Protocol for whole-cell patch-clamp recording and post hoc identification of hippocampal CA2 pyramidal neurons in adult mouse brain slices.

Urban road dust as a dynamic reservoir for microplastics: Quantifying precipitation washout, recovery patterns, and traffic influence in Bahía Blanca, Argentina.

From categories to producers: Dual regulatory and Anthropocene-based classification of macrolitter on an uninhabited Caribbean island.

Extraction-free, rapid LAMP-CRISPR/Cas12a assay for detection of pseudorabies virus.

A triplex RPA-LFA assay for the simultaneous detection of forensically important necrophagous insects in complex DNA mixtures.

To investigate the changes in choroidal optical coherence tomography (OCT) radiomic features, their correlations with visual acuity and utility in identifying pathological myopia (PM). A total of 288 myopic participants aged 18-50 years were included. Choroidal radiomic features were extracted and screened from OCT images via PyRadiomics and machine learning. Selected features were analysed for their associations with axial length, spherical equivalent, age and best corrected visual acuity (BCVA). Classification models were built using these features and their performance to identify PM was evaluated and compared with clinical parameters through five-fold cross-validation using metrics including area under the curve (AUC), accuracy, recall and F1 score. A total of 464 radiomic features were extracted and four choroidal intensity and shape features significantly correlated with axial length (p<0.001) were selected. Smaller maximum diameter, higher coarseness and greater perimeter surface ratio were significantly associated with worse BCVA (p<0.05). Radiomic features showed better performance than clinical features in both the internal test set (AUC=0.970 vs 0.938) and external validation set (AUC=0.858 vs 0.753) in identifying PM. Combining radiomic features with risk factors improved classification performance (AUC=0.990 internally and 0.892 externally). Among the included factors, intensity feature was most predictive for PM. OCT-based choroidal intensity and shape features were significantly correlated with axial elongation and visual impairment and outperformed clinical parameters in identifying PM. These features could serve as reliable biomarkers for monitoring high myopia progression.

Microplastics (MPs) are increasingly recognized as widespread contaminants in aquatic environments, including remote freshwater systems, where their presence is linked to waste generation and transport pathways. This study examined the occurrence, spatial-temporal distribution, polymer composition, and ecological risks of MPs in surface sediments from Black Lake and Devil Lake, two high-altitude glacial lakes in Durmitor National Park, Montenegro. Sediment samples were collected across three seasons and analyzed using standardized methods, including density separation, visual identification, and polymer characterization. MPs abundance averaged 5.1 ± 1.4 items per 100g of dry sediment in Black Lake and 3.8 ± 0.5 items per 100g in Devil Lake. Fibers and fragments were dominant morphotypes, with particles sized 1-3mm prevailing. Blue particles were most frequent. Five polymer types were identified, with polyethylene as the dominant polymer. Pollution load index values indicated moderate contamination, while polymer hazard and ecological risk indices suggested high to very high environmental risk. The presence of MPs in protected, high-altitude glacial lakes highlights their vulnerability to diffuse pollution sources, including tourism and long-range transport. The findings provide baseline data for alpine freshwater environments and underline the importance of integrating MPs pollution into waste management and environmental protection strategies.

