NGEF is a potential prognostic biomarker and could serve as an indicator for immunotherapy and chemotherapy in lung adenocarcinoma.

BACKGROUND: Neuronal guanine nucleotide exchange factor (NGEF) plays a key role in several cancers; however, its role in lung adenocarcinoma (LUAD) remains unclear. The aim of this study was to evaluate the efficacy of NGEF as a prognostic biomarker and potential therapeutic target for LUAD. METHODS: NGEF expression data for multiple cancers and LUAD were downloaded from multiple databases. The high- and low-NGEF expression groups were constructed based on median NGEF expression in LUAD samples, and then performed Kaplan-Meier survival analysis. Differentially expressed genes (DEGs) from the two NGEF expression groups were screened and applied to construct a protein-protein interaction network. The primary pathways were obtained using gene set enrichment analysis. The associations between NGEF expression and clinical characteristics, immune infiltration, immune checkpoint inhibitors (ICIs), sensitivity to chemotherapy, and tumor mutation burden (TMB) were investigated using R. Levels of NGEF expression in the lung tissue was validated using single-cell RNA sequencing, quantitative polymerase chain reaction (qPCR), immunohistochemical staining, and western blot analysis. RESULTS: The expression of NGEF mRNA was upregulated in multiple cancers. mRNA and protein expression levels of NGEF were higher in patients with LUAD than in controls, as validated using qPCR and western blot. High NGEF expression was an independent prognostic factor for LUAD and was associated with advanced tumor stage, large tumor size, more lymph node metastasis, and worse overall survival (OS). A total of 182 overlapping DEGs were screened between The Cancer Genome Atlas and GSE31210, among which the top 20 hub genes were identified. NGEF expression was mainly enriched in the pathways of apoptosis, cell cycle, and DNA replication. Moreover, elevated NGEF expression were associated with a high fraction of activated memory CD4+ T cells and M0 macrophages; elevated expression levels of the ICIs: programmed cell death 1 and programmed cell death 1 ligand 1 expression; higher TMB; and better sensitivity to bortezomib, docetaxel, paclitaxel, and parthenolide, but less sensitivity to axitinib and metformin. CONCLUSION: NGEF expression is upregulated in LUAD and is significantly associated with tumor stages, OS probability, immune infiltration, immunotherapy response, and chemotherapy response. NGEF may be a potential diagnostic and prognostic biomarker and therapeutic target in LUAD.

Knowledge-embedded spatio-temporal analysis for euploidy embryos identification in couples with chromosomal rearrangements.

BACKGROUND: The goal of the assisted reproductive treatment is to transfer one euploid blastocyst and to help infertile women giving birth one healthy neonate. Some algorithms have been used to assess the ploidy status of embryos derived from couples with normal chromosome, who subjected to preimplantation genetic testing for aneuploidy (PGT-A) treatment. However, it is currently unknown whether artificial intelligence model can be used to assess the euploidy status of blastocyst derived from populations with chromosomal rearrangement. METHODS: From February 2020 to May 2021, we collected the whole raw time-lapse videos at multiple focal planes from in vitro cultured embryos, the clinical information of couples, and the comprehensive chromosome screening results of those blastocysts that had received PGT treatment. Initially, we developed a novel deep learning model called the Attentive Multi-Focus Selection Network (AMSNet) to analyze time-lapse videos in real time and predict blastocyst formation. Building upon AMSNet, we integrated additional clinically predictive variables and created a second deep learning model, the Attentive Multi-Focus Video and Clinical Information Fusion Network (AMCFNet), to assess the euploidy status of embryos. The efficacy of the AMCFNet was further tested in embryos with parental chromosomal rearrangements. The receiver operating characteristic curve (ROC) was used to evaluate the superiority of the model. RESULTS: A total of 4112 embryos with complete time-lapse videos were enrolled for the blastocyst formation prediction task, and 1422 qualified blastocysts received PGT-A ( n = 589) or PGT for chromosomal structural rearrangement (PGT-SR, n = 833) were enrolled for the euploidy assessment task in this study. The AMSNet model using seven focal raw time-lapse videos has the best real-time accuracy. The real-time accuracy for AMSNet to predict blastocyst formation reached above 70% on the day 2 of embryo culture, and then increased to 80% on the day 4 of embryo culture. Combing with 4 clinical features of couples, the AUC of AMCFNet with 7 focal points increased to 0.729 in blastocysts derived from couples with chromosomal rearrangement. CONCLUSION: Integrating seven focal raw time-lapse images of embryos and parental clinical information, AMCFNet model have the capability of assessing euploidy status in blastocysts derived from couples with chromosomal rearrangement.

