Optimization Therapy by Coupling Intermittent Androgen Suppression with Impulsive Chemotherapy for a Prostate Cancer Model.

Intermittent androgen suppression in the prostate cancer is often relapsed by the increasing of prostate specific antigen level during the on-treatment. Historically, chemotherapy has had a limited role in the treatment of prostate cancer. However, new agents are showing promise in patients with advanced disease. Intermittent androgen suppression plus chemotherapy in pulsed pattern has become an indispensable clinical scheme for prostate cancer, which is presented to describe the transformation mechanism for three kinds of cancer cells in this paper. The model is then extended to include the residual effect of chemotherapy which suppresses the cancer cells production, thereby preventing the relapse. The optimal controls represent the efficiencies of both intermittent androgen suppression and chemotherapy in suppressing relapse of prostate cancer. Based on an optimal algorithm, numerical simulations are implemented not only to show the optimal durations of on- and off-treatment and chemotherapy dosages but also to present the effectiveness of different strategies in inhibiting the relapse for three types of patients. Results reveal that the optimal intermittent androgen suppression scheme with alterable treatment cycles is pivotal for type I and II patients, in part because it can greatly reduce the on-treatment time and degrade the level of prostate specific antigen. Furthermore, optimal hybrid schedule even averts the relapse of prostate cancer for type II and III patients. Finally, comparing the prostate specific antigen under intermittent androgen suppression schedule with residual effect of chemotherapy to one without residual effect of chemotherapy demonstrates the validity of both our model and algorithms in lessening the prostate specific antigen and decreasing the chemotherapy dosages.

Data-based modeling of breast cancer and optimal therapy.

Excessive accumulation of ß-catenin proteins is a vital driver in the development of breast cancer. Many clinical assessments incorporating immunotherapy with targeted mRNA of ß-catenin are costly endeavor. This paper develops novel mathematical models for different treatments by invoking available clinical data to calibrate models, along with the selection and evaluation of therapy strategies in a faster manner with lower cost. Firstly, in order to explore the interactions between cancer cells and the immune system within the tumor microenvironment, we construct different types of breast cancer treatment models based on RNA interference technique and immune checkpoint inhibitors, which have been proved to be an effective combined therapy in pre-clinical trials associated with the inhibition of ß-catenin proteins to enhance intrinsic anti-tumor immune response. Secondly, various techniques including MCMC are adopted to estimate multiple parameters and thus simulations in agreement with experimental results sustain the validity of our models. Furthermore, the gradient descent method and particle swarm algorithm are designed to optimize therapy schemes to inhibit the growth of tumor and lower the treatment cost. Considering the mechanisms of drug resistance in vivo, simulations exhibit that therapies are ineffective resulting in cancer relapse in the prolonged time. For this reason, parametric sensitivity analysis sheds light on the choice of new treatments which indicate that, in addition to inhibiting ß-catenin proteins and improving self-immunity, the injection of dendritic cells promoting immunity may provide a novel vision for the future of cancer treatment. Overall, our study provides witness of principle from a mathematical perspective to guide clinical trials and the selection of treatment regimens.

Predictive Recognition of DNA-binding Proteins Based on Pre-trained Language Model BERT.

Identifying proteins is crucial for disease diagnosis and treatment. With the increase of known proteins, large-scale batch predictions are essential. However, traditional biological experiments being time-consuming and expensive are difficult to accomplish this task efficiently. Nevertheless, deep learning algorithms based on big data analysis have manifested potential in this aspect. In recent years, language representation models, especially BERT, have made significant advancements in natural language processing. In this paper, using three protein segmentation methods and three encoder numbers, nine BERT models with different sizes are constructed to predict whether known proteins are DNA-binding proteins or not. Furthermore, based on the concept of protein motifs, multi-scale convolutional networks are fused into the models to extract the local features of DNA-binding proteins. Finally, we find that the larger the number of encoders, the better the model predictions under the condition of considering each amino acid in the protein as a word. Our proposed algorithm achieves 81.88% sensitivity and 0.39 MCC value on the test set. Furthermore, it achieves 62.41% accuracy on the independent test set PDB2272. It is evident that our proposed method can be a tool to assist in the identification of DNA-binding proteins.

