The GRAS transcription factor CsTL regulates tendril formation in cucumber.

Cucumber (Cucumis sativus, Cs) tendrils are slender vegetative organs that typically require manual removal to ensure orderly growth during greenhouse cultivation. Here, we identified cucumber tendril-less (tl), a Tnt1 retrotransposon-induced insertion mutant lacking tendrils. Map-based cloning identified the mutated gene, CsaV3_3G003590, which we designated as CsTL, which is homologous to Arabidopsis thaliana LATERAL SUPPRESSOR (AtLAS). Knocking out CsTL repressed tendril formation but did not affect branch initiation, whereas overexpression (OE) of CsTL resulted in the formation of two or more tendrils in one leaf axil. Although expression of two cucumber genes regulating tendril formation, Tendril (CsTEN) and Unusual Floral Organs (CsUFO), was significantly decreased in CsTL knockout lines, these two genes were not direct downstream targets of CsTL. Instead, CsTL physically interacted with CsTEN, an interaction that further enhanced CsTEN-mediated expression of CsUFO. In Arabidopsis, the CsTL homolog AtLAS acts upstream of REVOLUTA (REV) to regulate branch initiation. Knocking out cucumber CsREV inhibited branch formation without affecting tendril initiation. Furthermore, genomic regions containing CsTL and AtLAS were not syntenic between the cucumber and Arabidopsis genomes, whereas REV orthologs were found on a shared syntenic block. Our results revealed not only that cucumber CsTL possesses a divergent function in promoting tendril formation but also that CsREV retains its conserved function in shoot branching.

Computer-based facial recognition as an assisting diagnostic tool to identify children with Noonan syndrome.

BACKGROUND: Noonan syndrome (NS) is a rare genetic disease, and patients who suffer from it exhibit a facial morphology that is characterized by a high forehead, hypertelorism, ptosis, inner epicanthal folds, down-slanting palpebral fissures, a highly arched palate, a round nasal tip, and posteriorly rotated ears. Facial analysis technology has recently been applied to identify many genetic syndromes (GSs). However, few studies have investigated the identification of NS based on the facial features of the subjects. OBJECTIVES: This study develops advanced models to enhance the accuracy of diagnosis of NS. METHODS: A total of 1,892 people were enrolled in this study, including 233 patients with NS, 863 patients with other GSs, and 796 healthy children. We took one to 10 frontal photos of each subject to build a dataset, and then applied the multi-task convolutional neural network (MTCNN) for data pre-processing to generate standardized outputs with five crucial facial landmarks. The ImageNet dataset was used to pre-train the network so that it could capture generalizable features and minimize data wastage. We subsequently constructed seven models for facial identification based on the VGG16, VGG19, VGG16-BN, VGG19-BN, ResNet50, MobileNet-V2, and squeeze-and-excitation network (SENet) architectures. The identification performance of seven models was evaluated and compared with that of six physicians. RESULTS: All models exhibited a high accuracy, precision, and specificity in recognizing NS patients. The VGG19-BN model delivered the best overall performance, with an accuracy of 93.76%, precision of 91.40%, specificity of 98.73%, and F1 score of 78.34%. The VGG16-BN model achieved the highest AUC value of 0.9787, while all models based on VGG architectures were superior to the others on the whole. The highest scores of six physicians in terms of accuracy, precision, specificity, and the F1 score were 74.00%, 75.00%, 88.33%, and 61.76%, respectively. The performance of each model of facial recognition was superior to that of the best physician on all metrics. CONCLUSION: Models of computer-assisted facial recognition can improve the rate of diagnosis of NS. The models based on VGG19-BN and VGG16-BN can play an important role in diagnosing NS in clinical practice.

Facial recognition models for identifying genetic syndromes associated with pulmonary stenosis in children.

