A Nuclear rDNA Phylogeny of Smilax (Smilacaceae)

Smilacaceae are a taxonomically confused, cosmopolitan family of Liliales characterized by climbing habit, reticulate leaf venation, paired petiolar tendrils, unisexual flowers, and superior ovaries. Deviations from this generalized morphology have led to the division of Smilacaceae into at least seven different genera and five sections within the large genus Smilax. In particular, taxa with connate tepals (Heterosmilax), more than six stamens (Pleiosmilax, Oligosmilax), or herbaceous habit (Hemexia) have been variously classified. Using DNA sequences of 96 taxa from the nuclear rDNA ITS gene region, parsimony analyses provide moderate resolution, but generally poor bootstrap support for phylogenetic relationships in the family. Rhipogonum and Lapageria are closely related to Smilax, but may be better classified in separate families. Within Smilax few of the sections are monophyletic, but three major lineages are evident. The first is a primarily Old World clade that contains species of Heterosmilax, the erect, woody species of Smilax from Asia, and the herbaceous Smilax species. Within this clade there is evidence that the S. herbacea complex represents a single eastward dispersal from Asia to North America. A primarily New World clade (or paraphyletic grade in some trees) is also present in the ITS cladogram. From within this group there is evidence of a single westward dispersal from North America to Asia. The third lineage is represented by S. aspera, which is the sole member of Smilax section Smilax, and is sister to the entire genus. Its position in the cladogram is intriguing and may help to shed light on the family\u27s greater evolutionary and biogeographic history

Risk prediction for <1 cm lateral lymph node metastasis in papillary thyroid microcarcinoma

BackgroundBecause the diameter of the suspicious lymph nodes is less than 1 cm and adjacent to important structures in the neck, the diagnosis of small LLNM is important but difficult without the help of fine needle aspiration (FNA). There are no relevant reports of risk factors that predict the risk of suspicious &lt;1 cm LLNM.MethodsA total of 159 PTMC patients with suspicious &lt;1 cm LLNM were included in the study. Multivariate logistic regression analysis was used to identify ultrasound independent predictors of LLNM. A predictive model was developed according to multivariate logistic regression and evaluated by Hosmer-Lemeshow fit test.ResultsAge ≤ 38 years old, the largest PTMC was located in the upper part, and the presence of liquefaction or microcalcification in suspicious lymph nodes were independent risk factors for LLNM (univariate analysis P = 0.00, 0.00, 0.00; multivariate analysis P = 0.00, 0.02, 0.00. OR = 4.66 [CI: 1.78-12.21], 3.04 [CI: 1.24-7.46], 6.39 [CI: 1.85-22.00]). The predictive model for the diagnosis of suspicious &lt;1 cm lymph nodes was established as: P = ex/(1 + ex). X = -1.29 + (1.11 × whether the largest tumor is located in the upper part) + (1.54 × whether the age is ≤ 38 years) + (1.85 × whether the suspicious lymph nodes have liquefaction/microcalcification). The Hosmer-Lemeshow fit test was used to test the predicted ability, and it found that the predictive model had a good fit and prediction accuracy (X2 = 6.214, P = 0.623 &gt; 0.05). Chi squared trend analysis showed that the increase in the number of risk factors gradually increased the malignancy possibility of suspicious &lt;1 cm lymph nodes (chi squared trend test, P = 0.00).ConclusionsAge ≤ 38 years old, the largest PTMC located in the upper part, and the presence of liquefaction or microcalcification in suspicious lymph nodes were independent risk factors for suspicious &lt;1 cm LLNM in PTMC patients. Our result show that it is feasible to evaluate the malignant possibility of these lymph nodes using the number of risk factors

Oil absorption stability of modified cellulose porous materials with super compressive strength in the complex environment

