Optical-pulse-based reconfigurable phase control for independent beamforming and band fusion in a dual-band phased array receiver.

This Letter proposes an optical-pulse-based reconfigurable phase control method, enabling a dual-band phased array receiver to operate in two modes: dual-band-independent operation and dual-band fusion. The method utilizes optical pulses and optical delay to compensate for phase differences across frequency bands. An electrical phase shifter is employed to compensate for phase residual in both bands. All phase operations to both bands are processed concurrently in one link, thereby maintaining inter-band phase coherence. Experimental results verify the ability of dual-band-independent beamforming and inter-band phase coherence maintaining. A four-channel dual-band (X- and Ku-band) phased array antenna (PAA) receiver is constructed to measure PAA patterns and demonstrate band fusion. The pulse compression results in all directions reveal a doubled improvement in range resolution, which shows the potential for enhancement of radar performance.

The association between the fibrinogen-to-albumin ratio and delirium after deep brain stimulation surgery in Parkinson's disease.

Introduction: Postoperative delirium (POD) remains one of the most prevalent neuropsychiatric complications after deep brain stimulation (DBS) surgery. The fibrinogen-to-albumin ratio (FAR) has been shown to significantly correlate with the prognosis of many diseases related to inflammation. However, the association between FAR and POD remains unclear. We aimed to explore the association between POD and FAR in patients with Parkinson's disease (PD) undergoing DBS surgery. Methods: Patients with PD who underwent DBS surgery in our hospital were included in this retrospective study. FAR was calculated from the blood sample collected on admission. The association between baseline FAR and delirium after surgery was assessed by binary logistic regression analysis, interaction analysis, and stratified analyses. Results: Of 226 patients, 37 (16.4%) suffered from delirium after surgery. The average age of the participants was 63.3 ± 7.2 years, and 51.3% were male patients. Multivariate logistic regression analysis indicated that patients in the highest FAR tertile had a higher risk of POD compared with patients in the lowest FAR tertile (OR = 3.93, 95% CI: 1.24 ~ 12.67). Subgroup analysis demonstrated that FAR and the preoperative Mini-Mental State Examination score (p = 0.013) had an association with delirium after surgery. Conclusion: Our data suggest that a higher preoperative FAR was significantly associated with delirium after DBS surgery. FAR on admission is a useful candidate biomarker to identify patients with PD who are at a high risk of delirium following DBS surgery.

Association Between Hemoglobin-Albumin-Lymphocyte-Platelet Index and Mortality in Hospitalized COVID-19 Omicron BA.2 Infected Patients.

Background: The hemoglobin-albumin-lymphocyte-platelet (HALP) index is a novel biomarker reflecting systemic inflammation and nutritional status which are important for coronavirus disease 2019 (COVID-19) mortality. However, the association between HALP and mortality in patients with COVID-19 has yet to be investigated. Methods: A cohort of COVID-19 Omicron BA.2 infected patients admitted to the Shanghai Fourth People's Hospital, School of Medicine, Tongji University from April 12, 2022 to June 17, 2022 was retrospectively analyzed. Laboratory examinations on hospital admission, including hemoglobin, albumin, and lymphocyte and platelet, were collected. The association between baseline HALP and in-hospital poor overall survival (OS) was assessed using Kaplan-Meier curves, Cox regression models, interaction, and stratified analyses. Results: A total of 2147 patients with COVID-19 Omicron BA.2 infection were included in the final analyses, and mortality in the hospital was 2.65%. Multivariate analysis indicated that low HALP index was independently associated with in-hospital mortality of COVID-19 patients [hazard ratio (HR) = 2.08; 95% confidence interval (CI) = 1.17-3.73]. Subgroup analysis demonstrated that low HALP index was an independent risk factor for in-hospital mortality in COVID-19 patients with age ≥70 (HR = 2.22, CI = 1.18-4.15) and severe cases (HR = 2.09, CI = 1.13-3.86). Conclusion: HALP index is independently related to in-hospital poor OS for COVID-19 Omicron BA.2 infected patients, especially for age ≥70 and severe cases. HALP index on hospital admission is a useful candidate biomarker for identifying high risk of mortality in COVID-19 Omicron BA.2 infected patients.

