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ABSTRACTActinobacillus pleuropneumoniae (APP) is an important pathogen of the porcine respiratory disease complex, which leads to huge economic losses worldwide. We previously demonstrated that Pichia pastoris-producing bovine neutrophil β-defensin-5 (B5) could resist the infection by the bovine intracellular pathogen Mycobacterium bovis. In this study, the roles of synthetic B5 in regulating mucosal innate immune response and protecting against extracellular APP infection were further investigated using a mouse model. Results showed that B5 promoted the production of tumour necrosis factor (TNF)-α, interleukin (IL)-1β, and interferon (IFN)-β in macrophages as well as dendritic cells (DC) and enhanced DC maturation in vitro. Importantly, intranasal B5 was safe and conferred effective protection against APP via reducing the bacterial load in lungs and alleviating pulmonary inflammatory damage. Furthermore, in the early stage of APP infection, we found that intranasal B5 up-regulated the secretion of TNF-α, IL-1β, IL-17, and IL-22; enhanced the rapid recruitment of macrophages, neutrophils, and DC; and facilitated the generation of group 3 innate lymphoid cells in lungs. In addition, B5 activated signalling pathways associated with cellular response to IFN-β and activation of innate immune response in APP-challenged lungs. Collectively, B5 via the intranasal route can effectively ameliorate the immune suppression caused by early APP infection and provide protection against APP. The immunization strategy may be applied to animals or human respiratory bacterial infectious diseases. Our findings highlight the potential importance of B5, enhancing mucosal defence against intracellular bacteria like APP which causes early-phase immune suppression.

Abstract Kiwifruit decay caused by endophytic fungi is affected by exogenous pathogens that trigger changes in fungal community composition and interact with the endophytic fungal community. Four fungal pathogens of kiwifruit were identified. These were Aspergillus japonicus, Aspergillus flavus, Botryosphaeria dothidea, and Penicillium oxalicum. Except for P. oxalicum, the remaining three species represent newly described pathogens of kiwifruit. All four fungal species caused disease and decay in mature kiwifruit. Results of the fungal community analysis indicated that three pathogens that A. japonicus, A. flavus and P. oxalicum were the most dominant, however, other fungal species that did not cause disease symptoms were also present. Positive interactions between fungal species were found in asymptomatic, symptomatic, and infected kiwifruit. The ability of all four pathogens to infect kiwifruit was confirmed in an inoculation experiment. The presence of any one of the four identified pathogens accelerated decay development and limited the postharvest longevity of harvested kiwifruit. Results of the study identified and confirmed the ability of four fungal species to infect and cause decay in harvested kiwifruit. Changes in the structure and composition of the kiwifruit microbiome during the decay process were also characterized. This provides a foundation for the further study of the microbiome of kiwifruit and their involvement in postharvest diseases.

Klebsiella pneumoniae (K. pneumoniae), a kind of zoonotic bacteria, is among the most common antibiotic-resistant pathogens, and it causes nosocomial infections that pose a threat to public health. In this study, the roles of synthetic bovine neutrophil β-defensin-5 (B5) in regulating inflammatory response and metabolic response against multidrug-resistant K. pneumoniae infection in a mouse model were investigated. Mice were administrated intranasally with 20 μg of B5 twice and challenged with K. pneumoniae three days after B5 pretreatment. Results showed that B5 failed to directly kill K. pneumoniae in vitro, but it provided effective protection against multidrug-resistant K. pneumoniae via decreasing the bacterial load in the lungs and spleen, and by alleviating K. pneumoniae-induced histopathological damage in the lungs. Furthermore, B5 significantly enhanced the mRNA expression of TNF-α, IL-1β, Cxcl1, Cxcl5, Ccl17, and Ccl22 and obviously enhanced the rapid recruitment of macrophages and dendritic cells in the lungs in the early infection phase, but significantly down-regulated the levels of TNF-α, IL-1β, and IL-17 in the lungs in the later infection phase. Moreover, RNA-seq results showed that K. pneumoniae infection activated signaling pathways related to natural killer cell-mediated cytotoxicity, IL-17 signaling pathway, inflammatory response, apoptosis, and necroptosis in the lungs, while B5 inhibited these signaling pathways. Additionally, K. pneumoniae challenge led to the suppression of glycerophospholipid metabolism, the phosphotransferase system, the activation of microbial metabolism in diverse environments, and metabolic pathways in the lungs. However, B5 significantly reversed these metabolic responses. Collectively, B5 can effectively regulate the inflammatory response caused by K. pneumoniae and offer protection against K. pneumoniae. B5 may be applied as an adjuvant to the existing antimicrobial therapy to control multidrug-resistant K. pneumoniae infection. Our study highlights the potential of B5 in enhancing pulmonary bacterial clearance and alleviating K. pneumoniae-caused inflammatory damage.

