The causal relationship between psoriasis and chronic obstructive pulmonary disease: A bidirectional mendelian randomization study.

PURPOSE: Although many studies have investigated the association between psoriasis and chronic obstructive pulmonary disease (COPD), the causal relationship between psoriasis and COPD is still unknown. METHODS: We employed bidirectional Mendelian randomization to investigate the causal relationship between psoriasis and COPD. Genetic instruments for exposure were selected from two distinct genome-wide association study databases. Single nucleotide polymorphisms associated with exposures at the genome-wide significance level (p < 5 × 10^-8 ) and exhibiting low linkage disequilibrium (r^2  < 0.001) were chosen as instrumental variables. Causality was assessed using multiple MR methods, including Inverse-Variance Weighted (IVW), MR-Egger, Weighted Median, Simple Mode, and Weighted Mode. A significance level of p < 0.05 was considered statistically significant. Heterogeneity was examined using Cochran's Q test, and MR-Egger regression was employed to detect pleiotropy. The robustness and reliability of the results were further evaluated through leave-one-out analysis. RESULTS: We found a positive causal association between psoriasis and COPD [IVW: odds ratio (OR): 1.0006; p = 0.0056]. Heterogeneity and pleiotropy have not been discovered, so the results of the study are reliable. In the reverse analysis, no causal association between CPOD and psoriasis was found. CONCLUSION: Our findings revealed that psoriasis was associated with an elevated risk of COPD. However, no causal association between COPD and psoriasis was identified in our study.

Deep Learning of Multimodal Ultrasound: Stratifying the Response to Neoadjuvant Chemotherapy in Breast Cancer Before Treatment.

BACKGROUND: Not only should resistance to neoadjuvant chemotherapy (NAC) be considered in patients with breast cancer but also the possibility of achieving a pathologic complete response (PCR) after NAC. Our study aims to develop 2 multimodal ultrasound deep learning (DL) models to noninvasively predict resistance and PCR to NAC before treatment. METHODS: From January 2017 to July 2022, a total of 170 patients with breast cancer were prospectively enrolled. All patients underwent multimodal ultrasound examination (grayscale 2D ultrasound and ultrasound elastography) before NAC. We combined clinicopathological information to develop 2 DL models, DL_Clinical_resistance and DL_Clinical_PCR, for predicting resistance and PCR to NAC, respectively. In addition, these 2 models were combined to stratify the prediction of response to NAC. RESULTS: In the test cohort, DL_Clinical_resistance had an AUC of 0.911 (95%CI, 0.814-0.979) with a sensitivity of 0.905 (95%CI, 0.765-1.000) and an NPV of 0.882 (95%CI, 0.708-1.000). Meanwhile, DL_Clinical_PCR achieved an AUC of 0.880 (95%CI, 0.751-0.973) and sensitivity and NPV of 0.875 (95%CI, 0.688-1.000) and 0.895 (95%CI, 0.739-1.000), respectively. By combining DL_Clinical_resistance and DL_Clinical_PCR, 37.1% of patients with resistance and 25.7% of patients with PCR were successfully identified by the combined model, suggesting that these patients could benefit by an early change of treatment strategy or by implementing an organ preservation strategy after NAC. CONCLUSIONS: The proposed DL_Clinical_resistance and DL_Clinical_PCR models and combined strategy have the potential to predict resistance and PCR to NAC before treatment and allow stratified prediction of NAC response.

Navigating the immunometabolic heterogeneity of B cells in murine hepatocellular carcinoma at single cell resolution.

Induction of antitumor immunity is critical for the therapeutic efficacy of hepatocellular carcinoma (HCC) immunotherapy. The cellular metabolic state underpins the effector function of immune cells, yet our understanding of the phenotypic and metabolic heterogeneity of B cells within HCC microenvironment is poorly developed. Herein, we investigated the composition, distribution, phenotype, function and metabolic profiles of B-cell subsets in HCC and adjacent liver tissues from an orthotopic HCC mouse model using single-cell RNA sequencing (scRNA-seq). Our results identified six B-cell clusters, which can be classified into plasma cells and activated and exhausted B cells according to marker expression, functional and temporal distribution. Exhausted B cells exhibited low metabolic activities and impaired effector functions. Activated B and plasma cells showed higher metabolic activity than exhausted B cells, but there were clear differences in their metabolic profiles. In addition, we found that the effector function of exhausted B cells was further diminished in HCC tissues compared with adjacent liver tissues, but their metabolic activity was significantly enhanced. Collectively, we comprehensively characterized the metabolic profile and alterations in B-cell subsets in HCC, which contributes to the understanding of B-cell immunology in HCC and lays the foundation for exploring novel targets in HCC immunotherapy.

Nanosecond pulsed electric field ablation-induced modulation of sphingolipid metabolism is associated with Ly6c2+ mononuclear phagocyte differentiation in liver cancer.