Identifying gas hydrates and associated bottom simulating reflectors (BSRs) in seismic-reflection data requires the use of varied geophysical methods. Re-processed seismic data can primarily highlight seafloor features, but still show inherent signal limitations when resolving small-scale geological features. In contrast, seismic attributes applied to re-processed data have been successfully used in the recognition of hydrocarbon reservoirs. This work tests and applies an integrated seismic-attribute workflow for BSR mapping and qualitative interpretation of BSR-related anomalies, including those reflections commonly interpreted as representing free-gas beneath the BSR in the Makran Accretionary Wedge, offshore Pakistan. Seismic attributes were applied to the resolution-enhanced (re-processed) profile SO122-04a after a structure-oriented data conditioning that used dip-steered median filtering. They were followed by the extraction of amplitude attributes including Pseudo-Relief and Square-Root-of-Energy, and frequency decomposition including Fast Fourier Transform (FFT) and Continuous Wavelet Transform (CWT) evaluated at 20, 40, and 60Hz. The results in this work show that Pseudo-Relief emphasizes the geometric expression of seismic anomalies and BSRs by illuminating their curvature and shape, while Square-Root-of-Energy highlights spatial variations in reflection energy across BSR intervals. FFTs and CWTs show comparatively stronger amplitude expression of BSRs and underlying reflections in the 40 and 60Hz frequency bands. Finally, RGB color blending aids the visual identification of BSR-related anomaly zones (indicators of potential hydrate/free-gas systems), further supporting attribute-based mapping for the interpretation of BSR-related anomalies. Interpretations are qualitative in the absence of independent calibration data.

Understanding the origin, distribution, and biology of different cell populations in chimeric mice is critical for interpreting the pathological changes developed in these models. To this aim, the methodological work presented here illustrates the validation and application of a collection of labeling techniques to differentiate between specific mouse and human tissue/cell components in formalin-fixed paraffin-embedded samples from chimeric mice, especially those bearing human tumor and immune cells. First, broad approaches to identify cells of human origin using ubiquitous immunohistochemical targets such as HLA-A, Ku80, and human mitochondrial 60 kDa protein (hMito) were established using specimens from humanized mice and a human tissue microarray including both normal and neoplastic samples. Due to its crisp membranous immunoreactivity, HLA-A was the most useful marker for visual human cell identification; however, Ku80 and hMito may be suitable options when HLA-A is not expressed in the cells of interest. Importantly, using one or more of these markers provides a broad range of coverage for the vast majority of human-derived cells in chimeric mice. Second, tailored immunohistochemical or in situ hybridization methodologies to distinguish specific human or mouse cell subsets are presented, focusing on immune/inflammatory cells and human chimeric antigen receptor (CAR) T-cells. These diverse approaches are accompanied by descriptions of case examples highlighting practical diagnostic and experimental applications in the context of various humanized mouse models. While not comprehensive, this work represents a valuable starting reference for pathologists and investigators working with humanized mouse models and seeking to add spatial resolution to the complex landscape of chimeric tissues.

Amblyopia or "lazy eye" is a visual impairment due to abnormal visual development in childhood. If not treated early, it can cause permanent visual acuity loss. The main causes are uncorrected refractive errors and strabismus. Routine examinations at school are important to detect and prevent long-term impacts. Objective: To determine the prevalence of suspected amblyopia in students of SDN 11 and 17 Kebon Jeruk, West Jakarta, and its distribution based on age, gender, type, and clinical classification of amblyopia. Method: This study used a descriptive analytical design with a cross-sectional approach. Of the 840 students, 792 met the inclusion criteria through total sampling. The examination included visual acuity (Snellen Chart), refractive status (autorefractor), and identification of the type and classification of amblyopia. Results: It was found that 108 children (13.6%) were identified as suspected amblyopia. The proportion of cases was higher in girls (54.6%) than boys (45.4%). The age with the highest number of cases was 10 years (23.1%), followed by 7 years (21.3%). Based on the type, 53.7% were classified as anisometropic amblyopia and 46.3% were isometropic. In terms of classification, the majority (99.1%) were refractive amblyopia and only 0.9% were deprivation amblyopia.

