missing FTEs in some urban LHUs to over 38 FTEs in predominantly rural or livestock-oriented areas. The most critical gaps were consistently observed in the animal health sector (SA), which concentrated the majority of missing FTEs and highlighted a structural shortage of veterinary staff dedicated to animal health activities. The model, acknowledged by the Italian Court of Auditors as an excellent tool for rationalising food safety and veterinary public health services, offers an evidence-based, scalable solution for optimising human resource allocation. Its success depends on continued data integration, training, and formalization through regional and national agreements. This approach lays the groundwork for more efficient, accountable, and transparent management of public health personnel.

Pipes typically suffer from material degradation during service, necessitating reliable nondestructive inspection. Recently, zero group velocity (ZGV) combined harmonic generated by guided waves mixing was recognized as a potential mode for localized damage evaluation, but has rarely been developed. This work systematically investigates the generation mechanism and enhanced inspection performance of ZGV combined harmonics induced by counter-directional guided waves mixing in pipes, with comparative analysis against conventional combined harmonics. Theoretical analysis and modeling present that the nonlinear response of ZGV resonance is merely determined by the surface and volume power flux at the corresponding probe, endowing it exceptional sensitivity to localized degradation. Additionally, the non-propagating nature of ZGV combined harmonics eliminates cumulative effect-induced interference, overcoming a key limitation of conventional combined harmonics in degradation localization. With the optimized mode pairs and excitation conditions, numerical simulations compare the nonlinear responses and detection capabilities of ZGV and conventional combined harmonics. The nonlinear response of the ZGV combined harmonic greatly exceeds that of the conventional one, and increases more rapidly with the degradation severity or length. For both single localized degradation and two adjacent degradations, the peak positions of ZGV combined harmonic response align precisely with the degradation centers. In contrast, those of conventional combined harmonics exhibit large deviations from preset degradation centers due to cumulative effects. This study confirms that ZGV combined harmonics provide enhanced pipe inspection performance both sensitivity and localization accuracy. The findings provide an insight into advancing guided wave-based nondestructive testing techniques for localized degradation detection in pipes.

In 2023, there were 114 registered zoos in Korea, more than half of which are small indoor zoos. The welfare conditions of these indoor zoos have become a social issue. The amendment to the Zoo and Aquarium Act in 2023 introduced a licensing system based on the UK's zoo licensing framework, requiring zoos to meet specific criteria in order to obtain an operating license. This study conducts welfare assessments of zoo animals, allowing for the evaluation of the welfare status of each species and the welfare levels associated with the zoo environment. This study assessed animal welfare and microenvironmental conditions in South Korean zoos using a pilot inspector system integrating the Modified Animal Welfare Assessment Grid (M-AWAG) with environmental evaluations. Sixteen zoos were selected from 109 registered facilities, categorized into four groups based on accreditation status and size. Eleven commonly housed species were evaluated to maximize species diversity. Three trained inspectors-a wildlife veterinarian, a zookeeper, and an animal welfare researcher-conducted welfare assessments across four categories: physical, psychological, environmental, and procedural. Environmental variables measured included temperature, humidity, sound levels, illuminance, and odor concentration. The inter-rater reliability among inspectors was 0.942 with high objectivity. Results showed environmental (scores ranging from 4.58 to 6.95), psychological (3.38-4.42), and procedural (3.54-4.78) welfare scores were consistently poorer than physical scores (1.63-2.51) across all species, indicating significant areas for improvement. Analysis of environmental variables showed that illuminance, temperature, and humidity varied significantly among zoo groups Higher sound levels were associated with higher food and drink intake scores (r&#x202f;=&#x202f;0.28, p&#x202f;<&#x202f;0.001). High illuminance was negatively correlated with food and drink intake scores (r&#x202f;=&#x202f;-0.24, p&#x202f;=&#x202f;0.007). Relative humidity was negatively correlated with food and drink intake (r&#x202f;=&#x202f;-0.17, p&#x202f;=&#x202f;0.046) and activity level (r&#x202f;=&#x202f;-0.18, p&#x202f;=&#x202f;0.042). The results of this study, conducted as the first pilot project of its kind in Korea, demonstrate that microenvironmental factors can influence animal welfare and highlight the importance of individualized welfare assessments tailored to the ecological and behavioral needs of each species. The standardized welfare assessment protocol developed in this study can provide an empirical basis for evaluating and improving the welfare of animals of various species in domestic zoos, and easily measurable environmental-based assessments-such as temperature, humidity, light, and sound-provide essential data for improving actual animal welfare.

