Identifying Anti-NSCLC Bioactive Compounds in Scutellaria via 2D NMR-Based Metabolomic Analysis of Pharmacologically Classified Crude Extracts.

We presented a strategy utilizing 2D NMR-based metabolomic analysis of crude extracts, categorized by different pharmacological activities, to rapidly identify the primary bioactive components of TCM. It was applied to identify the potential bioactive components from Scutellaria crude extracts that exhibit anti-non-small cell lung cancer (anti-NSCLC) activity. Four Scutellaria species were chosen as the study subjects because of their close phylogenetic relationship, but their crude extracts exhibit significantly different anti-NSCLC activity. Cell proliferation assay was used to assess the anti-NSCLC activity of four species of Scutellaria. 1H-13C HSQC spectra were acquired for the chemical profiling of these crude extracts. Based on the pharmacological classification (PCA, OPLS-DA and univariate hypothesis test) were performed to identify the bioactive constituents in Scutellaria associated with the anti-NSCLC activity. As a result, three compounds, baicalein, wogonin and scutellarin were identified as bioactive compounds. The anti-NSCLC activity of the three potential active compounds were further confirmed via cell proliferation assay. The mechanism of the anti-NSCLC activity by these active constituents was further explored via flow cytometry and western blot analyses. This study demonstrated 2D NMR-based metabolomic analysis of pharmacologically classified crude extracts to be an efficient approach to the identification of active components of herbal medicine.

Deep-learning-based radiomics of intratumoral and peritumoral MRI images to predict the pathological features of adjuvant radiotherapy in early-stage cervical squamous cell carcinoma.

BACKGROUND: Surgery combined with radiotherapy substantially escalates the likelihood of encountering complications in early-stage cervical squamous cell carcinoma(ESCSCC). We aimed to investigate the feasibility of Deep-learning-based radiomics of intratumoral and peritumoral MRI images to predict the pathological features of adjuvant radiotherapy in ESCSCC and minimize the occurrence of adverse events associated with the treatment. METHODS: A dataset comprising MR images was obtained from 289 patients who underwent radical hysterectomy and pelvic lymph node dissection between January 2019 and April 2022. The dataset was randomly divided into two cohorts in a 4:1 ratio.The postoperative radiotherapy options were evaluated according to the Peter/Sedlis standard. We extracted clinical features, as well as intratumoral and peritumoral radiomic features, using the least absolute shrinkage and selection operator (LASSO) regression. We constructed the Clinical Signature (Clinic_Sig), Radiomics Signature (Rad_Sig) and the Deep Transformer Learning Signature (DTL_Sig). Additionally, we fused the Rad_Sig with the DTL_Sig to create the Deep Learning Radiomic Signature (DLR_Sig). We evaluated the prediction performance of the models using the Area Under the Curve (AUC), calibration curve, and Decision Curve Analysis (DCA). RESULTS: The DLR_Sig showed a high level of accuracy and predictive capability, as demonstrated by the area under the curve (AUC) of 0.98(95% CI: 0.97-0.99) for the training cohort and 0.79(95% CI: 0.67-0.90) for the test cohort. In addition, the Hosmer-Lemeshow test, which provided p-values of 0.87 for the training cohort and 0.15 for the test cohort, respectively, indicated a good fit. DeLong test showed that the predictive effectiveness of DLR_Sig was significantly better than that of the Clinic_Sig(P < 0.05 both the training and test cohorts). The calibration plot of DLR_Sig indicated excellent consistency between the actual and predicted probabilities, while the DCA curve demonstrating greater clinical utility for predicting the pathological features for adjuvant radiotherapy. CONCLUSION: DLR_Sig based on intratumoral and peritumoral MRI images has the potential to preoperatively predict the pathological features of adjuvant radiotherapy in early-stage cervical squamous cell carcinoma (ESCSCC).

TFIIB-Related Protein BRP5/PTF2 Is Required for Both Male and Female Gametogenesis and for Grain Formation in Rice.

