Reducing patient-specific QA workload through statistical process control and complexity metrics.

The proliferation of Internet of Things (IoT) devices raises significant security concerns, yet existing anomaly detection methods often overlook the data scarcity of low-power devices and the computational constraints of IoT gateways. This paper addresses these challenges by first analyzing traffic samples with t-SNE and Kernel Density Estimation, revealing that samples from other devices exhibit similarity to attack samples of a target device. Based on this observation, a novel contrastive learning-based unsupervised intrusion detection method named CLU-ID is proposed, which operates in two stages: contrastive pre-training and centroid-centered training. The contrastive pre-training stage employs a contrastive loss to distinguish target device flows from others, directing the model to focus on distinctive features of the target device rather than common features shared across devices. The centroid-centered training stage models the normal distribution of each device within a single neural network, generating central vectors that represent typical flow features and enabling anomaly detection as distribution outliers. Experimental results demonstrate that CLU-ID achieves state-of-the-art performance with limited training samples, outperforming Autoencoder (AE), Deep SVDD (DSVDD), Kitsune, and six traditional methods across both shared and individual model scenarios. Furthermore, the model significantly reduces parameter volume compared to AE and DSVDD, enhancing suitability for resource-constrained gateways, while also exhibiting effective detection capability for unseen attacks. • Addresses data scarcity and computational constraints in low-power IoT intrusion detection. • Proposes CLU-ID, a contrastive learning-based unsupervised intrusion detection method. • Employs contrastive pre-training to extract device-specific distinguishing features. • Uses centroid-centered training to model normal distributions and detect outliers as anomalies. • Achieves up to 30% performance improvement over AE and DSVDD with over 90% parameter reduction.

The future of maritime traffic will enter a transitional period characterised by a blend of manned ships and intelligent ships with varying degrees of autonomy. Existing research on maritime navigation situation assessment primarily focuses on traditional ships or a single-ship perspective, lacking a regulatory-oriented comprehensive assessment framework for high-density ship areas in mixed maritime traffic scenarios. These methods are difficult to meet the actual needs of shore-based supervision. Based on the RFRM (Risk Filtering, Ranking & Management) concept, this paper constructs a key risk indicator system affecting the overall navigation situation in high-density ship areas and designs mapping functions and calculation methods for multiple indicator values. The limitation of single weighting is overcome by integrating subjective and objective weights to form comprehensive weights. The Finite Interval Cloud Model (FICM) is adopted for quantitative rating of navigation situations in high-density areas, addressing the assumption deviation of traditional cloud models that rely on normal distribution. Case validation confirms the effectiveness of the proposed model. The research findings can provide decision support for regulators in supervising ships in mixed scenarios and address the research gaps in regulatory-oriented situation assessment under such scenarios.

This study aimed to investigate the internal void formation of two single-shade bulk-fill flowable composite resin and a high-strength restorative composite at different thickness levels by 3D micro-computed tomography (CT) analysis. Two different single-shade flowable bulk-fill composite resins (Charisma Bulk Flow One-CH and Omnichroma Flow Bulk-OM), and a high-strength restorative composite (G-aenial Universal Injectable-INJ) were used in this study. Cylinder-shaped samples were prepared at three different thicknesses (2, 4, and 6mm) from each test material (n = 3). Samples were scanned utilising a micro-CT device, and internal voids of the materials were evaluated. Data were analyzed with Jamovi v2.7.6 and Mi nitab v14. Robust ANOVA was applied when normality was not met for the interaction between material and thickness. Multiple comparisons were analyzed with Holm-corrected robust t-test. Generalized linear models were used when normal distribution was met. Multiple comparisons were employed with Tukey test. Significance level was set as p < 0.05. Material*thickness interaction did not significantly affect total porosity percentages of the tested groups (p > 0.05), whereas it influenced total number of voids significantly (p < 0.001). Regardless of different thicknesses, the composite resin's median total porosity and the number of total void values from the lowest to the highest total porosity percentages were as follows: INJ < OM < CH. The total number of voids increased as the thickness increased for the bulk-fill composite resins. Single-shade bulk-fill composite resins exhibited higher total porosity values.

Comprehensive characterization of interrelations between pork quality, flavor and colonic microbiota in Duroc × Landrace × Yorkshire pigs.

