Search Results (1,271)

This study investigates the effect of high-temperature hydrogen exposure on the structure and properties of Cr₂O₃/Al₂O₃-based detonation coatings deposited on AISI 316L stainless steel. Bilayer and gradient coatings were exposed to hydrogen at 1000 °C for 3, 4, and 5 h and subsequently characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM) with energy-dispersive X-ray spectroscopy (EDS), surface profilometry, and thermal desorption spectroscopy (TDS). One independent specimen was examined for each combination of coating architecture and hydrogen exposure duration. Therefore, the present study was designed as an exploratory comparative investigation rather than a statistically powered study. The principal α-Al₂O₃ and Cr₂O₃ phases remained detectable after all exposure durations, indicating preservation of the main oxide phases. SEM/EDS analysis revealed microcracks, local defects, and heterogeneous surface regions, with more pronounced localized damage in the bilayer coatings. The Ra values of the bilayer coatings were 1.385, 0.833, and 1.207 μm after 3, 4, and 5 h, respectively, whereas the corresponding values for the gradient coatings were 1.049, 1.337, and 1.049 μm. The minimum Ra of 0.833 μm after 4 h in the bilayer coating coincided with SEM/EDS evidence suggesting local coating damage and possible thinning. TDS showed the most intense hydrogen desorption for the gradient coating after 3 h. Overall, the observed results suggest that coating architecture influences surface evolution and hydrogen-retention behavior under the investigated high-temperature hydrogen exposure conditions. Full article

Background: Trabeculectomy is the standard surgical treatment for advanced glaucoma, yet the relationship between postoperative intraocular pressure (IOP) and long-term structural preservation remains unclear. We investigated longitudinal changes in peripapillary retinal nerve fiber layer (RNFL) thickness following trabeculectomy in primary open-angle glaucoma and evaluated whether lower postoperative IOP levels are associated with improved structural stability. Methods: This retrospective consecutive case series included 106 eyes undergoing trabeculectomy between 2010 and 2020 with follow-up of up to 5 years. RNFL thickness was assessed using spectral-domain optical coherence tomography at baseline and during follow-up. IOP, glaucoma medication use, best-corrected visual acuity, and visual field mean defect were recorded longitudinally. Results: Mean IOP decreased from 23.8 ± 0.9 mmHg preoperatively to 10.5 ± 0.7 mmHg at 1 month and remained significantly reduced at 5 years (13.8 ± 0.7 mmHg), with sustained reduction in medication use. RNFL thickness declined significantly from 64.4 ± 2.0 µm during the first postoperative year to 57.2 ± 1.6 µm and subsequently remained relatively stable. While eyes achieving a postoperative IOP <12 mmHg showed no statistically significant RNFL thinning compared to baseline, eyes with higher postoperative IOP exhibited greater structural decline despite meeting conventional success criteria. Conclusions: Trabeculectomy provides durable IOP reduction; however, early structural loss may still occur. Our findings suggest that lower postoperative IOP levels may be associated with greater structural preservation, although this relationship requires further investigation. Full article

