Explore the vast range of titles available.

Background Radiotherapy resistance is the main cause of low tumor regression for locally advanced rectum adenocarcinoma (READ). The biomarkers correlated to radiotherapy sensitivity and potential molecular mechanisms have not been completely elucidated. Methods A mRNA expression profile and a gene expression dataset of READ (GSE35452) were acquired from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Differentially expressed genes (DEGs) between radiotherapy responder and non-responder of READ were screened out. Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis for DEGs were performed. Random survival forest analysis was used to identified hub genes by randomForestSRC package. Based on CIBERSORT algorithm, Genomics of Drug Sensitivity in Cancer (GDSC) database, Gene set variation analysis (GSVA), enrichment analysis (GSEA), nomogram, motif enrichment and non-coding RNA network analyses, the associations between hub genes and immune cell infiltration, drug sensitivity, specific signaling pathways, prognosis prediction and TF – miRNA regulatory and ceRNA network were investigated. The expressions of hub genes in clinical samples were displayed with the online Human Protein Atlas (HPA). Results In total, 544 up-regulated and 575 down-regulated DEGs in READ were enrolled. Among that, three hubs including PLAGL2 , ZNF337 and ALG10 were identified. These three hub genes were significantly associated with tumor immune infiltration, different immune-related genes and sensitivity of chemotherapeutic drugs. Also, they were correlated with the expression of various disease-related genes. In addition, GSVA and GSEA analysis revealed that different expression levels of PLAGL2 , ZNF337 and ALG10 affected various signaling pathways related to disease progression. A nomogram and calibration curves based on three hub genes showed excellent prognosis predictive performance. And then, a regulatory network of transcription factor ( ZBTB6 ) - mRNA ( PLAGL2 ) and a ceRNA network of miRNA (has-miR-133b) - lncRNA were established. Finally, the results from HPA online database demonstrated the protein expression levels of PLAGL2, ZNF337 and ALG10 varied widely in READ patients. Conclusion These findings indicated that up-regulation of PLAGL2 , ZNF337 and ALG10 in READ associated with radiotherapy response and involved in multiple process of cellular biology in tumor. They might be potential predictive biomarkers for radiotherapy sensitivity and prognosis for READ.

The molecular mechanisms driving the development of cervical adenocarcinoma (CADC) and optimal patient management strategies remain elusive. In this study, we have identified circMAN1A2_009 as an oncogenic circular RNA (circRNA) in CADC. Clinically, circMAN1A2_009 showed significant upregulation in CADC tissues, with an impressive area under the curve value of 0.8075 for detecting CADC. Functional studies, involving both gain‐of‐function and loss‐of‐function experiments, revealed that circMAN1A2_009 suppressed reactive oxygen species accumulation and apoptosis, and boosted cell viability in CADC cells. Conversely, silencing circMAN1A2_009 reversed these effects. Further mechanistic investigations indicated that circMAN1A2_009 interacted with YBX1, facilitating the phosphorylation levels of YBX1 at serine 102 (p‐YBX1S102) and facilitating YBX1 nuclear localization through sequence 245–251. This interaction subsequently increased the activity of the glyoxalase 1 (GLO1) promoter, leading to the activation of GLO1 expression. Consistently, inhibition of either YBX1 or GLO1 mirrored the biological effects of circMAN1A2_009 in CADC cells. Additionally, knockdown of YBX1 or GLO1 partially reversed the oncogenic behaviors induced by circMAN1A2_009. In conclusion, our findings propose circMAN1A2_009 as a potential oncogene and a promising indicator for diagnosing and guiding therapy in CADC patients. CircMAN1A2_009, overexpressed in CADC, facilitates the nuclear translocation of YBX1, promoting its interaction with downstream GLO1 mRNA. This leads to the suppression of reactive oxygen species accumulation and the promotion of cell growth.

High-resolution numerical simulation data of a rainstorm triggering debris flow in Sichuan Province of China simulated by the Weather Research and Forecasting （WRF） Model were used to study the dominant cloud microphysical processes of the torrential rainfall.The results showed that：（1） In the strong precipitation period,particle sizes of all hydrometeors increased,and mean-mass diameters of graupel increased the most significantly,as compared with those in the weak precipitation period; （2） The terminal velocity of raindrops was the strongest among all hydrometeors,followed by graupel＇s,which was much smaller than that of raindrops.Differences between various hydrometeors＇ terminal velocities in the strong precipitation period were larger than those in the weak precipitation period,which favored relative motion,collection interaction and transformation between the particles.Absolute terminal velocity values of raindrops and graupel were significantly greater than those of air upward velocity,and the stronger the precipitation was,the greater the differences between them were; （3） The orders of magnitudes of the various hydrometeors＇ sources and sinks in the strong precipitation period were larger than those in the weak precipitation period,causing a difference in the intensity of precipitation.Water vapor,cloud water,raindrops,graupel and their exchange processes played a major role in the production of the torrential rainfall,and there were two main processes via which raindrops were generated：abundant water vapor condensed into cloud water and,on the one hand,accretion of cloud water by rain water formed rain water,while on the other hand,accretion of cloud water by graupel formed graupel,and then the melting of graupel formed rain water.