Inventory theory often assumes that demands during out-of-stock periods are backordered, although lost demands are arguably more realistic, especially in retail settings. This is, to a large extent, due to the fact that lost-sales systems are harder to analyse, especially if multiple orders can be outstanding. This paper considers the classic single-item, single-location, continuous review (R, Q) inventory model with lost sales under a cost minimization objective, where reorder level R is optimized given a fixed order quantity Q. Using approximation procedures, we derive a near-optimality condition that can be used to determine the reorder level. This can be seen as a modification and generalisation of the near-optimality condition presented by Hadley and Whitin (1963, Eq. (4-22)). A numerical analysis for a considerable set of instances shows that the new near-optimality condition outperforms that of Hadley and Whitin (1963), reducing the cost gap vs. the optimal solution from 0.12% to 0.01% under the assumptions of smooth demand (i.e. continuous or unit-sized demand) and at most a single order outstanding, and from 0.32% to 0.08% over all considered instances, including ones with multiple orders outstanding and lumpy demand. We also compare to settings where the cost expression uses the loss functions by Rosling (2002). This is optimal under the mentioned assumptions and has a cost gap of 0.24% over all considered instances. Since the conditions and the complex cost expression by Rosling (2002) are not easy to implement in real life, we also test simpler approximations based on Hadley and Whitin’s (1963) and the New near-optimality condition that take the quantile of a Normal distribution. These have cost gaps to the optimal solution of 0.71% and 0.32%, respectively.

Simulator-based training addresses a pressing need to move surgical skill acquisition from the operating room to the laboratory, reducing patient risk associated with traditional trial-and-error training methods. The Core Requisites of Orthopedic Wire Navigation Skills (CROWNS) curriculum employs a hybrid reality simulator to teach surgeons the foundational skill of placing a guidewire in bone via fluoroscopic imaging. Simulator-based training has already been shown to improve subsequent operating room performance, but objective benchmarks to assess skill proficiency are lacking. The objective of this study was to measure and compare expert and novice performance on a simulated hip wire navigation task as a basis to define candidate proficiency training benchmarks. This study was designed as an experimental assessment of a surgical simulation platform to differentiate between skill levels during a proctored examination. In all, 113 surgeons participated, including 68 orthopaedic residents (novices) and 45 experts (28 Orthopaedic Trauma Association fellows and 17 practicing orthopaedic surgeons). To achieve the primary objective of measuring and comparing expert and novice performance, all participants performed a simulated hip wire navigation task replicating guide wire placement for intertrochanteric fracture fixation. Experts completed three simulated patient scenarios: one represented a straightforward case with clear imaging and a typical femoral neck-shaft angle (∼ 135°), another required a steeper guidewire trajectory to match an increased neck-shaft angle, and another involved a slightly malreduced fracture with an imperfect lateral view, requiring greater judgment to determine appropriate wire placement. Novices completed only the straightforward case scenario. Performance metrics included tip-apex distance (TAD), fluoroscopic image count, time, decision errors, the angle of off-target adjustments, and the previously reported Image-based Decision Error Analysis (IDEA) composite score. The IDEA score is normalized, meaning that a participant's score reflects the number of SDs above or below the mean performance across a large resident cohort. Assuming a normal distribution of scores, 95% of participants would be expected to score between -2 and +2. Higher values indicate better performance generally associated with more surgical experience. The score combines three normalized components-TAD, angle error, and decision errors-where lower TAD and smaller or fewer errors yield a more positive score. Statistical analyses compared novice and expert cohorts to help inform proficiency benchmark recommendations. The expert cohort's simulator-based IDEA scores were then compared with prior intraoperative IDEA scores from residents. The expert cohort completed all three simulated patient scenarios with a mean ± SD time of 117 ± 60 seconds, using 17 ± 8 images, and achieving a TAD of 13 ± 5 mm. The expert cohort had 5 ± 4 decision errors, angle errors of 1.4° ± 1.8°, and an IDEA score of 0.65 ± 0.4. The novice cohort completed their single patient scenario with a time of 207 ± 88 seconds, using 22 ± 10 images, and achieving a TAD of 20 ± 7 mm. The novice cohort had 14 ± 7 decision errors, angle errors of 3.2° ± 3.0°, and an IDEA score of -0.21 ± 0.7. When comparing the performance of the expert and novice cohorts in the single shared case scenario, the expert cohort achieved a substantially higher IDEA score (0.62 ± 0.3 and -0.21 ± 0.7, respectively, mean difference 0.83 [95% confidence interval 0.58 to 1.07]; p < 0.001). Based on prior research linking simulator performance to actual surgical experience, the expert IDEA mean score of 0.65 was roughly comparable to a performance expected in novices after their first 11 completed wire navigation surgical cases. An IDEA score of 0.5 is proposed as a balanced proficiency benchmark, achieved by 75% of experts but only 13% of novices. The CROWNS simulator and IDEA composite score reliably distinguished expert from novice performance, underscoring the need for deliberate practice and targeted feedback to close the novice-expert gap. A benchmark IDEA score of 0.5 offers a rigorous yet attainable goal for residents, representing readiness for safe and efficient operating room participation. Objective, simulation-based proficiency standards-such as attaining an IDEA score of 0.5 for hip fracture pinning-can guide competency-based progression, ensuring that residents achieve the technical skill required for safe operating room performance. Future research should evaluate how such benchmarks translate to real-world outcomes and whether they can be optimized to further improve surgical training and patient safety.

