Search

Abstract Integrating genomics and histology for cancer prognosis demonstrates promise. Here, we develop a multi-classifier system integrating a lncRNA-based classifier, a deep learning whole-slide-image-based classifier, and a clinicopathological classifier to accurately predict post-surgery localized (stage I–III) papillary renal cell carcinoma (pRCC) recurrence. The multi-classifier system demonstrates significantly higher predictive accuracy for recurrence-free survival (RFS) compared to the three single classifiers alone in the training set and in both validation sets (C-index 0.831-0.858 vs. 0.642-0.777, p

To study the vacuum arc remelting process and improve the homogenization and cleanliness level of large-sized superalloy ingot， numerical simulation and industrial experiment were carried out to investigate the evolution of molten pool， solidification characteristics and inclusion distribution in a ϕ690 mm GH4738 superalloy ingot during vacuum arc remelting. The results show that as the melting progresses， the simulated molten pool morphology changes from a "shallow flat" shape to a "shallow U " shape. After melting for 500 minutes， the depth of the molten pool and the width of the mushy zone tend to stabilize， with maximum molten pool depth and mushy zone width being 200.88 mm and 72.38 mm respectively. During stable melting， the cooling rate at the surface and center of the casting ingot is 0.126K / s and 0.009 K/s， respectively， and after the height of the ingot is exceeds 0.4 m， the cooling rate of the casting ingot is basically stable.The trajectories of six particle size inclusions in the molten pool were simulated. Large size inclusions （>20 μm） were subjected to the fluid flow and buoyancy to move helically in the molten pool and were eventually trapped at the ingot sidewalls， while small size inclusions （≤10 μm） entered the molten pool and then moved inward along the bottom of the pool， eventually remaining inside the ingot， which is basically consistent with the detection value of the industrial ingot.

The estimation of postmortem interval (PMI) has long been a focal point in the field of forensic science. Following the death of an organism, microorganisms exhibit a clock-like proliferation pattern during the course of cadaver decomposition, forming the foundation for utilizing microbiology in PMI estimation. The establishment of PMI estimation models based on datasets from different seasons is of great practical significance. In this experiment, we conducted microbiota sequencing and analysis on gravesoil and mouse intestinal contents collected during both the winter and summer seasons and constructed a PMI estimation model using the Random Forest algorithm. The results showed that the MAE of the gut microbiota model in summer was 0.47 ± 0.26 d, R2 = 0.991, and the MAE of the gravesoil model in winter was 1.04 ± 0.22 d, R2 = 0.998. We propose that, in practical applications, it is advantageous to selectively build PMI estimation models based on seasonal variations. Additionally, through a combination of morphological observations, gravesoil microbiota sequencing results, and soil physicochemical data, we identified the time of cadaveric rupture for mouse cadavers, occurring at around days 24–27 in winter and days 6–9 in summer. This study not only confirms previous research findings but also introduces novel insights, contributing to the foundational knowledge necessary to advance the utilization of microbiota for PMI estimation.

Clear cell Renal Cell Carcinoma (ccRCC) is a highly heterogeneous disease, making it challenging to predict prognosis and therapy efficacy. In this study, we aimed to explore the role of 5-methylcytosine (m5C) RNA modification in ccRCC and its potential as a predictor for therapy response and overall survival (OS). We established a novel 5-methylcytosine RNA modification-related gene index (M5CRMRGI) and studied its effect on the tumor microenvironment (TME) using single-cell sequencing data for in-depth analysis, and verified it using spatial sequencing data. Our results showed that M5CRMRGI is an independent predictor of OS in multiple datasets and exhibited outstanding performance in predicting the OS of ccRCC. Distinct mutation profiles, hallmark pathways, and infiltration of immune cells in TME were observed between high- and low-M5CRMRGI groups. Single-cell/spatial transcriptomics revealed that M5CRMRGI could reprogram the distribution of tumor-infiltrating immune cells. Moreover, significant differences in tumor immunogenicity and tumor immune dysfunction and exclusion (TIDE) were observed between the two risk groups, suggesting a better response to immune checkpoint blockade therapy of the high-risk group. We also predicted six potential drugs binding to the core target of the M5CRMRGI signature via molecular docking. Real-world treatment cohort data proved once again that high-risk patients were appropriate for immune checkpoint blockade therapy, while low-risk patients were appropriate for Everolimus. Our study shows that the m5C modification landscape plays a role in TME distribution. The proposed M5CRMRGI-guided strategy for predicting survival and immunotherapy efficacy, we reported here, might also be applied to more cancers other than ccRCC.