A substantial proportion of esophageal adenocarcinoma (EAC) and high-grade dysplasia (HGD) cases in Barrett's esophagus (BE) are diagnosed after a dysplasia-negative endoscopy before the next recommended surveillance interval. These are classified as postendoscopy esophageal carcinoma (PEEC) or postendoscopy esophageal neoplasia (PEEN) and represent critical failures of BE surveillance. This review summarizes current definitions, epidemiology, potential etiologies, and evolving strategies to reduce PEEC/PEEN and improve the quality of BE surveillance. High-quality endoscopic examination using high-definition white-light endoscopy (HD-WLE) and virtual chromoendoscopy (CE), adherence to the Seattle biopsy protocol, adequate inspection time, and training in the recognition of visible lesions harboring prevalent dysplasia/EAC, are critical in reducing PEEC/PEEN. Initial data on the use of adjunctive tools (wide-area transepithelial sampling) and molecular biomarkers (p53, DNA methylation panels, and Tissue Systems Pathology tests) demonstrate promising results for detecting prevalent dysplasia and for reducing PEEC and PEEN. BE surveillance quality metrics, such as cancer and neoplasia detection rates, have been shown to be inversely associated with PEEN. PEEC and PEEN reflect critical gaps in the effectiveness of BE surveillance. Improving the quality of endoscopic surveillance is essential, including meticulous mucosal inspection, appropriate use of advanced imaging techniques, and adherence to systematic biopsy protocols, to minimize missed neoplasia.

Multiple contiguous aneurysms of the anterior communicating artery (ACoA) complex present significant technical challenges due to their irregular geometry and the density of critical hypothalamic perforators. Standard microscopic visualization is often limited by optical obstruction from clip blades or line-of-sight constraints during complex reconstruction. A 62-year-old woman presented with aneurysmal subarachnoid hemorrhage. Preoperative digital subtraction angiography and volumetric segmentation identified 4 distinct aneurysmal origins distributed across the ACoA-A2 complex. A subfrontal approach with opening of the proximal sylvian fissure was used to achieve adequate exposure of the aneurysm complex. Stepwise reconstruction was performed using 4 specific clips under intermittent bilateral A1 temporary occlusion. A minor residual neck remnant identified under the microscope was corrected. Endoscopic inspection was subsequently performed for final verification, confirming complete aneurysm obliteration and preservation of the deep perforating arteries. The patient was discharged with a modified Rankin Scale score of 0. Three-month follow-up digital subtraction angiography demonstrated complete angiographic occlusion. For multiple contiguous ACoA aneurysms, a systematic multimodal verification strategy incorporating subfrontal exposure with opening of the proximal sylvian fissure and endoscopic inspection may help ensure complete exclusion while protecting the hypothalamic circulation.