Empirical modeling of vehicle-bicycle interactions on high-speed rural roads: passing lateral distance and speed.

This study aimed to analyze the impact of various beverages on the color change, surface roughness, and bacterial adhesion of nanohybrid and microhybrid composites. Four resin composites with different filler characteristics were evaluated: two microhybrid composites (Dr. Müller Rhein [DRM] and Herculite Classic [HCM]) and two nanohybrid composites (Dr. Müller Kaiser [DRN] and Clearfil Majesty Esthetics [CME]). Each resin composite group was divided into four subgroups (n = 12) for immersion in beverages (control: distilled water; coffee, cola, and red wine). Initial color and surface roughness were measured, and measurements were repeated after 12 days of immersion. Saliva was applied to the sterilized sample surfaces to stimulate pellicle formation, and the samples were incubated. To evaluate bacterial adhesion, colony counts of Streptococcus mutans (ATCC 25175) were reported as CFU/ml. Additionally, one sample from each subgroup was cultured for scanning electron microscopy. Two-way analysis of variance was used to compare variables with normal distributions across resin composites and beverages; pairwise comparisons were performed using the Tukey test. Two-way robust ANOVA was used for variables that were not normally distributed, and pairwise comparisons were performed using the robust t-test with Holm's correction. Results for quantitative variables are presented as mean ± standard deviation and median (minimum: maximum). The significance level was set at p < 0.05. Dr. Müller Kaiser and Clearfil Majesty Esthetics were the most resistant to discoloration (p < 0.05). The highest surface roughness and bacterial adhesion were observed in the microhybrid composites Dr. Müller Rhein and Herculite Classic, while the lowest bacterial adhesion was observed in the nanohybrid composites (p < 0.05). Red wine and coffee exhibited the highest discoloration values, with no statistically significant difference between them (p > 0.05), whereas cola showed the greatest effect on surface roughness. Exposure of resin composites to various beverages can negatively affect surface properties, color stability, and promote biofilm formation. These findings provide preliminary insight into the effects of commonly consumed beverages on resin composites under controlled laboratory conditions. However, further in vivo and clinically simulated studies are required before definitive clinical recommendations can be made. Considering patients' dietary habits during material selection may improve long-term esthetic and biological outcomes.

