Even with the advantages of the sine cosine algorithm (SCA) in solving multimodal problems, there are some shortcomings for this method. We observe that the random patterns utilized in SCA cause an increasing attraction toward local optima. This study developed a rationalized version of this technique to deal with several representative benchmark cases with different dimensions. The improved algorithm combines the chaotic local search mechanism and Lévy flight operator with the core trends of SCA. The new variant is named as CLSCA. The Lévy flight with long jumps is adopted to boost the exploratory tendencies of the algorithm, while the chaotic local search mechanism is used as a local search for the destination point, which helps to further enhance the exploitation capability of SCA. Therefore, a suitable equilibrium between the exploration and exploitation can be kept in the CLSCA by two embedded patterns. To investigate the effectiveness and strength of the developed method, the CLSCA was tested on many benchmark functions, including different types of tasks such as single modal, multi-modal, hybrid, and composition functions. We compare the CLSCA with well-known optimizers, like particle swarm optimization (PSO) algorithm, grey wolf optimizer (GWO), SCA with differential evolution (SCADE), opposition-based SCA (OBSCA), fuzzy self-tuning PSO (FST_PSO), chaotic salp swarm algorithm (CSSA), and Chaotic whale optimizer (CWOA). Numerical experimental results demonstrate that the exploratory and exploitative properties of the classical SCA are clearly improved. The experimental results also show that our improved CLSCA is a better technique for different kinds of optimization tasks.

Lung squamous cell carcinoma (LUSC) is a leading cause of mobidity and mortality worldwide. Recently, there was a shift in the treatment pattern of immune therapy in LUSC patients; merely a small number of patients with non-small cell lung cancer (NSCLC) at advanced stages respond well to immune checkpoint blockade (ICB) therapy, and tumor mutation burden (TMB) is a valuable independent indicator of response to immune therapy. However, specific gene mutations and their relationship with TMB and tumor-infiltrating immunocytes in LUSC are still unclear. In the present paper, our team analyzed the somatically mutated genes from the ICGC (International Cancer Genome Consortium) and TCGA (The Cancer Genome Atlas) datasets and discovered that 15 frequent gene mutations occurred in both cohorts, including ZFHX4, MUC16, FLG, TP53, LRP1B, TTN, SYNE1, RYR2, CSMD3, USH2A, MUC17, DNAH5, FAM135B, COL11A1, and RYR3. Interestingly, only mutated TTN was related to higher TMB and prognostic outcomes among the 15 mutated genes. Moreover, according to the CIBERSORT algorithm, we revealed that TTN mutation enhanced the antitumor immune response. In conclusion, TTN may have important clinical implications for relevant immune therapy of lung squamous carcinoma.

This paper has proposed an effective intelligent prediction model that can well discriminate and specify the severity of Coronavirus Disease 2019 (COVID-19) infection in clinical diagnosis and provide a criterion for clinicians to weigh scientific and rational medical decision-making. With indicators as the age and gender of the patients and 26 blood routine indexes, a severity prediction framework for COVID-19 is proposed based on machine learning techniques. The framework consists mainly of a random forest and a support vector machine (SVM) model optimized by a slime mould algorithm (SMA). When the random forest was used to identify the key factors, SMA was employed to train an optimal SVM model. Based on the COVID-19 data, comparative experiments were conducted between RF-SMA-SVM and several well-known machine learning algorithms performed. The results indicate that the proposed RF-SMA-SVM not only achieves better classification performance and higher stability on four metrics, but also screens out the main factors that distinguish severe COVID-19 patients from non-severe ones. Therefore, there is a conclusion that the RF-SMA-SVM model can provide an effective auxiliary diagnosis scheme for the clinical diagnosis of COVID-19 infection.

Background: Baicalin is a flavonoid compound that exerts specific pharmacological effect in attenuating the proliferation, migration, and apoptotic resistance of hypoxia-induced pulmonary artery smooth muscle cells (PASMCs). However, the underlying mechanism has not been fully elucidated yet. Although our previous studies had indicated that activation of A2aR attenuates CXCR expression, little is known about the relationship between A2aR and SDF-1/CXCR4 axis in hypoxic PASMCs. In this study, we aimed to investigate the effect of A2aR on the SDF-1/CXCR4 axis in hypoxic PASMCs, the mechanism underlying this effect, and whether baicalin exerts its protective functions though A2aR. Methods: Rat PASMCs were cultured under normoxia/hypoxia and divided into nine groups: normoxia, hypoxia, hypoxia + AMD3100 (a CXCR4 antagonist), hypoxia + baicalin, hypoxia + negative virus, normoxia + A2aR knockdown, hypoxia + A2aR knockdown, hypoxia + CGS21680 (an A2aR agonist), and hypoxia + A2aR knockdown + baicalin. Lentiviral transfection methods were used to establish the A2aR knockdown model in PASMCs. Cells were incubated under hypoxic conditions for 24 h. Expression levels of A2aR, SDF-1, and CXCR4 were detected using RT-qPCR and western blot. The proliferation and migration rate were observed via CCK-8 and Transwell methods. Cell cycle distribution and cell apoptosis were measured by flow cytometry (FCM) and the In-Situ Cell Death Detection kit (Fluorescein). Results: Under hypoxic conditions, levels of A2aR, SDF-1, and CXCR4 were significantly increased compared to those under normoxia. The trend of SDF-1 and CXCR4 being inhibited when A2aR is up-regulated was more obvious in the baicalin intervention group. Baicalin directly enhanced A2aR expression, and A2aR knockdown weakened the function of baicalin. SDF-1 and CXCR4 expression levels were increased in the hypoxia + A2aR knockdown group, as were the proliferation and migration rates of PASMCs, while the apoptotic rate was decreased. Baicalin and CGS21680 showed opposite effects. Conclusions: Our data indicate that baicalin efficiently attenuates hypoxia-induced PASMC proliferation, migration, and apoptotic resistance, as well as SDF-1 secretion, by up-regulating A2aR and down-regulating the SDF-1/CXCR4 axis.