BACKGROUND:Naringin has been shown to promote the proliferation and osteogenic differentiation of bone marrow derived mesenchymal stem cells,making it a potential candidate for treating osteoporosis andenhancing fracture healing.However,its clinical application is limited by its low bioavailability.OBJECTIVE:To prepare chitosan/β-tricalcium phosphate scaffolds loaded with naringin and characterize their biological properties.METHODS:Chitosan/β-tricalcium phosphate scaffolds were prepared by freeze-drying and chemical crosslinking.The chitosan/β-tricalcium phosphate scaffolds were immersed in anhydrous ethanol solution containing naringin for 3 hours.After vacuum cold drying,chitosan/β-tricalcium phosphate/naringin scaffolds were obtained.The pore size,porosity,swelling rate,degradation rate,mechanical properties,and in vitro release capacity of naringin of the scaffolds were characterized.Rat bone marrow mesenchymal stem cells were inoculated on the surface of chitosan/β-tricalcium phosphate scaffolds and chitosan/β-tricalcium phosphate/naringin scaffolds,respectively,and cell proliferation,adhesion,activity and alkaline phosphatase activity after osteogenic differentiation were detected.RESULTS AND CONCLUSION:(1)The results of scanning electron microscopy showed that the naringin-chitosan/β-tricalcium phosphate composite scaffold had a porous mesh structure.The average pore diameter was(106.82±25.22)μm;the porosity was(76.26±4.81)％;24-hour swelling rate was(796.17±31.76)％;in vitro degradation rate of 7.71％at 4 weeks,and naringin could be slowly released in vitro for 9 days.There was no significant difference in the average pore size,porosity,24-hour swelling rate,in vitro degradation rate,compression strength and compression modulus at 4 weeks between the chitosan/β-tricalcium phosphate scaffold and the chitosan/β-tricalcium phosphate scaffold(P＞0.05).(2)Rat bone marrow mesenchymal stem cells adhered well to the surfaces of the two scaffolds and had good activity.Compared with the chitosan/β-tricalcium phosphate scaffold,the chitosan/β-tricalcium phosphate/naringin scaffold promoted the proliferation of rat bone marrow mesenchymal stem cells(P＜0.05),and increased the alkaline phosphatase activity of bone marrow mesenchymal stem cells after osteogenic differentiation(P＜0.05).(3)The results show that the chitosan/β-tricalcium phosphate/naringin scaffolds exhibit favorable physical properties and can effectively promote the adhesion,proliferation,and osteogenic differentiation of bone marrow-derived mesenchymal stem cells.

BACKGROUND:Naringin has been shown to promote the proliferation and osteogenic differentiation of bone marrow derived mesenchymal stem cells,making it a potential candidate for treating osteoporosis andenhancing fracture healing.However,its clinical application is limited by its low bioavailability.OBJECTIVE:To prepare chitosan/β-tricalcium phosphate scaffolds loaded with naringin and characterize their biological properties.METHODS:Chitosan/β-tricalcium phosphate scaffolds were prepared by freeze-drying and chemical crosslinking.The chitosan/β-tricalcium phosphate scaffolds were immersed in anhydrous ethanol solution containing naringin for 3 hours.After vacuum cold drying,chitosan/β-tricalcium phosphate/naringin scaffolds were obtained.The pore size,porosity,swelling rate,degradation rate,mechanical properties,and in vitro release capacity of naringin of the scaffolds were characterized.Rat bone marrow mesenchymal stem cells were inoculated on the surface of chitosan/β-tricalcium phosphate scaffolds and chitosan/β-tricalcium phosphate/naringin scaffolds,respectively,and cell proliferation,adhesion,activity and alkaline phosphatase activity after osteogenic differentiation were detected.RESULTS AND CONCLUSION:(1)The results of scanning electron microscopy showed that the naringin-chitosan/β-tricalcium phosphate composite scaffold had a porous mesh structure.The average pore diameter was(106.82±25.22)μm;the porosity was(76.26±4.81)％;24-hour swelling rate was(796.17±31.76)％;in vitro degradation rate of 7.71％at 4 weeks,and naringin could be slowly released in vitro for 9 days.There was no significant difference in the average pore size,porosity,24-hour swelling rate,in vitro degradation rate,compression strength and compression modulus at 4 weeks between the chitosan/β-tricalcium phosphate scaffold and the chitosan/β-tricalcium phosphate scaffold(P＞0.05).(2)Rat bone marrow mesenchymal stem cells adhered well to the surfaces of the two scaffolds and had good activity.Compared with the chitosan/β-tricalcium phosphate scaffold,the chitosan/β-tricalcium phosphate/naringin scaffold promoted the proliferation of rat bone marrow mesenchymal stem cells(P＜0.05),and increased the alkaline phosphatase activity of bone marrow mesenchymal stem cells after osteogenic differentiation(P＜0.05).(3)The results show that the chitosan/β-tricalcium phosphate/naringin scaffolds exhibit favorable physical properties and can effectively promote the adhesion,proliferation,and osteogenic differentiation of bone marrow-derived mesenchymal stem cells.

