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Advancements in psoriasis classification using custom transfer learning algorithms

Loss of IL1RA promotes prostate cancer growth and metastasis by activating Akt signaling pathway

Background. Interleukin-1 receptor antagonist (IL1RA) blocks the interaction between IL-1 and its receptors. It modulates inflammatory responses, cell proliferation, and the invasion of cancer cells. In this study, we examined the biological functions of IL1RA and the mechanisms that influence its effects on prostate cancer (PCa). Materials and methods. We performed RT-qPCR and Western blot analyses to evaluate IL1RA expression levels in various cell lines. For functional studies, we employed MTT, colony formation, soft agar, wound healing, and transwell migration and invasion assays. Then, we analyzed Western blots to elucidate the underlying mechanisms involved. Xenograft mouse models were ultimately established after the overexpression of IL1RA. Results. IL1RA was expressed at higher levels in prostate epithelial cells compared with the PCa cell lines. BPH cells with lower IL1RA expression exhibited an increased cell proliferation. The PCa cell lines C4-2B and LNCap, which overexpressed IL1RA, demonstrated suppressed tumorigenic properties in vitro. The in vivo experiment demonstrated an inhibitory effect on tumor growth in xenograft mice. Furthermore, Western blot results indicated elevated phosphorylated AKT levels in BPH cells with IL1RA knockdown, and phosphorylated AKT and GSK-3 β levels were reduced in C4-2B and LNCap cells that overexpressed IL1RA. Conclusion. This study revealed that IL1RA low expression is associated with PCa progression. Our finding has great clinical and translational significance. The potential clinical application of IL1RA as a therapeutic target for PCa requires further investigation.

The biostimulatory effect of microalgae extracts upgrades salt tolerance and antioxidant capacity in flowers and shoots of Cuminum cyminum L.

Wall-L merge sort: A tunable and adaptive sorting algorithm for diverse computing environments

Sorting algorithms play a crucial role in computing, but most are designed with rigid structure that are only efficient under certain conditions. Although some sorting algorithms perform well in some circumstances, they do not perform well on some resistant platforms. This study introduces Wall-L Merge Sort, which combines quadratic sorting with a modifiable multi-layer merging approach. By setting a single parameter, L , which determines the number of merge layers, Wall-L Sort shows a transition in the time complexity from O ( n 2 ) to O ( n log n ) without any modification in the unique idea. This degree of freedom enables a broad variety of input sizes to be encompassed and expands to several constraint platforms. The results show that Wall-L Sort and K-way Merge Sort have the built-in ability to handle different situations where other algorithms fail without assistance functions. Wall-L Merge Sort is the only sorting algorithm that combines complexity tuning, cache efficiency, recursion depth control, parallelism, and broad adaptability into one framework. It may not be the best choice for every situation, but its flexibility makes it a good fit for many different platforms, from small embedded systems to big computing systems. The theoretical and empirical evidence in this paper substantiates these advantages.

Effect of cutting process parameters on fatigue properties of quenched and tempered 42CrMo steel

Abstract Quenched and tempered 42CrMo steel is a commonly used material for critical components such as high-strength bolts, spindles, and transmission shafts, where fatigue failure induced by alternating loads is its primary failure mode. Machining, as a pivotal step connecting upstream and downstream processes in the manufacturing chain, significantly influences the fatigue performance of metal parts through the surface integrity it generates. Under wet cutting conditions, this study systematically conducted experiments to investigate the effect of cutting parameters—cutting speed ( v ), feed rate ( f ), and depth of cut ( $${\text{a}}_{\text{p}}$$ )—on fatigue performance, The influence of cutting parameters on surface roughness and residual stress was analyzed. Based on this analysis, a weighted standardization method integrating both roughness and residual stress was proposed for the comprehensive evaluation of fatigue life. The feasibility of this method was subsequently verified and analyzed through experiments. The results indicate that cutting speed exerts the most significant influence on surface roughness, while the distribution of residual stress is also considerably affected by cutting speed. Rotating bending fatigue tests and fracture analysis demonstrate that crack initiation and propagation result from the synergistic effect of surface roughness and residual stress, with surface residual compressive stress and its gradient distribution playing a dominant role in determining fatigue life. The novel weighted criterion proposed in this study exhibits strong consistency with fatigue life, providing both experimental evidence and a theoretical tool for optimizing cutting parameters and enhancing the service performance of 42CrMo critical components.

