IntroductionEvery year, many people die due to cancer in all of the world. So, the preparation and formulation of new chemotherapeutic supplements and drugs with remarkable effects to treat cancer are the priority of both developing and developed countries. In this study, zinc nanoparticles were synthesized in an aqueous medium using Fumaria officinalis leaf as stabilizing and reducing agents.Material and methodsThe green synthesized ZnNPs@ Fumaria officinalis were characterized using different techniques including UV-visible and FT-IR spectroscopy, X‐ray diffraction (XRD), scanning electron microscopy (SEM), and Energy Dispersive X-ray Spectrometry (EDS). The anticancer activity of ZnNPs@ Fumaria officinalis was evaluated against acute myeloid leukemia and acute T cell leukemia.ResultsAccording to the XRD analysis, 20.44 nm was measured for the crystal size of the nanoparticles. SEM images showed a uniform spherical morphology with an average size of 27.96 nm for ZnNPs@ Fumaria officinalis. In the cellular and molecular part of the recent study, the treated cells with ZnNPs@Fumaria officinalis were assessed by MTT assay for 48h about the cytotoxicity and anti-human acute leukemia properties on normal (HUVEC), acute myeloid leukemia (32D-FLT3-ITD and Human HL-60/vcr), and acute T cell leukemia (Jurkat, Clone E6-1 and J.RT3-T3.5) cell lines. The IC50 of ZnNPs@Fumaria officinalis were 227, 200, 250, and 336 µg/mL against 32D-FLT3-ITD, Human HL-60/vcr, Jurkat, Clone E6-1, and J.RT3-T3.5 cell lines, respectively.ConclusionsThe viability of malignant leukemia cell line reduced dose-dependently in the presence of ZnNPs@Fumaria officinalis. It appears that the anti-human acute leukemia effect of ZnNPs@Fumaria officinalis is due to their antioxidant effects.

Background Pulmonary sarcomatoid carcinoma (PSC) is a rare subtype of non-small cell lung cancers (NSCLC), but differs in terms of prognosis and treatment strategies. Due to the rarity of PSC, there are few reports focus on the CT radiomics of PSC. However, the preoperative diagnosis of PSC is important and remains challenging. The aim of the study is to explore the feasibility of preoperative differentiation of PSC from other NSCLC based on CT findings and radiomics, so as to improve the accuracy of radiological diagnosis of PSC. Methods 31 patients with PSC and 56 patients with other NSCLC were retrospectively analyzed. CT findings included tumor size, tumor location, calcification, vacuole/cavity, pleural invasion, and low-attenuation area (LAA) ratio. A total of 851 radiomics features were extracted from each CT phase data, including the plain scan (PS), arterial phase (AP) and venous phase (VP). The training and testing cohorts were created in an 8:2 ratio, and the top-ranked 11 features were selected using least absolute shrinkage and selection operator (LASSO) method. Seven machine learning algorithms (DT, GBDT, LDA, LR, RF, SVM, and XGBoost) were applied for the differential diagnosis of PSC and other NSCLC. Results The median survival times of PSC and other NSCLC were 8 months (95% CI 2.123–13.877) and 34 months (95% CI 22.920–45.080), respectively. The mean tumor size of PSC (2.0-9.3 cm) and other NSCLC (2.1–9.7 cm) was 5 cm, and the difference was not statistically significant. Compared to other NSCLC, PSC had a larger LAA ratio (P < 0.001), with an optimal cutoff value of 16.6%, and a sensitivity and specificity of 0.806 and 0.732, respectively. In CT radiomics, PS data combined with logistic regression (LR) algorithm yielded the highest diagnostic efficacy, and the area under the curve (AUC), accuracy, sensitivity and specificity were 0.972, 0.944, 0.833 and 1.000, respectively. Conclusions CT findings and radiomics showed efficient performance in the differential diagnosis of PSC from other NSCLC, which is helpful for the preoperative diagnosis of PSC.

Background Pulmonary sarcomatoid carcinoma (PSC) is a rare subtype of non-small cell lung cancer (NSCLC) but differs in terms of treatment strategies compared with conventional-NSCLC (c-NSCLC). However, preoperative CT differentiation between PSC and c-NSCLC remains a challenge. This study aimed to explore the CT findings and prognosis of PSC compared with c-NSCLC of similar tumor size. Methods Clinical data and CT findings of 31 patients with PSC and 87 patients with c-NSCLC were retrospectively analyzed. Clinical data included sex, age, and smoking history. CT findings included tumor size, tumor location, calcification, vacuole/cavity, pleural invasion, mean CT value, and low-attenuation area (LAA) ratio. Kaplan‒Meier curves and log-rank tests were used for survival analysis. A Cox regression model was constructed to evaluate prognostic risk factors associated with overall survival (OS). The Spearman correlation among clinicoradiological outcomes were analyzed. Results The mean tumor size of PSC and c-NSCLC were both 5.1 cm. The median survival times of PSC and c-NSCLC were 8 months and 34 months, respectively (P < 0.001). Calcification and vacuoles/cavities were rarely present in PSC. Pleural invasion occurred in both PSC and c-NSCLC (P = 0.285). The mean CT values of PSC and c-NSCLC on plain scan (PS), arterial phase (AP), and venous phase (VP) were 30.48 ± 1.59 vs. 36.25 ± 0.64 Hu (P = 0.002), 43.26 ± 2.96 vs. 58.71 ± 1.65 Hu (P < 0.001) and 50.26 ± 3.28 vs. 64.24 ± 1.86 Hu (P < 0.001), the AUCs were 0.685, 0.757 and 0.710, respectively. Compared to c-NSCLC, PSC had a larger LAA ratio, and the AUC was 0.802, with an optimal cutoff value of 20.6%, and the sensitivity and specificity were 0.645 and 0.862, respectively. Combined with the mean CT value and LAA ratio, AP + VP + LAA yielded the largest AUC of 0.826. The LAA ratio were not independent risk factors for PSC in this study. LAA ratio was negatively correlated with PS (r = -0.29), AP (r = -0.58), and VP (r = -0.66). LAA showed a weak positive correlation with tumor size(r = 0.27). Conclusions PSC has a poorer prognosis than c-NSCLC of similar tumor size. The mean CT value and LAA ratio contributes to preoperative CT differentiation of PSC and c-NSCLC.