AIM: To investigate the predictive value of serum histone deacetylase 1(HDAC1)and endothelial nitric oxide synthase(eNOS)for infectious endophthalmitis after cataract surgery.METHODS: A total of 362 cataract patients(362 eyes)admitted to our hospital from January 2020 to January 2023 were selected as the research objects. According to the occurrence of postoperative infectious endophthalmitis, they were divided into infection group(15 cases, 15 eyes)and non-infection group(347 cases, 347 eyes). Enzyme linked immunosorbent assay(ELISA)was applied to detect the levels of serum HDAC1 and eNOS in all subjects, and the levels of serum HDAC1 and eNOS in both groups were compared; the influencing factors of infectious endophthalmitis were analyzed by multivariate Logistic regression; the receiver operative curve(ROC)was applied to analyze the predictive value of serum HDAC1 and eNOS levels for postoperative infectious endophthalmitis in cataract patients.RESULTS: The levels of serum HDAC1 and eNOS in the infected group were obviously higher than those in the uninfected group(all P<0.01). Surgical time, vitreous overflow, HDAC1, and eNOS were all risk factors for postoperative infectious endophthalmitis(all P<0.05). ROC results showed that the AUC of HDAC1 and eNOS in predicting postoperative infectious endophthalmitis in cataract patients was 0.878 and 0.877, respectively, with sensitivity of 88.7% and 87.7%, specificity of 70.4% and 7.8%, respectively, while the AUC of the two combination in predicting postoperative infectious endophthalmitis in cataract patients was 0.978, with a sensitivity of 86.7% and a specificity of 85.3%.CONCLUSION:The serum levels of HDAC1 and eNOS in patients with infectious endophthalmitis after cataract surgery are obviously increased, and the combined detection of serum HDAC1 and eNOS can improve the predictive efficacy of infectious endophthalmitis in cataract patients after surgery. Both can provide reference for clinical diagnosis and treatment.

AIM: To investigate the predictive value of serum histone deacetylase 1(HDAC1)and endothelial nitric oxide synthase(eNOS)for infectious endophthalmitis after cataract surgery.METHODS: A total of 362 cataract patients(362 eyes)admitted to our hospital from January 2020 to January 2023 were selected as the research objects. According to the occurrence of postoperative infectious endophthalmitis, they were divided into infection group(15 cases, 15 eyes)and non-infection group(347 cases, 347 eyes). Enzyme linked immunosorbent assay(ELISA)was applied to detect the levels of serum HDAC1 and eNOS in all subjects, and the levels of serum HDAC1 and eNOS in both groups were compared; the influencing factors of infectious endophthalmitis were analyzed by multivariate Logistic regression; the receiver operative curve(ROC)was applied to analyze the predictive value of serum HDAC1 and eNOS levels for postoperative infectious endophthalmitis in cataract patients.RESULTS: The levels of serum HDAC1 and eNOS in the infected group were obviously higher than those in the uninfected group(all P<0.01). Surgical time, vitreous overflow, HDAC1, and eNOS were all risk factors for postoperative infectious endophthalmitis(all P<0.05). ROC results showed that the AUC of HDAC1 and eNOS in predicting postoperative infectious endophthalmitis in cataract patients was 0.878 and 0.877, respectively, with sensitivity of 88.7% and 87.7%, specificity of 70.4% and 7.8%, respectively, while the AUC of the two combination in predicting postoperative infectious endophthalmitis in cataract patients was 0.978, with a sensitivity of 86.7% and a specificity of 85.3%.CONCLUSION:The serum levels of HDAC1 and eNOS in patients with infectious endophthalmitis after cataract surgery are obviously increased, and the combined detection of serum HDAC1 and eNOS can improve the predictive efficacy of infectious endophthalmitis in cataract patients after surgery. Both can provide reference for clinical diagnosis and treatment.