Preclinical studies have proven that nanosecond pulsed electric field (nsPEF) ablation can be a safe and effective treatment for humans with unresectable liver cancer that are ineligible for thermal ablation. The concomitant activation of antitumor immunity by nsPEF can also potentially prevent tumor recurrence. However, whether nsPEF exhibits similar efficacy in a clinical setting remains to be investigated. A prospective clinical trial (clinicaltrials.gov identifier: NCT04039747) was conducted to evaluate the safety and efficacy of ultrasound (US)-guided nsPEF ablation in 15 patients with unresectable liver cancer that were ineligible for thermal ablation. We found that nsPEF ablation was safe and produced a 12-month recurrence-free survival (RFS) and local RFS of 60% (9/15) and 86.7% (13/15), respectively, in the enrolled patients. Integrative proteomic and metabolomic analysis showed that sphingolipid metabolism was the most significantly enriched pathway in patient sera after nsPEF without recurrence within 8 months. A similar upregulation of sphingolipid metabolism was observed in the intratumoral mononuclear phagocytes (MNPs), rather than other immune and nonimmune cells, of an nsPEF-treated mouse model. We then demonstrated that lymphocyte antigen 6 complex, locus C2-positive (Ly6c2+ ) monocytes first differentiated into Ly6c2+ monocyte-derived macrophages with an increase in sphingolipid metabolic activity, and subsequently into Ly6c2+ dendritic cells (DCs). Ly6c2+ DCs communicated with CD8+ T cells and increased the proportions of IFN-γ+ CD8+ memory T cells after nsPEF, and this finding was subsequently confirmed by depletion of liver Ly6c2+ MNPs. In conclusion, nsPEF was a safe and effective treatment for liver cancer. The alteration of sphingolipid metabolism induced by nsPEF was associated with the differentiation of Ly6c2+ MNPs, and subsequently induced the formation of memory CD8+ T cells with potent antitumor effect.

Deep learning radiomics of ultrasonography for comprehensively predicting tumor and axillary lymph node status after neoadjuvant chemotherapy in breast cancer patients: A multicenter study.

BACKGROUND: Neoadjuvant chemotherapy (NAC) can downstage tumors and axillary lymph nodes in breast cancer (BC) patients. However, tumors and axillary response to NAC are not parallel and vary among patients. This study aims to explore the feasibility of deep learning radiomics nomogram (DLRN) for independently predicting the status of tumors and lymph node metastasis (LNM) after NAC. METHODS: In total, 484 BC patients who completed NAC from two hospitals (H1: 297 patients in the training cohort and 99 patients in the validation cohort; H2: 88 patients in the test cohort) were retrospectively enrolled. The authors developed two deep learning radiomics (DLR) models for personalized prediction of the tumor pathologic complete response (PCR) to NAC (DLR-PCR) and the LNM status (DLR-LNM) after NAC based on pre-NAC and after-NAC ultrasonography images. Furthermore, they proposed two DLRNs (DLRN-PCR and DLRN-LNM) for two different tasks based on the clinical characteristics and DLR scores, which were generated from both DLR-PCR and DLR-LNM. RESULTS: In the validation and test cohorts, DLRN-PCR exhibited areas under the receiver operating characteristic curves (AUCs) of 0.903 and 0.896 with sensitivities of 91.2% and 75.0%, respectively. DLRN-LNM achieved AUCs of 0.853 and 0.863, specificities of 82.0% and 81.8%, and negative predictive values of 81.3% and 87.2% in the validation and test cohorts, respectively. The two DLRN models achieved satisfactory predictive performance based on different BC subtypes. CONCLUSIONS: The proposed DLRN models have the potential to accurately predict the tumor PCR and LNM status after NAC. PLAIN LANGUAGE SUMMARY: In this study, we proposed two deep learning radiomics nomogram models based on pre-neoadjuvant chemotherapy (NAC) and preoperative ultrasonography images for independently predicting the status of tumor and axillary lymph node (ALN) after NAC. A more comprehensive assessment of the patient's condition after NAC can be achieved by predicting the status of the tumor and ALN separately. Our model can potentially provide a noninvasive and personalized method to offer decision support for organ preservation and avoidance of excessive surgery.

Effect of pulsed field ablation on solid tumor cells and microenvironment.

Pulsed field ablation can increase membrane permeability and is an emerging non-thermal ablation. While ablating tumor tissues, electrical pulses not only act on the membrane structure of cells to cause irreversible electroporation, but also convert tumors into an immune active state, increase the permeability of microvessels, inhibit the proliferation of pathological blood vessels, and soften the extracellular matrix thereby inhibiting infiltrative tumor growth. Electrical pulses can alter the tumor microenvironment, making the inhibitory effect on the tumor not limited to short-term killing, but mobilizing the collective immune system to inhibit tumor growth and invasion together.

Neutrophil membrane-coated nanoparticles for enhanced nanosecond pulsed electric field treatment of pancreatic cancer.

OBJECTIVE: Pancreatic cancer is one of the leading causes of cancer-related deaths worldwide. Poor prognosis and low survival rates have driven the development of novel therapeutic strategies. Nanosecond pulsed electric field has emerged as a novel, minimal invasive and non-thermal treatment for solid tumors. It is of great significance to study the combination therapy of nsPEF and other treatment strategies for pancreatic cancer. METHODS: We developed neutrophil membrane-wrapped liposomal nanoparticles loaded with gemcitabine (NE/Lip-GEM) and investigated their use as a complementary agent for nsPEF treatment. RESULTS: Our results showed that neutrophil-mediated delivery of liposomal-gemcitabine (NE/Lip-GEM) efficiently inhibited the growth of pancreatic tumors in mice whose has been treated with incomplete nsPEF ablation. CONCLUSIONS: The combination of nsPEF and NE/Lip-GEM may be a promising synergistic strategy for pancreatic cancer therapy.