Nd laser posterior capsulotomy is the standard treatment for posterior capsule opacification following cataract surgery. Although generally considered safe, inadvertent laser impacts on the intraocular lens (IOL) optic may induce permanent surface defects that contribute to postoperative dysphotopsias and reduced visual quality. This experimental study investigated the optical consequences of Nd laser-induced damage on two commercially available hydrophobic acrylic IOLs, focusing on retinal light distribution and optical image quality. Two hydrophobic acrylic monofocal IOL models, the CT LUCIA (Carl Zeiss Meditec) and the AcrySof IQ (Alcon), were mounted on a customized experimental holder and exposed to standardized Nd laser applications consisting of 5, 10, or 15 laser shots. Laser interactions were documented using the PhysioGo.Lite laser platform combined with infrared thermal imaging. Untreated IOLs served as controls. Optical performance was subsequently evaluated using a standardized optical bench according to ISO recommendations. Point spread function (PSF) and modulation transfer function (MTF) analyses were performed to quantify retinal image quality, light scattering, and optical degradation. Retinal light distribution was assessed using a high-resolution projection screen simulating the retinal image. Laser exposure produced permanent focal defects on the anterior optical surface of both IOL models, resulting in measurable optical degradation. Even the lowest laser exposure (five shots) generated detectable alterations in light propagation, characterized by increased peripheral light scattering, enlargement of the PSF halo, reduced central peak intensity, and irregular light distribution across the simulated retinal plane. Descriptively, increasing numbers of laser impacts were associated with more pronounced optical disturbances, particularly in the AcrySof IQ samples. MTF analysis demonstrated a reduction in optical performance across multiple spatial frequencies, indicating deterioration of image contrast and resolving power. Although both hydrophobic acrylic IOL models exhibited optical alterations after laser exposure, descriptive differences in the magnitude and distribution of light scatter suggested a possible influence of material composition, refractive index, and surface microarchitecture. These observations should be considered preliminary because of the limited sample size and absence of inferential statistical analysis. Under the present experimental conditions, Nd:YAG laser-induced pitting was associated with measurable structural and optical alterations in two hydrophobic acrylic IOL models. Surface defects alter retinal light distribution, increase forward light scatter, and reduce optical quality, providing a possible optical mechanism that may contribute to postoperative dysphotopsias, although clinical visual symptoms were not directly evaluated in this study. These findings highlight the importance of meticulous laser focusing on the posterior capsule to minimize inadvertent IOL damage and preserve postoperative visual quality. Further investigations combining optical bench analyses with patient-reported visual outcomes are warranted to better define the clinical significance of laser-induced IOL pitting. Full article

High-sensitivity detection of metal contaminants during gas-insulated equipment (GIE) manufacturing is crucial to mitigating insulation risks. In this study, detection tests of metal contaminants are performed using the conventional partial discharge measurement (CPDM), UHF, and photon counting (PC) methods on a 252 kV GIS chamber. The results indicate that the PC method exhibits high sensitivity to micrometer-sized metal dust, while the UHF sensor performs better in detecting the millimeter-sized single wire-shaped particle. The CPDM method has no sensitivity advantage in either of the above cases. For sub-millimeter-sized block contaminants, all three methods exhibit comparable sensitivity. Moreover, a comprehensive statistical index is introduced to evaluate the discharge activity of different metal defects, enabling a more robust quantitative comparison. Full article