Synergistic anti-icing and snow-melting performance of two-component road markings enabled by PCMs and slow-release salts

Conventional road marking coatings suffer significant performance deterioration under winter conditions, including frost coverage, reduced retroreflectivity, and low-temperature embrittlement. This study presents a functional two-component road marking coating incorporating a composite anti-icing additive composed of a temperature-regulating phase-change material (TH-ME5) and a salt-based slow-release agent (T-SEN). The influence of additive content and the TH-ME5/T-SEN ratio on coating properties, road performance, and ice/snow mitigation was systematically evaluated. Results show that a total additive content of ≤20 wt.% maintains compliance with standard requirements for adhesion, flexibility, wear resistance, drying time, retroreflectivity, hiding power, alkali resistance, and UV aging. Ice adhesion tests reveal a two-stage anti-icing mechanism: TH-ME5 provides latent heat buffering during early freezing, while T-SEN governs long-term deicing. The optimal formulation—20 wt.% additive with a TH-ME5:T-SEN ratio of 1:3—achieved the lowest relative ice adhesion. Snow-melting simulations further demonstrate the coating’s ability to delay ice formation and reduce surface snow accumulation. This PCM–salt synergistic approach provides a feasible and scalable strategy for durable, self-deicing pavement markings in cold regions.

Satellite remote sensing enables monitoring of soil organic carbon decline in croplands of Jilin China

Abstract Soil organic carbon (SOC) is a key parameter for soil quality. As one of the major grain-producing regions of China, Jilin Province plays a critical role in ensuring national food security, making cropland SOC monitoring essential. Based on satellite remote sensing observations, this study reveals an overall 5.14% decline in SOC across croplands in Jilin Province over the past seven years. Losses were most pronounced in the west, while the central and eastern areas remained relatively stable. Conventional SOC estimation methods largely rely on machine learning, which can lack physical interpretability and reproducibility. PLSR-based SOC models achieved validation R 2 values of 0.40–0.61 with corresponding RMSEs of 0.30–0.38 across MODIS Terra, Landsat OLI, and Sentinel-2 MSI. The quantitative models exhibit satisfactory validation accuracy but limited spatial robustness across sensors in practical mapping. This study proposes a new broadband spectral index, the Ratio Soil Index (RSI), applied at 30-meter resolution. Using field synchronized SOC measurements and spectral analysis, we developed broadband indices from MODIS Terra, Landsat OLI, and Sentinel MSI. The RSI showed strong correlations with measured SOC, with coefficients of 0.72, 0.74, and 0.77 for the three sensors. Its spatial patterns were consistent with ground observations within the 95% confidence interval. The findings demonstrate that the RSI, with its concise formulation, reliable mapping performance, and ability to identify the variations of SOC, offers a scalable and reproducible metric for national SOC monitoring under changing agricultural management.

Predictors of exclusive breastfeeding among infants under-six months in rural Ethiopia

