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Evaluation of the properties of gel, film and paper coated with nanocellulose and nanochitin
Mucin expression in pancreatic ductal adenocarcinoma cell lines in 2D and 3D cultures: A proteomic and immunocytochemical analysis
Pancreatic ductal adenocarcinoma (PDAC) exhibits diverse phenotypes, including epithelial and mesenchymal characteristics, yet these features have not been effectively translated into clinical applications. Mucins are implicated in tumor progression and therapeutic resistance and are considered potential diagnostic and therapeutic targets. In this study, five epithelial and three mesenchymal PDAC cell lines were cultured under two-dimensional (2D) and three-dimensional (3D) conditions to investigate mucin expression. Proteomic analysis identified five mucins (MUC1, MUC4, MUC5B, MUC19, and MUC20) in 2D culture and eight (including MUC2, MUC5AC, and MUC13) in 3D culture. Candidate mucins were further validated by immunocytochemistry with H-score assessment. MUC1 was consistently expressed in all PDAC cell lines and showed marked upregulation in several lines under 3D culture. In mesenchymal PDAC cell lines, mucin expression was largely restricted to MUC1, whereas epithelial lines displayed broad 3D-induced reorganization. Notably, MUC5AC was absent in 2D culture but robustly induced in all epithelial PDAC cell lines under 3D conditions. Other mucins, including MUC2, MUC4, MUC5B, MUC13, MUC19, and MUC20, were variably upregulated, with epithelial lines demonstrating higher diversity and intensity of expression. These findings demonstrate that 3D culture effectively reveals the plasticity and heterogeneity of mucin expression in PDAC, highlighting its potential as a platform for biomarker discovery and the development of therapeutic strategies.
Nasopharyngeal metagenomics of symptomatic healthcare workers provides insights into the respiratory microbiome and antimicrobial resistance
‘Climate free fall’: why the biggest risk to our economies is yet to be recognized
Behavior of wild tigers in captivity at the Central Zoo, Nepal
The majority of tigers ( Panthera tigris ) maintained at the Central Zoo in Jawalakhel, Nepal represents rescues resulting from human-wildlife conflict situations. Their behavior in captivity can be influenced by a variety of factors including environmental and social variables such as visitor presence, sex of the tiger, and duration of captivity. In this study, we investigated how visitor presence, sex, and captivity duration influence the behavior pattern of captive tigers. Behavioral observations were conducted on four adult tigers (two males and two females) across summer and monsoon seasons using instantaneous scan sampling guided by a detailed ethogram and supported by CCTV recordings. Data were collected during both peak visiting hours and off-peak periods. Overall, the tigers exhibited high levels of inactivity, predominantly sleeping (43.7%) and resting (34.2%). Visitor presence significantly affected activity patterns of both male and female tigers (p < 0.001), marked by increased pacing and drinking behavior during peak hours. The off peak hours were dominated by resting and sleeping. Females tigers displayed more pacing and exploratory behaviors, while males were more sedentary. A recently rescued female (n = 1) exhibited higher level of play and exploration, whereas long-term captives showed elevated pacing and more repetitive and predictable routines (p < 0.001). Visitor presence, sex, and duration in captivity interactively shape the behavior expression and welfare indicators in these captive tigers. These findings highlight the importance of effective visitor management and environmental enrichment and play an important role in establishing welfare strategies to mitigate stress in captive tigers.
