Genetic Control of Alternative Splicing and its Distinct Role in Colorectal Cancer Mechanisms.

BACKGROUND & AIMS: Dysregulation of alternative splicing is implicated in many human diseases, and understanding the genetic variation underlying transcript splicing is essential to dissect the molecular mechanisms of cancers. We aimed to provide a comprehensive functional dissection of splicing quantitative trait loci (sQTLs) in cancer and focus on elucidating its distinct role in colorectal cancer (CRC) mechanisms. METHODS: We performed a comprehensive sQTL analysis to identify genetic variants that control messenger RNA splicing across 33 cancer types from The Cancer Genome Atlas and independently validated in our 154 CRC tissues. Then, large-scale, multicenter, multi-ethnic case-control studies (34,585 cases and 76,023 controls) were conducted to examine the association of these sQTLs with CRC risk. A series of biological experiments in vitro and in vivo were performed to investigate the potential mechanisms of the candidate sQTLs and target genes. RESULTS: The molecular characterization of sQTL revealed its distinct role in cancer susceptibility. Tumor-specific sQTL further showed better response to cancer development. In addition, functionally informed polygenic risk score highlighted the potentiality of sQTLs in the CRC prediction. Complemented by large-scale population studies, we identified that the risk allele (T) of a multi-ancestry-associated sQTL rs61746794 significantly increased the risk of CRC in Chinese (odds ratio, 1.20; 95% CI, 1.12-1.29; P = 8.82 × 10-7) and European (odds ratio, 1.11; 95% CI, 1.07-1.16; P = 1.13 × 10-7) populations. rs61746794-T facilitated PRMT7 exon 16 splicing mediated by the RNA-binding protein PRPF8, thus increasing the level of canonical PRMT7 isoform (PRMT7-V2). Overexpression of PRMT7-V2 significantly enhanced the growth of CRC cells and xenograft tumors compared with PRMT7-V1. Mechanistically, PRMT7-V2 functions as an epigenetic writer that catalyzes the arginine methylation of H4R3 and H3R2, subsequently regulating diverse biological processes, including YAP, AKT, and KRAS pathway. A selective PRMT7 inhibitor, SGC3027, exhibited antitumor effects on human CRC cells. CONCLUSIONS: Our study provides an informative sQTLs resource and insights into the regulatory mechanisms linking splicing variants to cancer risk and serving as biomarkers and therapeutic targets.

Construction of Oligorotaxanes with Hydrogen-Bonded Aramide Macrocycles through Threaded Host-Guest Complexation.

The development of efficient and selective organic synthetic approaches for complex molecules has garnered significant attention due to the need for precise control over molecular structures and functions. Rotaxanes, a type of mechanically interlocked molecules (MIMs), have shown promising applications in various fields including sensing, catalysis, and material science. However, the highly selective synthesis of oligo[n]rotaxanes (mostly n≥3) through controlling host-guest complexation and supramolecular threading assembly process still remains an ongoing challenge. In particular, the utilization of two-dimensional (2D) macrocycles with structural shape-persistency for the synthesis of oligo[n]rotaxanes is rare. In this concept, research on cooperatively threaded host-guest complexation with hydrogen-bonded (H-bonded) aramide macrocycles and selective synthetic protocols of oligo[n]rotaxanes has been summarized. The high efficiency and selectivity in synthesis are ascribed to the synergistic interplay of multiple non-covalent bonding interactions such as hydrogen bonding and intermolecular π-π stacking of macrocycles within the unique supramolecular structure of threaded host-guest complexes. This review focuses on the latest progress in the concepts, synthesis, and properties of H-bonded aramide macrocycle-based oligorotaxanes, and presents an in-depth outlook on challenges in this emerging field.

In Utero and Childhood/Adolescence Exposure to Tobacco Smoke, Genetic Risk, and Lung Cancer Incidence and Mortality in Adulthood.