ObjectiveTomatoes are one of the highest-yielding and most widely cultivated economic crops globally, playing a crucial role in agricultural production and providing significant economic benefits to farmers and related industries. However, early blight in tomatoes is known for its rapid infection, widespread transmission, and severe destructiveness, which significantly impacts both the yield and quality of tomatoes, leading to substantial economic losses for farmers. Therefore, accurately identifying early symptoms of tomato early blight is essential for the scientific prevention and control of this disease. Additionally, visualizing affected areas can provide precise guidance for farmers, effectively reducing economic losses. This study combines hyperspectral imaging technology with machine learning algorithms to develop a model for the early identification of symptoms of tomato early blight, facilitating early detection of the disease and visual localization of affected areas. MethodsTo address noise interference present in hyperspectral images, robust principal component analysis (RPCA) is employed for effective denoising, enhancing the accuracy of subsequent analyses. To avoid insufficient information representation caused by the subjective selection of regions of interest, the Otsu’s thresholding method is utilized to extract tomato leaves effectively from the background, with the average spectrum of the entire leaf taken as the primary object of study. Furthermore, a comprehensive spectral preprocessing workflow is established by integrating multivariate scatter correction (MSC) and standardization methods, ensuring the reliability and effectiveness of the data. Based on the processed spectral data, a discriminant model utilizing a linear kernel function support vector machine (SVM) is constructed, focusing on characteristic wavelengths to improve the model's discriminative capability. ResultsCompared to full-spectrum modeling, this approach results in an 8.33% increase in accuracy on the test set. After optimizing the parameters of the SVM model, when C=1.64, the accuracies of the training set and test set reach 91.67% and 94.44%, respectively, demonstrating a 1.19% increase in training set accuracy compared to the unoptimized model, while maintaining the same accuracy on the test set, effectively alleviating issues of underfitting. ConclusionThis study successfully establishes an early discriminant model for tomato early blight using hyperspectral imaging and achieves visualization of early symptoms. Experimental results indicate that the SVM discriminant model based on characteristic wavelengths and a linear kernel function can effectively identify early symptoms of tomato early blight. Visualization of these symptoms in terms of disease probability allows for a more intuitive detection of early diseases and timely implementation of corresponding control measures. This visual analysis not only enhances the efficiency of disease identification but also provides farmers with more straightforward and practical information, aiding them in formulating more reasonable prevention strategies. These research findings provide valuable references for the early identification and visualization of plant diseases, holding significant practical implications for monitoring, identifying, and scientifically preventing crop diseases. Future research could further explore how to apply this model to disease detection in other crops and how to integrate IoT technology to create intelligent disease monitoring systems, enhancing the scientific and efficient management of crops.

This article reports a first case of combined infection with severe fever with thrombocytopenia syndrome(SFTS)and scrub typhus in Dalian City.The patient was admitted to the hospital due to recurrent fever for 7 days and loss of consciousness for 1 day.Pathogen metagenomic sequencing(mNGS),SFTSV quantitative PCR,and enzyme-linked immunosorbent assay(ELISA)IgM tests were performed,showing positive results for Orientia tsutsugamushi and SFTSV nucleic acids.Based on clinical manifestations and epidemiological history,the patient was diagnosed with combined infections.

