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Springtails are a group of hexapods whose true diversity is currently underestimated. This is because the morphological characters normally used in species diagnosis do not have the necessary resolution. This situation is especially evident in Entomobryidae, which is currently the most diverse family of springtails. The combination of morphological and molecular data has allowed us to describe a new species of the genus Lepidocyrtus, based on specimens from Northern Italy, as well as to redefine the diagnosis of the European Lepidocyrtus lignorum-group.

Co-clinical trials are the concurrent or sequential evaluation of therapeutics in both patients clinically and patient-derived xenografts (PDX) pre-clinically, in a manner designed to match the pharmacokinetics and pharmacodynamics of the agent(s) used. The primary goal is to determine the degree to which PDX cohort responses recapitulate patient cohort responses at the phenotypic and molecular levels, such that pre-clinical and clinical trials can inform one another. A major issue is how to manage, integrate, and analyze the abundance of data generated across both spatial and temporal scales, as well as across species. To address this issue, we are developing MIRACCL (molecular and imaging response analysis of co-clinical trials), a web-based analytical tool. For prototyping, we simulated data for a co-clinical trial in “triple-negative” breast cancer (TNBC) by pairing pre- (T0) and on-treatment (T1) magnetic resonance imaging (MRI) from the I-SPY2 trial, as well as PDX-based T0 and T1 MRI. Baseline (T0) and on-treatment (T1) RNA expression data were also simulated for TNBC and PDX. Image features derived from both datasets were cross-referenced to omic data to evaluate MIRACCL functionality for correlating and displaying MRI-based changes in tumor size, vascularity, and cellularity with changes in mRNA expression as a function of treatment.

Background Hair color retention and rejuvenation have recently emerged as a new research hot spot, with many studies focused on developing treatments to prevent premature greying. Aim This study is aimed to explore the hair-blacking effects and possible mechanisms of Cerasus serrulata flower extracts (CSE) using a C57BL/6 premature greying mouse model. Materials and Methods The variations in hair follicle number, hair follicle depth and cortical thickness were evaluated by hematoxylin and eosin staining. The quantity changes of melanocytes were also evaluated using Masson-Fontana Melanin staining. The relative abundance of vascular endothelial growth factor (VEGF), β-catenin and tyrosinase transcripts following transformed growth were further quantified using RT-qPCR and immunofluorescence. The changes in protein level expression of β-catenin, VEGF and tyrosinase were also evaluated by western blot. Results The β-catenin and VEGF expression were significantly up-regulated in skin tissues treated with C. serrulata extracts, promoting hair growth and blackening. Conclusion CSE can promote the efficient hair-blacking likely via the VEGF pathway, making it a potential therapeutic candidate for treating premature greying.

— The fern genus Didymochlaena is characterized by having scaly rhizomes and petioles, tufted and bipinnate leaves, dimidiate pinnules, and fewer than 11 sori per pinnule. For a long time only one species was widely recognized in the genus. Most recently, six and seven species from Madagascar and Asia-Pacific regions, respectively, have been recognized based on morphological and/or molecular evidence. In this monographic study we recognize 22 species worldwide, six of which are described as new: D. alpina, D. amazonica, D. cameroonensis, D. comorensis, D. cubensis, and D. mesoamericana. A key to all species is provided, and detailed descriptions, illustrations, and information on their distribution and habitats are given.