Deep learning-based clinical-radiomics nomogram for preoperative prediction of lymph node metastasis in patients with rectal cancer: a two-center study.

Background: Precise preoperative evaluation of lymph node metastasis (LNM) is crucial for ensuring effective treatment for rectal cancer (RC). This research aims to develop a clinical-radiomics nomogram based on deep learning techniques, preoperative magnetic resonance imaging (MRI) and clinical characteristics, enabling the accurate prediction of LNM in RC. Materials and methods: Between January 2017 and May 2023, a total of 519 rectal cancer cases confirmed by pathological examination were retrospectively recruited from two tertiary hospitals. A total of 253 consecutive individuals were selected from Center I to create an automated MRI segmentation technique utilizing deep learning algorithms. The performance of the model was evaluated using the dice similarity coefficient (DSC), the 95th percentile Hausdorff distance (HD95), and the average surface distance (ASD). Subsequently, two external validation cohorts were established: one comprising 178 patients from center I (EVC1) and another consisting of 88 patients from center II (EVC2). The automatic segmentation provided radiomics features, which were then used to create a Radscore. A predictive nomogram integrating the Radscore and clinical parameters was constructed using multivariate logistic regression. Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were employed to evaluate the discrimination capabilities of the Radscore, nomogram, and subjective evaluation model, respectively. Results: The mean DSC, HD95 and ASD were 0.857 ± 0.041, 2.186 ± 0.956, and 0.562 ± 0.194 mm, respectively. The nomogram, which incorporates MR T-stage, CEA, CA19-9, and Radscore, exhibited a higher area under the ROC curve (AUC) compared to the Radscore and subjective evaluation in the training set (0.921 vs. 0.903 vs. 0.662). Similarly, in both external validation sets, the nomogram demonstrated a higher AUC than the Radscore and subjective evaluation (0.908 vs. 0.735 vs. 0.640, and 0.884 vs. 0.802 vs. 0.734). Conclusion: The application of the deep learning method enables efficient automatic segmentation. The clinical-radiomics nomogram, utilizing preoperative MRI and automatic segmentation, proves to be an accurate method for assessing LNM in RC. This approach has the potential to enhance clinical decision-making and improve patient care. Research registration unique identifying number UIN: Research registry, identifier 9158, https://www.researchregistry.com/browse-the-registry#home/registrationdetails/648e813efffa4e0028022796/.

A prognostic model for ovarian neoplasms established by an integrated analysis of 1580 transcriptomic profiles.

Even after debulking surgery combined with chemotherapy or new adjuvant chemotherapy paired with internal surgery, the average year of disease free survival in advanced ovarian cancer was approximately 1.7 years1. The development of a molecular predictor of early recurrence would allow for the identification of ovarian cancer (OC) patients with high risk of relapse. The Ovarian Cancer Disease Free Survival Predictor (ODFSP), a predictive model constructed from a special set of 1580 OC tumors in which gene expression was assessed using both microarray and sequencing platforms, was created by our team. To construct gene expression barcodes that were resistant to biases caused by disparate profiling platforms and batch effects, we employed a meta-analysis methodology that was based on the binary gene pair technique. We demonstrate that ODFSP is a reliable single-sample predictor of early recurrence (1 year or less) using the largest pool of OC transcriptome data sets available to date. The ODFSP model showed significantly high prognostic value for binary recurrence prediction unaffected by clinicopathologic factors, with a meta-estimate of the area under the receiver operating curve of 0.64 (P = 4.6E-05) and a D-index (robust hazard ratio) of 1.67 (P = 9.2E-06), respectively. GO analysis of ODFSP's 2040 gene pairs (collapsed to 886 distinct genes) revealed the involvement in small molecular catabolic process, sulfur compound metabolic process, organic acid catabolic process, sulfur compound biosynthetic process, glycosaminoglycan metabolic process and aminometabolic process. Kyoto encyclopedia of genes and genomes pathway analysis of ODFSP's signature genes identified prominent pathways that included cAMP signaling pathway and FoxO signaling pathway. By identifying individuals who might benefit from a more aggressive treatment plan or enrolment in a clinical trial but who will not benefit from standard surgery or chemotherapy, ODFSP could help with treatment decisions.

Predictive value of modified MRI-based split scar sign (mrSSS) score for pathological complete response after neoadjuvant chemoradiotherapy for patients with rectal cancer.