Rationale: The individual effects of early-life tobacco smoke exposure and its interactions with genetic factors on lung cancer in adulthood remain unclear. Objectives: To investigate the associations of early-life tobacco exposures as well as their interactions with polygenic risk scores (PRSs) with lung cancer incidence and mortality. Methods: A total of 432,831 participants from the UK Biobank study were included. We estimated the associations of in utero exposure to tobacco smoke, the age of smoking initiation and their interactions with PRSs with lung cancer incidence and mortality in adulthood using Cox proportional hazard models. Measurements and Main Results: Lung cancer incidence (hazard ratio [HR]: 1.59, 95% confidence interval [CI], 1.44-1.76) increased among participants with in utero tobacco exposure. Multivariable-adjusted HRs (with 95% CIs) of lung cancer incidence for smoking initiation in adulthood, adolescence, and childhood (versus never-smokers) were 6.10 (5.25-7.09), 9.56 (8.31-11.00), and 15.15 (12.90-17.79) (Ptrend < 0.001). Similar findings were observed in lung cancer mortality. Participants with high PRSs and in utero tobacco exposure (versus low PRSs participants without in utero exposure) had an HR of 2.35 for lung cancer incidence (95% CI, 1.97-2.80, Pinteraction = 0.089) and 2.43 for mortality (95% CI, 2.05-2.88, Pinteraction = 0.032). High PRSs with smoking initiation in childhood (versus never-smokers with low PRSs) had HRs of 18.71 for incidence (95% CI, 14.21-24.63, Pinteraction = 0.004) and 19.74 for mortality (95% CI, 14.98-26.01, Pinteraction = 0.033). Conclusions: In utero and childhood/adolescence exposure to tobacco smoke and its interaction with genetic factors may substantially increase the risks of lung cancer incidence and mortality in adulthood.

Thriving at work as a mediator of the relationship between psychological resilience and the work performance of clinical nurses.

OBJECTIVE: This study aims to investigate the relationship between psychological resilience, thriving at work, and work performance among nurses, as well as analyse the mediating role of thriving at work in the relationship between psychological resilience and the work performance of nurses. The findings are intended to serve as a reference for nursing managers to design tailored work performance intervention programs. METHOD: Using convenience sampling, 308 clinical nurses were selected from a tertiary hospital in Changsha City, Hunan Province, China, from February to April 2023. The Connor-Davidson Resilience Scale (CD-RISC), the Thriving at Work Scale, and the Work Performance Scale were employed for the questionnaire survey. Pearson correlation analysis was used to explore the relationship between psychological resilience, thriving at work and work performance. The SPSS 26.0 software's 'Process' plugin was utilised for mediation effect analysis. RESULTS: Significantly positive correlations were found between psychological resilience and thriving at work (r = 0.806, P < 0.01), thriving at work and work performance (r = 0.571, P < 0.01) as well as psychological resilience and work performance (r = 0.572, P < 0.01). Psychological resilience significantly predicted work performance positively (ß = 0.558, t = 11.165, P < 0.01), and this prediction remained significant when thriving at work (the mediating variable), was introduced (ß = 0.371, t = 4.772, P < 0.01). Psychological resilience significantly predicted thriving at work positively (ß = 0.731, t = 20.779, P < 0.01), and thriving at work significantly predicted work performance positively (ß = 0.256, t = 3.105, P < 0.05). The mediating effect size of thriving at work between psychological resilience and work performance was 33.49% (P < 0.05). CONCLUSION: Thriving at work plays a partial mediating role between psychological resilience and work performance. The level of work performance among clinical nurses was relatively high. Nursing managers can enhance thriving at work by fostering psychological resilience among clinical nurses, thereby further improving their work performance to ensure high-quality and efficient nursing care.

Saturated fatty acid intake, genetic risk and colorectal cancer incidence: A large-scale prospective cohort study.

Previous investigations mainly focused on the associations of dietary fatty acids with colorectal cancer (CRC) risk, which ignored gene-environment interaction and mechanisms interpretation. We conducted a case-control study (751 cases and 3058 controls) and a prospective cohort study (125 021 participants) to explore the associations between dietary fatty acids, genetic risks, and CRC. Results showed that high intake of saturated fatty acid (SFA) was associated with a higher risk of CRC than low SFA intake (HR =1.22, 95% CI:1.02-1.46). Participants at high genetic risk had a greater risk of CRC with the HR of 2.48 (2.11-2.91) than those at low genetic risk. A multiplicative interaction of genetic risk and SFA intake with incident CRC risk was found (PInteraction = 7.59 × 10-20 ), demonstrating that participants with high genetic risk and high SFA intake had a 3.75-fold greater risk of CRC than those with low genetic risk and low SFA intake. Furthermore, incorporating PRS and SFA into traditional clinical risk factors improved the discriminatory accuracy for CRC risk stratification (AUC from 0.706 to 0.731). Multi-omics data showed that exposure to SFA-rich high-fat dietary (HFD) can responsively induce epigenome reprogramming of some oncogenes and pathological activation of fatty acid metabolism pathway, which may contribute to CRC development through changes in gut microbiomes, metabolites, and tumor-infiltrating immune cells. These findings suggest that individuals with high genetic risk of CRC may benefit from reducing SFA intake. The incorporation of SFA intake and PRS into traditional clinical risk factors will help improve high-risk sub-populations in individualized CRC prevention.