Background: Infertility is a common reproductive health disorder that affects roughly 10–15% of couples during their reproductive period. Hysterosalpingography (HSG) is a widely utilized imaging modality for assessing uterine cavity morphology and fallopian tube patency and continues to play a central role in infertility investigations. Nevertheless, the interpretation of HSG findings may vary according to the experience and expertise of the evaluator, potentially leading to inconsistencies in clinical decision-making. Although artificial intelligence (AI) has demonstrated considerable potential in medical image analysis across various specialties, evidence regarding its application in the interpretation of HSG examinations remains scarce. Therefore, this study aimed to evaluate the level of agreement among radiologists, gynecologists, and an AI-based system in the assessment of identical HSG images. Methods: In this retrospective study, a total of 1443 HSG images obtained from 414 women who underwent hysterosalpingography as part of an infertility evaluation between January 2021 and January 2025 were reviewed. Cases with incomplete clinical records or suboptimal image quality were excluded from the analysis. All examinations were independently assessed by an experienced radiologist, a gynecologist specializing in infertility management, and a multimodal artificial intelligence system based on ChatGPT-5, with each evaluator blinded to the assessments of the others and to the patients’ clinical information. Image interpretation included the evaluation of contrast distribution, peritoneal spill, uterine cavity findings, tubal patency, and overall HSG impression, which were categorized according to predefined diagnostic criteria. The primary outcome was the degree of interobserver agreement among the evaluators. Agreement analyses were performed using Cohen’s kappa (κ) and Gwet’s AC1 coefficients. Analyses were conducted using IBM SPSS Statistics (version 30.0; IBM Corp., Armonk, NY, USA) and R statistical software (version 4.4.0; R Foundation for Statistical Computing, Vienna, Austria). Statistical significance was set at p < 0.05 (two-sided). Results: A total of 1443 HSG images obtained from 414 women were included in the final analysis. The mean age of the study population was 30.97 ± 5.59 years, and primary infertility accounted for 87.9% of cases. The average number of images acquired per examination was 3.49 ± 1.05. According to Cohen’s kappa analysis, the highest levels of agreement were observed for the assessment of image artifacts and contrast medium distribution. Agreement between the AI system and the radiologist was particularly strong for contrast medium distribution (κ = 0.757). For the overall interpretation of HSG findings, AI demonstrated substantial agreement with the radiologist (κ = 0.637), exceeding the level of agreement observed between the radiologist and the gynecologist (κ = 0.363). In contrast, concordance involving AI was lower for the evaluation of uterine abnormalities, intrauterine filling defects, and tubal patency. When agreement was reassessed using Gwet’s AC1 statistic, concordance coefficients were consistently higher than the corresponding kappa values across all evaluator pairs. Near-perfect agreement between AI and the radiologist was identified for contrast medium distribution (AC1 = 0.954), peritoneal spill (AC1 = 0.893), and patterns of peritoneal contrast passage (AC1 = 0.841). Procedures performed under local anesthesia yielded a significantly greater number of images than those conducted under general anesthesia (3.86 ± 0.86 vs. 3.08 ± 1.10, p < 0.001). No significant associations were detected between abnormal HSG findings and either infertility type or anesthetic technique. In multivariable analysis, the use of general anesthesia was independently associated with a lower image count, whereas the presence of tubal pathology emerged as an independent predictor of acquiring a greater number of images during the examination. Conclusions: Our findings indicate that AI-assisted interpretation of HSG images has the potential to complement expert assessment, showing substantial concordance in several key diagnostic domains. While the technology appears promising as a decision-support tool in infertility evaluation, further research and refinement are warranted, particularly regarding the assessment of tubal and uterine pathologies. Full article

Alternative fine aggregates are often assessed using compressive strength at a single reference age, which may conceal an early maximum followed by later strength loss. This preliminary screening study compared mortars containing river sand (RS), standard sand (StS), desert sand (DS), soil sand (SS), coal gangue sand (CGS), and metamorphic rock sand (MRS) under one nominal mixture design. For each aggregate, one mortar batch was prepared and nine 70.7 mm cubes were cast, with three specimens tested at 3, 7, and 28 d. RS, StS, DS, and SS continued to gain strength. Within the single CGS batch, strength decreased from 15.56 ± 0.31 MPa at 7 d to 11.68 ± 0.15 MPa at 28 d; within the single MRS batch, it decreased from 21.63 ± 0.48 MPa to 15.75 ± 0.20 MPa. The corresponding losses were 24.95% and 27.18%, and exploratory within-batch Tukey tests yielded p < 0.001. These statistics describe specimen-level variation within the tested batches and do not establish batch-to-batch reproducibility. Representative 28 d SEM fields, raw-aggregate EDS, and qualitative XRD provide contextual observations but lack the temporal and spatial resolution needed to reconstruct a defect-formation process between 7 and 28 d. Because aggregate moisture state, absorption, flow, air content, and compaction were not independently controlled, the results identify a screening signal that requires independent-batch validation rather than a general material mechanism. Full article