Summary: Background: Improved markers for predicting recurrence are needed to stratify patients with localised (stage I–III) renal cell carcinoma after surgery for selection of adjuvant therapy. We developed a novel assay integrating three modalities—clinical, genomic, and histopathological—to improve the predictive accuracy for localised renal cell carcinoma recurrence. Methods: In this retrospective analysis and validation study, we developed a histopathological whole-slide image (WSI)-based score using deep learning allied to digital scanning of conventional haematoxylin and eosin-stained tumour tissue sections, to predict tumour recurrence in a development dataset of 651 patients with distinctly good or poor disease outcome. The six single nucleotide polymorphism-based score, which was detected in paraffin-embedded tumour tissue samples, and the Leibovich score, which was established using clinicopathological risk factors, were combined with the WSI-based score to construct a multimodal recurrence score in the training dataset of 1125 patients. The multimodal recurrence score was validated in 1625 patients from the independent validation dataset and 418 patients from The Cancer Genome Atlas set. The primary outcome measured was the recurrence-free interval (RFI). Findings: The multimodal recurrence score had significantly higher predictive accuracy than the three single-modal scores and clinicopathological risk factors, and it precisely predicted the RFI of patients in the training and two validation datasets (areas under the curve at 5 years: 0·825–0·876 vs 0·608–0·793; p

Abstract Background The aim of this study was to identify the ferroptosis induced tumor microenvironment (FeME) landscape in bladder cancer (BCa) for mRNA vaccine development and selecting suitable patients for precision treatment. Methods Gene expression profiles and clinical information of 1216 BCa patients were extracted from TCGA-BLCA, three GEO databases and IMvigor210 cohort. We comprehensively established the FeME landscape of 1216 BCa samples based on 290 ferroptosis related genes (FRGs), and systematically correlated these regulation patterns with TME cell-infiltrating characteristics. Besides, we identified the patients’ ferroptosis risk index (FRI) to predict the prognosis of BCa for precise treatment. Results Six over-expressed and mutated tumor antigens associated with poor prognosis and infiltration of antigen presenting cells were identified in BCa. Furthermore, we demonstrated the evaluation of FeME within individual tumors could predict stages of tumor inflammation, subtypes, genetic variation, and patient prognosis. Then, 5-lncRNA signature was mined to produce the FRI. Low FRI was also linked to increased mutation load, better prognosis and enhanced response to anti-PD-L1 immunotherapy. Besides, an immunotherapy cohort confirmed patients with lower FRI demonstrated significant therapeutic advantages and clinical benefits. Conclusions TFRC, SCD, G6PD, FADS2, SQLE, and SLC3A2 are potent antigens for developing anti-BCa mRNA vaccine. Establishment of FRI will contribute to enhancing our cognition of TME infiltration characterization and guiding more effective immunotherapy strategies and selecting appropriate patients for tumor vaccine therapy.

Microbial communities can undergo significant successional changes during decay and decomposition, potentially providing valuable insights for determining the postmortem interval (PMI). The microbiota produce various gases that cause cadaver bloating, and rupture releases nutrient-rich bodily fluids into the environment, altering the soil microbiota around the carcasses. In this study, we aimed to investigate the underlying principles governing the succession of microbial communities during the decomposition of pig carcasses and the soil beneath the carcasses. At early decay, the phylum Firmicutes and Bacteroidota were the most abundant in both the winter and summer pig rectum. However, Proteobacteria became the most abundant in the winter pig rectum in late decay. Using genus as a biomarker to estimate the PMI could get the MAE from 1.375 days to 2.478 days based on the RF model. The abundance of bacterial communities showed a decreasing trend with prolonged decomposition time. There were statistically significant differences in microbial diversity in the two periods (pre-rupture and post-rupture) of the four groups (WPG 0–8Dvs. WPG 16–40D, p < 0.0001; WPS 0–16Dvs. WPS 24–40D, p = 0.003; SPG 0D vs. SPG 8–40D, p = 0.0005; and SPS 0D vs. SPS 8–40D, p = 0.0208). Most of the biomarkers in the pre-rupture period belong to obligate anaerobes. In contrast, the biomarkers in the post-rupture period belong to aerobic bacteria. Furthermore, the genus Vagococcus shows a similar increase trend, whether in winter or summer. Together, these results suggest that microbial succession was predictable and can be developed into a forensic tool for estimating the PMI.