Complexation and Separation of Palladium with Tridentate 2,6-Bis-triazolyl-pyridine Ligands: Synthesis, Solvent Extraction, Spectroscopy, Crystallography, and DFT Calculations.

Selective extraction of palladium from high-level liquid waste (HLLW) is desirable for the sustainable development of nuclear energy and resource recovery. In this work, three tridentate 2,6-bis-triazolyl-pyridine ligands (L-I, L-II, and L-III) bearing different alkyl side chains were synthesized and systematically studied for the complexation and extraction of palladium. Altering the alkyl side chains of the ligands led to pronounced differences in extraction performance. Among the three ligands, L-II decorated with two n-octyl groups exhibited the highest Pd(II) extraction efficiency at acidity levels of 1-5 M HNO3 and outstanding selectivity over 13 coexisting competing metal ions. Results from UV-vis titration experiments and theoretical calculations suggested that the differentiated extraction abilities of the ligands could be because of their different hydrophilicity rather than electron-donating effects. Slope analyses and electrospray ionization-high resolution mass spectrometry (ESI-HRMS) experiments revealed the formation of both L/Pd 1:1 and 2:1 species during the extraction process. These stoichiometries were further confirmed by job plots and NMR titration experiments. The ligands were found to aggregate slightly, especially at higher concentrations, which could result from multiple intermolecular hydrogen bonds as illustrated by X-ray crystallography. The configurations of PdL and PdL2 were further elucidated by analysis of single crystal structure and density-functional theory (DFT) calculations, respectively, where the first coordination sphere of Pd(II) was surrounded by four nitrogen or oxygen atoms in a quadrangular manner. This study provides an alternative method to separate palladium from HLLW and brings a new understanding of the coordination and complexation behaviors of Pd(II) with tridentate nitrogen ligands.

Selective Complexation and Separation of Uranium(VI) from Thorium(IV) with New Tetradentate N,O-Hybrid Diamide Ligands: Synthesis, Extraction, Spectroscopy, and Crystallographic Studies.

An unmet challenge in the thorium-uranium fuel cycle is the efficient separation of uranium from thorium. Herein, two new tetradentate N,O-hybrid ligands, N,N'-diethyl-N,N'-di-p-tolyl-2,2'-bipyridine-6,6'-dicarboxamide (Et-Tol-BPDA) and N,N'-diethyl-N,N'-di-p-tolyl-2,2'-bipyrimidine-4,4'-dicarboxamide (Et-Tol-BPymDA), comprising a bipyridine or bipyrimidine core and amide moieties were designed and synthesized for selectively complexing and separating U(VI) from Th(IV). The high U(VI)/Th(IV) extraction selectivity was achieved by Et-Tol-BPDA (SFU/Th = 33 at 3 M HNO3) and Et-Tol-BPymDA (SFU/Th = 73 at 3 M HNO3) in nitric acid solutions. The extraction process for U(VI) or Th(IV) with these two ligands primarily proceeded through the solvation mechanism, as evidenced by slope analyses. Thermodynamic studies for the extraction of U(VI) and Th(IV) revealed a spontaneous process. Results from UV-vis spectroscopic titration and slope analyses demonstrated that U(VI) and Th(IV) each form a 1:1 complex with the two ligands both in the monophasic organic solution and the biphasic extraction system. The stability constants of the 1:1 complexes of Et-Tol-BPDA or Et-Tol-BPymDA with U(VI) were found to be larger than those with Th(IV), which coincide well with the high U(VI)/Th(IV) extraction selectivity. The solid-state structures of Et-Tol-BPDA, Et-Tol-BPymDA, and 1:1 complexes of the two ligands with U(VI) or Th(IV) were analyzed by X-ray diffraction technique. The results from this work implicate the potential of bipyridine- and bipyrimidine-derived diamide ligands for uranium/thorium separation.

Risk SNP in a transcript of RP11-638I2.4 increases lncRNA-YY1 interaction and pancreatic cancer susceptibility.