Facile synthesis of phycocyanin/polydopamine hierarchical nanocomposites for synergizing PTT/PDT against cancer.

The exceptional biocompatibility and biosafety of natural proteins have made them a popular choice for tumor therapy in recent years, but their therapeutic effectiveness is severely constrained by factors including physiological instability, insufficient delivery, limited accumulation in tumor cells, etc. Here, a novel Mn-doped phycocyanin (Pc)/polydopamine (PDA) hierarchical nanostructure (MnPc@P) with excellent optical absorption, photothermal conversion, and photodynamic performances, is first designed and fabricated by a simply one-pot reaction, which not only successfully encapsulates natural protein Pc with intact activity in the nanostructure of MnPc@P but also gives them better biocompatibility. Upon laser irradiation, PDA-mediated hyperthermia and Pc-induced ROS elevation in tumor cells have been demonstrated, leading to drastic tumor cell death via combined PTT/PDT effect, greater than single PTT or PDT. In general, the expert fusion of Pc and PDA into a single nanomedicine opens fascinating perspectives in the delivery of natural proteins and tumor therapy.

FOXD1 is a prognostic biomarker and correlated with macrophages infiltration in head and neck squamous cell carcinoma.

BACKGROUND: Forkhead Box D1 (FOXD1) is differentially expressed in various tumors. However, its role and correlation with immune cell infiltration remains uncertain in head and neck squamous cell carcinoma (HNSC). METHODS: FOXD1 expression was analyzed in The Cancer Genome Atlas (TCGA) pan-cancer data. The clinical prognosis influence of FOXD1 was evaluated by clinical survival data of TCGA. Enrichment analysis of FOXD1 was performed using R packages 'clusterProfiler'. We downloaded the immune cell infiltration score of TCGA samples from published articles, and analyzed the correlation between immune cell infiltration level and FOXD1 expression. RESULTS: FOXD1 was highly expressed and associated with poorer overall survival (OS, P<0.0001), disease-specific survival (DSS, P=0.00011), and progression-free interval (PFI, P<0.0001) in HNSC and some other tumors. In addition, FOXD1 expression was significantly correlated with infiltration of immune cells. Tumor-associated macrophages (TAMs) infiltration increased in tissues with high FOXD1 expression in HNSC. Immunosuppressive genes such as PD-L1, IL-10, TGFB1, and TGFBR1 were significantly positively correlated with FOXD1. CONCLUSIONS: Our study suggests FOXD1 to be an oncogene and act as an indicator of poor prognosis in HNSC. FOXD1 might contribute to the TAM infiltration in HNSC. High FOXD1 may be associated with tumor immunosuppression status.

Amount of Escape Estimation Based on Bayesian and MCMC Approaches for RNA Interference.

The amount of short interfering RNA (siRNA) escaping from the endosome has a significant impact on the efficiency of RNAi. In general, the initial injected amount of siRNAs during the experiment is known, and also the amount of siRNAs after the experiment can be revealed by the level of mRNA measured. However, it is impossible to measure the amount of siRNAs that escape from the endosome and really take part in the chemical reaction of RNAi by detecting the biological organism and its tissues. Inspired by the bottleneck effect in the virus, we introduce the Bayesian approach to infer the amount of escape based on a single type and multiple types of siRNA, respectively. With the consideration of the large calculation quantity of the accurate posterior distribution and the unavailable analytic expression of the likelihood function, our article proposes to take samples by the improved Markov chain Monte Carlo (MCMC) method. The article takes the silencing gene of the synthesis of chitin and the interfering multiple target oncogene as numerical examples to show that our improved MCMC method has higher operation efficiency compared to the Bayesian approach. Our research models siRNA endosome escape using statistical methods for the first time. It perhaps provides a theoretical basis to decrease the cost of a biotic experiment for the future and the standardized statistical approaches for the amount of escape estimation.

Prevalence and Genetic Diversity of Cronobacter Species Isolated From Four Infant Formula Production Factories in China.