Transcription factor IIB (TFIIB) is a general transcription factor for RNA polymerase II, exerting its influence across various biological contexts. In the majority of eukaryotes, TFIIB typically has two homologs, serving as general transcription factors for RNA polymerase I and III. In plants, however, the TFIIB-related protein family has expanded greatly, with 14 and 9 members in Arabidopsis and rice, respectively. BRP5/pollen-expressed transcription factor 2 (PTF2) proteins belong to a subfamily of TFIIB-related proteins found only in plants and algae. The prior analysis of an Arabidopsis atbrp5 mutant, characterized by a T-DNA insertion at the 5' untranslated region, demonstrated the essential role of BRP5/PTF2 during the process of pollen germination and embryogenesis in Arabidopsis. Using a rice transformation system based on CRISPR/Cas9 technology, we have generated transgenic rice plants containing loss-of-function frameshift mutations in the BRP5/PTF2 gene. Unlike in the Arabidopsis atbrp5 mutant, the brp5/ptf2 frameshift mutations were not transmitted to progeny in rice, indicating an essential role of BRP5/PTF2 in both male and female gamete development or viability. The silencing of rice BRP5/PTF2 expression through RNA interference (RNAi) had little effect on vegetative growth and panicle formation but strongly affected pollen development and grain formation. Genetic analysis revealed that strong RNAi silencing of rice BRP5/PTF2 was still transmissible to progeny almost exclusively through female gametes, as found in the Arabidopsis atbrp5 knockdown mutant. Thus, reduced rice BRP5/PTF2 expression impacted pollen preferentially by interfering with male gamete development or viability. Drawing upon these findings, we posit that BRP5/PTF2 assumes a distinct and imperative function in the realm of plant sexual reproduction.

Genome-wide investigation and expression profiling of LOR gene family in rapeseed under salinity and ABA stress.

The Brassica napus (B. napus) LOR (Lurp-One-Related) gene family is a little-known gene family characterized by a conserved LOR domain in the proteins. Limited research in Arabidopsis showed that LOR family members played important roles in Hyaloperonospora parasitica (Hpa) defense. Nevertheless, there is a paucity of research investigating the role of the LOR gene family towards their responses to abiotic stresses and hormone treatments. This study encompassed a comprehensive survey of 56 LOR genes in B. napus, which is a prominent oilseed crop that holds substantial economic significance in China, Europe, and North America. Additionally, the study evaluated the expression profiles of these genes in response to salinity and ABA stress. Phylogenetic analysis showed that 56 BnLORs could be divided into 3 subgroups (8 clades) with uneven distribution on 19 chromosomes. 37 out of 56 BnLOR members have experienced segmental duplication and 5 of them have undergone tandem repeats events with strong evidence of purifying selection. Cis-regulatory elements (CREs) analysis indicated that BnLORs involved in process such as light response, hormone response, low temperature response, heat stress response, and dehydration response. The expression pattern of BnLOR family members revealed tissue specificity. RNA-Seq and qRT-PCR were used to validate BnLOR gene expression under temperature, salinity and ABA stress, revealing that most BnLORs showed inducibility. This study enhanced our comprehension of the B. napus LOR gene family and could provide valuable information for identifying and selecting genes for stress resistant breeding.

GIPMA: Global Intensity-Guided Peak Matching and Alignment for 2D 1H-13C HSQC-Based Metabolomics.