Surface-enhanced Raman spectroscopy (SERS) is a promising method for identifying microplastics (MPs). Nonetheless, traditional solid SERS substrate-based detection often struggles with individual MPs, making it particularly difficult to detect multiple MPs simultaneously and in a recyclable manner. To achieve ultrasensitive, multiplex, and easily recyclable SERS detection of MPs in environmental water samples, an in situ construction method was developed to create a particle-in-cavity (PIC) structure. This involved the simultaneous formation of PDMS cavities and the in situ embedding of Ag NPs into these cavities. The PIC structure not only successfully combined "surface hot spots" and "volume hot spots" but also enhanced SERS uniformity and resistance to ultrasonication due to the in situ embedding strategy. The flexible PIC-structured film allowed for the simultaneous SERS detection of three MPs (polystyrene, polypropylene, and polyethylene) in both seawater and tap water, with detection limits (LODs) of 0.1, 0.5, and 0.5 μg/mL, respectively. Recyclable SERS detection of the MPs for five recycles was easily accomplished through ultrasonication cleaning with xylene. Utilizing the SERS spectra of the three MPs, machine learning algorithms enabled precise quantification of the MPs in environmental water. Visual identification was conducted using Raman mapping for the mixture of the three MPs. This detection method, which integrates the unique PIC structure and machine learning, paves the way for future advancements in ultrasensitive and easily recyclable SERS detection for environmental monitoring.

Exploring the Promise of Improvisational Theatre Applications in Health Care: A Scoping Review.

Real-time object detection has become an important component of modern safety and surveillance systems since one has to identify dangerous events such as fire and smoke, and human presence as swiftly as possible to avoid damage and ensure timely response. The innovations in deep learning, particularly, the one-stage detectors, have significantly improved the accuracy and efficiency of the visual identification tasks under dynamic conditions. With the latest deep learning models based on the state-of-the-art YOLOv8 and YOLOv9, this paper suggests a real-time person, fire, and smoke detection system. A carefully chosen set of data on Roboflow is employed to enhance model generalization under a variety of lighting and environmental conditions. The models are trained and evaluated in Python-based environment using Google Colab that enables efficient experimentation and scalability. To test the performance under practical limitations, e.g. low-resolution camera input, occlusions, etc., a comparison analysis is conducted to assess the accuracy of detection, the speed of inference, and robustness. To enhance the situational awareness and effectiveness in responding, the developed system has a real-time alert system which triggers alerts when dangerous conditions are detected. The findings demonstrate the potential of highly advanced YOLO designs to deliver high-performance and real-time safety monitoring systems that can be applicable to emergency management and surveillance systems

Effective professional plaque removal is of major importance in the prevention of white spot lesions and gingivitis in patients with fixed orthodontic appliances. However, visual identification of plaque can be difficult, especially around brackets, ligatures and wires. The purpose of the present randomized clinical trial was to evaluate the effectiveness of a plaque disclosing agent (PDA) as a visual guide for biofilm removal. Thirty-two systematically and periodontally healthy adults with fixed orthodontic appliances and Plaque Index (PI) > = 25% were enrolled from October 2020 to May 2022, the subjects were equally randomized into test and control group. Primary outcome was the change in the difference in percentage of residual plaque area (RPA) between the two study groups. In the test group, a PDA was applied before professional oral hygiene, whilst the control group received a hygiene session without disclosing. The PDA was then re-applied at the end of the treatment in both groups, and the RPA was assessed via Image-J software analysis of standardized frontal photos and compared between groups. The average RPA in the test group was 3.9% (CI 95% 2.6%; 5.1%), which resulted significantly lower than in the control group, where it reached 12.0% (CI 95% 8.0%-16.0%) (p-value < 0.001). The percentage of area with residual plaque was modelled using a beta-regression model. The use of plaque disclosing agents as guidance for professional oral hygiene treatment leads to improved plaque removal in patients with fixed orthodontic appliances. NCT05428189, 2022-06-08, retrospectively registered.