Climatic comfortability serves as a crucial factor in tourism decision making; however, there remains a gap in evaluating the climate comfort conditions specifically for forest therapy. We developed a new index—Forest Therapy Climate Comfort Index (FTCCI)—to evaluate the climate comfort conditions for forest therapy by integrating the Temperature (T), Temperature and Humidity Index (THI), and Wind Efficiency Index (WEI). A total of 26 potential forest therapy bases were selected from the protected areas in Xinjiang and divided into five clusters: Aksu cluster, Hami cluster, Altai cluster, Ili and its surrounding cluster, and Urumqi and its surrounding cluster. Based on the monthly observation data from 25 surface meteorological stations in Xinjiang, spanning from 1994 to 2023, employing the Co-Kriging interpolation method, we explored the spatial–temporal variation in FTCCI from June to September and made clear the climate comfort duration across 26 bases in Xinjiang. The results indicated that (1) The variation in T, THI, and WEI in 26 bases demonstrated a consistent pattern of temporal variation. July emerged as the optimal month, followed closely by August, with most indices in both months falling within the comfort level. Conversely, September proved to be the least favorable month due to frigid conditions and discomfort for the human body, whereas June’s sensation was slightly more tolerable. (2) The distribution of T, THI, and WEI showed regional heterogeneity. The Urumqi and its surrounding cluster displayed the most favorable conditions for forest therapy, whereas the Aksu cluster showed the poorest performance. (3) There were differences in both FTCCI and climate comfort duration among various clusters in Xinjiang. Overall, excluding Tomur Peak and Nalati (July and August), the remaining 24 bases offered ideal climate comfort conditions for forest therapy from mid to late June through August. Notably, the bases in Urumqi and its surrounding cluster had the longest climate comfort duration, ranging from 3.5 to 4 months. Therefore, reliance on the unique climate, resource, and geographical condition of each base is crucial in creating special forest therapy products that cater to the diverse health needs of tourists.

Air oxygen content, an essential index for measuring air quality, is affected by vegetation and the environment in the forest. However, the scientific understanding of the influential mechanism of air oxygen content in different vegetation types is still not clear. Focusing on four different vegetation types: broad-leaved forest, coniferous forest, coniferous and broad-leaved mixed forest, and non-forest land within Shimen National Forest Park, China, the temporal dynamics of air oxygen content and its relationship with four environmental factors (temperature, relative humidity, wind speed, and negative air ion concentration) in different vegetation types were explored based on path analysis and decision analysis. The results showed that there was a noteworthy impact of vegetation types on air oxygen content, with coniferous and broad-leaved mixed forest (21.33 ± 0.42%) presenting the highest levels. The air oxygen content indicated a fundamentally consistent temporal pattern across different vegetation types, with the highest diurnal variation occurring at noon. It reached its peak in August and hit its nadir in December, with summer > spring > autumn > winter. In broad-leaved forest, the air oxygen content was determined by temperature, wind speed, negative air ion concentration, and relative humidity; in both coniferous forest and coniferous and broad-leaved mixed forest, the air oxygen content was affected by temperature, wind speed, and relative humidity; in non-forest land, the air oxygen content was influenced by temperature and wind speed. Generally, temperature was the dominant factor affecting air oxygen content in different vegetation types, and its positive impact tremendously exceeded other environmental factors. Wind speed had a positive impact on air oxygen content in three forest communities but a negative effect on non-forest land. Relative humidity acted as a limiting factor for air oxygen content within three forest communities. Negative air ion concentration showed a significant positive correlation on air oxygen content in broad-leaved forest. Therefore, when planning urban forests to improve air quality and construct forest oxygen bars, it is recommended that the tree species composition should be given priority to the coniferous and broad-leaved mixed pattern. Meanwhile, make sure the understory space is properly laid out so that the forest microclimates are conducive to the release of oxygen by plants through photosynthesis.