Geochemical and remote sensing insights into the hydrothermal origin of Banded Iron Formations at the Fatira Site, Northern Egyptian Nubian Shield
Abstract This study combines remote sensing, geochemical data, mineralogical analyses, and field investigations to examine the hydrothermal origin of the Banded Iron Formations (BIFs) in the Fatira region of the Egyptian Nubian Shield. The BIFs are situated within sheared metavolcanic sheeted dykes that form part of the ophiolitic mélange belt. These rocks show varying degrees of shearing due to the Fatira Shear Zone, which mainly consists of protomylonite, mylonite, and ultramylonite. The area is included in the Barud Gneissic Complex, along the ENE-trending dextral shear zone of the Qena-Safaga Line. The protolith underwent crustal shortening, leading to dextral movement along the Fatira Shear Zone. A study utilizing Landsat-8 imagery and the SAM method applied to ASTER data identified iron oxides, along with zones of chlorite and CO3-OH-bearing minerals, in the study area. The SAM algorithm mapped the distribution of common iron minerals, showing moderate to high lineament density and metavolcanic composition. The BIFs appear as thick single bands (3–4 m) along the outer edges of the shear zone and as thinner multiple bands within its central part. Field observations and mineralogical analyses indicate that the single bands consist of martitized magnetite-silicate facies, suggesting they are relatively unaltered. In contrast, the multiple bands comprise magnetite-chert-jasper facies, indicating significant alteration. Geochemical analyses support a hydrothermal genesis for the BIFs, with shearing planes acting as pathways for fluid flow. It is inferred that the protoliths of the BIFs originate from the host sheared metavolcanic sheeted dykes, which were affected by hydrothermal solutions. This research highlights the importance of integrating fieldwork, remote sensing, and laboratory analyses to understand better the formation and distribution of BIFs in tectonically active regions. The findings shed light on hydrothermal-related BIF formation within the sheared metavolcanic setting of the Wadi Fatira area and provide a useful framework for understanding iron mineralization in similar tectonically controlled environments.
Africa’s response to this Ebola outbreak shows how to shape global health
Construction of MUC2 promoter-type periodontitis DNA vaccine candidate plasmid and preliminary exploration of the effect of Lactobacillus rhamnosus on its expression
Background Conventional cytomegalovirus (CMV) promoter-type periodontitis vaccines can offer protection against periodontitis; however, their antigen genes can be widely expressed in the body, resulting in significant toxic effects. This study aimed to modify the CMV promoter for targeted expression of antigen genes, investigate appropriate methods for regulating its expression, and explore its regulatory mechanism. Methods We searched for the MUC2 promoter sequence in the UCSC database, replaced the original broad-spectrum CMV promoter type pVAX1-CMV pro - HA2-fimA periodontitis DNA vaccine candidate with the MUC2 promoter sequence, and constructed the pVAX1-MUC2 pro -HA2-fimA-EGFP periodontitis DNA vaccine. We assessed the intestinal-specific expression ability of the MUC2 promoter-type periodontitis DNA vaccine candidate by transfecting different tissue cells. Subsequently, various concentrations of Lactobacillus rhamnosus supernatant were used to stimulate intestinal tissue cells transfected with the MUC2pro periodontitis DNA vaccine. Real-time quantitative polymerase chain reaction (RT-qPCR) and Western blotting (WB) assays were used to confirm the regulatory effect of Lactobacillus rhamnosus supernatant. After transfection, TGF-β pathway inhibitors were used to target intestinal tissue cells cultured with Lactobacillus rhamnosus supernatant. RT-qPCR and WB assays were used to assess the impact of TGF-β inhibitors on the regulatory effect of Lactobacillus rhamnosus supernatant. All statistical analyses were performed using SPSS 29.0, with one-way ANOVA for multiple group comparisons and LSD/Tamhane’s T2 for post-hoc tests; P < 0.05 was considered statistically significant. Results After transfection with the MUC2 promoter-type periodontitis vaccine plasmid, green fluorescence was exclusively detected in LS-174T cells, while it was absent in LX-2, BEAS-2B, and U251 cells. Compared with the control group of LS 174T cells transfected without SN, the 2% SN group exhibited no enhancement of vaccine antigen expression ( P > 0.05). The 4% SN group enhanced vaccine antigen expression ( P < 0.05), while the 8% SN group significantly promoted vaccine antigen expression ( P < 0.01). Compared with the control group without SN, the PFD group exhibited no inhibitory effect on vaccine antigen expression in transfected LS-174T cells ( P > 0.05). Compared with the 8% SN group, the expression of vaccine antigens in LS-174T cells transfected with the 8% SN + PFD group was significantly inhibited ( P < 0.05). Conclusions Periodontitis DNA vaccine candidate plasmid pVAX1-MUC2 pro - HA2-fimA-EGFP was engineered for intestinal tissue-specific expression. Additionally, an appropriate concentration of Lactobacillus rhamnosus supernatant enhances MUC2 promoter-type periodontitis DNA vaccine candidate expression, which may be associated with TGF-β pathway regulation. EGFP was used as a surrogate marker for HA2/fimA expression in this study, and P2A cleavage efficiency was assumed but not validated, representing an indirect readout of antigen expression.
