An immune-related exosome signature predicts the prognosis and immunotherapy response in ovarian cancer.

BACKGROUND: Cancer-derived exosomes contribute significantly in intracellular communication, particularly during tumorigenesis. Here, we aimed to identify two immune-related ovarian cancer-derived exosomes (IOCEs) subgroups in ovarian cancer (OC) and establish a prognostic model for OC patients based on immune-related IOCEs. METHODS: The Cancer Genome Atlas (TCGA) database was used to obtain RNA-seq data, as well as clinical and prognostic information. Consensus clustering analysis was performed to identify two IOCEs-associated subgroups. Kaplan-Meier analysis was used to compare the overall survival (OS) between IOCEs-high and IOCEs-low subtype. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted to investigate the mechanisms and biological effects of differentially expressed genes (DEGs) between the two subtypes. Besides, an IOCE-related prognostic model of OC was constructed by Lasso regression analysis, and the signature was validated using GSE140082 as the validation set. RESULTS: In total, we obtained 21 differentially expressed IOCEs in OC, and identified two IOCE-associated subgroups by consensus clustering. IOCE-low subgroup showed a favorable prognosis while IOCE-high subgroup had a higher level of immune cell infiltration and immune response. GSEA showed that pathways in cancer and immune response were mainly enriched in IOCE-high subgroup. Thus, IOCE-high subgroup may benefit more in immunotherapy treatment. In addition, we constructed a risk model based on nine IOCE-associated genes (CLDN4, AKT2, CSPG5, ALDOC, LTA4H, PSMA2, PSMA5, TCIRG1, ANO6). CONCLUSION: We developed a novel stratification system for OV based on IOCE signature, which could be used to estimate the prognosis as well as immunotherapy for OC patient.

Printed Thick Film Resistance Temperature Detector for Real-Time Tube Furnace Temperature Monitoring.

Accurately acquiring crucial data on tube furnaces and real-time temperature monitoring of different temperature zones is vital for material synthesis technology in production. However, it is difficult to achieve real-time monitoring of the temperature field of tube furnaces with existing technology. Here, we proposed a method to fabricate silver (Ag) resistance temperature detectors (RTDs) based on a blade-coating process directly on the surface of a quartz ring, which enables precise positioning and real-time temperature monitoring of tube furnaces within 100-600 °C range. The Ag RTDs exhibited outstanding electrical properties, featuring a temperature coefficient of resistance (TCR) of 2854 ppm/°C, an accuracy of 1.8% FS (full scale), and a resistance drift rate of 0.05%/h over 6 h at 600 °C. These features ensured accurate and stable temperature measurement at high temperatures. For demonstration purposes, an array comprising four Ag RTDs was installed in a tube furnace. The measured average temperature gradient in the central region of the tube furnace was 5.7 °C/mm. Furthermore, successful real-time monitoring of temperature during the alloy sintering process revealed approximately a 20-fold difference in resistivity for silver-palladium alloys sintered at various positions within the tubular furnace. The proposed strategy offers a promising approach for real-time temperature monitoring of tube furnaces.

Predicting Durable Clinical Benefits of Postoperative Adjuvant Chemotherapy in Non-small Cell Lung Cancer: A Nomogram Based on CT Imaging and Immune Type.

PURPOSE: To develop a model based on conventional CT signs and the tumor microenvironment immune types (TIMT) to predict the durable clinical benefits (DCB) of postoperative adjuvant chemotherapy in non-small cell lung cancer (NSCLC). METHODS AND MATERIALS: A total of 205 patients with NSCLC underwent preoperative CT and were divided into two groups: DCB (progression-free survival (PFS) ≥ 18 months) and non-DCB (NDCB, PFS <18 months). The density percentiles of PD-L1 and CD8 + tumor-infiltrating lymphocytes (TIL) were quantified to estimate the TIMT. Clinical characteristics and conventional CT signs were collected. Multivariate logistic regression was employed to select the most discriminating parameters, construct a predictive model, and visualize the model as a nomogram. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were used to evaluate prediction performance and clinical utility. RESULTS: Precisely 118 patients with DCB and 87 with NDCB in NSCLC received postoperative adjuvant chemotherapy. TIMT was statistically different between the DCB and NDCB groups (P < 0.05). Clinical characteristics (neuron-specific enolase, squamous cell carcinoma antigen, Ki-76, and cM stage) and conventional CT signs (spiculation, bubble-like lucency, pleural retraction, maximum diameter, and CT value of the venous phase) varied between the four TIMT groups (P < 0.05). Furthermore, clinical characteristics (lymphocyte count [LYMPH] and cM stage) and conventional CT signs (bubble-like lucency and Pleural effusion) differed between the DCB and NDCB groups (P < 0.05). Multivariate analysis revealed that TIMT, cM stage, LYMPH, and pleural effusion were independently associated with DCB and were used to construct a nomogram. The area under the curve (AUC) of the combined model was 0.70 (95%CI: 0.64-0.76), with sensitivity and specificity of 0.73 and 0.60, respectively. CONCLUSION: Conventional CT signs and the TIMT offer a promising approach to predicting clinical outcomes for patients treated with postoperative adjuvant chemotherapy in NSCLC.

Predicting lymphovascular invasion in N0 stage non-small cell lung cancer: A nomogram based on Dual-energy CT imaging and clinical findings.

PURPOSE: To construct a nomogram for predicting lymphovascular invasion (LVI) in N0 stage non-small cell lung cancer (NSCLC) using dual-energy computed tomography (DECT) findings combined with clinical findings. METHODS: We retrospectively recruited 135 patients with N0 stage NSCLC from two hospitals underwent DECT before surgery and were divided into development cohort (n = 107) and validation cohort (n = 28). The clinical findings (baseline characteristics, biochemical markers, serum tumor markers and Immunohistochemical markers), DECT-derived parameters (iodine concentration [IC], effective atomic number [Eff-Z] and normalized iodine concentration [NIC], iodine enhancement [IE] and NIC ratio [NICr]) and Fractal dimension (FD) were collected and measured. A nomogram was constructed using significant findings to predict LVI in N0 stage NSCLC and was externally validated. RESULTS: Multivariable analysis revealed that lymphocyte count (LYMPH, odds ratio [OR]: 3.71, P=0.014), IC in arterial phase (ICa, OR: 1.25, P=0.021), NIC in venous phase (NICv, OR: 587.12, P=0.009) and FD (OR: 0.01, P=0.033) were independent significant factors for predicting LVI in N0 stage NSCLC, and were used to construct a nomogram. The nomogram exhibited robust predictive capabilities in both the development and validation cohort, with AUCs of 0.819 (95 % CI: 72.6-90.4) and 0.844 (95 % CI: 68.2-95.8), respectively. The calibration plots showed excellent agreement between the predicted probabilities and the actual rates of positive LVI, on external validation. CONCLUSIONS: Combination of clinical and DECT imaging findings could aid in predicting LVI in N0 stage NSCLC using significant findings of LYMPH, ICa, NICv and FD.