The aim of this study was to investigate the prevalence and genotypic characteristics of Cronobacter isolated from powdered infant formula (PIF) manufacturing facilities and to identify a potential source of contamination. A total of 42 Cronobacter isolates (5%) were detected in 835 environmental samples collected during the surveillance study. These isolates included C. sakazakii (n = 37), C. malonaticus (n = 3), and C. turicensis (n = 2). The isolates were divided into 14 sequence types (STs) by multi-locus sequence typing (MLST) and 21 pulsotypes (PTs) using pulsed-field gel electrophoresis (PFGE). The dominant C. sakazakii sequence types were ST3 (n = 12) and ST21 (n = 10), followed by ST136 (n = 6). The major PTs were PT22 (n = 12) and PT17 (n = 4) based on 100% similarity. Strains isolated from samples collected at the same production facility showed closer phylogenetic relation than those collected from distinct facilities. The result of extensive traceback sampling showed that PIF residues (PIF dust in production areas), fluid beds, drying areas, floors, and soil samples collected adjacent to the production facilities were the primary positive areas for Cronobacter. The present study outlines an effective approach to determine prevalence and genetic diversity of Cronobacter isolates associated with contamination of PIF.

Periodicity and dosage optimization of an RNAi model in eukaryotes cells.

BACKGROUND: As a highly efficient and specific gene regulation technology, RNAi has broad application fields and good prospects. The effect of RNAi enhances as the dosage of siRNA increases, while an exorbitant siRNA dosage will inhibit the RNAi effect. So it is crucial to formulate a dose-effect model to describe the degradation effects of the target mRNA at different siRNA dosages. RESULTS: In this work, a simple RNA interference model with hill kinetic function (Giulia Cuccato et al. (2011)) is extended. Firstly, by introducing both the degradation time delay τ1 of mRNA caused by siRNA and the transportation time delay τ2 of mRNA from the nucleus to the cytoplasm during protein translation, one acquires a novel delay differential equations (DDEs) model with physiology lags. Secondly, qualitative analyses are executed to identify regions of stability of the positive equilibrium and to determine the corresponding parameter scales. Next, the approximate period of the limit cycle at Hopf bifurcation points is computed. Furthermore we analyze the parameter sensitivity of the limit cycle. Finally, we propose an optimal strategy to select siRNA dosage which arouses significant silencing efficiency. CONCLUSIONS: Our researches indicate that when the dosage of siRNA is large, oscillating periods are identical for disparate number of siRNA target sites even if it greatly impacts the critical siRNA dosage which is the switch of oscillating behavior. Furthermore, parametric sensitivity analyses of limit cycle disclose that both of degradation lag and maximum degradation rate of mRNA due to RNAi are principal elements on determining periodic oscillation. Our explorations will provide evidence for gene regulation and RNAi.

Pest management of a prey-predator model with sexual favoritism.

Although sex of prey is an important factor for the risk of predating, few articles consider the consequences of sexual favoritism and the corresponding effects on the impulsive predator-prey dynamics and its utility in biological control. This paper investigates the pest management strategy of a prey-predator system model with sexual favoritism. An impulsive differential equation which models the process of periodically releasing natural enemies and spraying pesticides at different fixed time for pest control is proposed and investigated. It is proved that the pest-eradication periodic solution is globally asymptotically stable under the assumption that the release amount of the predator is greater than some critical value. Permanent conditions are established under the assumption that the release amount of the predator is less than another critical value. In particular, two single control strategies are proposed. Furthermore, we compare three pest control strategies and find that if we choose narrow-spectrum pesticides that targeted to a specific pest's life cycle to kill the pest, then the combined strategy is preferable. Finally, the corresponding system with no sexual favoritism is investigated. The results indicate that we can release fewer amount of the predators to eliminate the preys with sexual favoritism than without and any strong sexual favoritism will drive the pest towards extinction. In view of the biological meaning, the sexual favoritism plays a more active role in suppressing insect pests.

Extinction and permanence of one-prey multi-predators of Holling type II function response system with impulsive biological control.

In this paper, one investigates the dynamic behaviors of one-prey multi-predator model with Holling type II functional response by introducing impulsive biological control strategy (periodic releasing natural enemies at different fixed time). By using Floquet theorem and small amplitude perturbation method, it is proved that there exists an asymptotically stable pest-eradication periodic solution when the impulsive period is less than some critical value and permanence condition is established via the method of comparison involving multiple Liapunov functions. It is shown that multi-predator impulsive control strategy is more effective than the classical and single one.