A nutritionally-explicit test of the food-safety trade-off under predation risk
Development and validation of the AI literacy, risk perception, and academic confidence questionnaire for Chinese pre-service teachers
Artificial intelligence (AI) is becoming increasingly relevant to teacher education, yet evidence remains limited on how pre-service teachers’ AI literacy, risk perception, and academic confidence can be assessed within a coherent but multidimensional framework. This study examined the psychometric properties of the AI Literacy, Risk Perception, and Academic Confidence Questionnaire (AIRPAC-Q) among Chinese pre-service teachers. A cross-sectional survey was conducted with 528 participants recruited from teacher education programmes in China. The sample was randomly divided into an exploratory factor analysis (EFA) subsample (n = 258) and a confirmatory factor analysis (CFA) subsample (n = 270). Psychometric evaluation included expert-based content validation, pilot refinement, EFA, CFA, reliability testing, convergent and discriminant validity, concurrent validity, and known-group validity analyses. The final questionnaire retained 14 items across three complementary dimensions: AI literacy, risk perception, and academic confidence. Expert ratings showed acceptable content validity (I-CVI = 0.83–1.00; S-CVI/Ave = 0.94). The hypothesized three-factor model showed an acceptable fit to the data (χ² = 146.32, df = 74, χ²/df = 1.98, CFI = 0.952, TLI = 0.941, RMSEA = 0.060, SRMR = 0.047) and outperformed two-factor and one-factor alternatives. Cronbach’s α values ranged from 0.82 to 0.88, composite reliability values ranged from 0.83 to 0.89, and average variance extracted values ranged from 0.56 to 0.59. Participants with prior AI use experience scored higher on AI literacy and academic confidence but slightly lower on risk perception than those without such experience. These findings support the AIRPAC-Q as a context-specific multidimensional tool for assessing competence, caution, and confidence in AI-supported teacher education.
Integrated air-coupled ultrasonic transducer with front-end preamplification for structural health monitoring
Robust design of LAMP assays for in-field detection of major bacterial vascular diseases of banana
Bacterial diseases of banana are a growing global threat, causing yield losses and increased management costs. Major diseases include Moko, banana blood disease (BBD), and banana Xanthomonas wilt (BXW), caused by Ralstonia solanacearum , Ralstonia syzygii subsp. celebesensis , and Xanthomonas vasicola pv. musacearum , respectively. Effective surveillance requires point-of-care diagnostics such as loop-mediated isothermal amplification (LAMP) for on-site use. We aimed to develop three LAMP assays to specifically detect the bacteria responsible for Moko, BBD and BXW, directly from banana tissues, using a simplified DNA extraction protocol. The BBD – and BXW-LAMP assays demonstrated 100% specificity, yielding negative results for a broad range of non-target bacteria, including closely related species as well as pathogenic and endophytic strains associated with banana, and positive results for all the tested target strains. For Moko disease, a duplex-LAMP assay was developed to detect all strains from the four globally most relevant sequevars: IIB-3, IIB-4, IIA-6, and IIA-24. The duplex-LAMP successfully detected all target strains, except one that was shown to be non-pathogenic to Cavendish bananas. All non-target strains tested negative, with the exception of a delayed signal for one strain belonging to Ralstonia thomasi , not associated with banana environment (hospital strain). These results were supported by an extensive in silico analysis conducted on 9,668 Burkholderiaceae and 7,483 Xanthomonadaceae genomes. Detection limits ranged from 0.1 pg/µl to 1 pg/µl DNA, and from 10 4 to 10 5 CFU/ml on banana tissues spiked with calibrated bacterial suspensions, depending on the assay. The LAMP assays prove highly effective for detecting target pathogens in both artificially inoculated banana plants and field samples, offering a promising tool for improving disease management strategies.