With the growing demand for face editing applications, faceinpainting has become an increasingly important subfield within image inpainting research. While many existing methods use semantic segmentation guidance, they apply uniform weighting across all regions of the map. Since the missing areas of the image lack meaningful features, this uniform treatment provides insufficient guidance in missing areas, leading to unrealistic or structurally incoherent results. Moreover, these methods generally lack the capacity to adapt inpainting results to individual user preferences, thereby limiting their effectiveness in personalized face editing. To address these limitations, we propose SegPainter, a Mamba-based architecture for user-controllable face inpainting that enables customized restoration guided by user-defined semantic segmentation maps generated using the Image Segmentation Annotation Tool (ISAT) integrated with Meta's Segment Anything Model (SAM). Specifically, we propose Hard Mask Soft One-Hot Encoding (HMSOE) to adaptively weight regions in the segmentation map based on whether they correspond to known or missing areas of the masked image. This strategy amplifies semantic guidance in missing regions while attenuating it in known regions to avoid over-constraining existing content. We further introduce Semantic-Guided State Space Model (SG SSM) to dynamically modulate the Mamba layer with semantic features, adapting guidance to the masked image. To enhance the quality of inpainting results, we also propose Tri-Scan Inspection (TSI), a scanning mechanism designed to capture both global and local dependencies while preserving spatial continuity and facial structure. Extensive experiments on the CelebAMask-HQ and FFHQ datasets demonstrate that our framework outperforms state-of-the-art methods, producing sharper and more semantically consistent face inpainting results. Codes are available at the link: https://github.com/langka9/segpainter.git.

Security screening systems require reliable and real-time detection of threats in complex X-ray imagery and surrounding environments. Manual inspection of baggage images is often a overhead due to operator fatigue, cluttered objects and overlapping items. Recent advances in deep learning and intelligent sensing technologies provide opportunities for automated threat detection in such environments. In this work, we propose a multi-layer airport security framework integrating three complementary detection modules: YOLO-based X-ray prohibited-item detection, video anomaly detection for behavioural monitoring, and IoT-based environmental anomaly sensing. In addition to this, a blockchain-secured logging mechanism is incorporated to ensure tamper-proof storage of security events. The X-ray detection module employs YOLOv11-s trained on the CLCXray dataset to identify prohibited items in baggage. Behavioural anomalies in surveillance footage are detected using a 3D-CNN autoencoder, while environmental anomalies from IoT sensors are identified using an LSTM autoencoder. Outputs from these modules are integrated using a unified multimodal risk scoring mechanism. Experimental results demonstrate that YOLOv11-s achieves mAP[Formula: see text] of 78.91% and YOLOv8-s achieves mAP[Formula: see text] of 78.49% with strong detection reliability across varying confidence thresholds, both the models have comparable performance and either of it could be chosen. The IoT anomaly detection produces reconstruction errors in the range of 0.019-0.03, with anomalies identified when the error exceeds a threshold of 0.041 and for video anomaly its 0.0040-0.00685. The proposed multimodal fusion module achieves ROC-AUC score of 0.864, which demonstrated an improved detection reliability compared to individual modalities. Also, the framework achieves an average detection latency of 0.014 ms per event, while the blockchain logging module records security events with an average latency of 0.179 ms and supports up to 5723.47 transactions per second. These results demonstrate that the proposed framework provides an effective, scalable, and secure solution for automated airport security monitoring systems.

This study evaluated the effectiveness of a brief, Theory of Planned Behavior-based educational program on weight management and related health outcomes among university employees. In this quasi-experimental study conducted at two major universities in Erbil, Iraq, 200 employees with a body mass index (BMI)&#x2009;&#x2265;&#x2009;25&#xa0;kg/m&#xb2; self-selected into an intervention (n&#x2009;=&#x2009;100) or control (n&#x2009;=&#x2009;100) group. The intervention consisted of five individual 35-40-minute sessions delivered over 12 weeks and covered obesity awareness, culturally adapted nutrition education, physical activity, and behavior-change strategies. The control group received only standard written materials. Primary outcomes were changes in body weight, BMI, and waist circumference. Secondary outcomes included lipid profile, fasting glucose, quality of life (Impact of Weight on Quality of Life-Lite [IWQOL-Lite]), dietary quality, and physical activity. All assessments were performed at baseline and 12 weeks. The intervention was associated with a mean weight loss of 7.46&#xa0;kg (95% CI 6.44-8.48) compared with a gain of 0.58&#xa0;kg in the control group (adjusted difference&#x2009;-&#x2009;8.04&#xa0;kg; p&#x2009;<&#x2009;0.001; Cohen's d&#x2009;=&#x2009;2.40). 79% of intervention participants lost&#x2009;&#x2265;&#x2009;5% of their initial body weight (versus 0% in controls), and 41% lost&#x2009;&#x2265;&#x2009;10%. Significant improvements were also observed in BMI, waist circumference, lipid profile, quality of life, and dietary quality (all p&#x2009;<&#x2009;0.001; d&#x2009;>&#x2009;1.8). Mediation analysis indicated that improvement in dietary quality accounted for 82% of the observed association between group assignment and change in BMI. A brief, low-cost, culturally adapted educational intervention delivered in the workplace was associated with exceptionally large weight loss, cardiometabolic benefits, and psychosocial gains, with perfect retention. These findings suggest that this model may offer a promising approach for obesity management in Middle Eastern settings. However, confirmation in randomized controlled trials with longer follow-up is required before firm conclusions regarding scalability and effectiveness can be drawn. The study was not prospectively registered in a clinical trial registry because it employed a quasi-experimental design with participant self-selection rather than random allocation. However, the full study protocol including all primary and secondary outcomes, eligibility criteria, intervention details, and the statistical analysis plan was finalized, approved by the Hawler Medical University Ethics Committee (reference HMU-REC-2024-18, 15 September 2024), and locked prior to the start of participant recruitment and data collection. No outcomes were added, removed, or modified after data inspection, and no post-hoc analyses were conducted beyond those pre-specified in the protocol. The manuscript adheres fully to the TREND reporting standards for non-randomized evaluations.