The occurrence of oil spills has severe damage upon both the environment and human health. Hence, the development of a green, recyclable, complex environment resistant, and efficient oil–water separation aerogel is required in order to effectively absorb marine or industrial oil. In this study, modified cellulose/N,N'-methylenebisacrylamide/tannin (PCMT) composite porous materials were prepared utilizing the sol–gel method and were modified with tertbutyl acrylate. PCMT possesses a three-dimensional interpenetrating porous structure, exhibiting remarkable oil–water separation performance and excellent compressive strength (PCMT can capable of bearing 7000 times its own weight; PCMT can endure 290.3 kPa pressure at 80% strain when the amount of tannin is 0.2 g). The unique pore structure of PCMT engenders differential oil adsorption capacities (PCMT0, PCMT0.05, PCMT0.1, and PCMT0.2 evince higher adsorption capacities for petroleum ether and dichloromethane, n-hexane and dichloromethane, toluene, and toluene and dichloromethane, respectively). Of critical import, PCMT demonstrates exceptional adaptability to complex environments, wherein the porous materials maintain good hydrophobicity and oil absorption capacity under conditions of vigorous stirring, a wide pH range (1–14), a wide temperature range (4–160 °C), ultraviolet irradiation (8 h), and tape peeling (10 times). Moreover, the porous materials may be employed for the recovery of oil through simple mechanical extrusion, thus demonstrating certain economic significance and the application potential in the treatment of oil spills.publishedVersio

Whole slide image-based weakly supervised deep learning for predicting major pathological response in non-small cell lung cancer following neoadjuvant chemoimmunotherapy: a multicenter, retrospective, cohort study

ObjectiveDevelop a predictive model utilizing weakly supervised deep learning techniques to accurately forecast major pathological response (MPR) in patients with resectable non-small cell lung cancer (NSCLC) undergoing neoadjuvant chemoimmunotherapy (NICT), by leveraging whole slide images (WSIs).MethodsThis retrospective study examined pre-treatment WSIs from 186 patients with non-small cell lung cancer (NSCLC), using a weakly supervised learning framework. We employed advanced deep learning architectures, including DenseNet121, ResNet50, and Inception V3, to analyze WSIs on both micro (patch) and macro (slide) levels. The training process incorporated innovative data augmentation and normalization techniques to bolster the robustness of the models. We evaluated the performance of these models against traditional clinical predictors and integrated them with a novel pathomics signature, which was developed using multi-instance learning algorithms that facilitate feature aggregation from patch-level probability distributions.ResultsUnivariate and multivariable analyses confirmed histology as a statistically significant prognostic factor for MPR (P-value&lt; 0.05). In patch model evaluations, DenseNet121 led in the validation set with an area under the curve (AUC) of 0.656, surpassing ResNet50 (AUC = 0.626) and Inception V3 (AUC = 0.654), and showed strong generalization in external testing (AUC = 0.611). Further evaluation through visual inspection of patch-level data integration into WSIs revealed XGBoost’s superior class differentiation and generalization, achieving the highest AUCs of 0.998 in training and robust scores of 0.818 in validation and 0.805 in testing. Integrating pathomics features with clinical data into a nomogram yielded AUC of 0.819 in validation and 0.820 in testing, enhancing discriminative accuracy. Gradient-weighted Class Activation Mapping (Grad-CAM) and feature aggregation methods notably boosted the model’s interpretability and feature modeling.ConclusionThe application of weakly supervised deep learning to WSIs offers a powerful tool for predicting MPR in NSCLC patients treated with NICT

Smilax gaudichaudiana Kunth 1850

3. Smilax gaudichaudiana Kunth (1850: 252). Homotypic synonym: Heterosmilax gaudichaudiana (Kunth) Maxim (1872: 176). Type: lectotype designated by Koyama (1983: 123). CHINA. Macau: 1844, Gaudichaud 57 (lectotype P!, isolectotype P!)Published as part of Qi, Zhechen, Li, Pan & Fu, Chengxin, 2013, New combinations and a new name in Smilax for species of Heterosmilax in Eastern and Southeast Asian Smilacaceae (Liliales), pp. 58-60 in Phytotaxa 117 (2) on page 58, DOI: 10.11646/phytotaxa.117.2.4, http://zenodo.org/record/507922

Smilax micrantha Blume 1827

7. Smilax micrantha Blume (1827: 18) Homotypic synonym: Heterosmilax micrantha (Blume) Bakh. f. (1950: 400). Type: INDONESIA. Java, (holotype L!). Note: Koyama (1984) considered that Smilax micrantha may be a true Smilax allied to S. myosotiflora A.DC. of section Coilanthus.Published as part of Qi, Zhechen, Li, Pan & Fu, Chengxin, 2013, New combinations and a new name in Smilax for species of Heterosmilax in Eastern and Southeast Asian Smilacaceae (Liliales), pp. 58-60 in Phytotaxa 117 (2) on page 59, DOI: 10.11646/phytotaxa.117.2.4, http://zenodo.org/record/507922