PURPOSE: To measure the diagnostic performance of modified MRI-based split scar sign (mrSSS) score for the prediction of pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) for patients with rectal cancer. METHODS: The modified MRI-based split scar sign (mrSSS) score, which consists of T2-weighted images (T2WI)-based score and diffusion-weighted images (DWI)-based score. The sensitivity, specificity, and accuracy of modified mrSSS score, endoscopic gross type, and MRI-based tumor regression grading (mrTRG) score, in the prediction of pCR, were compared. The prognostic value of the modified mrSSS score was also studied. RESULTS: A total of 189 patients were included in the study. The Kendall's coefficient of interobserver concordance of modified mrSSS score, T2WI -based score, and DWI-based score were 0.899, 0.890, and 0.789 respectively. And the maximum and minimum k value of the modified mrSSS score was 0.797 (0.742-0.853) and 0.562 (0.490-0.634). The sensitivity, specificity, and accuracy of prediction of pCR were 0.66, 0.97, and 0.90 for modified mrSSS score; 0.37, 0.89, and 0.78 for endoscopic gross type (scar); and 0.24, 0.92, and 0.77 for mrTRG score (mrTRG = 1). The modified mrSSS score had significantly higher sensitivity than the endoscopic gross type and the mrTRG score in predicting pCR. Patients with lower modified mrSSS scores had significantly longer disease-free survival (P < 0.05). CONCLUSION: The modified mrSSS score showed satisfactory interobserver agreement and higher sensitivity in predicting pCR after nCRT in patients with rectal cancer. The modified mrSSS score is also a predictor of disease-free survival.

Microbiome composition is shaped by geography and population structure in the parasitic wasp Asobara japonica, but not in the presence of the endosymbiont Wolbachia.

The microbial community composition is crucial for diverse life-history traits in many organisms. However, we still lack a sufficient understanding of how the host microbiome is acquired and maintained, a pressing issue in times of global environmental change. Here we investigated to what extent host genotype, environmental conditions, and the endosymbiont Wolbachia influence the bacterial communities in the parasitic wasp Asobara japonica. We sampled multiple wasp populations across 10 locations in their natural distribution range in Japan and sequenced the host genome (whole genome sequencing) and microbiome (16S rRNA gene). We compared the host population structure and bacterial community composition of wasps that reproduce sexually and are uninfected with Wolbachia with wasps that reproduce asexually and carry Wolbachia. The bacterial communities in asexual wasps were highly similar due to a strong effect of Wolbachia rather than host genomic structure. In contrast, in sexual wasps, bacterial communities appear primarily shaped by a combination of population structure and environmental conditions. Our research highlights that multiple factors shape the bacterial communities of an organism and that the presence of a single endosymbiont can strongly alter their compositions. This information is crucial to understanding how organisms and their associated microbiome will react in the face of environmental change.

Prediction of tumor budding in patients with rectal adenocarcinoma using b-value threshold map.

OBJECTIVE: To investigate the feasibility of b-value threshold (bThreshold) map in preoperative evaluation of tumor budding (TB) in patients with locally advanced rectal cancer (LARC). METHODS: Patients with LARC were enrolled and underwent diffusion-weighted imaging (DWI). Contrast-to-noise ratio (CNR) between the lesions and normal tissues was assessed using DWI and bThreshold maps. TB was counted and scored using hematoxylin and eosin staining. Reproducibility for the apparent diffusion coefficient (ADC), bThreshold values, and region-of-interest (ROI) sizes were compared. Differences in ADC and bThreshold values with low-intermediate and high TB grades and the correlations between mean ADC and bThreshold values with TB categories were analyzed. Diagnostic performance of ADC and bThreshold values was assessed using area under the curve (AUC) and decision curve analysis. RESULTS: Fifty-one patients were evaluated. The CNR on bThreshold maps was significantly higher than that on DW images (9.807 ± 4.811 vs 7.779 ± 3.508, p = 0.005). Reproducibility was excellent for the ADC (ICC 0.933; CV 8.807%), bThreshold values (ICC 0.958; CV 7.399%), and ROI sizes (ICC 0.934; CV 8.425%). Significant negative correlations were observed between mean ADC values and TB grades and positive correlations were observed between mean bThreshold values and TB grades (p < 0.05). bThreshold maps showed better diagnostic performance than ADC maps (AUC, 0.914 vs 0.726; p = 0.048). CONCLUSIONS: In LARC patients, bThreshold values could distinguish different TB grades better than ADC values, and bThreshold maps may be a preoperative, non-invasive approach to evaluate TB grades. KEY POINTS: • Compared with diffusion-weighted images, bThreshold maps improved visualization and detection of rectal tumors. • Agreement and diagnostic performance of bThreshold values are superior to apparent diffusion coefficient in assessing tumor budding grades in patients with locally advanced rectal cancer. • bThreshold maps could be used to evaluate tumor budding grades non-invasively before operation.