Background Exclusive breastfeeding (EBF) is the practice of providing infants with only breast milk for the first six months of their life, except for medically prescribed drugs or supplements. Globally, EBF prevalence varies with low awareness in developing countries including Ethiopia. Hence, this study identified the significant predictors of the duration of EBF among infants fewer than six months in rural Ethiopia. Methods A survival analysis was used to identify the significant predictors of EBF in rural Ethiopia using the 2019 Ethiopia Mini Demographic and Health Survey (EMDHS) dataset. Results During the survey period in rural Ethiopia, a significant proportion of mothers (15.1%) do not exclusively breastfeed their infants, which still represents a major health problem. The final multivariable Weibull parametric survival model identified significant predictors of the duration of EBF in rural Ethiopia, such as, mothers with radio access (HR = 1.274, p-value 0.020), mothers delivered by C-section (HR = 0.573, p-value <0.001), mothers residing in Amhara (HR = 0.406, p-value = 0.025), Oromia (HR = 0.379, p-value = 0.017), Somali (HR = 0.112, p-value < 0.001), SNNPR (HR = 0.296, p-value = 0.002), Gambela (HR = 0.285, p-value = 0.002), Harari (HR = 0.220, p-value < 0.001), and Dire Dawa (HR = 0.234, p-value < 0.001), EIBF immediately within the first hour (HR = 1.554, p-value < 0.001), infants aged 0–1 year (HR = 0.226, p-value < 0.001), birth interval of 24 months and more (HR = 0.819, p-value = 0.034), and mothers who were currently married (HR = 0.514, p-value = 0.016). Conclusion The duration of EBF among infants less than six months in rural Ethiopia is significantly influenced by household access to radio, cesarean delivery, region, early initiation of breastfeeding, child’s age, preceding birth interval, and marital status at the 5% significance level. To improve EBF practices, there must be targeted interventions that are relevant to regions and better support for 15.1% of mothers.

Correction: Effect of multiple calcination cycles on CO2 capture efficiency during carbonation of MgO in a mineral looping process

Relationships among fertility concerns, fear of cancer recurrence, social support, self-efficacy, and family resilience among Chinese adolescents and young adults with cancer: A structural equation modeling

Fertility concerns, fear of cancer recurrence, social support, and self-efficacy are key factors that influence family resilience in adolescents and young adults with cancer. However, their combined effects and underlying mechanisms remain unclear. This study constructed a structural equation model to examine these relationships and provide evidence for the development of targeted psychosocial interventions in clinical practice. A cross-sectional survey was conducted from April 2024 to March 2025 using convenience sampling of 259 adolescents and young adults with cancer at a tertiary-level specialized cancer hospital in Hunan Province. All the participants completed validated measures of fertility concerns, fear of cancer recurrence, social support, self-efficacy, and family resilience. A structural equation model was constructed using the AMOS software to test the hypothesized pathways. Correlation analysis revealed significant associations between all variables. The structural equation model demonstrated good fit. Path analysis revealed that fertility concerns and fear of recurrence exerted significant negative effects on family resilience, while social support and self-efficacy exerted significant positive effects. Further analysis revealed that social support and self-efficacy partially mediated the relationships between fertility concerns, fear of cancer recurrence, and family resilience. Family resilience among adolescent and young adult patients with cancer was significantly associated with fertility concerns, fear of cancer recurrence, social support, and self-efficacy. Social support and self-efficacy can mitigate and mediate the negative effects of fertility concerns and the fear of cancer recurrence on family resilience. Therefore, future clinical interventions should prioritize enhancing social support and self-efficacy among adolescents and young adults with cancer to alleviate fertility concerns and fear of cancer recurrence, thereby strengthening family resilience.

Correction: Microwave assisted green synthesis of silver nanoparticles using Trigonella Hamosa L. plant extract for the photodegradation of some water pollutants

Boosting brain tumor segmentation: A novel 3D pooling approach with U-net 3D

Brain tumor segmentation is a crucial task in medical imaging that has a significant impact on diagnosis and treatment planning. This study introduces a novel 3D pooling layer within the U-Net 3D architecture to enhance segmentation accuracy from multimodal MRI. The method addresses the limitations of conventional pooling techniques by considering the interdependencies between MRI pixels, thereby improving the model’s ability to capture complex tumor structures. To increase robustness to intensity variation, two complementary normalization pipelines were trained independently with identical networks, and predictions from selected epochs were fused by simple probability averaging to form the final ensemble. Evaluation was conducted on BraTS2020 using five-fold cross-validation. On the validation set, the ensemble achieved Dice (ET/TC/WT)=0.8299/0.8882/0.8986 and HD95=4.40/4.95/11.14, reflecting consistent gains over max-pooling variants and comparing favorably with recent methods while using a lightweight fusion mechanism. These results confirm the effectiveness of the proposed 3D pooling approach and pave the way for more robust algorithms in automated brain tumor segmentation.