Estimation of optimal treatment regimes with electronic medical record data using the residual life value estimator.

Clinicians and patients must make treatment decisions at a series of key decision points throughout disease progression. A dynamic treatment regime is a set of sequential decision rules that return treatment decisions based on accumulating patient information, like that commonly found in electronic medical record (EMR) data. When applied to a patient population, an optimal treatment regime leads to the most favorable outcome on average. Identifying optimal treatment regimes that maximize residual life is especially desirable for patients with life-threatening diseases such as sepsis, a complex medical condition that involves severe infections with organ dysfunction. We introduce the residual life value estimator (ReLiVE), an estimator for the expected value of cumulative restricted residual life under a fixed treatment regime. Building on ReLiVE, we present a method for estimating an optimal treatment regime that maximizes expected cumulative restricted residual life. Our proposed method, ReLiVE-Q, conducts estimation via the backward induction algorithm Q-learning. We illustrate the utility of ReLiVE-Q in simulation studies, and we apply ReLiVE-Q to estimate an optimal treatment regime for septic patients in the intensive care unit using EMR data from the Multiparameter Intelligent Monitoring Intensive Care database. Ultimately, we demonstrate that ReLiVE-Q leverages accumulating patient information to estimate personalized treatment regimes that optimize a clinically meaningful function of residual life.

Methods for the estimation of direct and indirect vaccination effects by combining data from individual- and cluster-randomized trials.

Both individually and cluster randomized study designs have been used for vaccine trials to assess the effects of vaccine on reducing the risk of disease or infection. The choice between individually and cluster randomized designs is often driven by the target estimand of interest (eg, direct versus total), statistical power, and, importantly, logistic feasibility. To combat emerging infectious disease threats, especially when the number of events from one single trial may not be adequate to obtain vaccine effect estimates with a desired level of precision, it may be necessary to combine information across multiple trials. In this article, we propose a model formulation to estimate the direct, indirect, total, and overall vaccine effects combining data from trials with two types of study designs: individual-randomization and cluster-randomization, based on a Cox proportional hazards model, where the hazard of infection depends on both vaccine status of the individual as well as the vaccine status of the other individuals in the same cluster. We illustrate the use of the proposed model and assess the potential efficiency gain from combining data from multiple trials, compared to using data from each individual trial alone, through two simulation studies, one of which is designed based on a cholera vaccine trial previously carried out in Matlab, Bangladesh.

Functionalized Periodic Mesoporous Silica Nanoparticles for Inhibiting the Progression of Atherosclerosis by Targeting Low-Density Lipoprotein Cholesterol.

Atherosclerotic disease is a substantial global burden, and existing treatments, such as statins, are recommended to lower low-density lipoprotein cholesterol (LDL-C) levels and inhibit the progression of atherosclerosis. However, side effects, including gastrointestinal unease, potential harm to the liver, and discomfort in the muscles, might be observed. In this study, we propose a novel method using periodic mesoporous silica nanoparticles (PMS) to create heparin-modified PMS (PMS-HP) with excellent biocompatibility, enabling selective removal of LDL-C from the blood. In vitro, through the introduction of PMS-HP into the plasma of mice, we observed that, compared to PMS alone, PMS-HP could selectively adsorb LDL-C while avoiding interference with valuable components such as plasma proteins and high-density lipoprotein cholesterol (HDL-C). Notably, further investigations revealed that the adsorption of LDL-C by PMS-HP could be well-fitted to quasi-first-order (R2 = 0.993) and quasi-second-order adsorption models (R2 = 0.998). Likewise, in vivo, intravenous injection of PMS-HP enabled targeted LDL-C adsorption (6.5 ± 0.73 vs. 8.6 ± 0.76 mM, p < 0.001) without affecting other plasma constituents, contributing to reducing intravascular plaque formation (3.66% ± 1.06% vs. 1.87% ± 0.79%, p < 0.05) on the aortic wall and inhibiting vascular remodeling (27.2% ± 6.55% vs. 38.3% ± 1.99%, p < 0.05). Compared to existing lipid adsorption techniques, PMS-HP exhibited superior biocompatibility and recyclability, rendering it valuable for both in vivo and in vitro applications.