DNA Methylation Profiling in a Cigarette Smoke-Exposed Mouse Model of Airway Inflammation.

Purpose: DNA methylation, a major epigenetic modification, has been documented to play an important role in chronic obstructive pulmonary disease (COPD). In this study, we aimed to profile the DNA methylation patterns in a mouse model of airway inflammation induced by cigarette smoke (CS), a foremost risk factor of COPD. Material and Methods: To establish a model of airway inflammation, wild-type mice were exposed to mainstream CS or room air for 2 hours twice daily, 6 days per week for consecutive 4 weeks. Lung tissues of the mice were collected for genome-wide DNA methylation analysis by liquid hybridization capture-based bisulfite sequencing, which were used for intersection analysis with gene expression by cDNA microarray to identify candidate methylated genes. Then, functional enrichment analyses with protein-protein interaction (PPI) network regarding these genes were conducted to explore the potential mechanisms. Results: After 4-week CS exposure, the level of DNA methylation accompanied by a subacute airway inflammation was markedly enhanced, and 2002 differentially methylated genes (DMGs) were annotated, including 565 DMGs contained methylations in gene promoters, which were used for intersection with the differentially expressed genes. Then, 135 candidate methylated genes were further selected by the intersection, among which 58 genes with functional methylated modification were finally identified. Further analyses revealed candidate methylated genes were significantly enriched in a complicated network of signals and processes, including interleukins, toll-like receptors, T-cells differentiation, oxidative stress, mast cells activation, stem cells proliferation, etc., as well as the 58 functional methylated genes were partially located at key positions in PPI network, especially CXCL1, DDX58 and JAK3. Conclusion: This study suggests CS exposure significantly enhances DNA methylated level, and the potential functional methylated genes are closely related to complicated inflammatory-immune responses, which may provide some new experimental evidence in understanding the epigenetic mechanisms of CS-induced airway inflammation in COPD.

Signature based on RNA-binding protein-related genes for predicting prognosis and guiding therapy in non-small cell lung cancer.

Background: Studies have reported that RNA-binding proteins (RBPs) are dysregulated in multiple cancers and are correlated with the progression and prognosis of disease. However, the functions of RBPs in non-small cell lung cancer (NSCLC) remain unclear. The present study aimed to explore the function of RBPs in NSCLC and their prognostic and therapeutic value. Methods: The mRNA expression profiles, DNA methylation data, gene mutation data, copy number variation data, and corresponding clinical information on NSCLC were downloaded from The Cancer Genome Atlas, Gene Expression Omnibus, and the University of California Santa Cruz Xena databases. The differentially expressed RBPs were identified between tumor and control tissues, and the expression and prognostic value of these RBPs were systemically investigated by bioinformatics analysis. A quantitative polymerase chain reaction (qPCR) was performed to validate the dysregulated genes in the prognostic signature. Results: A prognostic RBP-related signature was successfully constructed based on eight RBPs represented as a risk score using least absolute shrinkage and selection operator (LASSO) regression analysis. The high-risk group had a worse overall survival (OS) probability than the low-risk group (p < 0.001) with 1-, 3-, and 5-year area under the receiver operator characteristic curve values of 0.671, 0.638, and 0.637, respectively. The risk score was associated with the stage of disease (p < 0.05) and was an independent prognostic factor for NSCLC when adjusted for age and UICC stage (p < 0.001, hazard ratio (HR): 1.888). The constructed nomogram showed a good predictive value. The P53, focal adhesion, and NOD-like receptor signaling pathways were the primary pathways in the high-risk group (adjusted p value <0.05). The high-risk group was correlated with increased immune infiltration (p < 0.05), upregulated relative expression levels of programmed cell death 1 (PD1) (p = 0.015), cytotoxic T-lymphocyte-associated protein 4 (CTLA4) (p = 0.042), higher gene mutation frequency, higher tumor mutational burden (p = 0.034), and better chemotherapy response (p < 0.001). The signature was successfully validated using the GSE26939, GSE31210, GSE30219, and GSE157009 datasets. Dysregulation of these genes in patients with NSCLC was confirmed using the qPCR in an independent cohort (p < 0.05). Conclusion: An RBP-related signature was successfully constructed to predict prognosis in NSCLC, functioning as a reference for individualized therapy, including immunotherapy and chemotherapy.

A ferroptosis-related gene signature for overall survival prediction and immune infiltration in lung squamous cell carcinoma.