In July 2013, an extreme precipitation event occurred in Sichuan, China, causing severe flooding, debris flows, and substantial losses. Moisture sources and transport of this event and quantification of the contribution from moisture sources were studied using the Lagrangian method. The results show that the vast majority of particles influencing this event originated from the relatively lower troposphere south of the precipitation region and can be traced back to the Arabian Sea. Moreover, the total moisture from the source regions accounts for more than 80% of the precipitation; moisture originating from the India Peninsula–Bay of Bengal–Indo‐China Peninsula region had the highest contribution.

Background An increase in the prevalence of lung cancer that is not smoking-related has been noticed in recent years. Unfortunately, these patients are not included in low dose computer tomography (LDCT) screening programs and are not actually considered in early diagnosis. Therefore, improved early diagnosis methods are urgently needed for non-smokers. It is necessary to establish a prediction model for non-smoking individuals at intermediate to high risk of developing lung cancer (LC) and develop a tool to address the significant gap in evaluating pulmonary nodules in non-smokers. Methods We retrospectively investigated 1121 patients with pulmonary nodules, who underwent LDCT examinations between September 2019 and March 2023. Five artificial intelligence (AI) algorithms were used to build two kinds of models and identify which one was better at diagnosing non-smoking pulmonary nodules patients. In the first model, we assigned 554 non-smoking individuals to a training cohort and 150 non-smoking patients to an independent validation cohort. The second model included 971 patients for the training set and 150 non-smoking patients for an independent validation set. All LDCT images of participants were obtained for AI analysis. AI of LDCT scans, liquid biopsy, and clinical characteristics were collected for model building. Results Among LC patients, 58,4% were non-smokers. Non-smoking patients had a high incidence of LC (71.4%), and women showed a significant excess risk compared with non-smoking men in terms of LC risk. Furthermore, our results indicated that the model built using random forest (RF) method, which integrates clinical characteristics (age, extra-thoracic cancer history, gender), radiological characteristics of pulmonary nodules (nodule diameter, nodule count, upper lobe location, malignant sign at the nodule edge, subsolid status), the artificial intelligence analysis of LDCT data, and liquid biopsy achieved the best diagnostic performance in the independent external non-smokers validation cohort (sensitivity 92%, specificity 97%, area under the curve [AUC] = 0.99). Conclusions These results could significantly improve early non-smoker LC diagnosis and treatment for non-smoker patients with malignant nodules. The established multi-omics model is a noninvasive prediction tool for non-smoking malignant pulmonary nodule diagnosis. Validation revealed that these models exhibited excellent discrimination and calibration capacities, especially the first model built using the RF method, suggesting their clinical utility in the early screening and diagnosis of non-smoking LC.

Intramuscular fat (IMF) deposition critically influences pork quality, necessitating reliable in vitro models to investigate its regulatory mechanisms. However, developing cost-effective porcine intramuscular pre-adipocyte (IMPA) seed cells capable of sustained proliferation and complete adipogenic differentiation remains technically challenging. Here, we established an immortalized porcine IMPA cell line through lentiviral-mediated transduction of simian virus 40 large T antigen (SV40T). This cell line retained differentiation capacity for over 25 passages, addressing limitations in primary cell lifespan. Functional evaluations demonstrated robust proliferative activity via CCK-8 assays, sustained telomerase activity, and minimal apoptosis, confirming stable growth characteristics during long-term culture. Furthermore, adipogenic induction assays revealed preserved differentiation potential, evidenced by lipid droplet accumulation and adipogenic marker expression. Collectively, this immortalized porcine IMPA cell line provides a valuable and reproducible model for elucidating molecular mechanisms underlying IMF deposition, offering significant utility for agricultural and biomedical research.