Two-dimensional (2D) 1H-13C heteronuclear single quantum coherence (HSQC) has been increasingly applied to metabolomics studies because it can greatly improve the resolving capability compared with one-dimensional (1D) 1H NMR. However, preprocessing methods such as peak matching and alignment tools for 2D NMR-based metabolomics have lagged behind similar methods for 1D 1H NMR-based metabolomics. Correct matching and alignment of 2D NMR spectral features across multiple samples are particularly important for subsequent multivariate data analysis. Considering different intensity dynamic ranges of a variety of metabolites and the chemical shift variation across the spectra of multiple samples, here, we developed an efficient peak matching and alignment algorithm for 2D 1H-13C HSQC-based metabolomics, called global intensity-guided peak matching and alignment (GIPMA). In GIPMA, peaks identified in all spectra are pooled together and sorted by intensity. Chemical shift of a stronger peak is regarded to be more accurate and reliable than that of a weaker peak. The strongest undesignated peak is chosen as the reference of a new cluster if it is not located within the chemical shift tolerance of any existing peak cluster (PC), or otherwise it is matched to an existing PC and the aligned chemical shift of the PC is updated as the intensity-weighted average of the chemical shifts of all peaks in the cluster. Setting an optimum chemical shift tolerance (Δδo) is critical for the peak matching and alignment across multiple samples. GIPMA dynamically searches for and intelligently selects the Δδo for peak matching to maximize the number of valid peak clusters (vPC), that is, spectral features, among multiple samples. By GIPMA, fully automatic peakwise matching and alignment do not require any spectrum as initial reference, while the chemical shift of each PC is updated as the intensity-weighted average of the chemical shifts of all peaks in the same PC, which is warranted to be statistically more accurate. Accurate chemical shifts for each representative spectral feature will facilitate subsequent peak assignment and are essential for correct metabolite identification and result interpretation. The proposed method was demonstrated successfully on the spectra of six model mixtures consisting of seven typical metabolites, yielding correct matching of all known spectral features. The performance of GIPMA was also demonstrated on 2D 1H-13C HSQC spectra of 87 real extracts of 29 samples of five Dendrobium species. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) of the 87 matched and aligned spectra by GIPMA generates correct classification of the 29 samples into five groups. In summary, the proposed algorithm of GIPMA provided a practical peak matching and alignment method to facilitate 2D NMR-based metabolomics studies.

The Mediator Complex: A Central Coordinator of Plant Adaptive Responses to Environmental Stresses.

As sessile organisms, plants are constantly exposed to a variety of environmental stresses and have evolved adaptive mechanisms, including transcriptional reprogramming, in order to survive or acclimate under adverse conditions. Over the past several decades, a large number of gene-specific transcription factors have been identified in the transcriptional regulation of plant adaptive responses. The Mediator complex plays a key role in transducing signals from gene-specific transcription factors to the transcription machinery to activate or repress target gene expression. Since its first purification about 15 years ago, plant Mediator complex has been extensively analyzed for its composition and biological functions. Mutants of many plant Mediator subunits are not lethal but are compromised in growth, development and response to biotic and abiotic stress, underscoring a particularly important role in plant adaptive responses. Plant Mediator subunits also interact with partners other than transcription factors and components of the transcription machinery, indicating the complexity of the regulation of gene expression by plant Mediator complex. Here, we present a comprehensive discussion of recent analyses of the structure and function of plant Mediator complex, with a particular focus on its roles in plant adaptive responses to a wide spectrum of environmental stresses and associated biological processes.

Subphrenic Lymph Node Metastasis Predicts Poorer Prognosis for Nasopharyngeal Carcinoma Patients With Metachronous Metastasis.

