A hybrid success history-based adaptive differential evolution and manta ray foraging optimization for multi-objective truss optimization problems

In this paper, a hybrid multi-objective metaheuristic algorithm based on the manta ray foraging optimization (MRFO) and the success history-based parameter adaptive differential evolution (SHADE) is developed to solve multi-objective truss optimization problems, called MO-SHADE-MRFO. SHADE is a variant of differential evolution with high performance in solving optimization problems, and MRFO is a novel metaheuristic algorithm inspired from the behavior of manta rays. In the proposed algorithm, the updating mechanism of MRFO is embedded into the SHADE, to enhance global convergence of SHADE for multi-objective truss optimization problems. The design problem is to minimize both structural mass and compliance subjected to stress constraints. Six benchmark truss optimization problems, including 10-bar, 25-bar, 37-bar, 120-bar, 200-bar and 942-bar trusses, are utilized to test the effectiveness of the proposed algorithm. The performance of the proposed algorithm is compared with nine state-of-the-art algorithms, in terms of metrics including hypervolume, inverted generational distance, and spacing-to-extent. The experiment results demonstrate that the proposed algorithm can obtain the best statistical values of metrics and the lowest standard deviation values in most test problems, which is more accurate than the compared algorithms. The Pareto solutions obtained by the proposed algorithm are well-distributed and smooth in each problem

Modulation of Immune Checkpoints by Chemotherapy in Human Colorectal Liver Metastases.

Metastatic colorectal cancer (CRC) is a major cause of cancer-related death, and incidence is rising in younger populations (younger than 50 years). Current chemotherapies can achieve response rates above 50%, but immunotherapies have limited value for patients with microsatellite-stable (MSS) cancers. The present study investigates the impact of chemotherapy on the tumor immune microenvironment. We treat human liver metastases slices with 5-fluorouracil (5-FU) plus either irinotecan or oxaliplatin, then perform single-cell transcriptome analyses. Results from eight cases reveal two cellular subtypes with divergent responses to chemotherapy. Susceptible tumors are characterized by a stemness signature, an activated interferon pathway, and suppression of PD-1 ligands in response to 5-FU+irinotecan. Conversely, immune checkpoint TIM-3 ligands are maintained or upregulated by chemotherapy in CRC with an enterocyte-like signature, and combining chemotherapy with TIM-3 blockade leads to synergistic tumor killing. Our analyses highlight chemomodulation of the immune microenvironment and provide a framework for combined chemo-immunotherapies

Selinexor in Advanced, Metastatic Dedifferentiated Liposarcoma: A Multinational, Randomized, Double-Blind, Placebo-Controlled Trial

PURPOSE Antitumor activity in preclinical models and a phase I study of patients with dedifferentiated liposarcoma (DD-LPS) was observed with selinexor. We evaluated the clinical benefit of selinexor in patients with previously treated DD-LPS whose sarcoma progressed on approved agents. METHODS SEAL was a phase II-III, multicenter, randomized, double-blind, placebo-controlled study. Patients age 12 years or older with advanced DD-LPS who had received two-five lines of therapy were randomly assigned (2:1) to selinexor (60 mg) or placebo twice weekly in 6-week cycles (crossover permitted). The primary end point was progression-free survival (PFS). Patients who received at least one dose of study treatment were included for safety analysis (ClinicalTrials.gov identifier: ). RESULTS Two hundred eighty-five patients were enrolled (selinexor, n = 188; placebo, n = 97). PFS was significantly longer with selinexor versus placebo: hazard ratio (HR) 0.70 (95% CI, 0.52 to 0.95; one-sided P = .011; medians 2.8 v 2.1 months), as was time to next treatment: HR 0.50 (95% CI, 0.37 to 0.66; one-sided P < .0001; medians 5.8 v 3.2 months). With crossover, no difference was observed in overall survival. The most common treatment-emergent adverse events of any grade versus grade 3 or 4 with selinexor were nausea (151 [80.7%] v 11 [5.9]), decreased appetite (113 [60.4%] v 14 [7.5%]), and fatigue (96 [51.3%] v 12 [6.4%]). Four (2.1%) and three (3.1%) patients died in the selinexor and placebo arms, respectively. Exploratory RNA sequencing analysis identified that the absence of CALB1 expression was associated with longer PFS with selinexor compared with placebo (median 6.9 v 2.2 months; HR, 0.19; P = .001). CONCLUSION Patients with advanced, refractory DD-LPS showed improved PFS and time to next treatment with selinexor compared with placebo. Supportive care and dose reductions mitigated side effects of selinexor. Prospective validation of CALB1 expression as a predictive biomarker for selinexor in DD-LPS is warranted. (C) 2022 by American Society of Clinical Oncolog