BACKGROUND: Ferroptosis is associated with cancer initiation and progression. However, the molecular mechanism and prognostic value of ferroptosis-related genes in lung squamous cell carcinoma (LUSC) are poorly understood. METHODS: The mRNA expression profiles, methylation data, and clinical information of patients with LUSC were downloaded from TCGA and GEO database. Ferroptosis-related differentially expressed genes (DEGs) were identified between cancerous and non-cancerous tissues, and their prognostic value was systemically investigated by bioinformatic analyses. RESULTS: A ferroptosis-related gene signature (ALOX5, TFRC, PHKG2, FADS2, NOX1) was constructed using multivariate Cox regression analysis and represented as a risk score. Overall survival (OS) probability was significantly lower in the high-risk group than in the low-risk group (P<0.001), and receiver operating characteristic curve showed a good predictive capacity (AUC = 0.739). The risk score was an independent prognostic factor for LUSC. A nomogram was constructed to predict the OS probabilities at 1, 3, and 5 years. High-risk score was associated with increased immune infiltration, lower methylation levels, higher immune checkpoint genes expression levels, and better chemotherapy response. Cell adhesion molecules, focal adhesion, and extracellular matrix receptor interaction were the main pathways in the high-risk group. The signature was validated using the TCGA test cohort, entire TCGA cohort, GSE30219, GSE157010, GSE73403, and GSE4573 datasets. The gene disorders in patients with LUSC were validated using real-time PCR and single-cell RNA sequencing analysis. CONCLUSIONS: A ferroptosis-related gene signature was constructed to predict OS probability in LUSC. This could facilitate novel therapeutic methods and guide individualized therapy.

Multi-Omics Analysis and Machine Learning Prediction Model for Pregnancy Outcomes After Intracytoplasmic Sperm Injection-in vitro Fertilization.

Background: To explore the methylation profiles in cumulus cells (CCs) of women undergoing intracytoplasmic sperm injection-in vitro fertilization (ICSI-IVF) and establish a prediction model of pregnancy outcomes using machine learning approaches. Methods: Methylation data were retrieved from the Gene Expression Omnibus (GEO) database, and differentially methylated genes (DMGs) were subjected to gene set analyses. Support vector machine (SVM), random forest (RF), and logistic regression (LR) were used to establish the prediction model, and microarray data from GEO was analyzed to identify differentially expressed genes (DEGs) associated with the dichotomous outcomes of clinical pregnancy (pregnant vs. non-pregnant). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis provided multi-dimensional validation for selected DMGs. Results: A total of 338 differentially methylated CpG sites associated with 146 unique genes across the genome were identified. Among the identified pathways, the prominent ones were involved in the regulation of cell growth and oocyte development (hsa04340, hsa04012, hsa04914, hsa04614, hsa04913, hsa04020, and hsa00510). The area under the curve (AUC) of machine learning classifiers was 0.94 (SVM) vs. 0.88 (RF) vs. 0.97 (LR). 196 DEGs were found in transcriptional microarray. Mapped genes were selected through overlapping enriched pathways in transcriptional profiles and methylated data of CCs, predictive of successful pregnancy. Conclusions: Methylated profiles of CCs were significantly different between women receiving ICSI-IVF procedures that conceived successfully and those that did not conceive. Machine learning approaches are powerful tools that may provide crucial information for prognostic assessment. Pathway analysis may be another way in multiomics analysis of cumulus cells.

BmNPV Orf 65 (Bm65) Is Identified as an Endonuclease Directly Facilitating UV-Induced DNA Damage Repair.

Baculoviruses have been used as biopesticides for the control of Lepidoptera larvae. However, solar UV radiation reduces the activity of baculovirus. In this study, an UV endonuclease, Bm65, was found encoded in the genome of Bombyx mori nuclear polyhedrosis virus (BmNPV). Bm65 (the ortholog of AcMNPV orf79) was guided by a key nuclear localization signal to enter the nucleus and accumulated at UV-induced DNA damage sites. Subsequent results further showed that Bm65-mediated DNA damage repair was not the only UV damage repair pathway of BmNPV. BmNPV also used host DNA repair proteins to repair UV-induced DNA damage. In summary, these results revealed that Bm65 was very important in UV-induced DNA damage repair of BmNPV, and BmNPV repaired UV-damaged DNA through a variety of ways. IMPORTANCE Baculovirus biopesticides are environmentally friendly insecticides and specifically infect invertebrates. UV radiation from the sunlight greatly reduces the activity of baculovirus biopesticides. However, the molecular mechanisms of most baculoviruses to repair UV-induced DNA damage remain unclear. Nucleotide excision repair (NER) is a major DNA repair pathway that removes UV-induced DNA lesions. At present, there are few reports about the nucleotide excision repair pathway in viruses. Here, we showed for the first time that the baculovirus Bm65 endonuclease actually cleaved UV-damaged DNA. Meanwhile, we found that BmNPV used both viral-encoded enzymes and host DNA damage repair proteins to reverse UV-induced DNA damage. These results will provide a reference for the research of UV damage repair of other viruses.