Deep networks dominate photovoltaic (PV) thermographic fault detection but need large annotated datasets and resist interpretation. We present a lightweight, interpretable infrared workflow combining support-vector-machine (SVM) module/background segmentation from four handcrafted features with an adaptive grid analysis labelling regions as nominal-intensity, high-intensity anomaly or low-intensity anomaly relative to a module-internal reference; the anomaly classes are inspection candidates, not confirmed faults. Evaluation used 21 close-range images of one 20 W module—recorded with the camera’s visible-light edge fusion active, so they are fused infrared/visible frames—and all 596 of a public five-sector UAV dataset. Segmentation against manual masks reached a mean intersection-over-union of 0.64; a feature ablation shows intensity statistics dominate, and an end-to-end Otsu pipeline gives almost the same high-intensity share (4.54% versus 4.50%): the SVM contributes reproducibility—removing the manual segmentation threshold, though not the empirical +48/−60 offsets—not accuracy. High-intensity regions concentrated in the module’s lower half, co-locating with a bus-bar defect known from hardware inspection—suggestive, not validated. The single-module, image-level close-range evaluation is optimistic, and the UAV shares, from a separately trained SVM, illustrate cross-domain application only. Segmentation runs at about 15 images per second on CPU. The method is a relative-intensity thermal screening workflow, not a validated defect-diagnosis or plant-health assessment method, and applies only where acquisition is controlled and the offsets are recalibrated for the target camera and palette. Full article

Pre-trained code models are increasingly used in software engineering, yet their incremental value beyond traditional software metrics for future-version class-level defect prediction remains unclear. This study evaluates CodeBERT, GraphCodeBERT, and CodeT5 using 16,237 class-version instances from six open-source Java systems and 11 chronological train–validation–test splits. We analyze model-specific input coverage and long-code representations and test whether learned code features add value beyond metric and size controls. Overflow affected 50.7% of instances for CodeBERT and GraphCodeBERT and 39.0% for CodeT5. Defective instances overflowed more often than clean instances, and this association remained after adjustment for size and project-version effects. Long-code strategies yielded small and inconsistent gains, none of which survived Holm correction. The best code-only model achieved a mean Matthews correlation coefficient (MCC) of 0.315, compared with 0.451 for metric-based Random Forest. Fusion produced no robust incremental gain. Multi-seed fine-tuning improved mean MCC for all encoders, but none of the paired gains remained statistically significant after Holm correction, and the best fine-tuned model remained below the metric baselines. These findings indicate that pre-trained code models should be evaluated with explicit input-coverage reporting, chronological validation, strong metric baselines, and incremental-value testing. Full article

This study proposes a quality-aware multimodal framework for health-state assessment and remaining useful life (RUL) prediction of cold-region tunnels. The framework integrates structural-response, environmental, apparent-defect, and engineering-inspectiondata, with the apparent-defect pathway jointly encoding raw images through a convolutional neural network and structured defect variables. Five data-quality dimensions-completeness, accuracy, consistency, timeliness, and traceability are incorporated intoreliability-guided multimodal fusion. Their base weights were re-audited through two rounds of expert consultation, each comprising 323 valid questionnaires. The Cr-weighted group analytic hierarchy process yielded weights of 0.0548, 0.1326, 0.1372, 0.2279, and 0.4474, respectively, with a group consistency ratio of 0.0455; the ranking remained stable under one-at-a-time +10% perturbations. In the primary tunnel case study, the framework achieved 89.7% health-state accuracy, a 6.3% RUL mean absolute percentage error, and 84.1% accuracy under Gaussian perturbation of standardized numerical inputs at a noise scale of 0.15. To further examine the reliability contribution of data-quality information, an independent field panel comprising 600 segment-month observations from 25 segments across three operational tunnels was evaluated using target-excluded specifications, two-way fixed effects, leave-one-tunnel-out validation, multiple baseline models, and five fixed random seeds. A one-standard-deviation increase in lagged quality instability was associated with a 0.0151 increase in the subsequent state-error index (95% CI: 0.0118-0.0184; p < 0.001). In cross-tunnel random-forest tests, incorporating quality information increased mean R² from 0.8277 to 0.8323 for state-error prediction and from 0.8517 to 0.8673 for RUL-contraction prediction, with both improvements significant in paired tests (p < 0.001). Split-conformal intervals achieved mean cross-tunnel coverage of 95.8% and 95.9%, respectively. These findings demonstrate that data-quality information provides a modest but statistically supported improvement in cross-tunnel reliability, whilethe principal contribution lies in integrating auditable data governance, reliability-aware fusion, and engineering decision support within a unified tunnel health-management framework. Full article