A self-adaptive quantum equilibrium optimizer with artificial bee colony for feature selection

Feature selection (FS) is a popular data pre-processing technique in machine learning to extract the optimal features to maintain or increase the classification accuracy of the dataset, which is a combinatorial optimization problem, requiring a powerful optimizer to obtain the optimum subset. The equilibrium optimizer (EO) is a recent physical-based metaheuristic algorithm with good performance for various optimization problems, but it may encounter premature or the local convergence in feature selection. This work presents a self-adaptive quantum EO with artificial bee colony for feature selection, named SQEOABC. In the proposed algorithm, the quantum theory and the self-adaptive mechanism are employed into the updating rule of EO to enhance convergence, and the updating mechanism from the artificial bee colony is also incorporated into EO to achieve appropriate FS solutions. In the experiments, 25 benchmark datasets from the UCI repository are investigated to verify SQEOABC, which is compared with several state-of-the-art metaheuristic algorithms and the variants of EO. The statistical results of fitness values and accuracy demonstrate that SQEOABC has better performance than the compared algorithms and the variants of EO. Finally, a real-world FS problem from COVID-19 illustrates the effectiveness and superiority of SQEOABC

Multi-objective SHADE with manta ray foraging optimizer for structural design problems

This paper presents a hybrid multi-objective success history-based parameter adaptive differential evolution (SHADE) with manta ray foraging optimizer (MRFO) for structural design problems, called MO-SHADE-MRFO. In the proposed algorithm, the updating rules of SHADE, a variant of differential evolution with great performance, are combined with the operators from MRFO, a recent swarm-based metaheuristic algorithm inspired from the manta ray with cyclone, chain and somersault foraging behaviors, which can balance the exploration and exploitation of the algorithm for structural design problems. Furthermore, MO-SHADE-MRFO utilizes the external archive to save and update the obtained Pareto fronts during the optimization process. The proposed algorithm is verified by multi-objective truss optimization problems with two objectives of minimizing the structural weight and the compliance, including 10-bar, 25-bar, 37-bar, 120-bar, 200-bar and 942-bar truss problems. Moreover, 9 different multi-objective metaheuristic algorithms are implemented to compare with the proposed algorithm, where three metrics are used to measure the performance of the algorithms, including hypervolume (HV), inverted generational distance (IGD), and spacing-to-extent (STE). According to the experimental results, MO-SHADE-MRFO can provide the best statistical values of HV, IGD and STE in most cases, ranking the first among the compared algorithms. Besides, the proposed algorithm also gives well-distributed Pareto solutions for the tested problems, illustrating the effectiveness of the hybrid updating rules of SHADE and MRFO

Human Gut Microbiota and Gastrointestinal Cancer

Human gut microbiota play an essential role in both healthy and diseased states of humans. In the past decade, the interactions between microorganisms and tumors have attracted much attention in the efforts to understand various features of the complex microbial communities, as well as the possible mechanisms through which the microbiota are involved in cancer prevention, carcinogenesis, and anti-cancer therapy. A large number of studies have indicated that microbial dysbiosis contributes to cancer susceptibility via multiple pathways. Further studies have suggested that the microbiota and their associated metabolites are not only closely related to carcinogenesis by inducing inflammation and immune dysregulation, which lead to genetic instability, but also interfere with the pharmacodynamics of anticancer agents. In this article, we mainly reviewed the influence of gut microbiota on cancers in the gastrointestinal (GI) tract (including esophageal, gastric, colorectal, liver, and pancreatic cancers) and the regulation of microbiota by diet, prebiotics, probiotics, synbiotics, antibiotics, or the Traditional Chinese Medicine. We also proposed some new strategies in the prevention and treatment of GI cancers that could be explored in the future. We hope that this review could provide a comprehensive overview of the studies on the interactions between the gut microbiota and GI cancers, which are likely to yield translational opportunities to reduce cancer morbidity and mortality by improving prevention, diagnosis, and treatment. Keywords: Inflammation, Immune regulation, Microbial metabolites, Carcinogenesis, Traditional Chinese Medicin

Human Gut Microbiota and Gastrointestinal Cancer.