To assess the radiosensitivity of different pancreatic cancer (PC) cell lines and examine their differences regarding their sensitivity to radiation, and to quantify the inter-study variability. The parameters for the linear quadratic (LQ) model and a nonlinear log-logistic regression (LL) model were derived using the data from clonogenic assays reported in the literature. In this study, the cell survival data from 108 clonogenic assays for four PC cell lines: Panc-1, MiaPaCa-2, BxPc-3, and AsPc-1, were fitted to the LQ model and the LL model performing bootstrapping. The lognormal, the generalized extreme value and the normal distributions were parametrized to describe the distribution of model parameters. The best representation was used to determine the median value of the parameters and their interquartile (IQR) ranges for each cell line. For the LQ model, no differences in the radiosensitivity between the four cell lines were found. A medianαof 0.14 Gy-1[IQR 0.08-0.25],βof 0.04 Gy-2[IQR 0.03-0.06] andα/βof 3.2 Gy [IQR 1.6-6.7] were obtained. For the LL model, MiaPaCa-2 showed slightly higher radiosensitivity compared to BxPc-3 and AsPc-1 cell lines. The median value for all PC cell lines ofϕ2was 2.44 Gy [IQR 1.99-3.09] andϕ3was 2.54 [IQR 2.16-3.03]. The LQ model remains a robust method to assess radiosensitivity of cells, but due to its susceptibility to experimental heterogeneity, caution must be taken during the selection of a properα/βvalue. The LL model can offer a more reliable method than the LQ model to capture radiobiological differences between cells' response to radiation. There is a need for standardization of the clonogenic assays procedure, and a more thorough report of the setup used.

In medicine, multiple continuous outcomes are often repeatedly measured on each subject over time to assess disease severity. Usually, it is of interest to investigate the association between those outcomes, which may be measured at different time points, resulting in unbalanced data. The multivariate linear mixed-effects model (MLMM) is a popular framework for this analysis. It considers the unbalanced nature of the data and accounts for the association of the outcomes via the random effects, often assuming a multivariate normal distribution. However, measuring and understanding the degree of connection between longitudinal outcomes remains challenging. We propose to enhance the MLMM by incorporating various interpretable association structures. Specifically, we consider that multiple longitudinal outcomes are related to the primary outcome through their current value, cumulative effect (total or partial), or both. Our research is motivated by Pompe disease, a rare, inheritable, progressive metabolic myopathy. Clinically, it is important to investigate how patient-reported outcome measures (primary outcomes) are associated with physical outcomes to determine whether improvements in physical outcomes are accompanied by improvements in health-related quality of life and other patient experiences. We found a positive association between them. The proposed models are fitted under the Bayesian framework using Hamiltonian Monte Carlo.

Recent advances in data registration techniques have enabled researchers to analyze high-dimensional, low sample size (HDLSS) data, which poses challenges for traditional statistical and machine learning methods-especially in fields like neurodevelopmental disorder classification. Among various proposed solutions, the Data Maximum Dispersion Classifier (DMDC) has demonstrated superior performance. This study introduces two extensions: Sparse DMDC (SDMDC) and Longitudinal Sparse DMDC (LSDMDC), with the latter leveraging multi-way data frameworks. Simulated HDLSS datasets were generated from multivariate normal distributions with sparse structures. Evaluation of SDMDC involved varying class imbalance, predictor count, and correlation structures. Performance was measured via average F-measure and signal-to-noise ratio (SNR). Results showed that SDMDC outperformed other sparse models. Moreover, promising improvement was observed in model classification performance and the selection of effective predictors when the correlation between predictor variables was increased. In longitudinal simulations, LSDMDC maintained performance even as the number of predictors increased along the first dimension, effectively handling rank-1 and rank-2 data. Notably, LSDMDC achieved the best results under autoregressive correlation. In conclusion, SDMDC demonstrated clear advantages over other sparse classifiers, while LSDMDC proved highly effective for complex longitudinal structures.

Phenotypic trade-offs, predicted to occur due to resource limitation, are not commonly detected. For this, the Y-model provides a powerful heuristic by showing that greater variation in resource acquisition than variation in allocation, masks trade-offs. However, the Y-model contains several other parameters beyond variation in acquisition and allocation, whose independent and interactive influences remain unclear. We simulate the means of, variation in, and correlation between, acquisition and allocation, to systematically explore how these parameters influence the phenotypic association between two traits. We find that the mean resources acquired by a population has no direct mathematical influence on phenotypic correlations. Instead: the mean of the resource allocation of a population; the correlation between allocation and acquisition; and the ratio of variation in acquisition to variation in acquisition plus allocation, directly impact phenotypic correlations. Importantly, a three-way interaction between these parameters provides a better prediction of the phenotypic correlation than their independent effects. We validate our simulations using an empirical dataset and analytical solutions to demonstrate their robustness. Using several biological examples such as resource limitation, we show how Y-model parameters might manifest their influence on phenotypic correlations in empirical data. Despite the simplicity of our simulations and assumptions (e.g. sampling from normal distributions), our study provides a quantitative extension of the Y-model paradigm to understand the detectability of phenotypic trade-offs.