Cold Sintering Mediated Engineering of Polycrystalline SnSe with High Thermoelectric Efficiency.

Despite the attractive thermoelectric properties in single crystals, the fabrication of high-performance polycrystalline SnSe by a cost-effective strategy remains challenging. In this study, we prepare the undoped SnSe ceramic with remarkable thermoelectric efficiency by the combination of a cold sintering process (CSP) and thermal annealing. The high sintering pressure during CSP induces not only highly oriented grains but also a high concentration of lattice dislocations and stacking faults, which leads to large lattice strain that can shorten the phonon relaxation time. Meanwhile, the thermal annealing breaks the highly resistive SnOx layers at grain boundaries, which improves the electrical conductivity and power factor. In addition, the grain growth during annealing further turns the broken SnOx layers into nanoparticles, which further lowers the thermal conductivity by enhanced scattering. As a result, a peak ZT of 1.3 at 890 K and a high average ZT of 0.69 are achieved in the polycrystalline SnSe, suggesting great potential in mid-temperature power generation. This work may pave the way for the mass production of SnSe-based ceramics for thermoelectric devices.

Predictive model of acute kidney injury in critically ill patients with acute pancreatitis: a machine learning approach using the MIMIC-IV database.

BACKGROUND: Acute kidney injury (AKI) is a common and serious complication in severe acute pancreatitis (AP), associated with high mortality rate. Early detection of AKI is crucial for prompt intervention and better outcomes. This study aims to develop and validate predictive models using machine learning (ML) to identify the onset of AKI in patients with AP. METHODS: Patients with AP were extracted from the MIMIC-IV database. We performed feature selection using the random forest method. Model construction involved an ensemble of ML, including random forest (RF), support vector machine (SVM), k-nearest neighbors (KNN), naive Bayes (NB), neural network (NNET), generalized linear model (GLM), and gradient boosting machine (GBM). The best-performing model was fine-tuned and evaluated through split-set validation. RESULTS: We analyzed 1,235 critically ill patients with AP, of which 667 cases (54%) experienced AKI during hospitalization. We used 49 variables to construct models, including GBM, GLM, KNN, NB, NNET, RF, and SVM. The AUC for these models was 0.814 (95% CI, 0.763 to 0.865), 0.812 (95% CI, 0.769 to 0.854), 0.671 (95% CI, 0.622 to 0.719), 0.812 (95% CI, 0.780 to 0.864), 0.688 (95% CI, 0.624 to 0.752), 0.809 (95% CI, 0.766 to 0.851), and 0.810 (95% CI, 0.763 to 0.856) respectively. In the test set, the GBM's performance was consistent, with an area of 0.867 (95% CI, 0.831 to 0.903). CONCLUSIONS: The GBM model's precision is crucial, aiding clinicians in identifying high-risk patients and enabling timely interventions to reduce mortality rates in critical care.

DYNAMIC PREDICTION OF RESIDUAL LIFE WITH LONGITUDINAL COVARIATES USING LONG SHORT-TERM MEMORY NETWORKS.

Sepsis, a complex medical condition that involves severe infections with life-threatening organ dysfunction, is a leading cause of death worldwide. Treatment of sepsis is highly challenging. When making treatment decisions, clinicians and patients desire accurate predictions of mean residual life (MRL) that leverage all available patient information, including longitudinal biomarker data. Biomarkers are biological, clinical, and other variables reflecting disease progression that are often measured repeatedly on patients in the clinical setting. Dynamic prediction methods leverage accruing biomarker measurements to improve performance, providing updated predictions as new measurements become available. We introduce two methods for dynamic prediction of MRL using longitudinal biomarkers. in both methods, we begin by using long short-term memory networks (LSTMs) to construct encoded representations of the biomarker trajectories, referred to as "context vectors." In our first method, the LSTM-GLM, we dynamically predict MRL via a transformed MRL model that includes the context vectors as covariates. In our second method, the LSTM-NN, we dynamically predict MRL from the context vectors using a feed-forward neural network. We demonstrate the improved performance of both proposed methods relative to competing methods in simulation studies. We apply the proposed methods to dynamically predict the restricted mean residual life (RMRL) of septic patients in the intensive care unit using electronic medical record data. We demonstrate that the LSTM-GLM and the LSTM-NN are useful tools for producing individualized, real-time predictions of RMRL that can help inform the treatment decisions of septic patients.