ObjectiveTo establish a geographical origin identification model for Foeniculi Fructus from Haiyuan， providing a new technical reference for the protection of Haiyuan's geo-authentic medicinal materials and its designation as a national geographical indication agricultural product. MethodsSamples of Foeniculi Fructus were collected from eight producing areas， including Minqin （Gansu）， Bozhou （Anhui）， Qingdao （Shandong）， Dezhou （Shandong）， Urumqi （Xinjiang）， Nujiang （Yunnan）， Gutuo （Inner Mongolia）， and Haiyuan （Ningxia）. Gas chromatography-ion mobility spectrometry （GC-IMS） was used to detect the volatile organic compounds （VOCs） in samples from these geographic origins. VOCs were qualitatively analyzed through dual matching with the National Institute of Standards and Technology （NIST） mass spectral database and the IMS drift time database. Using the Reporter module and Gallery Plot visualization tools within the LAV analytical platform， VOC fingerprint profiles characterizing geographic origins were constructed. A non-targeted analytical strategy was adopted， and 97 VOCs detected via GC-IMS were subjected to principal component analysis （PCA） and partial least squares discriminant analysis （PLS-DA） based on their differential distribution patterns to construct an origin identification model for Foeniculi Fructus from Haiyuan region. Key discriminative markers were screened using variable importance in projection （VIP） values greater than 1. ResultsA total of 97 VOCs were identified， including alcohols， aldehydes， ketones， esters， organic acids， terpenoids， ethers， alkenes， and benzenes. The PLS-DA model， based on VOCs data obtained by GC-IMS， effectively distinguished Foeniculi Fructus in Haiyuan region from those of other origins. During cross-validation， the model achieved a prediction parameter （Q²） of 0.976 and a goodness-of-fit parameter （R²） of 0.936， with no overfitting observed in permutation testing. Twelve key flavor markers with VIP > 1 were identified as characteristic indicators of Haiyuan origin. ConclusionA stable and highly predictive origin identification model for Foeniculi Fructus from Haiyuan was successfully established using GC-IMS technology， PLS-DA， and VIP-based marker screening. This model provides a novel technical strategy for accurately distinguishing Foeniculi Fructus in Haiyuan region from other regional varieties and offers new technical support for its protection as a geo-authentic medicinal material and a nationally designated geographical indication agricultural product in China.

ObjectiveTo establish a geographical origin identification model for Foeniculi Fructus from Haiyuan， providing a new technical reference for the protection of Haiyuan's geo-authentic medicinal materials and its designation as a national geographical indication agricultural product. MethodsSamples of Foeniculi Fructus were collected from eight producing areas， including Minqin （Gansu）， Bozhou （Anhui）， Qingdao （Shandong）， Dezhou （Shandong）， Urumqi （Xinjiang）， Nujiang （Yunnan）， Gutuo （Inner Mongolia）， and Haiyuan （Ningxia）. Gas chromatography-ion mobility spectrometry （GC-IMS） was used to detect the volatile organic compounds （VOCs） in samples from these geographic origins. VOCs were qualitatively analyzed through dual matching with the National Institute of Standards and Technology （NIST） mass spectral database and the IMS drift time database. Using the Reporter module and Gallery Plot visualization tools within the LAV analytical platform， VOC fingerprint profiles characterizing geographic origins were constructed. A non-targeted analytical strategy was adopted， and 97 VOCs detected via GC-IMS were subjected to principal component analysis （PCA） and partial least squares discriminant analysis （PLS-DA） based on their differential distribution patterns to construct an origin identification model for Foeniculi Fructus from Haiyuan region. Key discriminative markers were screened using variable importance in projection （VIP） values greater than 1. ResultsA total of 97 VOCs were identified， including alcohols， aldehydes， ketones， esters， organic acids， terpenoids， ethers， alkenes， and benzenes. The PLS-DA model， based on VOCs data obtained by GC-IMS， effectively distinguished Foeniculi Fructus in Haiyuan region from those of other origins. During cross-validation， the model achieved a prediction parameter （Q²） of 0.976 and a goodness-of-fit parameter （R²） of 0.936， with no overfitting observed in permutation testing. Twelve key flavor markers with VIP > 1 were identified as characteristic indicators of Haiyuan origin. ConclusionA stable and highly predictive origin identification model for Foeniculi Fructus from Haiyuan was successfully established using GC-IMS technology， PLS-DA， and VIP-based marker screening. This model provides a novel technical strategy for accurately distinguishing Foeniculi Fructus in Haiyuan region from other regional varieties and offers new technical support for its protection as a geo-authentic medicinal material and a nationally designated geographical indication agricultural product in China.