Chili plants are a commodity susceptible to plant pest organism attacks that can significantly reduce productivity. Visual identification of chili diseases by farmers is often inaccurate due to symptom similarity across disease categories, necessitating a technology-based approach capable of performing classification automatically and accurately. This study proposes a hybrid model combining EfficientNetB4 and Vision Transformer for chili leaf disease classification into four categories healthy, yellowish, curl leaf, and spot leaf. EfficientNetB4 extracts local features through compound scaling and MBConv blocks, while ViT models global relationships among image regions through self-attention, enabling a semantically meaningful integration of local and global feature representations that addresses the individual limitations of CNN and transformer-based architectures. The dataset integrates 4,000 secondary images from GitHub and 800 primary images collected directly from chili cultivation fields in Central Java, with splitting performed separately per source to ensure proportional distribution across subsets. To evaluate generalization capability, the model was assessed across three scenarios: training and testing on secondary data only 98.25%, testing on primary field data without prior field exposure 87.50%, and training and testing on integrated data 99.17%, with a perfect accuracy of 100% on the primary-only test set. These results demonstrate that incorporating field-collected data into training directly bridges the generalization gap caused by domain shift between laboratory and real-world conditions, outperforming both single-architecture and previous hybrid approaches reported in prior studies. The findings provide a methodological foundation for developing robust automated disease detection systems applicable across diverse agricultural crops and real-world farming environments.

Fusarium oxysporum is a representative soil-borne fungal pathogen that causes strawberry wilt, a disease characterized by an extremely high mortality rate that poses a severe threat to the sustainable development of the global strawberry industry. However, traditional detection methods are often time-consuming and dependent on specialized laboratory equipment, while existing soil nucleic acid extraction protocols are highly susceptible to interference from inhibitors in complex matrices, leading to low detection efficiency or high false-negative rates. To address these limitations, this study developed a novel on-site detection platform based on in-situ biological enrichment and purification-free nucleic acid release. A specialized enrichment rod targeting Fusarium oxysporum was developed to leverage the tropic growth characteristics of the pathogen, achieving physical separation from the soil matrix and effectively eliminating interference from complex soil inhibitors such as humic acids. The enriched pathogens release nucleic acids via a rapid lysis buffer, which are then neutralized and used directly as templates for RPA-CRISPR/Cas12b isothermal detection, enabling visual field identification through lateral flow strips. This method requires no specialized instrumentation, achieving a detection limit of 8.5 CFU/g and a sensitivity of 70 copies. After completing 48 hours of in situ enrichment, the entire process from rod retrieval to detection completion requires only 40 minutes (with hands-on operation time <10 minutes). By effectively circumventing soil inhibitor interference and simplifying complex nucleic acid extraction into a rapid, integrated protocol, this platform provides a critical technical solution for the on-site monitoring and precise control of soil-borne pathogens.

ABSTRACT Nitrogen and sulfur co‐doped carbon dots (NS‐CDs) were successfully synthesized from L‐cysteine and ethanolamine via a one‐pot hydrothermal method. In mixed ethanol/H 2 O solvent, NS‐CDs exhibit selective and sensitive fluorescence quenching response towards TNP and 4‐NP over other explosives. The low detection limits were as low as 0.272 µmol/L for TNP and 0.339 µmol/L for 4‐NP, demonstrating the high sensitivity of NS‐CDs. A comparison of the fluorescence and absorbance spectra suggests that the fluorescence quenching mechanism involves absorption competitive quenching (ACQ) and fluorescence resonance energy transfer (FRET). NS‐CDs showed satisfactory recoveries in the detection of TNP and 4‐NP in real water samples. Addtionally, NS‐CDs enabled visual identification of TNP and 4‐NP through a color change in a test paper model. This work demonstrates that nitrogen and sulfur co‐doping is an effective strategy for preparing sensitive fluorescent chemosensors based on carbon dots.

Covalent organic polymer cascade MnO2 nanosheets-based fluorescence-colorimetric dual-mode sensing system for highly sensitive detection of organophosphorus pesticides.

Abnormal visual identification method for loose screws and condensation inside battery packs

Efficient electronic configuration and adsorption energy balance enabled by trimetallic nanozymes: Toward AI-powered visual identification of Pseudomonas aeruginosa