Abstract Background Circular RNAs (circRNAs) have been indicated as potentially critical mediators in various types of tumor progression, generally acting as microRNA (miRNA) sponges to regulate downstream gene expression. However, the aberrant expression profile and dysfunction of circRNAs in human clear cell renal cell carcinoma (ccRCC) need to be further investigated. This study mined key prognostic circRNAs and elucidates the potential role and molecular mechanism of circRNAs in regulating the proliferation and metastasis of ccRCC. Methods circCHST15 (hsa_circ_0020303) was identified by mining two circRNA microarrays from the Gene Expression Omnibus database and comparing matched tumor versus adjacent normal epithelial tissue pairs or matched primary versus metastatic tumor tissue pairs. These results were validated by quantitative real-time polymerase chain reaction and agarose gel electrophoresis. We demonstrated the biological effect of circCHST15 in ccRCC both in vitro and in vivo. To test the interaction between circCHST15 and miRNAs, we conducted a number of experiments, including RNA pull down assay, dual-luciferase reporter assay and fluorescence in situ hybridization. Results The expression of circCHST15 was higher in ccRCC tissues compared to healthy adjacent kidney tissue and higher in RCC cell lines compared to normal kidney cell lines. The level of circCHST15 was positively correlated with aggressive clinicopathological characteristics, and circCHST15 served as an independent prognostic indicator for overall survival and progression-free survival in patients with ccRCC after surgical resection. Our in vivo and in vitro data indicate that circCHST15 promotes the proliferation, migration, and invasion of ccRCC cells. Mechanistically, we found that circCHST15 directly interacts with miR-125a-5p and acts as a microRNA sponge to regulate EIF4EBP1 expression. Conclusions We found that sponging of miR-125a-5p to promote EIF4EBP1 expression is the underlying mechanism of hsa_circ_0020303-induced ccRCC progression. This prompts further investigation of circCHST15 as a potential prognostic biomarker and therapeutic target for ccRCC.

Considering the limitation of COVID-19 so that undergraduates can't return to the campus to continue classroom learning, on-line teaching based on Zoom Meetings mixed with rain classroom is being adopted to teach medical immunology and microbiology in students majoring in Chinese Medicine. At the same time, flipped classroom is also applied to this on-line teaching. In this article, it is described in detail how this blending teaching is constructed. What's more, personal teaching reflection and summary is also analyzed.

Taking two kinds of typical involute splines applied in the power split system as the research object，the theoretical analysis models of the load distribution factor in the tooth direction are established respectively，and the effects of load position，torque distribution and torsional rigidity of splines on the load distribution in the tooth direction are studied. The results show that，for the first kind of involute spline pair，with the increase of load distribution position and load distribution ratio，the maximum load distribution factor first decreases and then increases. There are optimal load position and optimal load distribution ratio to minimize the maximum load distribution factor，with the reduction ratio of 56.67% and 51.83% respectively. When the left torsional stiffness is 450×106 N/rad and the right torsional stiffness is 522×106 N/rad，the maximum load distribution factor reaches the minimum value of 2.08，and the maximum value increases by 31.8% compared with the minimum value. For the second involute spline pair，the maximum load distribution factor increases gradually with the increase of the left torque position，and the increase proportion is 14.36%，the influence of load distribution on the maximum load distribution factor is very small. With the increase of torsional rigidity，the maximum load distribution factor increases by 16.86%.

Our brains constantly generate predictions of sensory input that are compared with actual inputs, propagate the prediction-errors through a hierarchy of brain regions, and subsequently update the internal predictions of the world. However, the essential feature of predictive coding, the notion of hierarchical depth and its neural mechanisms, remains largely unexplored. Here, we investigated the hierarchical depth of predictive auditory processing by combining functional magnetic resonance imaging (fMRI) and high-density whole-brain electrocorticography (ECoG) in marmoset monkeys during an auditory local-global paradigm in which the temporal regularities of the stimuli were designed at two hierarchical levels. The prediction-errors and prediction updates were examined as neural responses to auditory mismatches and omissions. Using fMRI, we identified a hierarchical gradient along the auditory pathway: midbrain and sensory regions represented local, shorter-time-scale predictive processing followed by associative auditory regions, whereas anterior temporal and prefrontal areas represented global, longer-time-scale sequence processing. The complementary ECoG recordings confirmed the activations at cortical surface areas and further differentiated the signals of prediction-error and update, which were transmitted via putative bottom-up γ and top-down β oscillations, respectively. Furthermore, omission responses caused by absence of input, reflecting solely the two levels of prediction signals that are unique to the hierarchical predictive coding framework, demonstrated the hierarchical top-down process of predictions in the auditory, temporal, and prefrontal areas. Thus, our findings support the hierarchical predictive coding framework, and outline how neural networks and spatiotemporal dynamics are used to represent and arrange a hierarchical structure of auditory sequences in the marmoset brain.

The estimation of a postmortem interval (PMI) is particularly important for forensic investigations. The aim of this study was to assess the succession of bacterial communities associated with the decomposition of mouse cadavers and determine the most important biomarker taxa for estimating PMIs. High-throughput sequencing was used to investigate the bacterial communities of gravesoil samples with different PMIs, and a random forest model was used to identify biomarker taxa. Redundancy analysis was used to determine the significance of environmental factors that were related to bacterial communities. Our data showed that the relative abundance of Proteobacteria, Bacteroidetes and Firmicutes showed an increasing trend during decomposition, but that of Acidobacteria, Actinobacteria and Chloroflexi decreased. At the genus level, Pseudomonas was the most abundant bacterial group, showing a trend similar to that of Proteobacteria. Soil temperature, total nitrogen, NH4+-N and NO3−-N levels were significantly related to the relative abundance of bacterial communities. Random forest models could predict PMIs with a mean absolute error of 1.27 days within 36 days of decomposition and identified 18 important biomarker taxa, such as Sphingobacterium, Solirubrobacter and Pseudomonas. Our results highlighted that microbiome data combined with machine learning algorithms could provide accurate models for predicting PMIs in forensic science and provide a better understanding of decomposition processes.

