Circumventing drug resistance in gastric cancer: A spatial multi-omics exploration of chemo and immuno-therapeutic response dynamics.

BACKGROUND: Gastric Cancer (GC) characteristically exhibits heterogeneous responses to treatment, particularly in relation to immuno plus chemo therapy, necessitating a precision medicine approach. This study is centered around delineating the cellular and molecular underpinnings of drug resistance in this context. METHODS: We undertook a comprehensive multi-omics exploration of postoperative tissues from GC patients undergoing the chemo and immuno-treatment regimen. Concurrently, an image deep learning model was developed to predict treatment responsiveness. RESULTS: Our initial findings associate apical membrane cells with resistance to fluorouracil and oxaliplatin, critical constituents of the therapy. Further investigation into this cell population shed light on substantial interactions with resident macrophages, underscoring the role of intercellular communication in shaping treatment resistance. Subsequent ligand-receptor analysis unveiled specific molecular dialogues, most notably TGFB1-HSPB1 and LTF-S100A14, offering insights into potential signaling pathways implicated in resistance. Our SVM model, incorporating these multi-omics and spatial data, demonstrated significant predictive power, with AUC values of 0.93 and 0.84 in the exploration and validation cohorts respectively. Hence, our results underscore the utility of multi-omics and spatial data in modeling treatment response. CONCLUSION: Our integrative approach, amalgamating mIHC assays, feature extraction, and machine learning, successfully unraveled the complex cellular interplay underlying drug resistance. This robust predictive model may serve as a valuable tool for personalizing therapeutic strategies and enhancing treatment outcomes in gastric cancer.

Research on the trajectory and influential factors of poly-victimization: A longitudinal study of Chinese adolescents.

BACKGROUND: Poly-victimization is more detrimental to adolescents' physical and mental health than is a single type of victimization. However, there has been limited research on the trajectory of poly-victimization among Chinese adolescents. OBJECTIVE: Identify the different developmental trajectories of poly-victimization among Chinese adolescents over time and examine the influencing factors of poly-victimization trajectories. METHODS: Data from four surveys conducted between 2020 and 2022, encompassing a cohort of 319 adolescents who had experienced poly-victimization, were utilized to identify their developmental trajectories via group-based trajectory modeling. Potential influencing factors were screened and compared using ANOVA or chi-square tests, while factors affecting the developmental trajectories of poly-victimization were analyzed through multinomial logistic regression. RESULTS: We identified three poly-victimization trajectories among adolescents: increasing poly-victimization (n = 39, 12.2 %), relieved poly-victimization (n = 228, 71.5 %), and fluctuating poly-victimization (n = 52, 16.3 %). Our findings indicate that boys, and those with poor class grade ranking, a lower level of parental education, lower household economy, smoking, drinking, suicide attempts, and suicide ideation, constitute the primary focus for the prevention and treatment of poly-victimization. CONCLUSION: We identified three poly-victimization trajectories, highlighting a significant heterogeneity in poly-victimization development. Understanding the characteristics of these developmental trajectories is crucial for realizing the dynamics of different poly-victimization subgroups and informing effective interventions.

Short-term effects of extreme meteorological factors on daily outpatient visits for anxiety in Suzhou, Anhui Province, China: a time series study.

Anxiety disorders are a major public health concern in China. Previous studies have provided evidence for associations between ambient temperature and anxiety outpatient visits, but no studies have examined short-term effects of other meteorological factors such as sunshine duration, wind speed, and precipitation on increased anxiety outpatient visits. We aimed to assess the association between climatic factors and outpatient visits for anxiety in Suzhou, a city with a temperate climate in Anhui Province, China. Daily anxiety outpatient visits, meteorological factors, and air pollutants from 2017 to 2019 were collected. A quasi-Poisson generalized linear regression model combined with distributed lag non-linear model (DLNM) was used to quantify the effects of extreme meteorological factors (sunshine duration, wind speed, and precipitation) on anxiety outpatient visits. All effects were presented as relative risk (RR), with the 90th and 10th percentiles of meteorological factors compared to the median. Subgroup analyses by age and gender were performed to identify susceptible subgroups. A total of 11,323 anxiety outpatient visits were reported. Extremely low sunshine duration and low and high wind speed increased the risk of anxiety outpatient visits. The strongest cumulative effects occurred at lag 0-14 days, and the corresponding RRs of extremely low sunshine duration and low and high wind speed were 1.417 (95% CI: 1.056-1.901), 1.529 (95% CI: 1.028-2.275), and 1.396 (95% CI: 1.007-1.935), respectively. Subgroup analyses showed that males and people aged ≥45 years appeared to be more susceptible to the cumulative effects of extremely low sunshine duration. In addition, the adverse effects of extreme wind speed were more pronounced in the cold season. This study provides evidence that extreme climatic factors have a lagged effect on anxiety outpatient visits. In the context of climate change, these findings may help develop weather-based early warning systems to minimize the effects of extreme meteorological factors on anxiety.

Multi-omics analysis reveals the chemoresistance mechanism of proliferating tissue-resident macrophages in PDAC via metabolic adaptation.

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer that typically demonstrates resistance to chemotherapy. Tumor-associated macrophages (TAMs) are essential in tumor microenvironment (TME) regulation, including promoting chemoresistance. However, the specific TAM subset and mechanisms behind this promotion remain unclear. We employ multi-omics strategies, including single-cell RNA sequencing (scRNA-seq), transcriptomics, multicolor immunohistochemistry (mIHC), flow cytometry, and metabolomics, to analyze chemotherapy-treated samples from both humans and mice. We identify four major TAM subsets within PDAC, among which proliferating resident macrophages (proliferating rMφs) are strongly associated with poor clinical outcomes. These macrophages are able to survive chemotherapy by producing more deoxycytidine (dC) and fewer dC kinases (dCKs) to decrease the absorption of gemcitabine. Moreover, proliferating rMφs promote fibrosis and immunosuppression in PDAC. Eliminating them in the transgenic mouse model alleviates fibrosis and immunosuppression, thereby re-sensitizing PDAC to chemotherapy. Consequently, targeting proliferating rMφs may become a potential treatment strategy for PDAC to enhance chemotherapy.

Large Fragment InDels Reshape Genome Structure of Porcine Alveolar Macrophage 3D4/21 Cells.

The porcine monomyeloid cell line, or 3D4/21 cells, is an effective tool to study the immune characteristics and virus infection mechanism of pigs. Due to the introduction of the neomycin resistance gene and the SV40 large T antigen gene, its genome has undergone essential changes, which are still unknown. Studying the variation in genome structure, especially the large fragments of insertions and deletions (InDels), is one of the proper ways to reveal these issues. In this study, an All-seq method was established by combining Mate-pair and Shotgun sequencing methods, and the detection and verification of large fragments of InDels were performed on 3D4/21 cells. The results showed that there were 844 InDels with a length of more than 1 kb, of which 12 regions were deletions of more than 100 kb in the 3D4/21 cell genome. In addition, compared with porcine primary alveolar macrophages, 82 genes including the CD163 had lost transcription in 3D4/21 cells, and 72 genes gained transcription as well. Further referring to the Hi-C structure, it was found that the fusion of the topologically associated domains (TADs) caused by the deletion may lead to abnormal gene function. The results of this study provide a basis for elaborating the genome structure and functional variation in 3D4/21 cells, provide a method for rapid and convenient detection of large-scale InDels, and provide useful clues for the study of the porcine immune function genome and the molecular mechanism of virus infection.