BACKGROUND: Williams-Beuren syndrome, Noonan syndrome, and Alagille syndrome are common types of genetic syndromes (GSs) characterized by distinct facial features, pulmonary stenosis, and delayed growth. In clinical practice, differentiating these three GSs remains a challenge. Facial gestalts serve as a diagnostic tool for recognizing Williams-Beuren syndrome, Noonan syndrome, and Alagille syndrome. Pretrained foundation models (PFMs) can be considered the foundation for small-scale tasks. By pretraining with a foundation model, we propose facial recognition models for identifying these syndromes. METHODS: A total of 3297 (n = 1666) facial photos were obtained from children diagnosed with Williams-Beuren syndrome (n = 174), Noonan syndrome (n = 235), and Alagille syndrome (n = 51), and from children without GSs (n = 1206). The photos were randomly divided into five subsets, with each syndrome and non-GS equally and randomly distributed in each subset. The proportion of the training set and the test set was 4:1. The ResNet-100 architecture was employed as the backbone model. By pretraining with a foundation model, we constructed two face recognition models: one utilizing the ArcFace loss function, and the other employing the CosFace loss function. Additionally, we developed two models using the same architecture and loss function but without pretraining. The accuracy, precision, recall, and F1 score of each model were evaluated. Finally, we compared the performance of the facial recognition models to that of five pediatricians. RESULTS: Among the four models, ResNet-100 with a PFM and CosFace loss function achieved the best accuracy (84.8%). Of the same loss function, the performance of the PFMs significantly improved (from 78.5% to 84.5% for the ArcFace loss function, and from 79.8% to 84.8% for the CosFace loss function). With and without the PFM, the performance of the CosFace loss function models was similar to that of the ArcFace loss function models (79.8% vs 78.5% without PFM; 84.8% vs 84.5% with PFM). Among the five pediatricians, the highest accuracy (0.700) was achieved by the senior-most pediatrician with genetics training. The accuracy and F1 scores of the pediatricians were generally lower than those of the models. CONCLUSIONS: A facial recognition-based model has the potential to improve the identification of three common GSs with pulmonary stenosis. PFMs might be valuable for building screening models for facial recognition. Key messages What is already known on this topic:  Early identification of genetic syndromes (GSs) is crucial for the management and prognosis of children with pulmonary stenosis (PS). Facial phenotyping with convolutional neural networks (CNNs) often requires large-scale training data, limiting its usefulness for GSs. What this study adds:  We successfully built multi-classification models based on face recognition using a CNN to accurately identify three common PS-associated GSs. ResNet-100 with a pretrained foundation model (PFM) and CosFace loss function achieved the best accuracy (84.8%). Pretrained with the foundation model, the performance of the models significantly improved, although the impact of the type of loss function appeared to be minimal. How this study might affect research, practice, or policy:  A facial recognition-based model has the potential to improve the identification of GSs in children with PS. The PFM might be valuable for building identification models for facial detection.

A critical review of the bioactive ingredients and biological functions of camellia oleifera oil.

Camellia oleifera oil is a pure and natural high-grade oil prevalent in South China. Camellia oleifera oil is known for its richness in unsaturated fatty acids and high nutritional value. There is increasing evidence indicating that a diet rich in unsaturated fatty acids is beneficial to health. Despite the widespread production of Camellia oleifera oil and its bioactive components, reports on its nutritional components are scarce, especially regarding systematic reviews of extraction methods and biological functions. This review systematically summarized the latest research on the bioactive components and biological functions of Camellia oleifera oil reported over the past decade. In addition to unsaturated fatty acids, Camellia oleifera oil contains six main functional components contributing to its antioxidant, antibacterial, anti-inflammatory, antidiabetic, anticancer, neuroprotective, and cardiovascular protective properties. These functional components are vitamin E, saponins, polyphenols, sterols, squalene, and flavonoids. This paper reviewed the biological activity of Camellia oleifera oil and its extraction methods, laying a foundation for further development of its bioactive components.

Discrepancy between arterial oxygen saturation and pulse oximetry measurement in a Chinese pediatric patient cohort.

Background: Increasing evidence suggest a racial bias in pulse oximetry measurement, but this was under investigated in Asian pediatric populations. Methods: Via the Pediatric Intensive Care database, this retrospective study included pediatric patient records of arterial oxygen saturation (SaO2) and oxygen saturation on pulse oximetry (SpO2) measured within 10 min. Discrepancy was examined, and potential predictors of occult hypoxemia (defined as SaO2 <88% with the paired SpO2 ≥92%) as well as its association with outcomes were explored by logistic regression. Results: A total of 390 patients were included with 454 pairs of SaO2-SpO2 readings. The study population consisted of Han Chinese (99.0%) and 43.6% were female. Occult hypoxemia was observed in 20.0% of the patients, with a mean SaO2 of 71.4 ± 15.8%. Potential predictors of occult hypoxemia included female, being first admitted to cardiac ICU, congenital heart disease, increased heart rate, while patients with prior surgery records were less likely to experience occult hypoxemia. Patients with occult hypoxemia had numerically higher in-ICU mortality (16.7% versus 10.9%) and in-hospital mortality (17.9% versus 10.9%), but the associations were not statistically significant. Conclusions: There was a substantial proportion of hypoxemia that was not detected by pulse oximetry in the Chinese pediatric patients, which might be predicted by several characteristics and seemed to associate with mortality.