Tens of thousands of long non-coding RNAs (lncRNAs) have been identified through RNA-seq analysis, but the biological and pathological significance remains unclear. By integrating the genome-wide lncRNA data with a cross-ancestry meta-analysis of PDAC GWASs, we depicted a comprehensive atlas of pancreatic ductal adenocarcinoma (PDAC)-associated lncRNAs, containing 1,204 lncRNA (445 novel lncRNAs and 759 GENCODE annotated lncRNAs) and 4,368 variants. Furthermore, we found that PDAC-associated lncRNAs could function by altering chromatin activity, transcription factors, and RNA-binding proteins binding affinity. Importantly, genetic variants linked to PDAC are preferentially found at PDAC-associated lncRNA regions, supporting the biological and clinical relevance of PDAC-associated lncRNAs. Finally, we prioritized a novel transcript (MICT00000110172.1) of RP11-638I2.4 as a potential tumor promoter. MICT00000110172.1 is able to reinforce the interaction with YY1, which could reverse the effect of YY1 on pancreatic cancer cell cycle arrest to promote the pancreatic cancer growth. G > A change at rs2757535 in the second exon of MICT00000110172.1 induces a spatial structural change and creates a target region for YY1 binding, which enforces the effect of MICT00000110172.1 in an allele-specific manner, and thus confers susceptibility to tumorigenesis. In summary, our results extend the repertoire of PDAC-associated lncRNAs that could act as a starting point for future functional explorations, and the identification of lncRNA-based target therapy.

HSPA12A was identified as a key driver in colorectal cancer GWAS loci 10q26.12 and modulated by an enhancer-promoter interaction.

Although genome-wide association studies (GWASs) have identified over 100 colorectal cancer (CRC) risk loci, an understanding of causal genes or risk variants and their biological functions in these loci remain unclear. Recently, genomic loci 10q26.12 with lead SNP rs1665650 was identified as an essential CRC risk loci of Asian populations. However, the functional mechanism of this region has not been fully clarified. Here, we applied an RNA interfering-based on-chip approach to screen for the genes essential for cell proliferation in the CRC risk loci 10q26.12. Notably, HSPA12A had the most significant effect among the identified genes and functioned as a crucial oncogene facilitating cell proliferation. Moreover, we conducted an integrative fine-mapping analysis to identify putative casual variants and further explored their association with CRC risk in a large-scale Chinese population consisting of 4054 cases and 4054 controls and also independently validated in 5208 cases and 20,832 controls from the UK biobank cohort. We identified a risk SNP rs7093835 in the intron of HSPA12A that was significantly associated with an increased risk of CRC (OR 1.23, 95% CI 1.08-1.41, P = 1.92 × 10-3). Mechanistically, the risk variant could facilitate an enhancer-promoter interaction mediated by the transcriptional factor (TF) GRHL1 and ultimately upregulate HSPA12A expression, which provides functional evidence to support our population findings. Collectively, our study reveals the important role of HSPA12A in CRC development and illustrates a novel enhancer-promoter interaction module between HSPA12A and its regulatory elements rs7093835, providing new insights into the etiology of CRC.

CancerImmunityQTL: a database to systematically evaluate the impact of genetic variants on immune infiltration in human cancer.

Tumor-infiltrating immune cells as integral component of the tumor microenvironment are associated with tumor progress, prognosis and responses to immunotherapy. Genetic variants have been demonstrated to impact tumor-infiltrating, underscoring the heritable character of immune landscape. Therefore, identification of immunity quantitative trait loci (immunQTLs), which evaluate the effect of genetic variants on immune cells infiltration, might present a critical step toward fully understanding the contribution of genetic variants in tumor development. Although emerging studies have demonstrated the determinants of germline variants on immune infiltration, no database has yet been developed to systematically analyze immunQTLs across multiple cancer types. Using genotype data from TCGA database and immune cell fractions estimated by CIBERSORT, we developed a computational pipeline to identify immunQTLs in 33 cancer types. A total of 913 immunQTLs across different cancer types were identified. Among them, 5 immunQTLs are associated with patient overall survival. Furthermore, by integrating immunQTLs with GWAS data, we identified 527 immunQTLs overlapping with known GWAS linkage disequilibrium regions. Finally, we constructed a user-friendly database, CancerImmunityQTL (http://www.cancerimmunityqtl-hust.com/) for users to browse, search and download data of interest. This database provides an informative resource to understand the germline determinants of immune infiltration in human cancer and benefit from personalized cancer immunotherapy.

