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Rosacea is a chronic inflammatory skin disorder, whose underlying cellular and molecular mechanisms remain obscure. Here, we generate a single-cell atlas of facial skin from female rosacea patients and healthy individuals. Among keratinocytes, a subpopulation characterized by IFNγ-mediated barrier function damage is found to be unique to rosacea lesions. Blocking IFNγ signaling alleviates rosacea-like phenotypes and skin barrier damage in mice. The papulopustular rosacea is featured by expansion of pro-inflammatory fibroblasts, Schwann, endothelial and macrophage/dendritic cells. The frequencies of type 1/17 and skin-resident memory T cells are increased, and vascular mural cells are characterized by activation of inflammatory pathways and impaired muscle contraction function in rosacea. Most importantly, fibroblasts are identified as the leading cell type producing pro-inflammatory and vasodilative signals in rosacea. Depletion of fibroblasts or knockdown of PTGDS, a gene specifically upregulated in fibroblasts, blocks rosacea development in mice. Our study provides a comprehensive understanding of the aberrant alterations of skin-resident cell populations and identifies fibroblasts as a key determinant in rosacea development.

BackgroundRosacea is a chronic inflammatory disease with variable clinical presentations, including transient flushing, fixed erythema, papules, pustules, and phymatous changes on the central face. Owing to the diversity in the clinical manifestations of rosacea, the lack of objective biochemical examinations, and nonspecificity in histopathological findings, accurate identification of rosacea is a big challenge. Artificial intelligence has emerged as a potential tool in the identification and evaluation of some skin diseases such as melanoma, basal cell carcinoma, and psoriasis. ObjectiveThe objective of our study was to utilize a convolutional neural network (CNN) to differentiate the clinical photos of patients with rosacea (taken from 3 different angles) from those of patients with other skin diseases such as acne, seborrheic dermatitis, and eczema that could be easily confused with rosacea. MethodsIn this study, 24,736 photos comprising of 18,647 photos of patients with rosacea and 6089 photos of patients with other skin diseases such as acne, facial seborrheic dermatitis, and eczema were included and analyzed by our CNN model based on ResNet-50. ResultsThe CNN in our study achieved an overall accuracy and precision of 0.914 and 0.898, with an area under the receiver operating characteristic curve of 0.972 for the detection of rosacea. The accuracy of classifying 3 subtypes of rosacea, that is, erythematotelangiectatic rosacea, papulopustular rosacea, and phymatous rosacea was 83.9%, 74.3%, and 80.0%, respectively. Moreover, the accuracy and precision of our CNN to distinguish rosacea from acne reached 0.931 and 0.893, respectively. For the differentiation between rosacea, seborrheic dermatitis, and eczema, the overall accuracy of our CNN was 0.757 and the precision was 0.667. Finally, by comparing the CNN diagnosis with the diagnoses by dermatologists of different expertise levels, we found that our CNN system is capable of identifying rosacea with a performance superior to that of resident doctors or attending physicians and comparable to that of experienced dermatologists. ConclusionsThe findings of our study showed that by assessing clinical images, the CNN system in our study could identify rosacea with accuracy and precision comparable to that of an experienced dermatologist.

We have developed a scalable detector readout design for a 100 ps coincidence time resolution (CTR) time of flight (TOF) positron emission tomography (PET) detector technology. The basic scintillation detectors studied in this paper are based on 2 × 4 arrays of 3 × 3 × 10 mm3'fast-LGSO:Ce' scintillation crystals side-coupled to 6 × 4 arrays of 3 × 3 mm2silicon photomultipliers (SiPMs). We employed a novel mixed-signal front-end electronic configuration and a low timing jitter Field Programming Gate Array-based time to digital converter for data acquisition. Using a22Na point source, >10 000 coincidence events were experimentally acquired for several SiPM bias voltages, leading edge time-pickoff thresholds, and timing channels. CTR of 102.03 ± 1.9 ps full-width-at-half-maximum (FWHM) was achieved using single 3 × 3 × 10 mm3'fast-LGSO' crystal elements, wrapped in Teflon tape and side coupled to a linear array of 3 SiPMs. In addition, the measured average CTR was 113.4 ± 0.7 ps for the side-coupled 2 × 4 crystal array. The readout architecture presented in this work is designed to be scalable to large area module detectors with a goal to create the first TOF-PET system with 100 ps FWHM CTR.

To mitigate soil degradation and decrease dependency on chemical inputs in agriculture, this study examined the joint effects of coconut shell biochar and Bacillus strain Ya-1 on soil fertility, rhizosphere bacterial communities, and the growth of chili (Capsicum annuum L.). A controlled pot experiment with four treatments was conducted: control (CK), biochar only (C), Bacillus strain Ya-1 only (B), and a combination of both (BC). The BC treatment significantly enhanced the soil carbon and available phosphorus contents by approximately 20% and the soil nitrogen content and pH by 18% and 0.3 units, respectively, compared to the control. It also increased microbial biomass carbon and nitrogen by 25% and 30%, respectively, indicating improved soil microbial diversity as shown by the highest Pielou evenness index and Shannon index values. The combined application of biochar and the Ya-1 strain resulted in a 15% increase in chili plant height and a 40% improvement in root dehydrogenase activity, suggesting enhanced nutrient uptake and metabolism. Metabolic profiling showed shifts in stress response and nutrient assimilation under different treatments. Collectively, these results indicate the potential of biochar and microbial inoculants to significantly promote soil and plant health, providing a sustainable strategy to improve agricultural productivity and reduce reliance on chemical inputs. [ABSTRACT FROM AUTHOR]