Inhibition of myocardial remodeling through miR-150/TET3 axis after AMI.

BACKGROUND: Current studies have suggested that miRNA is beneficial in inhibiting myocardial remodeling after myocardial infarction (AMI), however, its underlying mechanism is unclear. OBJECTIVES: We aimed to investigate whether miR-150 can inhibit myocardial remodeling after myocardial infarction and whether this process is regulated by the miR-150/TET3 pathway. METHODS: On the first day, C57BL/6 AMI mice(n = 15) were administrated with miR-150, and another 15 AMI mice were administrated with the same volume of control Agomir. Left ventricular ejection fraction (LVEF%) and myocardial remodeling were compared after one week; TET3 (ten-eleven translocation 3) and VEGF-α (vascular endothelial growth factor-α) were also determined in the infracted heart simultaneously. The neovascularization in the infarcted area at day 21 was compared through CD31 using fluorescence microscopy; Activated monocytes stimulated with LPS were transfected with miR-150. Laser scanning confocal microscopy was used to detect the intracytoplasmic imaging of miR-150 in Ly6Chigh monocytes. Expression of the miR-150 in the monocytes was measured using Q-PCR. After 48 h, the proportion of Ly6Chigh/low monocytes was determined using flow cytometry. Expression of TET3 in Ly6Chigh/low monocytes was measured using Q-PCR and Western blot. After the downregulation of TET3 specifically, the levels of Ly6Chigh/low monocytes were further determined. RESULTS: We first observed an increased trend of mice survival rate in the miR-150 injection group, but it didn't reach a statistical difference (66.7% vs. 40.0%, p = 0.272). However, AMI mice administrated with miR-150 displayed better LVEF% (51.78%±2.90% vs. 40.28%±4.20%, p<0.001) and decreased infarct size% (25.47 ± 7.75 vs. 50.39 ± 16.91, p = 0.002). After miR-150 was transfected into monocytes, the percentage of Ly6Clow monocytes increased significantly after 48 h (48.5%±10.1% vs. 42.5%±8.3%, p < 0.001). Finally, Western blot analysis (0.56 ± 0.10/ß-actin vs. 0.99 ± 0.12/ß-actin, p < 0.001) and real-time PCR (1.09 ± 0.09/GAPDH vs. 2.53 ± 0.15/GAPDH, p < 0.001, p < 0.001) both confirmed decreased expression of TET3 in monocytes after transfection with miR-150. After the downregulation of TET3 specifically, Ly6Clow monocytes showed a significant increase (16.73%±6.45% vs. 6.94%±2.99%, p<0.001, p < 0.001). CONCLUSIONS: miR-150 alleviated myocardial remodeling after AMI. Possible mechanisms are ascribed to the regulating of TET3 and VEGF-α in inflammatory monocytes.

The Potential Value of Systemic Inflammation Response Index on Delirium After Hip Arthroplasty Surgery in Older Patients: A Retrospective Study.

Purpose: To explore the relationship between the systemic inflammation response index (SIRI) and postoperative delirium (POD) in older patients with hip arthroplasty surgery. Patients and Methods: Older patients who underwent elective hip arthroplasty surgery were included in this retrospective study. SIRI, neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) were collected from blood routine examination at admission. Binary logistic regression analysis was performed to evaluate the association between SIRI levels and POD was analyzed. Results: Ultimately, 116 older patients who met the inclusion criteria were assessed. Thirty-four (29%) of 116 patients diagnosed with POD were defined as the POD group, and the rest consisted of the Non-POD group. Compared with non-POD patients, POD patients showed significantly higher levels of SIRI (P < 0.001) and NLR (P = 0.002) at admission. There was no significance in the levels of PLR between two groups. SIRI was independently associated with the occurrence of POD in multivariate logistic regression analysis [odds ratio (OR) = 3.34, 95% confidence interval (95% CI) = 1.26-8.85, P = 0.016]. Receiver operating characteristic curve analysis indicated that SIRI with an optimal cutoff value of 0.987 predicted the POD with a sensitivity of 88.2% and specificity of 74.4%, and the area under the curve was 0.82 (95% CI, 0.74-0.90, P < 0.01). Conclusion: Preoperative SIRI and NLR levels in the blood are associated with the occurrence of POD. Moreover, preoperative SIRI level is a useful candidate biomarker to identify delirium after elective hip arthroplasty surgery in older patients.