Pressure injuries represent a significant healthcare challenge requiring early detection to prevent severe complications. While thermal imaging shows promise for detecting early pressure related temperature changes, its robustness across varying imaging conditions and diverse patient populations remains unclear. This study systematically evaluated how imaging protocol variations (lighting, distance, positioning, camera type) and participant skin tone influence classification model performance for thermal cooling detection. Using a controlled cooling protocol to simulate early pressure injury temperature changes, we collected 1,680 images from 35 diverse participants across 12 imaging protocol variations. We compared two approaches: three deep learning models (MobileNetV2, InceptionNetV3, ResNet50) and a threshold-based approach using an optimal fixed threshold temperature differential. Deep learning models outperformed the threshold-based approach, achieving 98.699.6% accuracy compared to 95.6%, with superior performance across all imaging protocols and skin tone groups. Threshold-based approach showed camera-dependent misclassification patterns across skin tones. On the high-resolution FLIR E8XT, the MST 7-10 group had 8 of 11 misclassifications. This pattern shifted on the low-resolution FLIR ONE Pro, where the intermediate skin tone group (MST 6) had 22 of 44 total misclassifications. In contrast, deep learning models maintained consistent performance across all skin tone groups and imaging protocols. Visualization analysis of the deep learning models suggested that these models focused on thermal gradients at cooling region boundaries, suggesting that spatial temperature gradients, not single-value thresholds, are critical for accurate detection. These findings suggest the potential of deep learning-based approaches to maintain robust, equitable performance across diverse skin tones and imaging conditions. Pressure injuries are a major clinical challenge requiring early detection. Current visual inspection methods are unreliable, especially for patients with darker skin tones. Thermal imaging shows promise for detecting early temperature changes, but no studies have systematically evaluated how imaging variations affect detection accuracy across diverse populations. Using two cameras, FLIR E8XT (320&#xd7;240 pixels) and FLIR ONE Pro (160&#xd7;120 pixels), we collected 1,680 thermal images from 35 healthy adults across diverse skin tones within a controlled setting with simulated cooling and evaluated how imaging variations (lighting, distance, positioning, camera type) affect performance on two classification approaches: deep learning models that preserve spatial temporal context, and threshold-based approach using a single fixed temperature. Deep learning based approaches demonstrated superior robustness to camera type compared to the threshold based approach which exhibited hardware dependency, with performance deteriorating dramatically on the lower resolution FLIR ONE Pro camera. Deep learning-based approaches also showed consistent performance across all skin tone groups, indicating that both camera selection and labeling methodology are critical for clinical thermal imaging systems. Deep learning models preserve spatial temperature information and show promise for reliable pressure injury detection across diverse patient populations.

