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Apocynum venetum L. belongs to the Apocynaceae family and is a plant that is highly resistant to stress. It is important in the fields of ecology, feeding, industry and medicine. The molecular mechanism underlying salt tolerance has not been elucidated. In this study, RNA-seq based transcriptome sequencing of A. venetum leaves after 0, 2, 6, 12, 24 and 48 h of treatment with 300 mM NaCl was performed. We conducted a comprehensive analysis of the transcriptome expression profiles of A. venetum under salt stress using the WGCNA method and identified red, black, and brown as the core modules regulating the salt tolerance of A. venetum. A co-expression regulatory network was constructed to identify the core genes in the module according to the correlations between genes. The genes TRINITY_DN102_c0_g1 (serine carboxypeptidase), TRINITY_DN3073_c0_g1 (SOS signaling pathway) and TRINITY_DN6732_c0_g1 (heat shock transcription factor) in the red module were determined to be the core genes. Two core genes in the black module, TRINITY_DN9926_c0_g1 and TRINITY_DN7962_c0_g1, are pioneer candidate salt tolerance-associated genes in A. venetum. The genes in the brown module were mainly enriched in two pathways, namely photosynthesis and osmotic balance. Among them, the TRINITY_DN6321_c0_g2 and TRINITY_DN244_c0_g1 genes encode aquaporin, which is helpful for maintaining the cell water balance and plays a protective role in defending A. venetum under abiotic stress. Our findings contribute to the identification of core genes involved in the response of A. venetum to salt stress.

Osteoporosis is a complex endocrine disease characterized by a decline in bone mass and microstructural integrity. It constitutes a major global health problem. Recent progress in the field of artificial intelligence (AI) has opened new avenues for the effective diagnosis of osteoporosis via radiographs. This review investigates the application of AI classification of osteoporosis in radiographs. A comprehensive exploration of electronic repositories (ClinicalTrials.gov, Web of Science, PubMed, MEDLINE) was carried out in adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 statement (PRISMA). A collection of 31 articles was extracted from these repositories and their significant outcomes were consolidated and outlined. This encompassed insights into anatomical regions, the specific machine learning methods employed, the effectiveness in predicting BMD, and categorizing osteoporosis. Through analyzing the respective studies, we evaluated the effectiveness and limitations of AI osteoporosis classification in radiographs. The pooled reported accuracy, sensitivity, and specificity of osteoporosis classification ranges from 66.1% to 97.9%, 67.4% to 100.0%, and 60.0% to 97.5% respectively. This review underscores the potential of AI osteoporosis classification and offers valuable insights for future research endeavors, which should focus on addressing the challenges in technical and clinical integration to facilitate practical implementation of this technology.

Context Swertia mussotii Franch. (Gentianaceae) is a source of the traditional Tibetan medicine, ZangYinChen, and is used to treat chronic hepatitis and many types of jaundice.Objective This study explored the therapeutic effects and mechanism of S. mussotii on non-alcoholic fatty liver disease in diet-induced hypercholesterolaemia.Materials and methods After a week of adaptive feeding, 32 Sprague-Dawley rats were divided into four groups: (1) Control, (2) Control-S, (3) Model, and (4) Model-S. During the 12 experimental weeks, we established the Model using a high-fat diet. Control-S and Model-S were given 1.0 g/kg S. mussotii water extract via gavage starting in the fifth week until the end of experiment.Results When compared with Model rats, the S. mussotii water extract led to a reduction in high-density lipoproteins (43.9%) and albumin (13.9%) and a decrease in total cholesterol (54.0%), triglyceride (45.6%), low-density lipoproteins (8.6%), aspartate aminotransferase (11.0%), alanine aminotransferase (15.5%), alkaline phosphatase (19.1%), total protein (6.4%), and glucose (20.8%) in serum. A reduction in three cytokines (IL-1β, IL-6, and TNFα) was detected. Histopathological examination showed that liver steatosis was significantly relieved in S. mussotii-treated high-fat diet rats. S. mussotii also caused a downregulation in the expression of TLR4 (43.2%), MyD88 (33.3%), and a decrease in phosphorylation of NF-κB.Discussion and conclusions Our findings indicate that S. mussotii may act as a potential anti-inflammation drug via inhibition of the TLR4/MyD88/NF-κB pathway. Further in vivo and in vitro studies are needed to validate its potential in clinical medicine.