Human gut microbiota play an essential role in both healthy and diseased states of humans. In the past decade, the interactions between microorganisms and tumors have attracted much attention in the efforts to understand various features of the complex microbial communities, as well as the possible mechanisms through which the microbiota are involved in cancer prevention, carcinogenesis, and anti-cancer therapy. A large number of studies have indicated that microbial dysbiosis contributes to cancer susceptibility via multiple pathways. Further studies have suggested that the microbiota and their associated metabolites are not only closely related to carcinogenesis by inducing inflammation and immune dysregulation, which lead to genetic instability, but also interfere with the pharmacodynamics of anticancer agents. In this article, we mainly reviewed the influence of gut microbiota on cancers in the gastrointestinal (GI) tract (including esophageal, gastric, colorectal, liver, and pancreatic cancers) and the regulation of microbiota by diet, prebiotics, probiotics, synbiotics, antibiotics, or the Traditional Chinese Medicine. We also proposed some new strategies in the prevention and treatment of GI cancers that could be explored in the future. We hope that this review could provide a comprehensive overview of the studies on the interactions between the gut microbiota and GI cancers, which are likely to yield translational opportunities to reduce cancer morbidity and mortality by improving prevention, diagnosis, and treatment

Human Gut Microbiota and Gastrointestinal Cancer

Human gut microbiota play an essential role in both healthy and diseased states of humans. In the past decade, the interactions between microorganisms and tumors have attracted much attention in the efforts to understand various features of the complex microbial communities, as well as the possible mechanisms through which the microbiota are involved in cancer prevention, carcinogenesis, and anti-cancer therapy. A large number of studies have indicated that microbial dysbiosis contributes to cancer susceptibility via multiple pathways. Further studies have suggested that the microbiota and their associated metabolites are not only closely related to carcinogenesis by inducing inflammation and immune dysregulation, which lead to genetic instability, but also interfere with the pharmacodynamics of anticancer agents. In this article, we mainly reviewed the influence of gut microbiota on cancers in the gastrointestinal (GI) tract (including esophageal, gastric, colorectal, liver, and pancreatic cancers) and the regulation of microbiota by diet, prebiotics, probiotics, synbiotics, antibiotics, or the Traditional Chinese Medicine. We also proposed some new strategies in the prevention and treatment of GI cancers that could be explored in the future. We hope that this review could provide a comprehensive overview of the studies on the interactions between the gut microbiota and GI cancers, which are likely to yield translational opportunities to reduce cancer morbidity and mortality by improving prevention, diagnosis, and treatment. Keywords: Inflammation, Immune regulation, Microbial metabolites, Carcinogenesis, Traditional Chinese Medicin

Gefitinib-Integrated Regimen versus Chemotherapy Alone in Heavily Pretreated Patients with Epidermal Growth Factor Receptor–Mutated Lung Adenocarcinoma: A Case-Control Study

BACKGROUND: The study aimed to compare the tolerability and efficacy of gefitinib combined with chemotherapy agents versus chemotherapy alone for the treatment of epidermal growth factor receptor (EGFR)–mutated lung adenocarcinoma in heavily pretreated patients. METHODS: The study was designed as a matched-pair case-control investigation to minimize intergroup heterogeneity. Patients were stratified into gefitinib plus chemotherapy and chemotherapy alone groups with matching for sex, age, ECOG performance status, progress-free survival (PFS) from previous EGFR tyrosine kinase inhibitor treatment, EGFR mutation types, and tumor metastasis status. RESULTS: Sixty-six patients were selected from our database using the matched-pair method. The median age was 61 years (95% confidence interval, 57-65 years). During a follow-up period of 14.5 months on average, the overall response rates of the gefitinib-integrated and chemotherapy alone groups were 9.1% and 6.5%, respectively (P > .05), whereas the corresponding disease-control rates were 39.4% and 30.3%, respectively (P > .05). No statistically significant differences in PFS (median, 4.2 vs 3.3 months; P = .06) and overall survival (median, 10.4 vs 7.9 months; P = .44) were observed between two groups. The 6-month survival rates of the gefitinib-integrated and chemotherapy alone groups were 21.2% and 12.1%, respectively (P < .05). Side effects were mild, and all treatments were well tolerated. CONCLUSIONS: Our results indicated that gefitinib-integrated therapy offered a trend to better PFS and an improved 6-month survival rate in heavily pretreated patients with metastatic EGFR-mutated lung adenocarcinoma. All treatments were well tolerated. Future prospective studies are warranted to confirm our findings