Terminal sterilisation is a critical process for pharmaceutical products intended for parenteral or invasive administration, yet conventional sterilisation approaches can compromise thermolabile or chemically sensitive drug substances. In this study, the compatibility of crystalline and amorphous solid forms of five drug substances (betamethasone dipropionate (BMD), estradiol (E2), itraconazole (ITZ), vismodegib (VDG), and zolmitriptan (ZMT)) with sterilising gamma irradiation and ethylene oxide (EtO) cycles was evaluated in terms of chemical and physical stability. Physical stability was assessed by visual inspection of crystalline powders and amorphous solids pre- and post-sterilisation and by differential scanning calorimetry (DSC) to characterise the melting behaviour of crystalline drugs, and glass transition and recrystallisation events in amorphous forms. Chemical stability was examined using reverse phase-high-performance liquid chromatography (RP-HPLC), liquid chromatography-mass spectrometry (LC-MS), and proton nuclear magnetic resonance spectroscopy (&#xb9;H-NMR). Gamma irradiation and EtO preserved the chemical integrity of BMD, E2, ITZ, and VDG. A reversible colour change observed for ITZ was attributed to radical formation as confirmed by gentle heating, HPLC and LC-MS. E2 demonstrated alterations in crystallinity and thermal behaviour after sterilisation, despite maintaining chemical integrity. ZMT exhibited pronounced sensitivity to sterilisation: gamma irradiation induced physical softening and oxidative degradation, particularly in the amorphous form, while EtO sterilisation induced ZMT hydroxyethylation, as confirmed by LC-MS identification of multiple alkylated derivatives. Overall, sterilisation compatibility was governed primarily by molecular structure rather than the drug solid-state. These findings provide an insight for selecting sterilisation strategies and facilitate the translation of crystalline and amorphous pharmaceutical solids toward clinical application.

Brucellosis is a chronic infectious disease of cattle that may influence not only animal health but also the nutritional and sanitary quality of meat. While veterinary-sanitary implications of infected carcasses are well documented, limited information is available on the biochemical composition of meat derived from chronically infected animals. This study aimed to evaluate the veterinary-sanitary status, organoleptic characteristics, and biochemical composition of beef obtained from cattle with chronic brucellosis compared with clinically healthy animals. An observational comparative cross-sectional study was conducted using post-slaughter samples collected within official veterinary surveillance programs. A total of 250 meat samples were subjected to veterinary-sanitary and organoleptic assessment, including animals diagnosed with brucellosis, leukemia, tuberculosis, and leptospirosis. Biochemical analysis was restricted to chronic brucellosis and matched controls (n = 100 per group). Standardized methods were used to determine proximate composition, mineral content, vitamin levels, fatty acid profile, and amino acid composition. Statistical analysis was performed using independent Student's t-test, with significance set at p &#x2264; 0.05, and false discovery rate correction applied for multiple comparisons. Veterinary-sanitary assessment revealed a higher proportion of carcass alterations and conditional suitability in infected animals compared with controls. Organoleptic evaluation indicated mild but consistent changes in color, texture, and overall quality of meat from infected cattle. Biochemical analysis demonstrated significant alterations in nutrient composition in the infected group, including reduced protein content and modifications in lipid fractions. Changes in fatty acid composition were observed, with variations in saturated and unsaturated fatty acids and altered polyunsaturated to saturated fatty acids and n-6 to n-3 ratios. Mineral and vitamin profiles also exhibited measurable differences between groups. Effect size analysis confirmed moderate to large differences for several key nutritional parameters, indicating biologically relevant impacts of chronic infection on meat quality. Chronic brucellosis is associated with measurable alterations in the biochemical composition and veterinary-sanitary quality of beef. Although meat from infected animals may remain conditionally suitable for consumption following regulatory assessment, its nutritional value can be compromised. These findings highlight the importance of integrating veterinary disease status into meat quality evaluation frameworks and support the need for continued surveillance and risk-based assessment in meat inspection systems.

