Lipid-associated macrophages for osimertinib resistance and leptomeningeal metastases in NSCLC.

Leptomeningeal metastases (LMs) remain a devastating complication of non-small cell lung cancer (NSCLC), particularly following osimertinib resistance. We conducted single-cell RNA sequencing on cerebrospinal fluid (CSF) from EGFR-mutant NSCLC with central nervous system metastases. We found that macrophages of LMs displayed functional and phenotypic heterogeneity and enhanced immunosuppressive properties. A population of lipid-associated macrophages, namely RNASE1_M, were linked to osimertinib resistance and LM development, which was regulated by Midkine (MDK) from malignant epithelial cells. MDK exhibited significant elevation in both CSF and plasma among patients with LMs, with higher MDK levels correlating to poorer outcomes in an independent cohort. Moreover, MDK could promote macrophage M2 polarization with lipid metabolism and phagocytic function. Furthermore, malignant epithelial cells in CSF, particularly after resistance to osimertinib, potentially achieved immune evasion through CD47-SIRPA interactions with RNASE1_M. In conclusion, we revealed a specific subtype of macrophages linked to osimertinib resistance and LM development, providing a potential target to overcome LMs.

Unilateral boundary time series forecasting.

Time series forecasting is an essential tool across numerous domains, yet traditional models often falter when faced with unilateral boundary conditions, where data is systematically overestimated or underestimated. This paper introduces a novel approach to the task of unilateral boundary time series forecasting. Our research bridges the gap in existing methods by proposing a specialized framework to accurately forecast within these skewed datasets. The cornerstone of our approach is the unilateral mean square error (UMSE), an asymmetric loss function that strategically addresses underestimation biases in training data, improving the precision of forecasts. We further enhance model performance through the implementation of a dual model structure that processes underestimated and accurately estimated data points separately, allowing for a nuanced analysis of the data trends. Additionally, feature reconstruction is employed to recapture obscured dynamics, ensuring a comprehensive understanding of the data. We demonstrate the effectiveness of our methods through extensive experimentation with LightGBM and GRU models across diverse datasets, showcasing superior accuracy and robustness in comparison to traditional models and existing methods. Our findings not only validate the efficacy of our approach but also reveal its model-independence and broad applicability. This work lays the groundwork for future research in this domain, opening new avenues for sophisticated analytical models in various industries where precise time series forecasting is crucial.

[Impact of Mild SARS-CoV-2 Infection on Hematological Parameters and Suitability of Apheresis Platelets Donation in Blood Donors].

OBJECTIVE: To investigate the effects of mild SARS-CoV-2 infection on hematological parameters of adult blood donors and the suitability of apheresis platelet donation, the changes of the hematological parameters in blood donors with mild infection of the SARS-CoV-2 Omicron variant strain were evaluated. METHODS: Seventy-two blood donors with mild COVID-19 symptoms who donated consecutive apheresis platelets for 3 times from December 2022 to January 2023, 42 cases among which were included in the infection-positive group, and 30 cases in the suspected infection group. Forty-two donors un-vaccinated against SARS-CoV-2, un-infected, and donated three consecutive apheresis platelets from October to November 2022 were included in the control group. The changes of blood routine testing in the positive group and the suspected infection group were retrospectively compared before (Time1) and after (Time2 and Time3) the onset of symptoms, three consecutive times (Time1, Time2, Time3) in the control group by repeated measures analysis of variance. The Bayesian discriminant method was used to establish a discriminant equation to determine whether the recent infection of SARS-CoV-2 occurred or not. RESULTS: Simple effect of the number times of tests in the positive and suspected infection groups was significant（ Finfection-positive group=6.98, P < 0.001, partial η2=0.79, Fsuspected infection group=4.31, P < 0.001, partial η2=0.70）. The positive group and the suspected infection group had lower RBC, HCT, and HGB, and higher PLT and PCT at Time2 compared to Time1 and Time3(P < 0.05). The positive group and the suspected infection group showes RDW-CV and RDW-SD at Time3 higher than Time1 and Time2 (P < 0.001). The simple effect of the number times of tests in the control group was not significant ( F=0.96, P =0.55, partial η2=0.34). The difference of the whole blood count parameters in the control group for three times was not statistically significant (P >0.05). We established a discriminant equation to determine whether the recent infection of SARS-CoV-2 occurred or not. The equation had an eigenvalue of 0.22, a canonical correlation of 0.43 (χ2=27.81, P < 0.001), and an analysis accuracy of 72.9%. CONCLUSION: The hematological indicators of RBC, HCT, HGB, PLT, PCT, RDW-CV and RDW-SD in blood donors who had infected with mild COVID-19 showed dynamic changes. The discriminant equation for whether they are infected recently with COVID-19 has a high accuracy rate.