Swin Unet3D: a three-dimensional medical image segmentation network combining vision transformer and convolution.

BACKGROUND: Semantic segmentation of brain tumors plays a critical role in clinical treatment, especially for three-dimensional (3D) magnetic resonance imaging, which is often used in clinical practice. Automatic segmentation of the 3D structure of brain tumors can quickly help physicians understand the properties of tumors, such as the shape and size, thus improving the efficiency of preoperative planning and the odds of successful surgery. In past decades, 3D convolutional neural networks (CNNs) have dominated automatic segmentation methods for 3D medical images, and these network structures have achieved good results. However, to reduce the number of neural network parameters, practitioners ensure that the size of convolutional kernels in 3D convolutional operations generally does not exceed [Formula: see text], which also leads to CNNs showing limitations in learning long-distance dependent information. Vision Transformer (ViT) is very good at learning long-distance dependent information in images, but it suffers from the problems of many parameters. What's worse, the ViT cannot learn local dependency information in the previous layers under the condition of insufficient data. However, in the image segmentation task, being able to learn this local dependency information in the previous layers makes a big impact on the performance of the model. METHODS: This paper proposes the Swin Unet3D model, which represents voxel segmentation on medical images as a sequence-to-sequence prediction. The feature extraction sub-module in the model is designed as a parallel structure of Convolution and ViT so that all layers of the model are able to adequately learn both global and local dependency information in the image. RESULTS: On the validation dataset of Brats2021, our proposed model achieves dice coefficients of 0.840, 0.874, and 0.911 on the ET channel, TC channel, and WT channel, respectively. On the validation dataset of Brats2018, our model achieves dice coefficients of 0.716, 0.761, and 0.874 on the corresponding channels, respectively. CONCLUSION: We propose a new segmentation model that combines the advantages of Vision Transformer and Convolution and achieves a better balance between the number of model parameters and segmentation accuracy. The code can be found at https://github.com/1152545264/SwinUnet3D .

Evaluation of career planning group counseling and its effectiveness for intern male nursing students.

OBJECTIVE: To investigate the influence of group counselling on the career planning and career maturity of male nursing students. METHOD: Sixty male nursing students were randomly selected from a specific-level first-class hospital in Hunan Province from July to August 2020 by using the convenience sampling method and were subsequently divided into the control group and the experimental group using the random number table method. The control group received routine pre-job training, including aspects concerning the hospital profile, nurse etiquette, nursing core systems, professional ethics, nursing emergency treatment and career prospects and planning. In the experimental group, career planning group counselling was added after the regular pre-service training (once a week) with each session lasting 2 h for a total of six training sessions. At six weeks and three months after the intervention, the career status evaluation scale and the college students' career maturity scale were used to compare the career planning and career maturity status of the two groups of male nursing students. RESULTS: After six weeks and three months of intervention, all the dimensions and total scores of both the career status evaluation scale and the career maturity scale in the experimental group were superior to those in the control group with statistically significant differences (all P < 0.05). The repeated measures of variance analysis indicated that the differences in the total score for career planning and the four dimensions in terms of intergroup effect, time effect and interaction effect between the two groups were statistically significant (P < 0.05). The intergroup effect, time effect and interaction effect of the total score for vocational maturity, career goal, career confidence, career value, career freedom and career reference of the two groups were statistically significant (P < 0.05), while the time effect of the relative dependency dimension was also statistically significant (P < 0.05). CONCLUSION: Group counselling can significantly improve the career planning and career maturity status of male nursing students and has a certain long-term effect.

Development of diagnostic algorithm using machine learning for distinguishing between active tuberculosis and latent tuberculosis infection.