Convectional heat transfer continues to receive extensive research attention due to its importance in vital applications such as solar energy collectors and cooling of nuclear reactors. This research investigates natural convection and associated entropy generation within a porous wavy enclosure with a star-shaped hot cylinder. The fluid saturated in the porous medium is assumed to be a water-Cu-Al2O3 hybrid nanofluid. The energy balance equation accounts for thermal non-equilibrium between the hybrid nanofluid and the local structure of the porous medium (LTNE). The effects of the number of lobes of the star-shaped cylinder (N), the Rayliegh number (Ra), the Darcy number (Da), the porosity of the medium (&#x3b5;), and the volume fraction of the different nanoparticles (&#x3c6;Al2O3 and &#x3c6;Cu) are inspected using numerical FEM analysis and optimized by the aid of artificial neural network (ANN). The results show that it is not possible to increase the Nusselt number without increasing entropy, and it is not possible to decrease entropy without decreasing heat transfer. The objective function (OBF), defined as the ratio of the Nusselt number to the total entropy generation, indicates that the best design is achieved with low obstacle waviness and low nanoparticle loading. The number of lobes and the nanoparticle volume fraction often increase entropy faster than they improve heat transfer. The maximum OBF&#x2009;=&#x2009;3.877 (Nuavg&#x2009;=&#x2009;38.705, ST&#x2009;=&#x2009;9.9824) occurs at N&#x2009;=&#x2009;1, &#x3c6;Al2O3&#x2009;=&#x2009;0, &#x3c6;Cu&#x2009;=&#x2009;0, Ra&#x2009;=&#x2009;103, Da&#x2009;=&#x2009;10-4, &#x3b5;&#x2009;=&#x2009;0.1. This study demonstrates the advantages of using artificial neural networks (ANN) to optimize the design of heat exchangers filled with nanofluids. This approach minimizes losses caused by thermal and flow irreversibility, thereby contributing to energy savings.

Magnetic resonance spectroscopy techniques are widely used to non-invasively study brain metabolism. Despite advances in magnetic resonance spectroscopic imaging (MRSI), there is a notable absence of research on comparing fast non-Cartesian MRSI with single-voxel spectroscopy (SVS), limiting our understanding of its performance and applicability. In this study, we compared the spectral quality and metabolite concentrations obtained using short-TE 2D spiral MRSI and SVS in the same region in the human brain at 3T. Five healthy subjects were scanned at 3T. 2D spiral MRSI data were acquired in a transverse slice through the posterior cingulate cortex (PCC), while the SVS volume was placed within the PCC region. Both techniques employed the standardized semi-LASER sequence for localization. All data were processed in Matlab and fitted with LCModel. Visual inspection suggested comparable overall spectral quality between the two acquisitions. Quantitatively, however, the PCC voxel measured with MRSI exhibited lower signal-to-noise ratio (SNR) compared to SVS at identical scan times, when the SVS voxel matched the effective MRSI voxel. Consistent with lower SNR, metabolite quantification showed higher Cramer-Rao lower bounds with MRSI. In addition, concentrations of glutamate and glutamate plus glutamine were lower with MRSI. Our findings demonstrate that the quality of semi-LASER localized short-TE spiral MRSI spectra is very comparable to that of semi-LASER localized SVS spectra. Small metabolic-specific concentration differences may be due to different WM/GM tissue weighting within the voxel (slice selection profile in SVS vs. point-spread function in MRSI) and different SNR between the two techniques.