Meta analysis for insomnia Guizhi Gancao Longgu Muli decoction for insomnia A meta-analysis.

BACKGROUND: Guizhi Gancao Longgu Muli Decoction can make a good effect on the insomnia under the catalogue of traditional Chinese medicine. METHOD: To search the databases:Pubmed, Web of Science, EMBASE, the Cochrane Library, the China National Knowledge Infrastructure (CNKI), the China Biology Medicine disc (CBMdisc), the China Science and Technology Journal Database (VIP), the Wanfang. RESULTS: Fifteen randomized controlled trials were included, totally including 1164 participants. After summarizing the observational index revised according to the "Guiding Principles for Clinical Research of New Chinese Medicines", we found that the curative effect of the trial group is 2.29 times that of the control group in the fixed effect model which had a statistically significant difference [OR = 2.293681, 95%CI = 0.3266112-5.83]. And the Pittsburgh Sleep Quality Index (PSQI) which had 7 different dimensions, including subjective sleep quality[p = 0.001 < 0.05], sleep latency, sleep duration[p = 0.000 < 0.05], habitual SE[p = 0.000 < 0.05], sleep disorders[p = 0.002 < 0.05], use of sleep medications[p = 0.000 < 0.05], and daytime dysfunction[p = 0.000 < 0.05], showed a higher scores in the trial group than the one in the control group in every dimension. The final results of the total scores in PSQI also showed a higher scores in trial group with a p = 0.000 < 0.05 (Test of WMD), suggest a statistically significant difference. While the adverse effects showed a lower rate in the trial group than the one in the control group under a fixed-effect model, with a p = 0.000 < 0.05, indicate a statistically significant difference. CONCLUSION: The efficacy and safety of GGLMD in the trial groups are better than the modern western medicine in the control groups.

Is rectal filling optimal for MRI-based radiomics in preoperative T staging of rectal cancer?

PURPOSE: To determine whether rectal filling with ultrasound gel is clinically more beneficial in preoperative T staging of patients with rectal cancer (RC) using radiomics model based on magnetic resonance imaging (MRI). METHODS: A total of 94 RC patients were assigned to cohort 1 (leave-one-out cross-validation [LOO-CV] set) and 230 RC patients were assigned to cohort 2 (test set). Patients were grouped according to different pathological T stages. The radiomics features were extracted through high-resolution T2-weighted imaging for all volume of interests in the two cohorts. Optimal features were selected using the least absolute shrinkage and selection operator (LASSO) algorithm. Model 1 (without rectal filling) and model 2 (with rectal filling) were constructed. LOO-CV was adopted for radiomics model building in cohort 1. Thereafter, the cohort 2 was used to test and verify the effectiveness of the two models. RESULTS: Totally, 204 patients were enrolled, including 60 cases in cohort 1 and 144 cases in cohort 2. Finally, seven optimal features with LASSO were selected to build model 1 and nine optimal features were used for model 2. The ROC curves showed an AUC of 0.806 and 0.946 for model 1 and model 2 in cohort 1, respectively, and an AUC of 0.783 and 0.920 for model 1 and model 2 in cohort 2, respectively (p = 0.021). CONCLUSION: The radiomics model with rectal filling showed an advantage for differentiating T1 + 2 from T3 and had less inaccurate categories in the test cohort, suggesting that this model may be useful for T-stage evaluation.

The feasibility of MRI-based radiomics model in presurgical evaluation of tumor budding in locally advanced rectal cancer.

PURPOSE: To build and validate a magnetic resonance imaging-based radiomics model to preoperatively evaluate tumor budding (TB) in locally advanced rectal cancer (LARC). METHODS: Pathologically confirmed LARC cases submitted to preoperative rectal MRI in two distinct hospitals were enrolled in this retrospective study and assigned to cohort 1 (training set, n = 77; test set, n = 51) and cohort 2 (validation set, n = 96). Radiomics features were obtained from multiple sequences, comprising high-resolution T2, contrast-enhanced T1, and diffusion-weighted imaging (T2WI, CE-T1WI, and DWI, respectively). The least absolute shrinkage and selection operator (LASSO) was utilized to select the optimal features from T2WI, CE-T1WI, DWI, and the combination of multi-sequences, respectively. A support vector machine (SVM) classifier was utilized to construct various radiomics models for discriminating the TB grades. Receiver operating characteristic curve analysis and decision curve analysis (DCA) were carried out to determine the diagnostic value. RESULTS: Five optimal features associated with TB grade were determined from combined multi-sequence data. Accordingly, a radiomics model based on combined multi-sequences had an area under the curve of 0.796, with an accuracy of 81.2% in the validation set, showing a better performance in comparison with other models in both cohorts (p < 0.05). DCA exhibited a clinical benefit for this radiomics model. CONCLUSION: The novel MRI-based radiomics model combining multiple sequences is an effective and non-invasive approach for evaluating TB grade preoperatively in patients with LARC.