IntroductionMetastatic spinal cord compression (MSCC) is a disastrous complication of advanced malignancy. A deep learning (DL) algorithm for MSCC classification on CT could expedite timely diagnosis. In this study, we externally test a DL algorithm for MSCC classification on CT and compare with radiologist assessment.MethodsRetrospective collection of CT and corresponding MRI from patients with suspected MSCC was conducted from September 2007 to September 2020. Exclusion criteria were scans with instrumentation, no intravenous contrast, motion artefacts and non-thoracic coverage. Internal CT dataset split was 84% for training/validation and 16% for testing. An external test set was also utilised. Internal training/validation sets were labelled by radiologists with spine imaging specialization (6 and 11-years post-board certification) and were used to further develop a DL algorithm for MSCC classification. The spine imaging specialist (11-years expertise) labelled the test sets (reference standard). For evaluation of DL algorithm performance, internal and external test data were independently reviewed by four radiologists: two spine specialists (Rad1 and Rad2, 7 and 5-years post-board certification, respectively) and two oncological imaging specialists (Rad3 and Rad4, 3 and 5-years post-board certification, respectively). DL model performance was also compared against the CT report issued by the radiologist in a real clinical setting. Inter-rater agreement (Gwet’s kappa) and sensitivity/specificity/AUCs were calculated.ResultsOverall, 420 CT scans were evaluated (225 patients, mean age=60 ± 11.9[SD]); 354(84%) CTs for training/validation and 66(16%) CTs for internal testing. The DL algorithm showed high inter-rater agreement for three-class MSCC grading with kappas of 0.872 (p

Osteoporosis, marked by low bone mineral density (BMD) and a high fracture risk, is a major health issue. Recent progress in medical imaging, especially CT scans, offers new ways of diagnosing and assessing osteoporosis. This review examines the use of AI analysis of CT scans to stratify BMD and diagnose osteoporosis. By summarizing the relevant studies, we aimed to assess the effectiveness, constraints, and potential impact of AI-based osteoporosis classification (severity) via CT. A systematic search of electronic databases (PubMed, MEDLINE, Web of Science, ClinicalTrials.gov) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 39 articles were retrieved from the databases, and the key findings were compiled and summarized, including the regions analyzed, the type of CT imaging, and their efficacy in predicting BMD compared with conventional DXA studies. Important considerations and limitations are also discussed. The overall reported accuracy, sensitivity, and specificity of AI in classifying osteoporosis using CT images ranged from 61.8% to 99.4%, 41.0% to 100.0%, and 31.0% to 100.0% respectively, with areas under the curve (AUCs) ranging from 0.582 to 0.994. While additional research is necessary to validate the clinical efficacy and reproducibility of these AI tools before incorporating them into routine clinical practice, these studies demonstrate the promising potential of using CT to opportunistically predict and classify osteoporosis without the need for DEXA.

BackgroundMetastatic epidural spinal cord compression (MESCC) is a devastating complication of advanced cancer. A deep learning (DL) model for automated MESCC classification on MRI could aid earlier diagnosis and referral.PurposeTo develop a DL model for automated classification of MESCC on MRI.Materials and MethodsPatients with known MESCC diagnosed on MRI between September 2007 and September 2017 were eligible. MRI studies with instrumentation, suboptimal image quality, and non-thoracic regions were excluded. Axial T2-weighted images were utilized. The internal dataset split was 82% and 18% for training/validation and test sets, respectively. External testing was also performed. Internal training/validation data were labeled using the Bilsky MESCC classification by a musculoskeletal radiologist (10-year experience) and a neuroradiologist (5-year experience). These labels were used to train a DL model utilizing a prototypical convolutional neural network. Internal and external test sets were labeled by the musculoskeletal radiologist as the reference standard. For assessment of DL model performance and interobserver variability, test sets were labeled independently by the neuroradiologist (5-year experience), a spine surgeon (5-year experience), and a radiation oncologist (11-year experience). Inter-rater agreement (Gwet’s kappa) and sensitivity/specificity were calculated.ResultsOverall, 215 MRI spine studies were analyzed [164 patients, mean age = 62 ± 12(SD)] with 177 (82%) for training/validation and 38 (18%) for internal testing. For internal testing, the DL model and specialists all showed almost perfect agreement (kappas = 0.92–0.98, p < 0.001) for dichotomous Bilsky classification (low versus high grade) compared to the reference standard. Similar performance was seen for external testing on a set of 32 MRI spines with the DL model and specialists all showing almost perfect agreement (kappas = 0.94–0.95, p < 0.001) compared to the reference standard.ConclusionA DL model showed comparable agreement to a subspecialist radiologist and clinical specialists for the classification of malignant epidural spinal cord compression and could optimize earlier diagnosis and surgical referral.