Gastrointestinal (GI) cancers pose a significant health burden, highlighting the need for non-invasive biomarkers. Tongue inspection, a traditional diagnostic method in Chinese medicine, has been increasingly quantified via imaging and microbiome analysis. This review synthesizes evidence on tongue features and coating microbiota in GI cancer detection. We systematically searched PubMed, EMBASE, Cochrane and Chinese databases until July 2025 for case-control or cohort studies comparing tongue characteristics or microbiota between GI cancer patients and healthy controls. Data were pooled using fixed- or random-effects models. Sixteen studies (n = 4,994) were included. GI cancer patients showed significantly higher rates of abnormal tongue body morphology (OR = 5.33, 95% CI 3.26-8.72), abnormal tongue body color (OR = 17.85, 95% CI 7.01-45.54), abnormal tongue coating texture (OR = 5.98, 95% CI 4.02-8.91) and abnormal tongue coating color (OR = 3.24, 95% CI 2.00-5.26) versus controls. Although &#x3b1;-diversity did not differ, certain taxa (e.g. Actinobacteria, Prevotella_7) were reduced in cancer patients. Subgroup analyses by cancer type showed generally consistent directions of association for abnormal tongue manifestations in gastric, colorectal and esophageal cancers, despite significant heterogeneity. Abnormal tongue features and specific microbial shifts are associated with GI cancers, suggesting potential non-invasive tools for early detection. However, due to heterogeneity and methodological limitations, further large-scale prospective studies are needed for validation.

Rosacea is a chronic inflammatory skin disorder characterized by persistent facial erythema. Its clinical assessment relies on the Clinician's Erythema Assessment (CEA), a subjective scale prone to inter-observer variability. To address the need for diagnostic consistency, this study developed a multimodal artificial intelligence framework for objective CEA grading using standardized VISIA&#xae; imaging. We analyzed a retrospective cohort of 1,001 patients. To establish a robust reference standard, three expert dermatologists conducted a multi-step collective audit to reach a unanimous consensus for each case. The framework integrated handcrafted image-derived tabular features with deep learning representations. During training, spatial data augmentations and Focal Loss were implemented to address dataset imbalance and mitigate overfitting. Our results demonstrated that the multimodal fusion model achieved statistically significant improvements over the strong image-only baseline (McNemar's p = 0.031 ; DeLong's p = 0.024 ), yielding a Macro-AUC of 0.902 (95% CI: 0.862-0.937). Furthermore, to address the ordinal nature of the disease severity, the fusion model achieved a Quadratic Weighted Kappa (QWK) of 0.800 and an Intraclass Correlation Coefficient (ICC) of 0.801 (95% CI: 0.720-0.860), indicating excellent alignment with expert consensus. Error analysis revealed that over 95% of misclassifications in intermediate grades (CEA3) were restricted to adjacent categories, reflecting strong clinical safety. Interpretability analysis via layer-wise relevance propagation confirmed the model's focus on clinically recognized erythema-prone regions. This study establishes a robust proof-of-concept tool that transforms rosacea assessment from subjective inspection into an objective digital measurement, offering significant translational potential for clinical trials and teledermatology.

Sickle cell anemia is caused by a single missense mutation in the human beta globin gene (HBB) that replaced glutamic acid with valine at position 7(p.Glu7Val), producing sickle hemoglobin (HbS) and promoting polymer formation under deoxygenated conditions. In the present in silico study, genomic and protein sequences of HBB (normal beta globin), HbS variant beta globin (HbS) and hemoglobin alpha globin (HBA) were retrieved from NCBI in FASTA format and analyzed comparatively. physicochemical properties were computed using ExPASY protparam, secondary structure features were predicted using PSIPRED, and three-dimensional /quaternary structural modeling was performed using SWISS-MODEL with template selection from PDB. The ProtParam analysis display that the p.Glu7Val substitution in HBBs reduced the number of acidic residues (Asp+Glu) and shifted the theoretical PI upward(HBB:6.740, HBBs:7.131) accompanied by a&#x223c;30&#x202f;Da decrease in molecular weight and a modest increase in hydrophobicity related indices (aliphatic index and GRAVY). PSIPRED predicted predominantly alpha helical globin folds for all subunits, with only minor differences between HBB and HbS. Structural inspection of the SWISS-MODEL assemblies localized Val 7 at the Beta chain N-terminus and consistent with the classical mechanism whereby this hydrophobic substitution creates a surface stick region that can engage hydrophobic patches on neighboring deoxy Hb molecules, providing structural context consistent with HbS polymerization and downstream erythrocyte sickling. Overall, this computational workflow provides a clear sequence to structure interpretation of the HbS mutation and highlights measurable physicochemical shifts associated with SCA.