Background/Objectives: Postoperative free flap monitoring relies on clinical assessment of colour, turgor, and capillary refill time (CRT). The high frequency of required assessments renders this process labour-intensive and inherently subjective, with sensitivity dependent on observer experience and fatigue. This study evaluated the feasibility of a large multimodal AI (Artificial Intelligence) model (Gemini 3 Flash, Google AI Studio) applied without any task-specific training or fine-tuning, with each video analysed in an independent session, to establish whether meaningful diagnostic agreement is achievable before any domain-specific training is introduced, with the ultimate goal of supporting the development of a machine-based secondary safety net that augments, rather than replaces, the primary clinical assessment of the responsible surgeon in postoperative free flap surveillance. Methods: One hundred and forty-three postoperative video recordings from 143 different patients who underwent fasciocutaneous free flap reconstruction for extraoral defects were analysed. The cohort was deliberately enriched for pathological cases to ensure adequate representation of each vascular compromise category. Assessment was based on intrapatient comparison between flap and adjacent native tissue, without task-specific training or prior clinical information. AI outputs were compared against consensus assessments of three senior plastic surgeons (two associate professors, one full professor) for four parameters: flap colour, turgor, CRT interpretation, and clinical diagnosis. Agreement was evaluated using Cohen’s kappa (κ). Receiver operating characteristic (ROC) analysis assessed the discriminative performance of the AI confidence score. Results: Statistically significant agreement was demonstrated across all four parameters (p < 0.001 for all): flap colour (κ = 0.613, 95% CI: 0.489–0.737, 80.4%), turgor (κ = 0.676, 95% CI: 0.558–0.794, 83.9%; assessed from visual surrogates rather than direct palpation), CRT interpretation (κ = 0.487, 95% CI: 0.360–0.614, 74.1%), and clinical diagnosis (κ = 0.524, 95% CI: 0.399–0.649, 74.1%). Normal flaps were correctly identified in 81.0% of cases and venous compromise in 69.4%. ROC analysis identified a confidence score percentile cutoff of 89 as the optimal threshold for discriminating compromised from normal flaps, with scores below 89 indicating a compromised (pathological) result (AUC = 0.667, 95% CI: 0.579–0.755; sensitivity 77.0%, specificity 52.4%; overall accuracy 62.9%). Conclusions: A structured-prompted multimodal LLM (Large Language Model) demonstrated statistically significant agreement with the consensus judgement of senior plastic surgeons in postoperative free flap assessment without task-specific training, relying solely on intrapatient visual comparison. These findings constitute a proof-of-concept for AI-based video analysis as a potential adjunctive approach in free flap surveillance, warranting prospective validation with independent clinical outcome data before any claim of clinical utility, with possible broader applicability across clinical domains pending such validation. Key limitations include the absence of independent clinical outcome or confirmation of diagnostic categories, deliberately enriched sampling, a small arterial insufficiency subgroup, and the current absence of a formal data processing agreement for the cloud-based AI platform used. Full article