The different prognostic factors between metastatic and nonmetastatic disease of esophageal neuroendocrine carcinoma.

PURPOSE: The specific risk factors of metastatic and nonmetastatic esophageal neuroendocrine carcinoma (NEC) are still uncertain. Whether primary site surgery is necessary for all patients with esophageal NEC is unknown. METHODS: Patients with esophageal NEC in the Surveillance, Epidemiology, and End Results (SEER) database from 2004 to 2014 were selected. STATA 12 was used to analyze the clinical and pathological features of esophageal NEC. RESULTS: In total, 241 patients with esophageal NEC were included. Metastatic patients had shorter overall survival than nonmetastatic patients (6.03 versus 11.90 months, respectively). Prognostic factors varied between metastatic and nonmetastatic esophageal NEC. The location of the primary tumor is a key point for the prognosis of esophageal NEC. For nonmetastatic esophageal NEC, patients with tumors in the upper third of the esophagus had the worst survival, and patients with metastatic esophageal NEC with a primary tumor in the lower part of the esophagus tended to have an increased risk of death. Moreover, age ≥68 years (hazard ratio [HR] = 2.05; 95% confidence interval [CI]: 1.28-3.31; P < 0.01) and large cell carcinoma (HR = 2.79; 95% CI: 1.30-6.00; P < 0.01) were independent risk factors in patients with metastatic esophageal NEC. Primary site resection benefited patients with nonmetastatic esophageal NEC (HR = 0.20; 95% CI: 0.07-0.56; P < 0.01) rather than patients with metastatic esophageal NEC (HR = 0.91; 95% CI: 0.29-2.83; P > 0.05). CONCLUSIONS: Our study presented that primary tumor location is an important risk factor for nonmetastatic esophageal NEC patients. Age and pathological type are important risk factors for patients with metastatic esophageal NEC. Nonmetastatic esophageal NEC will benefit from primary tumor resection. Systematic treatment is recommended for metastatic esophageal NEC.

The role of TMEM26 in disrupting tight junctions and activating NF-κB signaling to promote epithelial-mesenchymal transition in esophageal squamous cell carcinoma

Abstract Objectives Metastasis is one of the biggest challenges in the management of Esophageal Squamous Cell Carcinoma (ESCC), of which molecular mechanisms remain elusive. The present study aimed to explore the roles and underlying mechanisms of Transmembrane protein 26 (TMEM26) in ESCC. Method TMEM26 expressions in tumorous and adjacent tissues from patients with ESCC and in normal esophageal epithelial and ESCC cell lines were detected by immunostaining and western blotting, respectively. The Epithelial-Mesenchymal Transition (EMT), a critical process during metastasis, was investigated by wound healing and Transwell assays, and EMT-related proteins were examined after the TMEM26 alteration in ESCC cell lines. NF-κB signaling activation and Tight Junction (TJ) protein expression were analyzed by western blotting and immunofluorescence, respectively. In vivo verification was performed on the liver metastatic murine model. Results Compared with non-cancerous esophageal tissues and cells, the TMEM26 expression level was higher in ESCC samples and cell lines, where the plasma membrane localization of TMEM26 was observed. The EMT-related processes of ESCC cells were suppressed by RNAi depletion of TMEM26 but aggravated by TMEM26 overexpression. Mechanistically, TMEM26 promoted NF-κB signaling to accelerate EMT in ESCC cells. The plasma membrane presentation and assembly of TJ proteins were impaired by TMEM26. Conclusion Overall, TMEM26 acts as a critical determinant for EMT in ESCC cells by disrupting TJ formation and promoting NF-κB signaling, which may be a potential therapeutic target for treating metastatic ESCC.