The Differences of Quantitative Flow Ratio in Coronary Artery Stenosis with or without Atrial Fibrillation.

Quantitative flow ratio (QFR) is a new method for the assessment of the extent of coronary artery stenosis. But it may be obscured by the cardiac remodeling and abnormal blood flow of the coronary artery when encountering atrial fibrillation (AF). The present study aimed to examine the impact of these changed structures and blood flow of coronary arteries on QFR results in AF patients. Methods and Results. We evaluated QFR in 223 patients (112 patients with AF; 111 non-AF patients served as controls) who had undergone percutaneous coronary intervention (PCI) due to severe stenoses in coronary arteries. QFR of the target coronary was determined according to the flow rate of the contrast agent. Results showed that AF patients had significantly higher QFR values than control (0.792 ± 0.118 vs. 0.685 ± 0.167, p < 0.001). We further analyzed local QFR around the stenoses (0.858 ± 0.304 vs. 0.756 ± 0.146, p=0.002), residual QFR (0.958 ± 0.055 vs. 0.929 ± 0.093, p=0.005), and index QFR (0.807 ± 0.108 vs. 0.713 ± 0.152, p < 0.001) in these two groups of patients with and without AF. Further analysis revealed that QFR in AF patients was negatively correlated with coronary flow velocity (R = -0.22, p=0.02) and area of stenosis (R = -0.70, p < 0.001) but positively correlated with the minimum lumen area (MLA) (R = 0.47, p < 0.001). Conclusion. AF patients with coronary artery stenosis have higher QFR values, which are associated with decreased blood flow velocity, smaller stenosis, and larger MLA in AF patients upon cardiac remodeling.

Interpretable machine-learning model for real-time, clustered risk factor analysis of sepsis and septic death in critical care.

BACKGROUND AND OBJECTIVE: Interpretable and real-time prediction of sepsis and risk factor analysis could enable timely treatment by clinicians and improve patient outcomes. To develop an interpretable machine-learning model for the prediction and risk factor analysis of sepsis and septic death. METHODS: This is a retrospective observational cohort study based on the Medical Information Mart for Intensive Care (MIMIC-IV) dataset; 69,619 patients from the database were screened. The two outcomes include patients diagnosed with sepsis and the death of septic patients. Clinical variables from ICU admission to outcomes were analyzed: demographic data, vital signs, Glasgow Coma Scale scores, laboratory test results, and results for arterial blood gasses (ABGs). Model performance was compared using the area under the receiver operating characteristic curve (AUROC). Model interpretations were based on the Shapley additive explanations (SHAP), and the clustered analysis was based on the combination of K-means and dimensionality reduction algorithms of t-SNE and PCA. RESULTS: For the analysis of sepsis and septic death, 47,185 and 2480 patients were enrolled, respectively. The XGBoost model achieved a predictive value of area under the curve (AUC): 0.745 [0.731-0.759] for sepsis prediction and 0.8 [0.77, 0.828] for septic death prediction. The real-time prediction model was trained to predict by day and visualize the individual or combined risk factor effects on the outcomes based on SHAP values. Clustered analysis separated the two phenotypes with distinct risk factors among patients with septic death. CONCLUSION: The proposed real-time, clustered prediction model for sepsis and septic death exhibited superior performance in predicting the outcomes and visualizing the risk factors in a real-time and interpretable manner to distinguish and mitigate patient risks, thus promising immense potential in effective clinical decision making and comprehensive understanding of complex diseases such as sepsis.

SUMOylation of Smad2 mediates TGF-ß-regulated endothelial-mesenchymal transition.