The complete chloroplast genome sequence of Centipeda minima (L.) A. Braun et Aschers (Compositae) has been characterized by Illumina pair-end sequencing. The chloroplast genome is 152,351 bp in length, including a pair of inverted repeats (IRs) of 25,004 bp (12.1%) and a large single copy (LSC) and a small single copy (SSC) region of 83,972 bp (55.1%) and 18,371 bp (32.8%), respectively. The chloroplast genome includes 127 genes, which contain 83 protein-coding genes, 36 transfer RNA genes, and 8 ribosomal RNA genes. The nucleotide composition is asymmetric (31.1% A, 18.5% C, 19.0% G, 31.4% T). The overall GC content of C. minima chloroplast genome is 37.5%. Phylogenetic analysis illustrates that C. minima is closely related to other Asteraceae species, including Helianthus annuus subsp. texanus, Tithonia diversifolia and Xanthium sibiricum with a strong bootstrap value of 100.

At present, many decision-making fields require collecting and organizing the opinions and evaluation values of experts. However, they evaluation values given by experts used in the decision-making should be further analysed from a scientific angle. In this paper the evidence reasoning approach under two-dimensional frameworks (ERTDF) is used to mine the relative validity of the expert scoring value. This approach takes the self-evaluated familiarity with the decision-making objects to be evaluated and the authority of the position and title as the main characteristics of the expert evaluation body. The unique characteristic information of the above experts is transformed into evidence to assist in correcting the original score results and to express their respective reliability distribution. This method also could support some intelligent decisions under the evaluation background of experts in various fields. The application results show that this method could better consider the uncertainty of the evaluation results caused by the various characteristics of the individual experts, improve the effect of experts' scores, and make the comprehensive results of multiple experts' evaluation values more reasonable and accurate. [ABSTRACT FROM AUTHOR]

Previous studies have examined the neural correlates for crossmodal paired-associate (PA) memory and the temporal dynamics of its formation. However, the neural dynamics for feedback processing of crossmodal PA learning remain unclear. To examine this process, we recorded event-related scalp electrical potentials for PA learning of unimodal visual-visual pairs and crossmodal visual-tactile pairs when participants performed unimodal and crossmodal tasks. We examined event-related potentials (ERPs) after the onset of feedback in the tasks for three effects: feedback type (positive feedback vs. negative feedback), learning (as the learning progressed) and the task modality (crossmodal vs. unimodal). The results were as follows: (1) feedback type: the amplitude of P300 decreased with incorrect trials and the P400/N400 complex was only present in incorrect trials; (2) learning: progressive positive voltage shifts in frontal recording sites and negative voltage shifts in central and posterior recording sites were identified as learning proceeded; and (3) task modality: compared with the unimodal PA learning task, positive voltage shifts in frontal sites and negative voltage shifts in posterior sites were found in the crossmodal PA learning task. To sum up, these results shed light on cortical excitability related to feedback processing of crossmodal PA learning.

Visual stimuli are known to activate the auditory cortex of deaf people, presenting evidence of cross-modal plasticity. However, the mechanisms underlying such plasticity are poorly understood. In this functional MRI study, we presented two types of visual stimuli, language stimuli (words, sign language, and lip-reading) and a general stimulus (checkerboard) to investigate neural reorganization in the superior temporal cortex (STC) of deaf subjects and hearing controls. We found that only in the deaf subjects, all visual stimuli activated the STC. The cross-modal activation induced by the checkerboard was mainly due to a sensory component via a feed-forward pathway from the thalamus and primary visual cortex, positively correlated with duration of deafness, indicating a consequence of pure sensory deprivation. In contrast, the STC activity evoked by language stimuli was functionally connected to both the visual cortex and the frontotemporal areas, which were highly correlated with the learning of sign language, suggesting a strong language component via a possible feedback modulation. While the sensory component exhibited specificity to features of a visual stimulus (e.g., selective to the form of words, bodies, or faces) and the language (semantic) component appeared to recruit a common frontotemporal neural network, the two components converged to the STC and caused plasticity with different multivoxel activity patterns. In summary, the present study showed plausible neural pathways for auditory reorganization and correlations of activations of the reorganized cortical areas with developmental factors and provided unique evidence towards the understanding of neural circuits involved in cross-modal plasticity.