Mechanical characterization of PETG – carbon fiber composite parts using 3D printing for drone frame application

Intratumoral spatial heterogeneity at non-contrast CT predicts histological grading of invasive pulmonary adenocarcinoma: a multicenter retrospective study

Objectives The International Association for the Study of Lung Cancer (IASLC) grading system is key to the prognosis and treatment of Invasive Pulmonary Adenocarcinoma (IPA). However, current radiomics and other radiological approaches poorly capture tumor heterogeneity, limiting predictive power. This study aimed to develop an interpretable CT-based model that predicts the histological grading of IPA by decoding its intratumoral spatial heterogeneity. Materials and methods This multi‑center retrospective study enrolled 355 IPA patients, split into training/validation (7:3) and an independent test cohort. Tumors were graded as low‑grade (Ⅰ/Ⅱ) or high‑grade (Ⅲ) per IASLC criteria. Intratumoral subregions were generated via unsupervised clustering of CT images, and their spatial interaction heterogeneity was quantified using a Multi-regional Spatial Interaction (MSI) matrix. Five models (clinical‑radiological, radiomics, MSI, radiomics‑combined, MSI‑combined) were built using four preprocessors and five classifiers. The optimal model was selected based on the Receiver Operating Characteristic (ROC) curve in the validation cohort, with generalizability assessed in the test cohort. Performance was compared via the DeLong test, and SHapley Additive exPlanations (SHAP) analysis interpreted feature contributions. Results Three subregions were generated. The high-grade group exhibited a larger proportion of Subregion 1, while showing a smaller proportion of Subregion 2. The MSI model based on 10 MSI features achieved an AUC of 0.806 in the test cohort, outperforming clinical‑radiological, radiomics, and radiomics‑combined models (p = 0.002, 0.010, 0.022). Adding clinical‑radiological features did not improve the MSI model (p = 0.083). SHAP identified MSI_border_proportion_2_3 (relative border proportion between Subregions 2 and 3) as the most influential feature, with lower values indicating high‑grade IPA. Conclusion The CT-based MSI model can predict the histological grade of IPA by decoding the spatial interaction heterogeneity of different subregions in the tumor, thereby providing reliable imaging evidence for preoperative individualized risk assessment.

Super-twisting algorithm-based fuzzy sliding mode control for descriptor T-S fuzzy systems

Autapses enable temporal pattern recognition in spiking neural networks

Most sensory stimuli are temporal in structure. How action potentials encode the information incoming from sensory stimuli remains one of the central research questions in neuroscience. Precise spike timing is known to represent information in spiking neuronal networks, yet the information processing mechanisms of spiking neuronal networks is poorly understood. One feasible way to understand the processing mechanism of a spiking network is to associate the structural connectivity of the network with the corresponding functional behaviour. This work demonstrates the structure-function mapping of spiking networks evolved (or handcrafted) for a temporal pattern recognition task. The task is to recognise a specific order of the input signals so that the output neuron of the network spikes only for the correct placement and remains silent for all others. The minimal networks obtained for this task revealed two complementary roles of autapses in recognition. First, autapses enable a seamless transition to the next network state when a new input signal arrives. Second, in the absence of the input signal, they allow the network to maintain a network state for an extended period, a form of memory. To show that the recognition task is accomplished by transitions between network states, we map the network states of a functional spiking neural network (SNN) onto the states of a finite-state transducer (FST). Finally, based on our understanding, we define rules for constructing the topology of a network handcrafted for recognising a subsequence of signals in a particular order. The analysis of minimal networks recognising patterns of different lengths revealed a positive correlation between the pattern length and the number of autaptic connections in the network. Furthermore, in agreement with the behaviour of neurons in the network, we were able to associate specific functional roles of ’locking,’ ’switching,’ and ’accepting’ to neurons.