ObjectiveEarly detection of crop diseases is crucial for effective agricultural management and yield protection. While visible light imaging has been widely applied for disease detection due to its accessibility and non-destructive nature, most existing methods primarily focus on identifying diseases during the symptomatic phase, when visual symptoms are already prominent. However, detecting plant diseases during the incubation period—when symptoms are still subtle or invisible—remains a major challenge due to the lack of distinctive visual cues and limited research methodologies. This study aims to address this gap by proposing a novel three-channel recognition model to accurately identify early blight symptoms during the incubation stage in Solanaceae crops, particularly in chili and tomato, using only visible light images. MethodsWe established a controlled experimental setup in which healthy leaves and leaves inoculated with early blight pathogens were photographed continuously over time. A total of 1 258 visible light images were collected, capturing various stages of disease progression. From these images, lesion regions were manually annotated. To quantitatively characterize early and subtle color changes within the lesion areas, we extracted color moments—first-order (mean), second-order (standard deviation), and third-order (skewness)—from multiple color spaces, including Lab and HSV. By analyzing the temporal variation of these color moments across disease progression stages, we identified the first-order moment of the saturation (S) channel in the HSV color space as the most sensitive indicator of lesion development on inoculated leaves. Using this insight, we defined four disease categories: healthy, incubation stage, early stage, and late stage. Subsequently, a three-channel classification model was constructed by integrating features from three color channels that provided complementary information. Three-channel models were constructed based on R-G-B, L-a-b, and H-S-V color spaces, respectively, to evaluate performance across different crops and to determine which color representation provides the most discriminative power for identifying disease symptoms during the incubation period. ResultsThe proposed models demonstrated strong classification performance. The three-channel model built using the Lab color space achieved a 94.44% accuracy in recognizing the incubation stage of early blight in pepper, effectively distinguishing subtle pre-symptomatic features from healthy tissue. The model based on the HSV color space achieved 100% accuracy in detecting incubation-stage symptoms in tomato, underscoring the discriminative power of S-channel variations in this context. These results confirm the model’s capability to identify early blight before visible lesions become pronounced, which is essential for timely disease intervention. ConclusionThis study presents a new technical pathway for early-stage disease detection using visible light images by focusing on subtle color feature changes during the incubation period. The proposed three-channel recognition model effectively identifies early blight in both chili and tomato, offering a non-destructive, low-cost, and easily deployable solution for early warning and precision agriculture. Furthermore, this framework can be generalized to other crops and diseases where early detection plays a critical role in minimizing yield losses and ensuring sustainable production. The method lays a solid foundation for future research in pre-symptomatic plant disease recognition and provides valuable tools for intelligent crop monitoring and precision management systems.

BACKGROUND: Neoadjuvant chemoradiotherapy followed by radical surgery has been a common practice for patients with locally advanced rectal cancer, but the response rate varies among patients. This study aimed to develop a ResNet-Vision Transformer based magnetic resonance imaging (MRI)-endoscopy fusion model to precisely predict treatment response and provide personalized treatment. METHODS: In this multicenter study, 366 eligible patients who had undergone neoadjuvant chemoradiotherapy followed by radical surgery at eight Chinese tertiary hospitals between January 2017 and June 2024 were recruited, with 2928 pretreatment colonic endoscopic images and 366 pelvic MRI images. An MRI-endoscopy fusion model was constructed based on the ResNet backbone and Transformer network using pretreatment MRI and endoscopic images. Treatment response was defined as good response or non-good response based on the tumor regression grade. The Delong test and the Hanley-McNeil test were utilized to compare prediction performance among different models and different subgroups, respectively. The predictive performance of the MRI-endoscopy fusion model was comprehensively validated in the test sets and was further compared to that of the single-modal MRI model and single-modal endoscopy model. RESULTS: The MRI-endoscopy fusion model demonstrated favorable prediction performance. In the internal validation set, the area under the curve (AUC) and accuracy were 0.852 (95% confidence interval [CI]: 0.744-0.940) and 0.737 (95% CI: 0.712-0.844), respectively. Moreover, the AUC and accuracy reached 0.769 (95% CI: 0.678-0.861) and 0.729 (95% CI: 0.628-0.821), respectively, in the external test set. In addition, the MRI-endoscopy fusion model outperformed the single-modal MRI model (AUC: 0.692 [95% CI: 0.609-0.783], accuracy: 0.659 [95% CI: 0.565-0.775]) and the single-modal endoscopy model (AUC: 0.720 [95% CI: 0.617-0.823], accuracy: 0.713 [95% CI: 0.612-0.809]) in the external test set. CONCLUSION The MRI-endoscopy fusion model based on ResNet-Vision Transformer achieved favorable performance in predicting treatment response to neoadjuvant chemoradiotherapy and holds tremendous potential for enabling personalized treatment regimens for locally advanced rectal cancer patients.
Humans ; Rectal Neoplasms/diagnostic imaging* ; Magnetic Resonance Imaging/methods* ; Male ; Female ; Middle Aged ; Neoadjuvant Therapy/methods* ; Aged ; Adult ; Chemoradiotherapy/methods* ; Endoscopy/methods* ; Treatment Outcome