Forests in urban areas provide great healthcare benefits to citizens, but it is less well known whether this benefit is related to different geographical spaces. We selected exurban forest, suburban forest, downtown forest, and urban control in Guangzhou, China to analyze the change characteristics of negative air ion concentration (NAIC), air oxygen content (AOC), and human comfort index (HCI). Based on Criteria Importance Through Intercriteria Correlation (CRITIC) method, the urban forest comprehensive healthcare index (UFCHI) was established. Finally, the evaluation criteria for UFCHI were identified by cluster analysis. The results demonstrated that (1) The NAIC in exurban forest (2,713 ± 1,573 ions/cm3) and suburban forest (2,147 ± 923 ions/cm3) was evidently better than downtown forest (1,130 ± 255 ions/cm3) and urban control (531 ± 162 ions/cm3). (2) The AOC was in the order of exurban forest (21.17 ± 0.38%) > suburban forest (21.13 ± 0.30%) > downtown forest (21.10 ± 0.16%) > urban control (20.98 ± 0.12%). (3) The HCI in urban control (5.56 ± 2.32) and downtown forest (5.15 ± 1.80) is higher than suburban forest (4.02 ± 1.53) and exurban forest (3.71 ± 1.48). (4) The UFCHI in exurban forest (1.000), suburban forest (0.790), and downtown forest (0.378) were beneficial to human health to some extent, while urban control (0.000) was at Level IV, having no healthcare benefit. Except in winter, the UFCHI in exurban forest and suburban forest were all at Level II and above; while downtown forest and urban control were all at Level III and below at all seasons. Overall, urban forests in the exurbs and suburbs have better healthcare benefits than those in the downtowns. Furthermore, it is recommended that urban residents visit exurban and suburban forests for forest therapy in spring, summer, and autumn.

Introduction: Early predictive pathological complete response (pCR) is beneficial for optimizing neoadjuvant chemotherapy (NAC) strategies for breast cancer. The hematoxylin and eosin (HE)-stained slices of biopsy tissues contain a large amount of information on tumor epithelial cells and stromal. The fusion of pathological image features and clinicopathological features is expected to build a model to predict pCR of NAC in breast cancer.Methods: We retrospectively collected a total of 440 breast cancer patients from three hospitals who underwent NAC. HE-stained slices of biopsy tissues were scanned to form whole-slide images (WSIs), and pathological images of representative regions of interest (ROI) of each WSI were selected at different magnifications. Based on several different deep learning models, we propose a novel feature extraction method on pathological images with different magnifications. Further, fused with clinicopathological features, a multimodal breast cancer NAC pCR prediction model based on a support vector machine (SVM) classifier was developed and validated with two additional validation cohorts (VCs).Results: Through experimental validation of several different deep learning models, we found that the breast cancer pCR prediction model based on the SVM classifier, which uses the VGG16 model for feature extraction of pathological images at ×20 magnification, has the best prediction efficacy. The area under the curve (AUC) of deep learning pathological model (DPM) were 0.79, 0.73, and 0.71 for TC, VC1, and VC2, respectively, all of which exceeded 0.70. The AUCs of clinical model (CM), a clinical prediction model established by using clinicopathological features, were 0.79 for TC, 0.73 for VC1, and 0.71 for VC2, respectively. The multimodal deep learning clinicopathological model (DPCM) established by fusing pathological images and clinicopathological features improved the AUC of TC from 0.79 to 0.84. The AUC of VC2 improved from 0.71 to 0.78.Conclusion: Our study reveals that pathological images of HE-stained slices of pre-NAC biopsy tissues can be used to build a pCR prediction model. Combining pathological images and clinicopathological features can further enhance the predictive efficacy of the model.

IntroductionAI-assisted ultrasound diagnosis is considered a fast and accurate new method that can reduce the subjective and experience-dependent nature of handheld ultrasound. In order to meet clinical diagnostic needs better, we first proposed a breast lesions AI classification model based on ultrasound dynamic videos and ACR BI-RADS characteristics (hereafter, Auto BI-RADS). In this study, we prospectively verify its performance.MethodsIn this study, the model development was based on retrospective data including 480 ultrasound dynamic videos equivalent to 18122 static images of pathologically proven breast lesions from 420 patients. A total of 292 breast lesions ultrasound dynamic videos from the internal and external hospital were prospectively tested by Auto BI-RADS. The performance of Auto BI-RADS was compared with both experienced and junior radiologists using the DeLong method, Kappa test, and McNemar test.ResultsThe Auto BI-RADS achieved an accuracy, sensitivity, and specificity of 0.87, 0.93, and 0.81, respectively. The consistency of the BI-RADS category between Auto BI-RADS and the experienced group (Kappa:0.82) was higher than that of the juniors (Kappa:0.60). The consistency rates between Auto BI-RADS and the experienced group were higher than those between Auto BI-RADS and the junior group for shape (93% vs. 80%; P = .01), orientation (90% vs. 84%; P = .02), margin (84% vs. 71%; P = .01), echo pattern (69% vs. 56%; P = .001) and posterior features (76% vs. 71%; P = .0046), While the difference of calcification was not significantly different.DiscussionIn this study, we aimed to prospectively verify a novel AI tool based on ultrasound dynamic videos and ACR BI-RADS characteristics. The prospective assessment suggested that the AI tool not only meets the clinical needs better but also reaches the diagnostic efficiency of experienced radiologists.