Distinct changes in riparian sediment microbial communities with depth and time since dam removal

Structural damage detection and safety assessment method based on machine vision and machine learning

Structural damage detection and health assessment are crucial for maintaining infrastructure safety and durability. This study presents a novel multi-scale vision-based framework that combines deep learning and machine learning for accurate and interpretable structural safety evaluation. Specifically, we integrate ResNet-50 and SegFormer models to jointly achieve coarse-level damage classification and fine-grained pixel-level segmentation. Seven key damage parameters are quantitatively extracted from high-resolution images—such as crack length, spalling area, and rebar exposure—and serve as interpretable features for safety assessment. A Random Forest (RF) model is developed to establish a nonlinear mapping from these visual features to structural safety levels. Experimental results demonstrate that the RF-based safety assessment model outperforms other traditional machine learning approaches, achieving an accuracy of 87.0%, F1-score of 0.76, and AUC of 0.83, highlighting its strong generalization and classification capabilities. This work offers a comprehensive and generalizable solution for automated structural damage detection and safety evaluation.

Eco-friendly synthesis of Balanites aegyptiaca-derived selenium nanoparticles: extract and assessment of their anticancer, antimicrobial, cytogenetic and molecular docking insights

Abstract This study reports the eco-friendly synthesis of selenium nanoparticles (SeNPs) using the methanolic extract of Balanites aegyptiaca mesocarp and evaluates their biological activities. The synthesized spherical SeNPs (average size: 2.82 nm) were characterized by TEM, FESEM, and UV–Vis spectroscopy, confirming that phenolic compounds serve as both reducing and stabilizing agents. HPLC analysis revealed eight major phenolics, with gallic acid, chlorogenic acid, and daidzein being the predominant compounds. The SeNPs exhibited strong cytotoxicity against HCT-116 colorectal cancer cells (IC₅₀ = 30.03 µg/mL), potent antibacterial activity against Klebsiella pneumoniae , Escherichia coli , and Enterococcus faecalis , and induced concentration-dependent cytogenetic effects in Vicia faba root tips. Molecular docking studies suggested that phenolic compounds effectively interact with the CDK4 active site, supporting their potential anticancer properties. These findings highlight B. aegyptiaca -derived SeNPs as promising candidates for biomedical applications.

Iodine increases pulmonary type I interferon responses and decreases covid-19 disease severity: Results from an open label randomized clinical trial

Objective To investigate whether oral treatment with 12.5 mg iodine additional to standard of care is effective in reducing mortality and clinical deterioration of patients hospitalized with COVID-19. Methods We performed a single center, randomized clinical trial (EudraCT 2020-001852-16) in which patients with severe covid-19 in need of hospitalization were randomized in two groups. The first group received 12.5 mg oral iodine for 8 days, the second group did not receive iodine next to the standard of care. Primary endpoints were deterioration of disease defined as transfer from the ward to the intensive care unit (ICU) or death. Next to these parameters we collected parameters in line with the recommendations made by the WHO in the early days of the pandemic. On these additional datasets we performed an exploratory analysis and investigated possible confounders and trends. The inclusion phase of the study was between October 2020 and April 2022. Finally, in vitro validations were performed. Results Outcomes from 141 participants were analyzed, revealing no significant differences in mortality or transfers to intensive care between the iodine-treated group (67 patients) and the control group (74 patients). In an exploratory analysis we found that patients randomized to receive oral iodine had a significantly shorter stay at the ICU ( p  = 0.016). In vitro validations proved increased virus-induced type I interferon responses upon iodine administration in pulmonary cells. Conclusion These findings suggest that while iodine does not reduce mortality or ICU admissions, it may enhance antiviral immunity through increased type I interferon responses, contributing to shorter ICU stays in COVID-19 patients. The role of iodine in enhancing IFN-I mediated antiviral immunity warrants future research. Registration of trial: EudraCT Number: 2020-001852-16. https://www.clinicaltrialsregister.eu/ctr-search/search?query=2020-001852-16 Sponsor name: Maxima Medical Center. Date of Registration: April 1 st 2020.