Jing Huang, Zhijian Lin, Yu Wang, Xueli Ding, Bing Zhang School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, People’s Republic of ChinaCorrespondence: Bing Zhang, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, People’s Republic of China, Email zhangb@bucm.edu.cnBackground: Hyperuricemia (HUA) is a major public health issue with a high prevalence worldwide. Wuling San (WLS) is an effective treatment for HUA. However, the active compounds and the related mechanism are unclear. In this study, we aimed to explore the active compounds and the underlying pharmacological mechanisms of WLS against HUA.Methods: First, a network pharmacology approach was used to detect active compounds of WLS, and potential targets and signaling pathways involved in the treatment of HUA were predicted. Then, a molecular docking strategy was used to predict the affinity between active compounds and key targets. Finally, to verify the prediction, the HUA rat model was established.Results: 49 active compounds with 108 common targets were obtained. Besides, cerevisterol, luteolin, ergosterol peroxide, beta-sitosterol, and sitosterol were identified as key active compounds. In PPI analysis, TNF, IL6, CASP3, PPARG, STAT3, and other 12 core targets were obtained. GO enrichment analysis indicated that WLS was likely to interfere with oxidative stress in the treatment of HUA, and KEGG enrichment analysis indicated multiple inflammation-related signaling pathways possibly involved in the treatment of HUA by WLS, including TNF, and NOD-like receptor, HIF-1, PI3K-Akt, and IL-17 signaling pathways. The results of molecular docking indicated that the active compounds had good binding properties to their key targets. In the validation experiments, WLS significantly reduced the levels of serum uric acid (SUA) and serum malondialdehyde (MDA). Moreover, WLS not only significantly increased the levels of total antioxidant capacity (T-AOC) and superoxide dismutase (SOD), but also inhibited the expression of tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6).Conclusion: In the present study, we demonstrate that WLS has multicomponent, multitarget, and multi-pathway properties in the treatment of HUA. Its potential capability to reduce SUA could be ascribed to oxidative stress improvement and inflammation inhibition.Keywords: hyperuricemia, Wuling San, oxidative stress, inflammation

Background: Triple-negative breast cancer (TNBC) patients frequently receive combination chemotherapy treatment, but a direct comparison of response to carboplatin, docetaxel, and their combination in 50 TNBC patient-derived xenografts (PDXs) showed that combination treatment was largely ineffective at generating enhanced responses over the best single agent. This suggests de-escalation of chemotherapy may be possible if molecular mechanisms and biomarkers underlying response to individual treatments can be identified. To this end, we performed multi-omics profiling for the 50 TNBC PDXs. Methods: Orthotopic TNBC PDXs were treated with four weekly cycles of docetaxel, carboplatin, or the combination. Changes in tumor volume after 4 weeks of treatment were assessed quantitatively and by modified RECIST criteria. Genomic, transcriptomic, and mass-spectrometry-based proteomic profiling were performed on baseline tumors prior to treatments to identify associations with chemotherapy response at the gene and pathway level. ProMS was used to integrate both RNA and protein data to select a 5 RNA feature combination for optimized prediction of carboplatin response in a logistic regression model. Publicly available neoadjuvant chemotherapy clinical datasets with transcriptomic data and response information used for validation/testing included TNBC samples from: GSE18864, I-SPY2 (GSE194040), and BrighTNess (GSE164458). Results: Proteogenomic profiles revealed distinct genes associated with response to each agent and their combination, respectively, suggesting distinct molecular mechanisms underlying response to each treatment. A substantial number of genes associated with single agent and combination treatment were validated in multiple independent patient cohorts receiving platinum and taxane containing neoadjuvant therapy, confirming clinical relevance of our PDX panel. For the same treatment, different types of molecular data identified distinct sets of associated genes, providing highly complementary information. At the pathway level, RNA and protein data converged to metabolic and E2F/G2M related pathways which were upregulated in PDXs resistant or responsive to all treatment types, respectively, while variable levels of MYC-related proliferation pathways were observed across all treatments suggesting pathways that are common across and unique to different treatments. Several individual genes found to be higher in PDXs with better response to either single-agent had discriminatory power in external clinical TNBC datasets treated with similar neoadjuvant chemotherapy regimens. In addition, a logistic regression-based carboplatin response prediction model trained to select a group of 5 RNA markers (TKT, MAGI2, ATF6B, MCM7, LRP6) using both RNA and protein data performed the best in predicting response to cisplatin in a clinical TNBC dataset vs predicting response to other datasets with taxane and platinum + taxane combination containing chemotherapy regimens, demonstrating specificity of the prediction model. These results suggest potential individual biomarkers or biomarker combinations to select TNBC tumors that may respond to either single agent carboplatin, docetaxel, or their combination. PDXs refractory to all treatment arms had higher levels of proteostasis-related pathways including proteasome degradation and the unfolded protein response (UPR) related to endoplasmic reticulum stress and altered levels of chromatin regulation. Subsequent pharmacological targeting of the UPR pathway and targeting HDACs enhanced chemotherapy response. Conclusion: Proteogenomic characterization identifies molecular mechanisms and putative biomarkers for stratifying TNBC tumors for single or combination chemotherapy treatments, suggests targeted therapies to augment chemotherapy response, and provides a valuable resource for researchers and clinicians. Citation Format: Jonathan T. Lei, Chen Huang, Ramakrishnan R. Srinivasan, Suhas Vasaikar, Lacey E. Dobrolecki, Alaina N. Lewis, Na Zhao, Jin Cao, Susan G. Hilsenbeck, C. Kent Osborne, Mothaffar Rimawi, Matthew J. Ellis, Varduhi Petrosyan, Alexander B. Saltzman, Anna Malovannaya, John D. Landua, Bo Wen, Antrix Jain, Gerburg M. Wulf, Shunqiang Li, Daniel C. Kraushaar, Tao Wang, Xi Chen, Gloria V. Echeverria, Meenakshi Anurag, Bing Zhang, Michael T. Lewis. Patient-derived xenografts allow deconvolution of single agent and combination chemotherapy responses in triple-negative breast cancer [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P2-23-01.