Two-stage deep learning for circular landmark detection in hip radiographs
Tall and small trees are equally vulnerable to drought
Association between glycemic control and chronic kidney disease development in older patients with type 2 diabetes: A retrospective cohort study
Background In older patients with type 2 diabetes (T2D), physicians often de-emphasize strict glycemic control to avoid severe hypoglycemia. However, the incidence of renal impairment in diabetic octogenarians is underexplored, and the impact of glycemic control on kidney function in this age group remains unclear. This study assessed its effect on CKD development. Methods Retrospective, multicenter cohort study including patients (>80 years) with T2D between 2012 and 2016, at least one annual glycated hemoglobin (HbA1c) measurement, an estimated glomerular filtration rate (GFR) ≥60 mL/min/1.73m 2 , and ≥one annual GFR estimation during follow-up. Patients were classified according to glycemic control (poor and good); good control was set at HbA1c < 7.5%. Five-year follow-up data were collected from medical records. Results The study included 1062 patients, 435 (40.96%) in the poor glycemic control group and 627 (59.04%) in the good control group. CKD incidence was significantly higher among individuals with poor control (61.61%) compared to those with good control (52.47%) (p = 0.003). Logistic regression analyses showed that poor control is independently associated with higher odds of CKD onset (OR: 0.87 good vs. poor control, p = 0.010) and accelerates its progression (time-to-CKD HR: 0.78 good vs. poor control, p = 0.004). Conclusions Poor glycemic control is independently associated with CKD development and a shorter time to CKD onset, supporting its potential relevance for kidney-related outcomes in older patients with T2D. These findings highlight the need to consider glycemic control within a broader, individualized clinical approach in this population.
Low-cost approximate multipliers for quantum-dot cellular automata
Can a history of crop rotations improve the prediction of soil organic carbon in the Andes? integrating machine learning multi-annual crop classification as a proxy of soil management
Soil organic carbon (SOC) is a crucial component related to various processes that ensure soil health and function. Its modeling is vital for assessing and monitoring soil degradation caused by the potential impact of agricultural activities.This study aimed to model SOC in the Northern highlands of Peru, characterized by a high amount of SOC, which is being affected by crop expansion. Crop rotation (CR) history was linked to ground-truthed soil data via a multi-year crop classification model trained on data from 534 fields across 2022–2024. Each cropland field was represented as a polygon delineating its boundaries and indicating its dominant crop cover. Time series of multispectral Sentinel-2 Level-2A Top of Canopy imagery were used to derive phenological features—such as the timing of maximum canopy cover and the length of the growing period—based on Normalized Difference Vegetation Index (NDVI) time series. A Random Forest classifier was used as the baseline model. The cropland classification model demonstrated a strong overall performance, with F 1 scores ranging from 0.81 to 0.98 across the different classes. The model performed well for lupin and pasture but scored lower for beans and potatoes. Predictions of cropland classes from 2019 to 2022 were created, resulting in frequency layers that represent crop rotations. Four feature configurations were evaluated: (i) including all features as a benchmark, (ii) excluding climatology, (iii) excluding crop rotation history, and (iv) excluding soil properties. Configurations including all features and excluding crop rotation history showed the highest performance ( R 2 = 0.63), while those excluding climatology or soil properties performed worse ( R 2 ≈ 0.52 -- 0.53 ). Although soil features were the most important, fallow frequency emerged as the most critical predictor of SOC in crop rotations. When soil data were excluded, fallow frequency, combined with climatic features, explained over half of the SOC variability. The findings emphasize the importance of incorporating remote sensing-derived CR into SOC mapping efforts.
Mesoscale numerical study on the mechanical behavior of basalt fiber reinforced phosphogypsum-based materials
Resource optimization algorithm for RF-aided NOMA VLC network joint blocklength control and power allocation
To achieve high-reliability in the Industrial Internet of Things (IIoT) and satisfy the low-latency requirements of industrial equipment, this paper proposes a resource optimization scheme that jointly controls information transmission blocklength and power allocation. Specifically, Short Packet Communication (SPC) and Non-Orthogonal Multiple Access (NOMA) technologies are introduced to construct a Radio Frequency (RF)-aided Visible Light Communication (VLC) network system. The successful transmission probability of multiple User Equipment (UE) is analyzed, and the Service Capacity (SC) of each channel is quantified. Then, an optimization problem is formulated to maximize the SC, subject to constraints on statistical Quality of Service (QoS), Service Reliability (SR), and transmission power. To solve this optimization problem, we design a resource optimization algorithm joint blocklength and power allocation. Simulation results demonstrate that the proposed resource optimization scheme for NOMA VLC/RF networks outperforms NOMA VLC and OMA VLC in ensuring highly reliable data transmission. Furthermore, the proposed algorithm could maximize the SC of the NOMA VLC/RF networks by utilizing shorter blocklength.