Background/Objectives: This study aimed to compare the intraobserver and interobserver reliability and absolute agreement of the CT-based glenoid arc-angle method when the reference circle was constructed either directly on the injured glenoid or transferred from the healthy contralateral glenoid. The objective was to assess measurement reproducibility rather than anatomical validity, diagnostic accuracy, or clinical utility. Methods: Preoperative CT scans of 31 patients with anterior shoulder instability who underwent surgery were retrospectively analyzed. Three independent observers with different levels of experience performed measurements at two separate time points using two geometrical approaches. In the contralateral-reference method, the diameter of the best-fit circle was determined on the healthy contralateral glenoid and transferred to the injured side. In the injured-side method, the best-fit circle was determined directly on the injured glenoid. The defect angle and glenoid diameter were measured, and the defect angle was converted into an area-based percentage of glenoid bone loss. Intraobserver reliability was assessed across the two measurement sessions, whereas interobserver reliability was assessed across observer-specific means from the two sessions, using intraclass correlation coefficients with 95% confidence intervals. Absolute agreement, systematic bias, and measurement error between sessions were additionally evaluated using Bland–Altman analysis, the standard error of measurement, and the minimum detectable change. Results: The contralateral-reference method yielded numerically higher ICC point estimates for both measured parameters. For defect-angle measurements, the interobserver ICC based on observer-specific two-session mean estimates was 0.890 with the contralateral-reference method (95% CI: 0.811–0.942) and 0.603 with the injured-side method (95% CI: 0.399–0.767). For glenoid-diameter measurements, the corresponding interobserver ICCs were 0.900 (95% CI: 0.660–0.961) and 0.812 (95% CI: 0.672–0.900), respectively. Several confidence intervals crossed conventional reliability category boundaries and partially overlapped between methods; therefore, these findings represent numerical differences in point estimates rather than statistically established superiority. Conclusions: Within this cohort, the contralateral-reference method yielded numerically higher ICC point estimates, smaller session-related biases, and lower measurement-error estimates than the injured-side method. These descriptive findings do not establish statistically significant superiority, diagnostic accuracy, clinical utility, or treatment thresholds. The results are limited by the relatively small retrospective cohort of 31 surgically treated patients and the absence of anatomical or clinical outcome validation. Full article

Introduction: Pelvic organ prolapse (POP) involves the descent of pelvic organs into the vaginal cavity due to weakened pelvic support structures, particularly ligamentous and connective tissue laxity or defects. Surgical interventions are common, with approximately 200,000 procedures annually in the US. Surgical approaches prioritize apical support preservation, often using native tissue for reinforcement. The uterosacral ligament suspension (USLS) via vaginal approach offers advantages in cost and time. Presently, there is a resurgence of interest in transvaginal native-tissue procedures for pelvic floor reconstructive surgery, driven by factors such as cost-effectiveness and the desire to avoid complications associated with mesh. However, the declining use of this approach may result in insufficient exposure for residents and gynecologists to acquire vaginal surgical skills. Our study aims to comprehensively assess the learning curve associated with vaginal hysterectomy, considering the impact of experience on operative outcomes, complications, and patient results. Methods: This retrospective single-center study analyzes consecutive patients who underwent vaginal hysterectomy followed by high uterosacral ligament suspension for pelvic organ prolapse (POP) performed by one pelvic floor surgeon in training at Fondazione IRCCS San Gerardo dei Tintori Hospital in Monza, Italy, between November 2021 and February 2023. Patients underwent transvaginal hysterectomy and salpingectomy, with additional procedures as necessary, followed by high uterosacral ligament suspension. Follow-up assessments were performed periodically to evaluate symptoms and determine objective recurrence. Comparative analyses were conducted on preoperative POP-Q stages, demographic attributes, perioperative outcomes, and recurrence rates. Additionally, we utilized cumulative summation (CUSUM) analysis to examine the learning curve associated with vaginal hysterectomy accompanied by high uterosacral ligament suspension, focusing on surgical failure and operation duration. Results: A total of 43 patients were included in the study. The population had a median age of 62 years and a mean menopausal onset age of 49.7 years. Symptoms included bulging (100%), with 70% reporting bladder emptying difficulties. All patients exhibited stage II or higher anterior compartment prolapse, and the majority had stage II or higher central prolapse. Vaginal hysterectomy and uterosacral ligament suspension were performed on all patients, with additional repairs as needed. The median operative time was 119.45 ± 20.4 min, with a mean blood loss of 303.3 ± 107.9 mL. Postoperative assessment utilizing the POP-Q system revealed significant improvements in the anterior (p < 0.001 for Aa and Ba), central (p < 0.001 for C), and posterior (p < 0.001 for Ap and Bp) compartments compared to preoperative measures. Additionally, a decrease in the genital hiatus and an increase in the perineal body were noted (p < 0.001 for gh and pb). Functional outcomes demonstrated a substantial improvement in micturition symptoms and vaginal bulging compared to baseline (p < 0.001), although not all parameters reached statistical significance. Recurrence rates were 9.3% for the anterior compartment and 2.3% for the central compartment. The CUSUM analysis indicated proficiency in operation time after the 30th procedure, while surgical proficiency, defined by surgical success, entails no recurrence within 12 months postoperatively, was stabilized after 22 cases. Conclusions: Vaginal uterine ligament suspension not only stands as is a safe and effective procedure for pelvic organ prolapse but also boasts a brief learning curve, facilitating rapid enhancement in surgical proficiency within a condensed time frame, particularly in terms of blood loss, anatomical recurrence, and subsequent reintervention. Full article