Absence of complementary sex determination in two Leptopilina species (Figitidae, Hymenoptera) and a reconsideration of its incompatibility with endosymbiont-induced thelytoky.

Complementary sex determination (CSD) is a widespread sex determination mechanism in haplodiploid Hymenoptera. Under CSD, sex is determined by the allelic state of one or multiple CSD loci. Heterozygosity at one or more loci leads to female development, whereas hemizygosity of haploid eggs and homozygosity of diploid eggs results in male development. Sexual (arrhenotokous) reproduction normally yields haploid male and diploid female offspring. Under asexual reproduction (thelytoky), diploidized unfertilized eggs develop into females. Thelytoky is often induced by bacterial endosymbionts that achieve egg diploidization by gamete duplication. As gamete duplication leads to complete homozygosity, endosymbiont-induced thelytokous reproduction is presumed to be incompatible with CSD, which relies on heterozygosity for female development. Previously, we excluded CSD in four Asobara (Braconidae) species and proposed a two-step mechanism for Wolbachia-induced thelytoky in Asobara japonica. Here, we conclusively reject CSD in two cynipid wasp species, Leptopilina heterotoma and Leptopilina clavipes. We further show that thelytoky in L. clavipes depends on Wolbachia titer but that diploidization and feminization steps cannot be separated, unlike in A. japonica. We discuss what these results reveal about the sex determination mechanism of L. clavipes and the presumed incompatibility between CSD and endosymbiont-induced thelytoky in the Hymenoptera.

Predicting Treatment Response to Neoadjuvant Chemoradiotherapy in Rectal Mucinous Adenocarcinoma Using an MRI-Based Radiomics Nomogram.

OBJECTIVE: To build and validate an MRI-based radiomics nomogram to predict the therapeutic response to neoadjuvant chemoradiotherapy (nCRT) in rectal mucinous adenocarcinoma (RMAC). METHODS: Totally, 92 individuals with pathologically confirmed RMAC administered surgical resection upon nCRT in two different centers were assessed retrospectively (training set, n = 52, validation set, n = 40). Rectal MRI was performed pre-nCRT. Radiomics parameters were obtained from high-resolution T2-weighted images and selected to construct a radiomics signature. Then, radiomics nomogram construction integrated patient variables and the radiomics signature. The resulting radiomics nomogram was utilized to assess the tumor regression grade (TRG). Diagnostic performance was determined by generating receiver operating characteristic (ROC) curves and decision curve analysis (DCA). RESULTS: Six optimal features related to TRG were obtained to construct a radiomics signature. The nomogram combining the radiomics signature with age and mucin deposit outperformed the radiomics signature alone in the training (AUC, 0.950 vs 0.843, p < 0.05) and validation (AUC, 0.868 vs 0.719, p < 0.05) cohorts. DCA demonstrated a clinical utility for the radiomics nomogram model. CONCLUSIONS: The established quantitative MRI-based radiomics nomogram is effective in predicting treatment response to neoadjuvant therapy in patients with RMAC.

MRI-based nomogram analysis: recognition of anterior peritoneal reflection and its relationship to rectal cancers.