Kiwifruit bacterial canker, caused by Pseudomonas syringae pv. actinidiae (Psa), is a catastrophic disease affecting kiwifruit worldwide. As no effective cure has been developed, planting Psa-resistant cultivars is the best way to avoid bacterial canker in kiwifruit cultivation. However, the differences in the mechanism of resistance between cultivars is poorly understood. In the present study, five local kiwifruit cultivars were used for Psa resistance evaluation and classified into different resistance categories, tolerant (T), susceptible (S), and highly susceptible (HS), based on their various symptoms of lesions on the cane. Susceptible and highly susceptible varieties had a higher sucrose concentration, and a greater decrease in sucrose content was observed after Psa inoculation in phloem than in tolerant varieties. Three invertase activities and their corresponding gene expressions were detected in the phloem with lesions and showed the same trends as the variations in sucrose concentration. Meanwhile, after Psa inoculation, enzyme activities involved in antioxidant defense responses, such as PAL, POD, and CAT, were also altered in the phloem of the lesion position. With no differences among cultivars, PAL and POD activities in phloem first increased and then decreased after Psa inoculation. However, great differences in CAT activities were observed between T and S/HS categories. Our results demonstrate that sucrose content was negatively correlated with the disease resistance of different cultivars and that the increase in immune response enzymes is likely caused by increased sucrose metabolism in the phloem.

Spinal metastasis is the most common malignant disease of the spine. Recently, major advances in machine learning and artificial intelligence technology have led to their increased use in oncological imaging. The purpose of this study is to review and summarise the present evidence for artificial intelligence applications in the detection, classification and management of spinal metastasis, along with their potential integration into clinical practice. A systematic, detailed search of the main electronic medical databases was undertaken in concordance with the PRISMA guidelines. A total of 30 articles were retrieved from the database and reviewed. Key findings of current AI applications were compiled and summarised. The main clinical applications of AI techniques include image processing, diagnosis, decision support, treatment assistance and prognostic outcomes. In the realm of spinal oncology, artificial intelligence technologies have achieved relatively good performance and hold immense potential to aid clinicians, including enhancing work efficiency and reducing adverse events. Further research is required to validate the clinical performance of the AI tools and facilitate their integration into routine clinical practice.

Background: Early diagnosis of metastatic epidural spinal cord compression (MESCC) is vital to expedite therapy and prevent paralysis. Staging CT is performed routinely in cancer patients and presents an opportunity for earlier diagnosis. Methods: This retrospective study included 123 CT scans from 101 patients who underwent spine MRI within 30 days, excluding 549 CT scans from 216 patients due to CT performed post-MRI, non-contrast CT, or a gap greater than 30 days between modalities. Reference standard MESCC gradings on CT were provided in consensus via two spine radiologists (11 and 7 years of experience) analyzing the MRI scans. CT scans were labeled using the original reports and by three radiologists (3, 13, and 14 years of experience) using dedicated CT windowing. Results: For normal/none versus low/high-grade MESCC per CT scan, all radiologists demonstrated almost perfect agreement with kappa values ranging from 0.866 (95% CI 0.787–0.945) to 0.947 (95% CI 0.899–0.995), compared to slight agreement for the reports (kappa = 0.095, 95%CI −0.098–0.287). Radiologists also showed high sensitivities ranging from 91.51 (95% CI 84.49–96.04) to 98.11 (95% CI 93.35–99.77), compared to 44.34 (95% CI 34.69–54.31) for the reports. Conclusion: Dedicated radiologist review for MESCC on CT showed high interobserver agreement and sensitivity compared to the current standard of care.