Background: Idiopathic nephrotic syndrome (NS) in children is characterized by urinary protein loss and potential disruptions in trace element homeostasis. The dynamic changes in zinc, copper, and magnesium levels in relation to disease activity remain incompletely defined. Objective: This study aimed to evaluate serum zinc, copper, and magnesium and urinary copper and magnesium alterations in homeostasis in pediatric nephrotic syndrome and to examine their associations with disease stage, proteinuria, disease duration, renal function, and corticosteroid response. Materials and Methods: This is a prospective cohort study involving 108 participants, including 74 pediatric patients with idiopathic nephrotic syndrome and 34 healthy controls, comprising 164 clinical and biological assessments. Serum and urinary concentrations of Zn, Cu, and Mg were analyzed, alongside clearance parameters and the fractional excretion of magnesium. Statistical analysis included non-parametric tests, Spearman correlations, ROC analysis, and multivariable logistic regression. Results: Serum zinc levels were significantly lower during active disease phases and normalized during remission (p < 0.001); however, these differences in serum zinc concentration with stages of the NS disappeared after adjustment for serum protein levels (p = 0.424), suggesting a transport deficit secondary to hypoproteinemia. Although serum zinc concentrations were also significantly reduced in patients with concomitant infection, adjustment for serum protein levels attenuated this association, and the zinc-to-protein ratio did not differ significantly according to infection status (p = 0.08). Urinary copper levels and clearance were elevated during active disease and positively correlated with proteinuria (rho = 0.35–0.38; p < 0.001); the Cu/Zn ratio varied significantly across disease stages (p < 0.001) and was associated with both disease activity and a tendency toward corticosteroid resistance. Magnesium demonstrated a pattern of tubular conservation during active phases, with elevated fractional excretion values during remission (p < 0.001) and inverse correlations with proteinuria. Disease duration, but not relapse burden, was positively correlated with serum zinc and fractional magnesium excretion and inversely correlated with serum magnesium. In the multivariable analysis, serum proteins emerged as the sole independent predictor of disease activity, while age and the Cu/Zn ratio were associated with corticosteroid resistance. Conclusions: Pediatric nephrotic syndrome induces significant alterations in zinc, copper, and magnesium homeostasis, dependent on glomerular permeability and plasma protein status. Zinc changes with stages of NS likely reflect a secondary transport defect. In contrast, zinc changes with infection likely occurred because of a shift of zinc to the intracellular compartment. Urinary copper serves as a marker of glomerular permeability and magnesium highlights tubular adaptation. The Cu/Zn ratio and magnesium handling parameters may prove clinically useful in monitoring disease activity and treatment response. Full article