BACKGROUND: The discrimination between active tuberculosis (ATB) and latent tuberculosis infection (LTBI) remains challenging. The present study aims to investigate the value of diagnostic models established by machine learning based on multiple laboratory data for distinguishing Mycobacterium tuberculosis (Mtb) infection status. METHODS: T-SPOT, lymphocyte characteristic detection, and routine laboratory tests were performed on participants. Diagnostic models were built according to various algorithms. RESULTS: A total of 892 participants (468 ATB and 424 LTBI) and another 263 participants (125 ATB and 138 LTBI), were respectively enrolled at Tongji Hospital (discovery cohort) and Sino-French New City Hospital (validation cohort). Receiver operating characteristic (ROC) curve analysis showed that the value of individual indicator for differentiating ATB from LTBI was limited (area under the ROC curve (AUC) < 0.8). A total of 28 models were successfully established using machine learning. Among them, the AUCs of 25 models were more than 0.9 in test set. It was found that conditional random forests (cforest) model, based on the implementation of the random forest and bagging ensemble algorithms utilizing conditional inference trees as base learners, presented best discriminative power in segregating ATB from LTBI. Specially, cforest model presented an AUC of 0.978, with the sensitivity of 93.39% and the specificity of 91.18%. Mtb-specific response represented by early secreted antigenic target 6 (ESAT-6) and culture filtrate protein 10 (CFP-10) spot-forming cell (SFC) in T-SPOT assay, as well as global adaptive immunity assessed by CD4 cell IFN-γ secretion, CD8 cell IFN-γ secretion, and CD4 cell number, were found to contribute greatly to the cforest model. Superior performance obtained in the discovery cohort was further confirmed in the validation cohort. The sensitivity and specificity of cforest model in validation set were 92.80% and 89.86%, respectively. CONCLUSIONS: Cforest model developed upon machine learning could serve as a valuable and prospective tool for identifying Mtb infection status. The present study provided a novel and viable idea for realizing the clinical diagnostic application of the combination of machine learning and laboratory findings.

An imConvNet-based deep learning model for Chinese medical named entity recognition.

BACKGROUND: With the development of current medical technology, information management becomes perfect in the medical field. Medical big data analysis is based on a large amount of medical and health data stored in the electronic medical system, such as electronic medical records and medical reports. How to fully exploit the resources of information included in these medical data has always been the subject of research by many scholars. The basis for text mining is named entity recognition (NER), which has its particularities in the medical field, where issues such as inadequate text resources and a large number of professional domain terms continue to face significant challenges in medical NER. METHODS: We improved the convolutional neural network model (imConvNet) to obtain additional text features. Concurrently, we continue to use the classical Bert pre-training model and BiLSTM model for named entity recognition. We use imConvNet model to extract additional word vector features and improve named entity recognition accuracy. The proposed model, named BERT-imConvNet-BiLSTM-CRF, is composed of four layers: BERT embedding layer-getting word embedding vector; imConvNet layer-capturing the context feature of each character; BiLSTM (Bidirectional Long Short-Term Memory) layer-capturing the long-distance dependencies; CRF (Conditional Random Field) layer-labeling characters based on their features and transfer rules. RESULTS: The average F1 score on the public medical data set yidu-s4k reached 91.38% when combined with the classical model; when real electronic medical record text in impacted wisdom teeth is used as the experimental object, the model's F1 score is 93.89%. They all show better results than classical models. CONCLUSIONS: The suggested novel model (imConvNet) significantly improves the recognition accuracy of Chinese medical named entities and applies to various medical corpora.

Microbial community structure, co-occurrence network and fermentation characteristics of woody plant silage.

BACKGROUND: Feed shortage is a factor restricting animal production in the tropics, therefore how to use natural woody plant resources as animal feed is an important strategy. RESULTS: Under the dual stress of an anaerobic and acidic environment, the microbial response during the fermentation of paper mulberry (PM) silage was found to be sensitive. The Gram-negative bacteria and mould died, and the dominant microbial community rapidly shifted to Gram-positive bacteria, resulting in a large reduction in microbial diversity and abundance. Exogenous bran additives interfered with the stress effects of the woody silage environment. Wheat bran (WB) accelerated the response of microorganisms to the anaerobic stress, and lactic acid bacteria became the dominant microbial community, thereby enhancing the lactic acid fermentation of silage, affecting the metabolic pathways of microorganisms, and improving the flavour and quality of the silage. Addition of rice bran made Enterobacter and Clostridium species quickly respond to the stress of the silage environment and become the predominant bacterial groups. In particular, anaerobic and spore-forming Clostridium species showed a strong tolerance to the silage environment, leading to butyric acid fermentation and protein degradation of the silage, and reducing its fermentation quality. CONCLUSION: The PacBio single-molecule real-time (SMRT) sequencing technology accurately revealed the microbial co-occurrence network and fermentation mechanism of silage. Our results indicate that PM can be used in combination with WB to prepare high-quality silage for animal production. © 2021 Society of Chemical Industry.

Clinical characteristics and risk factors of fatal patients with COVID-19: a retrospective cohort study in Wuhan, China.