AIM: We retrospectively analyzed the distribution of distant lymph node metastasis and its impact on prognosis in patients with metastatic NPC after treatment. METHODS: From 2010 to 2016, 219 NPC patients out of 1,601 (182 from the Affiliated Cancer Hospital and Institute of Guangzhou Medical University, and 37 from the Affiliated Dongguan Hospital, Southern Medical University) developed distant metastasis after primary radiation therapy. Metastatic lesions were divided into groups according to location: bones above the diaphragm (supraphrenic bone, SUP-B); bones below the diaphragm (subphrenic bone, SUB-B); distant lymph nodes above the diaphragm (supraphrenic distant lymph nodes, SUP-DLN); distant lymph nodes below the diaphragm (subphrenic distant lymph nodes, SUB-DLN), liver, lung, and other lesions beyond bone/lung/distant lymph node above the diaphragm (supraphrenic other lesions, SUP-OL); other lesions beyond bone/liver/distant lymph node below the diaphragm (subphrenic other lesions, SUB-OL); the subtotal above the diaphragm (supraphrenic total lesions, SUP-TL); and the subtotal below the diaphragm (subphrenic total lesions, SUB-TL). Kaplan-Meier methods were used to estimate the probability of patients' overall survival (OS). Univariate and multivariate analyses were applied using the Cox proportional hazard model to explore prediction factors of OS. RESULTS: The most frequent metastatic locations were bone (45.2%), lung (40.6%), liver (32.0%), and distant lymph nodes (20.1%). The total number of distant lymph node metastasis was 44, of which 22 (10.0%) were above the diaphragm, 18 (8.2%) were below the diaphragm, and 4 (1.8%) were both above and below the diaphragm. Age (HR: 1.02, 95% CI: 1.00, 1.03, p = 0.012), N stage (HR: 1.26, 95% CI: 1.04, 1.54, p = 0.019), number of metastatic locations (HR: 1.39, 95% CI: 1.12, 1.73, p = 0.003), bone (HR: 1.65, 95% CI: 1.20, 2.25, p = 0.002), SUB-B (HR: 1.51, 95% CI: 1.07, 2.12, p = 0.019), SUB-DLN (HR: 1.72, 95% CI: 1.03, 2.86, p = 0.038), and SUB-O L(HR: 4.46, 95% CI: 1.39, 14.3, p = 0.012) were associated with OS. Multivariate analyses revealed that a higher N stage (HR: 1.23, 95% CI: 1.00, 1.50, p = 0.048), SUB-DLN (HR: 1.72, 95% CI: 1.02, 2.90, p = 0.043), and SUB-OL (HR: 3.72, 95% CI: 1.14, 12.16, p = 0.029) were associated with worse OS. CONCLUSION: Subphrenic lymph node metastasis predicts poorer prognosis for NPC patients with metachronous metastasis; however, this needs validation by large prospective studies.

The cytoprotective effects of dihydromyricetin and associated metabolic pathway changes on deoxynivalenol treated IPEC-J2 cells.

In this study, we investigated the cytoprotective effects of dihydromyricetin (DHM) against deoxynivalenol (DON)-induced toxicity and accompanied metabolic pathway changes in porcine jejunum epithelial cells (IPEC-J2). The cells were incubated in 250 ng/ml DON cotreated with 40 µM DHM, followed by toxicity analysis, oxidative stress reaction analysis, inflammatory response analysis and metabolomic analysis. The results showed that DHM significantly increased the cell viability (P < 0.01), the intracellular GSH level (P < 0.01) and decreased the intracellular ROS level (P < 0.01), the secretion of TNF-α, IL-8 (P < 0.01) and the apoptotic cell percentages (P < 0.01) in IPEC-J2 cells compared to that in the DON group. Metabolomic analysis revealed that DHM recovered the disorder of metabolic pathways such as glutamate metabolism, arachidonic metabolism and histidine metabolism caused by DON. In summary, DHM alleviated cell injury induced by DON and it is possibly through its antioxidant activity, anti-inflammatory activity or ability to regulate metabolic pathways.

Treatment effects of cumulative cisplatin dose during radiotherapy following induction chemotherapy in nasopharyngeal carcinoma: propensity score analyses.

BACKGROUND: The treatment effects of cumulative cisplatin dose (CCD) during radiotherapy (RT) following induction chemotherapy (IC) have not been determined for patients with locoregionally advanced nasopharyngeal carcinoma (NPC). METHODS: A total of 3460 patients with locoregionally advanced NPC who were treated with IC plus cisplatin-based concurrent chemoradiotherapy or RT alone were included in this retrospective study. Three CCD groups (0 mg/m2 ⩽ CCD <100 mg/m2, 100 mg/m2 ⩽ CCD <200 mg/m2, CCD ⩾200 mg/m2) were balanced through the inverse probability of treatment weighting based on propensity scores estimated by a general boosted model. The primary endpoint was overall survival (OS); the secondary endpoints were distant metastasis-free survival (DMFS) and locoregional recurrence-free survival (LRFS). RESULTS: CCD ⩾200 mg/m2 and <200 mg/m2 exhibited similar treatment effects for OS and DMFS, and were both superior to CCD <100 mg/m2 for OS and DMFS in patients with stage IVa NPC. The three CCD groups achieved similar treatment effects for patients with stage II-III NPC. After IC, CCD during RT appeared to exert little treatment effect on LRFS. CONCLUSION: The CCD during RT exerts treatment effects and improves OS by reducing the risk of distant metastasis for patients with stage IVa NPC following IC, and CCD <200 mg/m2 (mainly 160 mg/m2 in this group) is recommended. However, RT alone may be sufficient after IC in patients with stage II-III NPC.