Rab proteins are key members of the Ras superfamily that regulate vesicular trafficking in eukaryotic cells, ensuring accurate cargo transport and cellular homeostasis. To investigate the functional role of Rab proteins, RNA interference (RNAi) was employed to silence LmRab11A in Locusta migratoria, followed by integrated transcriptomic and metabolomic analyses. Transcriptomic profiling identified 54 downregulated and 71 upregulated genes upon LmRab11A knockdown. Based on Log2 fold change values and statistical significance, 15 downregulated genes were selected for further analysis. RT-qPCR validation confirmed that 9 of these genes were significantly downregulated following LmRab11A silencing. Subsequent RNAi-mediated knockdown of these 9 genes revealed that silencing of only LOCMI11062 (β-tubulin) resulted in 100% mortality and severe morphological defects in locust nymphs, indicating its critical role in development. Metabolomic analysis identified 11 significantly altered metabolites, including 8 exhibiting increased abundance and 3 exhibiting decreased abundance. Spearman correlation analysis between the 9 downregulated genes and the altered metabolites suggested potential associations between 6 metabolites and specific genes. Collectively, these findings demonstrate that suppression of LmRab11A disrupts downstream gene expression and alters the metabolite network, thereby impairing growth and development. Full article

Property condition assessment is a critical step in residential real estate investment underwriting, motivating after-repair value (ARV) estimates and rehabilitation cost projections. Traditional approaches rely on in-person inspections or manual photo review by experienced investors—processes that are time-consuming, subjective, and do not scale. Prior computer vision work on building analysis has focused on structural defect detection using convolutional neural networks but has not addressed the holistic, room-level condition assessment needed for residential investment decision-making. This paper presents a per-room analysis framework that leverages multimodal large language models (MLLMs) to assess the condition of residential properties from photographs. The framework analyzes each photo independently at the room level—detecting the room type, condition category, condition score, material features, and visible issues. Condition output is intended to feed a separate downstream rehabilitation cost and ARV estimation model that is outside the scope of this paper; the present empirical evaluation is restricted to per-photo condition assessment and inter-rater agreement with human experts. I evaluate the framework on two complementary datasets: (i) a primary per-image condition evaluation on 57 photographs from 14 real off-market properties in the Memphis, TN MSA, spanning three condition tiers (Fixer, Outdated, Standard), with independent labels from two experienced real estate investors; (ii) a secondary room classification evaluation on the public REI Dataset (51 attempted, 39 successful, 12 HTTP-503 failures). The room classification accuracy was 76.5% intention-to-analyze on REI (100% per-protocol on the 39 successful calls; 23.5% API failure rate) and 82.5% on the concierge dataset. The inter-rater agreement on the concierge dataset, with 95% bootstrap CIs (5000 resamples) and Spearman’s $\rho$ as primary score statistic, was as follows: Cohen’s $\kappa =0.773$ ($95\%$ CI $[0.64,0.90]$) between Labeler A and the MLLM (weighted $\kappa =0.853$ $[0.76,0.94]$; $\rho =0.906$); and $\kappa =0.502$ $[0.35,0.66]$ between Labeler B and the MLLM ($\rho =0.858$); both bracket the human–human reliability of $\kappa =0.590$ $[0.42,0.74]$ ($\rho =0.807$). The MLLM’s $\kappa$ asymmetry across the two labelers is statistically significant ($\Delta \kappa =0.271$, $95\%$ bootstrap CI $[0.115,0.429]$, $p=0.0004$), which I attribute to plausible training distribution and labeling style differences. A blind re-labeling sensitivity analysis on a stratified 15-image subsample yields anchoring-corrected $\kappa$ estimates of approximately $0.65$ (Labeler A) and $0.35$ (Labeler B); the headline anchored values therefore sit at the upper bound of plausible blind-equivalent agreement. Failure modes concentrate at the Outdated tier and at the Outdated–Standard boundary, where humans themselves disagree most, indicating intrinsic taxonomy ambiguity rather than a model artifact. I make no claim to multi-market generalization and present multi-market extension as ongoing work. Full article