BACKGROUND: Clinical trials are controlled patient studies aiming to objectively assess the effectiveness of treatment interventions. However, the average effectiveness observed at the group level does not directly apply to individual patients. Cancer clinical trials that include molecular profiling on the baseline tumor samples enable the discovery of treatment response-associated molecular features and the development of prediction models that help discriminate responders from non-responders in each treatment arm. Despite potential opportunities, there are many challenges associated with the predictive modeling of such data, as exemplified by the small sample size and large feature space. Advances in machine learning and artificial intelligence (AI) may provide new solutions to address these challenges. However, for machine learning and AI to have an impact, data needs to be carefully curated, high-quality, standardized, and easily accessible and understood by data scientists, who may not have the domain knowledge. METHODS: Here, we created a python package “ClinicalOmicsDB” to address the challenges of data accessibility and promote development and application of machine learning methods to omics data from clinical trial samples with treatment response information. The package makes data readily analyzable by data scientists so that they can develop, utilize, and optimize their algorithms for predicting treatment responses and discovering novel biomarkers. To promote a two-way dialogue, we have also developed several Jupyter Notebook tutorials for biologists or clinicians who wish to gain expertise in machine learning. Omics data from clinical studies are downloaded from Gene Expression Omnibus (GEO) and responses were determined based on clinical trial primary endpoints. Currently, the package has datasets from 22 breast cancer clinical trials, including a total of 5050 patients (Table 1). It will be continuously expanded to include additional trials for breast cancer and other cancer types. RESULTS: To evaluate package utility, we built machine learning models to predict neoadjuvant chemotherapy with four cycles of 5-fluorouracil/epirubicin/cyclophosphamide (FEC) followed by four cycles of docetaxel/capecitabine on US Oncology clinical trial 02-103 [GSE42822]. The best performing model was the Random Forest Classifier model, which had an AUC of 0.817. To determine the generalizability of machine learning models established from the package, we trained a Random Forest Classifier model using the GSE25055 breast cancer dataset and apply the model to a different breast cancer dataset, GSE20194, which yielded an AUC of 0.648. These results suggest utilizing machine learning on clinical omics datasets can provide predictive and generalizable models that could be implemented in clinical settings for future breast cancer patients. CONCLUSION: We are expanding the database for data scientists, biologists, and clinicians to practice novel biotechnology-derived therapies to facilitate the implementation of precision medicine approaches for future patients. As more people add new data to the package, we will work towards improving pharmaceutical and private companies’ clinical trial data sharing policies and practices to promote data sharing. Table 1. Available breast cancer datasets in ClinicalOmicsDB The current database has 22 breast cancer clinical trials with 5050 total patients. Therapy shows various cytotoxic and/or targeted treatments utilized in a clinical trial. The database will be continuously expanded to include additional trials for breast cancer and other cancer types. Citation Format: Chang In Moon, Byron Jia, Bing Zhang. ClinicalomicsDB - Bridging the gap between clinical omics data and machine learning [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P2-12-02.