Endothelial-mesenchymal transition (EndoMT) is a complex biological process in which endothelial cells are transformed into mesenchymal cells, and dysregulated EndoMT causes a variety of pathological processes. Transforming growth factor beta (TGF-ß) signaling effectively induces the EndoMT process in endothelial cells, and Smad2 is the critical protein of the TGF-ß signaling pathway. However, whether small ubiquitin-like modifier modification (SUMOylation) is involved in EndoMT remains unclear. Here, we show that Smad2 is predominantly modified by SUMO1 at two major SUMOylation sites with PIAS2α as the primary E3 ligase, whereas SENP1 (sentrin/SUMO-specific protease 1) mediates the deSUMOylation of Smad2. In addition, we identified that SUMOylation significantly enhances the transcriptional activity and protein stability of Smad2, regulating the expression of downstream target genes. SUMOylation increases the phosphorylation of Smad2 and the formation of the Smad2-Smad4 complex, thus promoting the nuclear translocation of Smad2. Ultimately, the wildtype, but not SUMOylation site mutant Smad2 facilitated the EndoMT process. More importantly, TGF-ß enhances the nuclear translocation of Smad2 by enhancing its SUMOylation and promoting the EndoMT process. These results demonstrate that SUMOylation of Smad2 plays a critical role in the TGF-ß-mediated EndoMT process, providing a new theoretical basis for the treatment and potential drug targets of EndoMT-related clinical diseases.

New application of a periodic mesoporous nanocrystal silicon-silica composite for hyperlipidemia.

The integration of the properties of silicon nano crystallinity with silica mesoporosity provides a wealth of new opportunities for emerging biomedicine. Cholesterol (CHO) and triglyceride (TG) levels have always been a challenge for cardiologists in the treatment of patients with chronic coronary artery disease (CAD). For patients with hyperlipidemia, statins and other lipid-lowering drugs are currently recommended. It should be noted, however, that significant side effects have been reported in the treatments, including liver damage, muscle pain, etc. We here found that our previously produced periodic mesoporous nanocrystalline silicon-silica, meso-ncSi/SiO2 (PMS), a nanocomposite material, has the properties of lowering CHO and TG, and is associated with better safety and biocompatibility compared to existing lipid-lowering drugs. After being incubated with PMS for 2 hours, CHO and TG levels in blood were significantly lower than before. In addition, CHO and TG adsorbed on with PMS could also be extracted and released, contributing to the recovery and recycling of PMS.

A novel m6A/m5C/m1A score signature to evaluate prognosis and its immunotherapy value in colon cancer patients.

BACKGROUND: Colon cancer features strong heterogeneity and invasiveness, with high incidence and mortality rates. Recently, RNA modifications involving m6A, m5C, and m1A play a vital part in tumorigenesis and immune cell infiltration. However, integrated analysis among various RNA modifications in colon cancer has not been performed. METHODS: RNA-seq profiling, clinical data and mutation data were obtained from The Cancer Genome Atlas and Gene Expression Omnibus. We first explored the mutation status and expression levels of m6A/m5C/m1A regulators in colon cancer. Then, different m6A/m5C/m1A clusters and gene clusters were identified by consensus clustering analysis. We further constructed and validated a scoring system, which could be utilized to accurately assess the risk of individuals and guide personalized immunotherapy. Finally, m6A/m5C/m1A regulators were validated by immunohistochemical staining and RT-qPCR. RESULTS: In our study, three m6A/m5C/m1A clusters and gene clusters were identified. Most importantly, we constructed a m6A/m5C/m1A scoring system to assess the clinical risk of the individuals. Besides, the prognostic value of the score was validated with three independent cohorts. Moreover, the level of the immunophenoscore of the low m6A/m5C/m1A score group increased significantly with CTLA-4/PD-1 immunotherapy. Finally, we validated that the mRNA and protein expression of VIRMA and DNMT3B increased in colon cancer tissues. CONCLUSIONS: We constructed and validated a stable and powerful m6A/m5C/m1A score signature to assess the survival outcomes and immune infiltration characteristics of colon cancer patients, which further guides optimization of personalized treatment, making it valuable for clinical translation and implementation.