Thin dielectric-layer-enabled low-voltage operation of fully printed flexible carbon nanotube thin-film transistors.

The quality of printable dielectric layer has become one of the major obstacles to achieving high-performance fully printed transistors. A thick dielectric layer will require high gate voltage to switch the transistors on and off, which will cause high power dissipation in printed devices. In response to this challenge, fully printed carbon nanotube (CNT)-based thin-film transistors (TFTs) have been fabricated on flexible membranes such as polyimide and liquid crystal polymer using aerosol jet printing. These devices can be operated at bias voltages below ±10 V (drain/gate voltages around ±6 V). This is much smaller than the previously reported values for fully printed CNT-TFTs because of using xdi-dcs (mixture of poly(vinylphenol)/poly (methylsilsesquioxane)) as the dielectric and using a single printing method. The lower voltage is a consequence of a thin dielectric layer (∼300 nm) and good uniformity in the printed CNT network. The printed CNT-TFTs show on/off ratio >105, and mobility >5 cm2V-1s-1. Layer-by-layer deposition of CNT allows highly uniform and dense network formation, and the optimization of the xdi-dcs concentration using natural butyl alcohol provides high-yield printing of a thin dielectric layer. Collectively, this work shows the potential of using fully printed CNT-TFTs in various flexible electronic applications such as wearable sensors, actuators, artificial skin, displays and wireless tags and antennas.

Inducible expression of defensins and cathelicidins by nutrients and associated regulatory mechanisms.

Host defense peptides (HDPs) are crucial components of the body's first line of defense that protect organisms from infections and mediate immune responses. Defensins and cathelicidins are the two most important families of HDPs in mammals. In this review, we summarize the nutrients that are involved in inducible expression of endogenous defensins and cathelicidins. In addition, the mitogen-activated protein kinases (MAPK), nuclear factor kappa B (NF-κB) and histone deacetylase (HDAC) signaling pathways that play vital roles in the induction of defensin and cathelicidin expression are highlighted. Endogenous defensins and cathelicidins induced by nutrients may be potential alternatives to antibiotic treatments against infection and diseases. This review mainly focuses on the inducible expression and regulatory mechanisms of defensins and cathelicidins in multiple species by different nutrients and the potential applications of defensin- and cathelicidin-inducing nutrients.

Quantitative Structure-Activity Relationship Study of Antioxidant Tripeptides Based on Model Population Analysis.

Due to their beneficial effects on human health, antioxidant peptides have attracted much attention from researchers. However, the structure-activity relationships of antioxidant peptides have not been fully understood. In this paper, quantitative structure-activity relationships (QSAR) models were built on two datasets, i.e., the ferric thiocyanate (FTC) dataset and ferric-reducing antioxidant power (FRAP) dataset, containing 214 and 172 unique antioxidant tripeptides, respectively. Sixteen amino acid descriptors were used and model population analysis (MPA) was then applied to improve the QSAR models for better prediction performance. The results showed that, by applying MPA, the cross-validated coefficient of determination (Q²) was increased from 0.6170 to 0.7471 for the FTC dataset and from 0.4878 to 0.6088 for the FRAP dataset, respectively. These findings indicate that the integration of different amino acid descriptors provide additional information for model building and MPA can efficiently extract the information for better prediction performance.