BACKGROUND: This study is aimed to explore the factors influencing the visualization of the anterior peritoneal reflection (APR) and evaluated the feasibility of measuring the distance from the anal verge to APR (AV-APR), the tumor height on MRI and the accuracy of determining the tumor location with regard to APR. METHODS: We retrospectively analyzed 110 patients with rectal cancer. A univariate and multivariate logistic regression was performed to identify the independent factors (age, sex, T stage, the degree of bladder filling, pelvic effusion, intraoperative tumor location, BMI, uterine orientation, the distance from seminal vesicle/uterus to rectum) associated with the visualization of the APR on MRI. The nomogram diagram and receiver operating characteristic curve (ROC curve) were established. Intraclass correlation coefficient (ICC) was used to evaluate the consistency of the distance of AV-APR. The Pearson correlation coefficient was used to characterize the agreement between measurements of the tumor height by colonoscopy and MRI. The Kappa statistics was used to evaluate the value of MRI in the diagnosis of the tumor location with regard to the APR. RESULTS: Multivariate logistic regression showed that BMI (P = 0.031, odds ratio, OR = 1.197), pelvic effusion (P = 0.020, OR = 7.107) and the distance from seminal vesicle/uterus to the rectum (P = 0.001, OR = 3.622) were correlated with the visualization of APR. The cut-off point of BMI and the distance from seminal vesicle/uterus to the rectum is 25.845 kg/m2 and 1.15 cm. The area under curve (AUC) (95% Confidence Interval, 95% CI) of the combined model is 0.840 (0.750-0.930). The favorable calibration of the nomogram showed a non-significant Hosmer-Lemeshow test statistic (P = 0.195). The ICC value (95% CI) of the distance of AV-APR measured by two radiologists was 0.981 (0.969-0.989). The height measured by MRI and colonoscopy were correlated with each other (r = 0.699, P < 0.001). The Kappa value was 0.854. CONCLUSIONS: BMI, pelvic effusion, and the distance from seminal vesicle/uterus to rectum could affect the visualization of APR on MRI. Also, it's feasible to measure the distance of AV-APR, the tumor height, and to evaluate the tumor location with regard to APR using MRI.

MRI-Based Radiomics of Rectal Cancer: Assessment of the Local Recurrence at the Site of Anastomosis.

RATIONALE AND OBJECTIVE: To investigate the significance of magnetic resonance imaging (MRI)-based radiomics model in differentiating local recurrence of rectal cancer from nonrecurrence lesions at the site of anastomosis. MATERIALS AND METHODS: A total of 80 patients with clinically suspected lesions of anastomosis underwent 3.0T pelvic MRI consisting of T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and contrast-enhanced T1-weighted volume interpolated body examination (VIBE) imaging. Radiomics features were extracted from volumes of interest (VOIs), delineated manually on multiple MRI sequences. Subsequently, principal component analysis reduced the dimensionality of features for T2WI, DWI, VIBE, and combined multisequences, respectively. On this basis, the extreme gradient boosting (XGBoost) classifier was trained to build ModelT2WI, ModelDWI, ModelVIBE, and Modelcombination. Receiver operating characteristic curves were generated to determine the diagnostic performance of various models. RESULTS: Principal component analysis selected eight, four, seven, and six principal components to construct the radiomics model for T2WI, DWI, VIBE, and combined multisequences, respectively. Modelcombination had an area under the receiver operating characteristic curve of 0.864, with sensitivity and specificity of 81.82% and 75.86% in the validation set, demonstrating a more optimal performance compared to other models (p< 0.05). The decision curve analysis confirmed the clinical usefulness of the model. CONCLUSION: This study demonstrated that MRI-based radiomics is a sophisticated and noninvasive tool for accurately distinguishing LR from nonrecurrence lesions at the site of anastomosis. Combining multiple sequences significantly improves its performance.

Tripterygium and its plant extraction for systemic lupus erythematosus: A protocol for systematic review and meta analysis.

BACKGROUND: Systemic Lupus Erythematosus (SLE) is a diffuse connetive tissue disease, which is difficult to be conquered. However, the traditional Chinese medicine is significant in the treatment. And the Chinese medicine tripterygium and its plant extraction can help us to overcome this disease, to some extent. METHODS: The deadline should be from inception to February 2020 by computer from the databases: the Cochrane Library, Pubmed, Embase, Web of Science in English and the Chinese National Knowledge Infrastructure, Wanfang Database, Chinese Biomedical Literature Database and Chinese Science, Chinese Traditional Medicine Database, Chinese Science and Technology Periodical Database in Chinese. Included criteria are randomized controlled trials. The primary outcomes are the clinical symptoms, systemic lupus erythematosus disease activity index and quality of life questionnaire (the top 10 frequency). We will use RevMan 5.0 statistical software for data synthesis, sensitivity analysis, meta regression, subgroup analysis, and risk of bias assessment. The publish bias will be assessed by a funnel plot and the funnel plot symmetries will be evaluated by Begg and Egger tests. We will use the Grading of Recommendations Assessment, Development and Evaluation system to assess the quality of evidence. RESULTS: This article will give a protocol for meta analysis which can make sure the efficacy and side effect of the tripterygium and its plant extraction for SLE. CONCLUSION: The efficacy and side effect of the tripterygium and its plant extraction for SLE will be evaluated. ETHICS AND DISSEMINATION: Without personal information involved, ethical approval and informed consent form is no need. The review will be submitted to a peer-reviewed journal prospectively to spread our findings. PROSPERO REGISTRATION NUMBER: PROSPERO CRD42020176444.