An environmentally friendly strategy for the photo-catalyzed three-component reaction between quinoxalin-2(1H)-ones, vinylarenes, with inexpensive and easily accessible ethyl bromodifluoroacetate/sodium difluoromethanesulfinate is described. This protocol exhibits mild conditions, high efficiency, and excellent functional group tolerance, providing a highly efficient approach for the synthesis of difluorobenzylated quinoxalin-2(1H)-ones by the formation of two carbon-carbon bonds. A radical mechanism is responsible for this three-component transformation.

From a rhetorical perspective, the article takes the puns in Xi You Ji (Journey to the West) and its two English translations as the object of study and divides the puns into three categories: homonymy, homophony, and paronomasia puns respectively. The two translations deal with the three types of puns in very different ways. Yu’s translation deals with the puns in the original by the strategy of direct translation or phonetic translation with annotations, which largely compensates for the lack of puns when they are transformed in a foreign context. While Jenner’s translation mostly adopts the direct translation strategy, and only a few puns have been translated with annotations. The translations of the typical puns in the original text are comparable between the two translations, and both of them try to achieve phonetic, meaning, and morphological equivalence with the original text. However, from an overall perspective, the richness of translation and compensation in Jenner’s translation is much inferior to those in Yu’s translation.

Donor lung ventilation and inflation during the warm ischemia could attenuate ischemia-reperfusion injury (IRI) after lung transplantation. Hydrogen sulfide (H2S), as a kind of protective gas, has demonstrated the antilung IRI effect. This study is aimed at observing the different methods of administering H2S in the setting of warm ischemia, ventilation, and inflation on the lung graft from a rat non-heart-beating donor. After 1 h of cardiac arrest, donor lungs in situ were inflated with 80 ppm H2S (FS group), ventilated with 80 ppm H2S (VS group), or deflated (control group) for 2 h. Then, the lung transplantation was performed after 3 h cold ischemia. The rats without ischemia and reperfusion were in the sham group. Pulmonary surfactant in the bronchoalveolar lavage fluid was measured in donor lung. The inflammatory response, cell apoptosis, and lung graft function were assessed at 3 h after reperfusion. The lung injury was exacerbated in the control group, which was attenuated significantly after the H2S treatment. Compared with the FS group, the pulmonary surfactant in the donor was deteriorated, the lung oxygenation function was decreased, and the inflammatory response and cell apoptosis were increased in the graft in the VS group ( P < 0.05 ). In conclusion, H2S inflation during the warm ischemia phase improved the function of lung graft via regulating pulmonary surfactant stability and decreased the lung graft IRI via decreasing the inflammatory response and cell apoptosis.

Natural hybrids in Triticeae have been frequently reported from the Qinghai–Tibet plateau, but minor variation in morphological features and homoploid hybridization have made it difficult to identify the origin and genome constitution of hybrids between Elymus and Campeiostachys. Specimens were investigated using morphology, cytogenetics and phylogenetic analyses to uncover the genome constitution and origin of ten putative natural hybrids (SH01-SH10) from the Qinghai–Tibet plateau. SH01, SH02, SH03, SH05 and SH06 (2n = 5x = 35, StStHHY) originated from Campeiostachys breviaristata (2n = 6x = 42, StStHHYY) and Elymus sibiricus (2n = 4x = 28, StStHH); SH04 and SH07 (2n = 5x = 35, StStHHY) originated from C. nutans (2n = 6x = 42, StStHHYY) and E. sibiricus; SH08 (2n = 5x = 35, StStHHY) originated from C. dahurica var. tangutorum (2n = 6x = 42, StStHHYY) and E. sibiricus; and SH09 and SH10 (2n = 4x = 28, StStHH) were the homoploid hybrids of E. sibiricus and an unknown Elymus sp. (2n = 4x = 28, StStHH). Karyotype variations in SH04 and SH07 might originate from the maternal progenitor. Frequent hybridization in the Qinghai–Tibet plateau might result from factors such as genome constitution, pollination habits, overlapping flowering stage, sympatric distribution and unique ecological conditions.