Intermittent hypoxia (IH) is a hallmark of obstructive sleep apnea (OSA), which is related to tumorigenesis and progression. We explored the possible mechanisms by which OSA may promote the development of non‐small cell lung cancer (NSCLC). In this study, NSCLC cells with and without miR‐106a‐5p inhibition were exposed to IH or room air (RA), and subsequently, exosomes were extracted and identified. Macrophages were incubated with these exosomes to detect the expression of the STAT3 signaling pathway and M2‐type macrophage markers, as well as the effect of the macrophages on the malignancy of NSCLC cells. A nude mouse tumorigenesis model was constructed to detect the effects of exosomal miR‐106a‐5p on M2 macrophage polarization and NSCLC cell malignancy. Our results showed that IH exosomes promoted the polarization of M2 macrophages, thereby promoting the proliferation, invasion, and metastasis of NSCLC cells. Further, Based on microarray analysis of RA and IH exosomes, we discovered that miR‐106a‐5p, transferred to the macrophages through exosomes, participated in this mechanism by promoting M2 macrophage polarization via down‐regulating PTEN and activating the STAT3 signaling pathway in vitro and in vivo. For patients with NSCLC and OSA, exosomal miR‐106a‐5p levels showed a positive relation to AHI. Exosomal miR‐106a‐5p represents a potential therapeutic target among patients with concomitant cancer and NSCLC.

Accurate time series forecasting is of great importance in real-world scenarios such as health care, transportation, and finance. Because of the tendency, temporal variations, and periodicity of the time series data, there are complex and dynamic dependencies among its underlying features. In time series forecasting tasks, the features learned by a specific task at the current time step (such as predicting mortality) are related to the features of historical timesteps and the features of adjacent timesteps of related tasks (such as predicting fever). Therefore, capturing dynamic dependencies in data is a challenging problem for learning accurate future prediction behavior. To address this challenge, we propose a cross-timestep feature-sharing multi-task time series forecasting model that can capture global and local dynamic dependencies in time series data. Initially, the global dynamic dependencies of features within each task are captured through a self-attention mechanism. Furthermore, an adaptive sparse graph structure is employed to capture the local dynamic dependencies inherent in the data, which can explicitly depict the correlation between features across timesteps and tasks. Lastly, the cross-timestep feature sharing between tasks is achieved through a graph attention mechanism, which strengthens the learning of shared features that are strongly correlated with a single task. It is beneficial for improving the generalization performance of the model. Our experimental results demonstrate that our method is significantly competitive compared to baseline methods.

Differences in rainfall budgets between convective and stratiform regions of a torrential rainfall event were investigated using high-resolution simulation data produced by the Weather Research and Forecasting (WRF) model. The convective and stratiform regions were reasonably separated by the radar-based convective–stratiform partitioning method, and the three-dimensional WRF-based precipitation equation combining water vapor and hydrometeor budgets was further used to analyze the rainfall budgets. The results showed that the magnitude of precipitation budget processes in the convective region was one order larger than that in the stratiform region. In convective/stratiform updraft regions, precipitation was mainly from the contribution of moisture-related processes, with a small negative contribution from cloud-related processes. In convective/ stratiform downdraft regions, cloud-related processes played positive roles in precipitation, while moisture-related processes made a negative contribution. Moisture flux convergence played a dominant role in the moisture-related processes in convective or stratiform updraft regions, which was closely related to large-scale dynamics. Differences in cloud-related processes between convective and stratiform regions were more complex compared with those in moisture-related processes. Both liquid- and ice-phase microphysical processes were strong in convective/stratiform updraft regions, and ice-phase processes were dominant in convective/stratiform downdraft regions. There was strong net latent heating within almost the whole troposphere in updraft regions, especially in the convective updraft region, while the net latent heating (cooling) mainly existed above (below) the zero-layer in convective/stratiform downdraft regions.

Purpose Radiotherapy induces multiple forms of tumor cell death, including immunogenic cell death (ICD) like GSDME-mediated pyroptosis and MLKL-mediated necroptosis; and ICD has been increasingly accepted as a crucial element leading to enhanced anti-tumor adaptive immunity. We aim to clarify whether a vaccine-like effect is intrinsic for radiation-induced tumor cell death, and to explore potential applications. Methods A randomized controlled trial was performed to validate the synergism between clinical radiotherapy and PD1 blockade in patients with resectable rectal cancer. Colorectal cancer cell lines and patient-derived organoids were used to study the impact of ionizing radiation on tumor cells. Syngeneic mouse tumor models were used to study the microenvironment modulating effect of localized irradiation, as well as the potential vaccine-like effect of the in vitro irradiated autologous tumor cells. Results For patients with locally advanced rectal cancer, adding immune checkpoint blockade to radiotherapy significantly increases tumor response rate. On cellular level, ionizing radiation induces GSDMD-mediated pyroptosis of colorectal cancer cells and increased the infiltration of cytotoxic T cells into tumor microenvironment. On syngeneic, immune-competent mouse tumor models, autologous tumor cells irradiated in vitro at a sterilization dosage work as a therapeutical vaccine to inhibit the growth of inoculated tumors. Conclusions Radiation-elicited tumor cell death exhibited an in situ vaccine effect to potentiate immune checkpoint blockade, and autologous tumor cells irradiated in vitro at a sterilization level may serve as a candidate source of immunogen for personalized therapeutical cancer vaccine.