BACKGROUND: The coronavirus disease 2019 (COVID-19) has caused a global pandemic, resulting in considerable mortality. The risk factors, clinical treatments, especially comprehensive risk models for COVID-19 death are urgently warranted. METHODS: In this retrospective study, 281 non-survivors and 712 survivors with propensity score matching by age, sex, and comorbidities were enrolled from January 13, 2020 to March 31, 2020. RESULTS: Higher SOFA, qSOFA, APACHE II and SIRS scores, hypoxia, elevated inflammatory cytokines, multi-organ dysfunction, decreased immune cell subsets, and complications were significantly associated with the higher COVID-19 death risk. In addition to traditional predictors for death risk, including APACHE II (AUC = 0.83), SIRS (AUC = 0.75), SOFA (AUC = 0.70) and qSOFA scores (AUC = 0.61), another four prediction models that included immune cells subsets (AUC = 0.90), multiple organ damage biomarkers (AUC = 0.89), complications (AUC = 0.88) and inflammatory-related indexes (AUC = 0.75) were established. Additionally, the predictive accuracy of combining these risk factors (AUC = 0.950) was also significantly higher than that of each risk group alone, which was significant for early clinical management for COVID-19. CONCLUSIONS: The potential risk factors could help to predict the clinical prognosis of COVID-19 patients at an early stage. The combined model might be more suitable for the death risk evaluation of COVID-19.

Chiroptical Sensing of Amino Acid Derivatives by Host-Guest Complexation with Cyclo[6]aramide.

A hydrogen-bonded (H-bonded) amide macrocycle was found to serve as an effective component in the host-guest assembly for a supramolecular chirality transfer process. Circular dichroism (CD) spectroscopy studies showed that the near-planar macrocycle could produce a CD response when combined with three of the twelve L-α-amino acid esters (all cryptochiral molecules) tested as possible guests. The host-guest complexation between the macrocycle and cationic guests was explored using NMR, revealing the presence of a strong affinity involving the multi-point recognition of guests. This was further corroborated by density functional theory (DFT) calculations. The present work proposes a new strategy for amplifying the CD signals of cryptochiral molecules by means of H-bonded macrocycle-based host-guest association, and is expected to be useful in designing supramolecular chiroptical sensing materials.

N6-methyladenosine mRNA methylation of PIK3CB regulates AKT signalling to promote PTEN-deficient pancreatic cancer progression.

OBJECTIVE: Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers worldwide. Thus far, most drugs have failed to significantly improve patient survival. N6-methyladenosine (m6A) plays an important role in the progression of PDAC, but its aberrant regulation driven by germline variants in human diseases remains unclear. DESIGN: We first performed an exome-wide association analysis in 518 PDAC patients with overall survival and replicated in an independent population containing 552 PDAC patients. Then, a series of biochemical experiments in vitro and in vivo were conducted to investigate potential mechanisms of the candidate variant and its target gene PIK3CB underlying the PDAC progression. Moreover, the PIK3CB-selective inhibitor KIN-193 was used to block PDAC tumour growth. RESULTS: We identified a missense variant rs142933486 in PIK3CB that is significantly associated with the overall survival of PDAC by reducing the PIK3CB m6A level, which facilitated its mRNA and protein expression levels mediated by the m6A 'writer' complex (METTL13/METTL14/WTAP) and the m6A 'reader' YTHDF2. The upregulation of PIK3CB is widely found in PDAC tumour tissues and significantly correlated with the poor prognosis of PDAC, especially in PTEN-deficient patients. We further demonstrated that PIK3CB overexpression substantially enhanced the proliferation and migration abilities of PTEN-deficient PDAC cells and activated AKT signalling pathway. Remarkably, KIN-193, a PIK3CB-selective inhibitor, is shown to serve as an effective anticancer agent for blocking PTEN-deficient PDAC. CONCLUSIONS: These findings demonstrate aberrant m6A homoeostasis as an oncogenic mechanism in PDAC and highlight the potential of PIK3CB as a therapeutic target for this disease.

Clinical characteristics and risk factors associated with COVID-19 disease severity in patients with cancer in Wuhan, China: a multicentre, retrospective, cohort study.