To determine gestational age-specific reference ranges and develop a Z-score equation for the fetal ductal pulsatility index (PI), between 24 and 33+6weeks of gestation. A prospective cross-sectional study conducted from 2023 to 2024, 226 singleton fetuses between 24 and 33+6weeks were enrolled. The ductus arteriosus (DA) was assessed in the longitudinal plane of the fetus using color Doppler to confirm its course and flow direction, and pulsed Doppler sampling was placed at the proximal DA with an insonation angle <15°. Three consecutive measurements of PI were recorded and averaged. Z-scores were derived from predicted means and standard deviations and reference percentiles (5th, 50th, and 95th) were computed for gestational age groups using quantile regression. Intraobserver reproducibility was evaluated in 30 randomly selected cases with an interval≥2weeks using the intraclass correlation coefficient (ICC). The mean gestational age was 28.2±3.1weeks. The ductal PI showed a mean of 2.4±0.26 with normal distribution (p=0.2525). The Z-score equation for PI was successfully derived Z-score = (Measured PI- 2.4)/0.26. Reference ranges for PI were: 24-27weeks (5th=2.0; 50th=2.4; 95th=2.7), 28-31weeks (5th=2.1; 50th=2.5; 95th=2.9), and 32-34weeks (5th=2.2; 50th=2.4; 95th=2.5). Intraobserver reproducibility for PI was excellent (ICC >0.90). We established gestational age-specific reference ranges and a Z-score equation for the ductal PI in normal fetuses between 24 and 33+6weeks. These findings provide a standardized tool for fetal cardiologists to identify subtle ductal flow abnormalities and support perinatal risk stratification.

The meniscus is essential for knee joint biomechanics, contributing to load distribution and stability. Variations such as the anterior medial meniscofemoral ligament (AMMFL) originating from the anterior horn of the medial meniscus (AHMM) are rare but significant. These variations may predispose individuals to medial meniscus injuries and knee dysfunction, though their biomechanical implications remain unclear. The AMMFL disrupts normal anchorage of the AHMM to the tibial plateau, increasing meniscal mobility and potentially altering load transmission and predisposing to degeneration or tears. Case 1: A 49-year-old woman presented with left knee pain without trauma. MRI identified a medial meniscus posterior root tear (MMPRT). During arthroscopy, an AMMFL was discovered connecting the AHMM to the intercondylar notch. The MMPRT was repaired with transtibial suture fixation, while the AMMFL was retained due to its non-impinging nature. Quantitative MRI revealed an AMMFL measuring 3mm × 2.1mm with low signal intensity. At 10-month follow-up, the patient reported significant pain relief and restored knee function (e.g., resolution of squatting difficulty, KOOS score improved from 48 to 84). Case 2: A 58-year-old woman with chronic right knee pain and osteoarthritis underwent unicompartmental knee arthroplasty. During surgery, a free AHMM attached to the intercondylar notch via a tendon-like AMMFL was observed and excised. Intraoperative measurement showed a 27mm ligament-like structure, confirmed histologically as dense parallel collagen bundles without fibrocartilage. At 8-month follow-up, the patient walked freely; full weight-bearing achieved by 4 weeks (Oxford Knee Score improved from 19 to 39). Treatment differed based on impingement status and degenerative context: retention after root repair in Case 1 versus excision during UKA in Case 2. Both cases revealed below-average medial posterior tibial slopes (MPTS, case 1 is 4.49° and case 2 is 2.17°), questioning the biomechanical role of AMMFL in MMPRT development. AMMFL is a rare anatomical variant with unclear biomechanical roles. The abnormal attachment of the AMMFL could potentially alter normal meniscal kinematics and load distribution. This anatomical configuration may be associated with posterior root tears even in knees with a low tibial slope, suggesting a possible biomechanical vulnerability that warrants further investigation. This potential biomechanical vulnerability-inferred from the anatomy and the presentation of MMPRT with low MPTS-highlights the importance of meticulously evaluating the posterior horn and root attachment in patients with an AMMFL on MRI. Although a causal link remains unproven, the association suggests these patients may be prone to tears despite the absence of other traditional risk factors. Awareness of this variant is crucial for accurate imaging diagnosis and arthroscopic evaluation, as it may identify knees at biomechanical risk for posterior root tears, warranting further investigation.