A multiplicative structural nested mean model for zero-inflated outcomes.

Zero-inflated nonnegative outcomes are common in many applications. In this work, motivated by freemium mobile game data, we propose a class of multiplicative structural nested mean models for zero-inflated nonnegative outcomes which flexibly describes the joint effect of a sequence of treatments in the presence of time-varying confounders. The proposed estimator solves a doubly robust estimating equation, where the nuisance functions, namely the propensity score and conditional outcome means given confounders, are estimated parametrically or nonparametrically. To improve the accuracy, we leverage the characteristic of zero-inflated outcomes by estimating the conditional means in two parts, that is, separately modelling the probability of having positive outcomes given confounders, and the mean outcome conditional on its being positive and given the confounders. We show that the proposed estimator is consistent and asymptotically normal as either the sample size or the follow-up time goes to infinity. Moreover, the typical sandwich formula can be used to estimate the variance of treatment effect estimators consistently, without accounting for the variation due to estimating nuisance functions. Simulation studies and an application to a freemium mobile game dataset are presented to demonstrate the empirical performance of the proposed method and support our theoretical findings.

Identification and validation of a new pyroptosis-associated lncRNA signature to predict survival outcomes, immunological responses and drug sensitivity in patients with gastric cancer.

BACKGROUND: Gastric cancer (GC) ranks fifth in prevalence among carcinomas worldwide. Both pyroptosis and long noncoding RNAs (lncRNAs) play crucial roles in the occurrence and development of gastric cancer. Therefore, we aimed to construct a pyroptosis-associated lncRNA model to predict the outcomes of patients with gastric cancer. METHODS: Pyroptosis-associated lncRNAs were identified through co-expression analysis. Univariate and multivariate Cox regression analyses were performed using the least absolute shrinkage and selection operator (LASSO). Prognostic values were tested through principal component analysis, a predictive nomogram, functional analysis and Kaplan‒Meier analysis. Finally, immunotherapy and drug susceptibility predictions and hub lncRNA validation were performed. RESULTS: Using the risk model, GC individuals were classified into two groups: low-risk and high-risk groups. The prognostic signature could distinguish the different risk groups based on principal component analysis. The area under the curve and the conformance index suggested that this risk model was capable of correctly predicting GC patient outcomes. The predicted incidences of the one-, three-, and five-year overall survivals exhibited perfect conformance. Distinct changes in immunological markers were noted between the two risk groups. Finally, greater levels of appropriate chemotherapies were required in the high-risk group. AC005332.1, AC009812.4 and AP000695.1 levels were significantly increased in gastric tumor tissue compared with normal tissue. CONCLUSIONS: We created a predictive model based on 10 pyroptosis-associated lncRNAs that could accurately predict the outcomes of GC patients and provide a promising treatment option in the future.

Immunotherapy combined with chemotherapy in the treatment for rare primary mucoepidermoid carcinoma of the liver: a case report.

Primary mucoepidermoid carcinoma of the liver (PMCL) is rare in the hepatic system, with no standard treatment and poor prognosis with a median overall survival of only 120 days. PMCL with immunotherapy has not been reported yet. Here, we present a case of PMCL treated by immunotherapy and chemotherapy. A 64-year-old male with PMCL underwent partial right hepatectomy and liver lesion resection on 19 June 2020. Two months later, the chest computed tomography indicated the presence of multiple nodules in both lungs with higher tumor markers. Considering the presence of a tumor metastasis, the patient received four courses of immunotherapy plus mGEMOX chemotherapy from 8 September 2020. The patient tolerated the combined therapy well, with red moles on the face and chest which were considered as grade 1 reactive cutaneous capillary endothelial proliferation. He also had grade 2 thrombocytopenia and leucopenia after the first course of chemotherapy, but no neutropenia, fatigue, vomiting or diarrhea. However, his disease progressed. The patient refused further treatment and died on 20 April 2021. The overall survival time after diagnosis was 301 days. We describe here the first case report on immunotherapy treatment for PMCL. That suggested immunotherapy combined with chemotherapy may be an option after a hepatic lobectomy for PMCL.