Objective: To investigate the expression of IL-33 in serum and skin lesions of patients with progressive psoriasis vulgaris (PsV) and its clinical significance. Methods: A total of 126 patients with PsV and 45 healthy individuals were enrolled from January 2018 to January 2019 which defined as PsV group and control group, respectively. The differences of IL-33 expression in serum between the two groups were analyzed and compared, and the differences of IL-33 expression in skin tissue between patients with PsV and lipoma patients were also compared. The serum IL-33 expression levels with PASI scores correlation, differences between clinical characteristics and influencing factors in patients with PsV were analyzed by Pearson correlation analysis, single factor analysis and logistic regression analysis, respectively. Receiver Operating Characteristic (ROC) curve was used to investigate the clinical efficacy of serum IL-33 levels in differentiating PsV from control group, mild from moderate to severe PsV. Results: Compared with the control group, the expression of IL-33 in the serum of the patients with PsV was significantly higher (t=17.51, P

Objective: To investigate the predictive value of neutrophil to lymphocyte ratio (NLR) in secondary thyroid abnormalities in female patients with vulvar lichen sclerosus (VLS). Methods: A total of 273 female patients with VLS were collected from January 2014 to June 2018. The follow-up was limited to patients secondary thyroid abnormalities or to Dec 31, 2018. Female patients with thyroid abnormalities secondary to VLS were setted as observation group and others were setted as control group. The difference of thyroid hormone and antibody levels between the two groups were compared. Pearson correlation was used to analyze the correlation between NLR and vulvar leukoplakia area, thyroid hormone and antibody levels. The predictive value of NLR in female patients with thyroid abnormalities secondary to VLS was analyzed by ROC curve. Results: The average age of VLS patients was (42.17±8.65) years old. The lesions were mostly located to the labia and vaginal orifice, most of which showed depigmentation or hypopigmentation. The prevalence of thyroid abnormalities was 13.92% (38/273). The follow-up time of thyroid abnormalities after female patients diagnosed with VLS was (15.21±3.48) months, and most patients showed with high level of TPO-Ab and TSH. Pearson correlation analysis showed that NLR was positively correlated with leukoplakia area, TSH and TPO-Ab levels (r=0.232, 0.531, 0.499 respectively). ROC curve area of predicting NLR in female patients with thyroid abnormalities secondary to VLS was 0.906 (95% CI: 0.841~0.971) by ROC curve result. When NLR was 5.585, the Yoden index, sensitivity and specificity was 0.736, 84.2% and 89.4%, respectively. Conclusions: The prevalence of thyroid dysfunction or antibody abnormalities in female patients with VLS is higher. NLR can effectively predict the occurrence of thyroid abnormalities in the female patients with VLS. Clinicians should routinely detect the level of NLR in patients with VLS.

Background: Metastatic epidural spinal cord compression (MESCC) is a disastrous complication of advanced malignancy. Deep learning (DL) models for automatic MESCC classification on staging CT were developed to aid earlier diagnosis. Methods: This retrospective study included 444 CT staging studies from 185 patients with suspected MESCC who underwent MRI spine studies within 60 days of the CT studies. The DL model training/validation dataset consisted of 316/358 (88%) and the test set of 42/358 (12%) CT studies. Training/validation and test datasets were labeled in consensus by two subspecialized radiologists (6 and 11-years-experience) using the MRI studies as the reference standard. Test sets were labeled by the developed DL models and four radiologists (2–7 years of experience) for comparison. Results: DL models showed almost-perfect interobserver agreement for classification of CT spine images into normal, low, and high-grade MESCC, with kappas ranging from 0.873–0.911 (p < 0.001). The DL models (lowest κ = 0.873, 95% CI 0.858–0.887) also showed superior interobserver agreement compared to two of the four radiologists for three-class classification, including a specialist (κ = 0.820, 95% CI 0.803–0.837) and general radiologist (κ = 0.726, 95% CI 0.706–0.747), both p < 0.001. Conclusion: DL models for the MESCC classification on a CT showed comparable to superior interobserver agreement to radiologists and could be used to aid earlier diagnosis.