BackgroundDistraction is a safe and effective technique that diverts a child’s attention from anxiety‐provoking stimuli. This study aimed to evaluate the efficacy of thumb sleeves as a visual distraction and Pop‐It toys as therapeutic play in reducing dental anxiety and pain in children aged 4–6 years undergoing mandibular alveolar nerve block.Materials and MethodsThis double‐blind trial included 100 children aged 4–6 years, randomly allocated into four parallel groups: Group I (Control)—Tell–Show–Do (TSD) alone; Group II—TSD with visual distraction (thumb sleeve); Group III—TSD with sensory distraction (Pop‐It toy); Group IV—TSD with both visual and sensory distractions. Pulse rate was measured at baseline (T0), after behavior management (T1), and 2 min after local anesthesia (T3). Dental anxiety was assessed using the Facial Image Scale at T0 and T3, and pain was measured using the Face, Legs, Activity, Cry, Consolability (FLACC) scale at T2. Statistical significance was set at p < 0.05. Kolmogorov–Smirnov was used to test the normal distribution, and the Kruskal–Wallis test was used to compare the studied groups.ResultsAnxiety and pain scores varied among the groups. Based on pulse rate, Group III (TSD with sensory distraction) showed the lowest anxiety after behavior management, while Group I (TSD alone) showed the highest. After 2 min of anesthesia, Group IV (combined visual and sensory distraction) demonstrated the lowest anxiety, whereas Group II showed the highest. According to the Facial Image Scale, Group IV recorded the lowest anxiety and Group III the highest at T3. In terms of pain (FLACC), Group IV showed the lowest scores, while Group I recorded the highest during anesthesia administration. However, the Kruskal–Wallis test revealed no statistically significant differences between groups in pulse rate, Facial Image Scale, or FLACC scores (p > 0.05).ConclusionAlthough no statistically significant differences were found between the studied groups, the use of distraction techniques combined with TSD showed a trend toward greater reduction in dental anxiety and pain compared with TSD alone. These approaches may be considered as supportive behavior management tools during inferior alveolar nerve block (IANB) in young children.Trial Registration: ClinicalTrials.gov identifier: NCT06976047

Abstract The application of spur-type varieties is crucial for achieving dwarfing and dense planting cultivation. However, the underlying mechanisms regulating the spur-type phenotype remain unclear. In this study, we created an F1 population by crossing ‘Hanfu’ (HF, spur-type) with ‘Wangxianghong’ (WXH, standard-type) and observed that the spur rate in F1 hybrid population followed a normal distribution. Though bulked segregant analysis (BSA) and RNA-sequencing analysis (RNA-seq), we identified 85 differentially expressed genes across three QTL regions were screened. Interestingly, small auxin-up RNA 14 (MdSAUR14) exhibited distinct expression patterns in ‘HF,’ ‘WXH,’ and their extreme hybrid populations, suggesting a close association between its expression levels and the spur-type phenotype. MdSAUR14-overexpressed apple lines exhibited greater plant height and increased average internode length compared to wild type (WT) plants. A double base substitutions (DBS) variation in the MdSAUR14 promoter is closely linked to the spur phenotype. Furthermore, the presence of the DBS impacts the binding ability of BRASSINAZOLE-RESISTANT 1 (MdBZR1), which subsequently influences MdSAUR14 activity. Our study elucidates DBS-related genetic variation and mechanisms underlying shoot elongation in spur-type varieties.