BACKGROUND: COVID-19 has spread globally. Epidemiological susceptibility to COVID-19 has been reported in patients with cancer. We aimed to systematically characterise clinical features and determine risk factors of COVID-19 disease severity for patients with cancer and COVID-19. METHODS: In this multicentre, retrospective, cohort study, we included all adult patients (aged ≥18 years) with any type of malignant solid tumours and haematological malignancy who were admitted to nine hospitals in Wuhan, China, with laboratory-confirmed COVID-19 between Jan 13 and March 18, 2020. Enrolled patients were statistically matched (2:1) with patients admitted with COVID-19 who did not have cancer with propensity score on the basis of age, sex, and comorbidities. Demographic characteristics, laboratory examinations, illness severity, and clinical interventions were compared between patients with COVID-19 with or without cancer as well as between patients with cancer with non-severe or severe COVID-19. COVID-19 disease severity was defined on admission on the basis of the WHO guidelines. Univariable and multivariable logistic regression, adjusted for age, sex, comorbidities, cancer type, tumour stage, and antitumour treatments, were used to explore risk factors associated with COVID-19 disease severity. This study was registered in the Chinese Clinical Trial Register, ChiCTR2000030807. FINDINGS: Between Jan 13 and March 18, 2020, 13 077 patients with COVID-19 were admitted to the nine hospitals in Wuhan and 232 patients with cancer and 519 statistically matched patients without cancer were enrolled. Median follow-up was 29 days (IQR 22-38) in patients with cancer and 27 days (20-35) in patients without cancer. Patients with cancer were more likely to have severe COVID-19 than patients without cancer (148 [64%] of 232 vs 166 [32%] of 519; odds ratio [OR] 3·61 [95% CI 2·59-5·04]; p<0·0001). Risk factors previously reported in patients without cancer, such as older age; elevated interleukin 6, procalcitonin, and D-dimer; and reduced lymphocytes were validated in patients with cancer. We also identified advanced tumour stage (OR 2·60, 95% CI 1·05-6·43; p=0·039), elevated tumour necrosis factor α (1·22, 1·01-1·47; p=0·037), elevated N-terminal pro-B-type natriuretic peptide (1·65, 1·03-2·78; p=0·032), reduced CD4+ T cells (0·84, 0·71-0·98; p=0·031), and reduced albumin-globulin ratio (0·12, 0·02-0·77; p=0·024) as risk factors of COVID-19 severity in patients with cancer. INTERPRETATION: Patients with cancer and COVID-19 were more likely to deteriorate into severe illness than those without cancer. The risk factors identified here could be helpful for early clinical surveillance of disease progression in patients with cancer who present with COVID-19. FUNDING: China National Natural Science Foundation.

Dynamic changes and characterization of the protein and carbohydrate fractions of native grass grown in Inner Mongolia during ensiling and the aerobic stage.

OBJECTIVE: To improve the utility of native grass resources as feed in China, we investigated the dynamics of protein and carbohydrate fractions among Inner Mongolian native grasses, during ensiling and the aerobic stage, using the Cornell Net Carbohydrate and Protein System. METHODS: Silages were prepared without or with lactic acid bacteria (LAB) inoculant. We analyzed the protein and carbohydrate fractions and fermentation quality of silages at 0, 5, 15, 20, 30, and 60 d of ensiling, and the stability at 0.5, 2, 5, and 10 d during the aerobic stage. RESULTS: Inner Mongolian native grass contained 10.8% crude protein (CP) and 3.6% water-soluble carbohydrates (WSC) on a dry matter basis. During ensiling, pH and CP and WSC content decreased (p<0.05), whereas lactic acid and ammonia nitrogen (N) content increased (p<0.05). Non-protein N (PA) content increased significantly, whereas rapidly degraded true protein (PB1), intermediately degraded true protein (PB2), total carbohydrate (CHO), sugars (CA), starch (CB1), and degradable cell wall carbohydrate (CB2) content decreased during ensiling (p<0.05). At 30 d of ensiling, control and LAB-treated silages were well preserved and had lower pH (<4.2) and ammonia-N content (<0.4 g/kg of fresh matter [FM]) and higher lactic acid content (>1.0% of FM). During the aerobic stage, CP, extract ether, WSC, lactic acid, acetic acid, PB1, PB2, true protein degraded slowly (PB3), CHO, CA, CB1, and CB2 content decreased significantly in all silages, whereas pH, ammonia-N, PA, and bound true protein (PC) content increased significantly. CONCLUSION: Control and LAB-treated silages produced similar results in terms of fermentation quality, aerobic stability, and protein and carbohydrate fractions. Inner Mongolian native grass produced good silage, nutrients were preserved during ensiling and protein and carbohydrate losses largely occurred during the aerobic stage.