With advancements in computing technology and the rapid progress of data science, machine learning has been widely applied in various fields, showing great potential, especially in digital healthcare. In recent years, conversational diagnostic systems have been used to predict diseases through symptom checking. Early systems predicted the likelihood of a single disease by minimizing the number of questions asked. However, doctors typically perform differential diagnoses in real medical practice, considering multiple possible diseases to address diagnostic uncertainty. This requires systems to ask more critical questions to improve diagnostic accuracy. Nevertheless, such systems in acute medical situations need to process information quickly and accurately, but the complexity of differential diagnosis increases the system's computational cost. To improve the efficiency and accuracy of telemedicine diagnostic systems, this study developed an optimized algorithm for the Top-K algorithm. This algorithm dynamically adjusts the number of the most likely diseases and symptoms by real-time monitoring of case progress, optimizing the diagnostic process, enhancing accuracy (99.81%), and increasing the exclusion rate of severe pathologies. Additionally, the Top-K algorithm optimizes the diagnostic model through a policy network loss function, effectively reducing the number of symptoms and diseases processed and improving the system's response speed by 1.3–1.9 times compared to the state-of-the-art differential diagnosis systems. [ABSTRACT FROM AUTHOR]

Rosacea is a chronic inflammatory skin disease. Systemic inflammation plays a vital role in the pathogenesis of rosacea. Many studies have reported hematological parameters as biomarkers for diseases with inflammatory processes. However, the diagnostic value of hematological parameters in rosacea remains a puzzle. This study involved 462 patients with rosacea, including erythematotelangiectatic rosacea (ETR, n = 179), papulopustular rosacea (PPR, n = 250), and phymatous rosacea (PhR, n = 33), and 924 healthy control subjects. Demographic, clinical, and laboratory information was collected and compared between rosacea subtypes. The hematological parameters of the patients and the healthy controls were compared retrospectively. The platelet volume (MPV) and platelet crit (PCT) were significantly upregulated, and the lower red cell distribution width (RDW) was significantly downregulated in rosacea compared to healthy controls, and they were identified as the diagnostic biomarkers for rosacea with area under the curve values of 0.828, 0.742, and 0.787, respectively. Comparing the hematological parameters among the three rosacea subtypes, we found that platelet‐to‐lymphocyte ratio and platelet‐to‐neutrophil ratio values in the ETR group were significantly higher than those in the PPR and PhR groups. The correlation between hematological parameters and clinical scores showed that RDW was negatively correlated with the Clinician Erythema Assessment score. However, there was no significant correlation between the Investigator Global Assessment score and hematological parameters. In conclusion, PCT, MPV, and RDW have diagnostic value for rosacea, and RDW is correlated with the severity of rosacea erythema, implying the potential applications of PCT, MPV, and RDW in the diagnosis and monitoring of rosacea. [ABSTRACT FROM AUTHOR]

The rational design of covalent organic frameworks (COFs) with hydrochromic properties is of significant value because of the facile and rapid detection of water in diverse fields. In this report, we present a thiazole-linked COF (TZ-COF-6) sensor with a large surface area, ultrahigh stability, and excellent crystallinity. The sensor was synthesized through a simple three-component reaction involving amine, aldehyde, and sulfur. The thiazole and methoxy groups confer strong basicity to TZ-COF-6 at the nitrogen sites, making them easily protonated reversibly by water. Therefore, TZ-COF-6 displayed color change visible to the naked eye from yellow to red when protonated, along with a red shift in absorption in the ultraviolet-visible diffuse reflectance spectra (UV-vis DRS) when exposed to water. Importantly, the water-sensing process was not affected by polar organic solvents, demonstrating greater selectivity and sensitivity compared to other COF sensors. Therefore, TZ-COF-6 was used to detect trace amounts of water in organic solvents. In strong polar solvents, such as N,N-dimethyl formamide (DMF) and ethanol (EtOH), the limit of detection (LOD) for water was as low as 0.06% and 0.53%, respectively. Even after 8 months of storage and 15 cycles, TZ-COF-6 retained its original crystallinity and detection efficiency, displaying high stability and excellent cycle performance. [ABSTRACT FROM AUTHOR]

Plant pathogenic fungi present a significant risk to crop productivity. The most cost-effective and efficient approach to mitigating plant diseases caused by these pathogenic fungi is using antifungal agrochemicals. In this study, we synthesized two sets of compounds incorporating malononitrile structures. Furthermore, we assessed the fungicidal properties of a subset of crucial intermediates and target compounds against specific agricultural fungi. Additionally, we employed predictive methods to estimate the oral LD50 values of selected target compounds in rats. The results demonstrated that the compounds E, particularly those with malononitrile and 1,2,3-triazole structures, exhibited significant inhibition (over 90%) against Rhizoctonia solani, including compound E11 with an EC50 value of 2.621 mg L−1. The toxicity prediction showed that E11 is a low-toxicity compound. Thus, E11 is a potential candidate for the further development of new R. solani selective inhibitory fungicides. [ABSTRACT FROM AUTHOR]