A Matrix-Variate Skew Contaminated Normal Distribution with Applications to Model-Based Clustering

Objective: To investigate the mechanism by which inhibition of Rho GTPase-activating protein 29 (ArhGAP29) affects epithelial-mesenchymal transition (EMT) and the progression of pulmonary fibrosis induced by silicon dioxide (SiO(2)) . Methods: From September 2024 to June 2025, forty SPF-grade male C57BL/6N mice were used to establish a silicosis model via intratracheal instillation of SiO(2). At 14 days after SiO(2) exposure, adeno-associated virus (AAV) carrying ArhGAP29 inhibitor was administered to infect the lungs of silicotic mice. Mice were divided into four groups (n=10 per group) : saline group (intratracheal instillation of sterile saline), SiO(2) group (intratracheal instillation of 50 μl 50 mg/ml SiO(2) suspension), SiO(2)+empty vector AAV group (AAV-BLANK group, SiO(2) modeling followed by negative control AAV intervention), and AAV-si-ArhGAP29 group (SiO(2) modeling followed by targeted ArhGAP29 interference AAV intervention) .Hematoxylin-eosin (HE) staining was performed to observe pathological changes in lung tissues. Hydroxyproline (HYP) content detection was used to evaluate the degree of pulmonary fibrosis. Western blotting was applied to detect the protein expression levels of ArhGAP29, α-smooth muscle actin (α-SMA) and E-cadherin (E-cad) in lung tissues. Immunofluorescence (IF) staining was conducted to detect the expression and localization of α-SMA and ArhGAP29. Intergroup comparisons between two groups with normal distribution and homogeneity of variance were analyzed by Student's t-test, while non-parametric tests were adopted for data not conforming to normal distribution. Results: Compared with the saline group, the expression levels of ArhGAP29 and α-SMA were significantly upregulated, while E-cad expression was markedly downregulated in lung tissues of silicotic mice (P<0.05). Compared with the AAV-BLANK group, the number and area of silicotic nodules were significantly reduced, pulmonary fibrosis was markedly alleviated, α-SMA expression was downregulated and E-cad expression was upregulated in the AAV-si-ArhGAP29 group, with statistically significant differences (P<0.05) . Conclusion: Inhibition of ArhGAP29 expression effectively alleviates SiO(2)-induced pulmonary fibrosis, and its mechanism may be associated with the suppression of epithelial-mesenchymal transition (EMT) .

An infection of the lung that mostly affects the alveolar space is known as pneumonia. According to the Global Burden of Disease Study, infection of the lower respiratory tract was the second most common cause of death and years of life lost. This study aims to study the serum N-terminal pro-B-type natriuretic peptide (NT-proBNP) level in pneumonia as well as its correlation with Confusion, Urea (Raised blood urea), Respiratory rate (>30/min), Blood pressure (low), 65 (age >65 years) (CURB-65) score for determining the severity of disease. An institution-based descriptive observational study with cross-sectional design was conducted in the Department of Medicine, RIMS, Ranchi, from October 2023 to September 2024. All adult patients aged 18 years and above were admitted with clinical and radiological signs of community-acquired pneumonia. Descriptive analysis was done in the form of proportion for categorical variables and median (interquartile range) and mean (standard deviation [SD]) for continuous variables. Continuous variables were checked for normal distribution, and nonparametric test (Mann-Whitney U test) was done accordingly. Spearman's correlation and Pearson correlation statistics was performed to assess the relationship between NT-proBNP and severity of the disease. The difference between proportions was analyzed using the Chi-square test, and P < 0.05 was considered statistically significant. Data were entered into MS Excel and analyzed using the SPSS version 20. The study was conducted on 145 patients. The mean (SD) NT-proBNP level was 937.04 (1243.03) pg/ml. Around 11% of the patients expired during hospital stay and 89% were discharged. The respective mean NT proBNP level for the particular variable like patients with age >60, tobacco consumption, diabetes, hypertension, higher grades of the Modified Medical Research Council Scale for Dyspnoea (MMRC) and higher score of CURB 65 score was more than their counterparts. The mean NT-proBNP level was significantly higher among the patients expired during hospital stay than the patients who were discharged from the hospital. NT-proBNP was positively and significantly correlated with age, CURB-65 score, hospital stay, and MMRC grade with Spearman's correlation coefficient of 0.57, 0.83, 0.73, and 0.79, respectively. The study demonstrated that levels of NT-proBNP positively correlated with CURB-65, MMRC grades, and mortality among the patients with pneumonia. Hence, NT-proBNP levels might be an effective parameter in predicting the disease course among the patients with pneumonia.