Cyclic adenosine monophosphate (cAMP) response element-binding (CREB) proteins are a family of mammalian transcription activators. We identified a novel human CREB gene (CREB4) that was 1592 bp long and encoded a protein of 395 amino acid residues. The protein shared high homology to mouse CREB3 (identity 62%, similarity 72%). The expression pattern of the human CREB4 gene showed transcripts in prostate, brain, pancreas, skeletal muscle, small intestine, testis, leukocyte, and thymus, whereas in heart, lung, liver, kidney, placenta, spleen, ovary, and colon, specific bands of the transcript could not be detected. The CREB4 gene consisted of ten exons and nine introns and was mapped to chromosome 1q21.3 by means of a bioinformatics analysis.

Research work on distinguishing humans from animals can help provide priority orders and optimize the distribution of resources in earthquake- or mining-related rescue missions. However, the existing solutions are few and their stability and accuracy of classification are less. This study proposes an accurate method for distinguishing stationary human targets from dog targets under through-wall condition based on ultra-wideband (UWB) radar. Eight humans and five beagles were used to collect 130 samples of through-wall signals using the UWB radar. Twelve corresponding features belonging to four categories were combined using the support vector machine (SVM) method. A recursive feature elimination (RFE) method determined an optimal feature subset from the twelve features to overcome overfitting and poor generalization. The results after ten-fold cross-validation showed that the area under the receiver operator characteristic (ROC) curve can reach 0.9993, which indicates that the two subjects can be distinguished under through-wall condition. The study also compared the ability of the proposed features of four categories when used independently in a classifier. Comparison results indicated that wavelet entropy-corresponding features among them have the best performance. The method and results are envisioned to be applied in various practical situations, such as post-disaster searching, hostage rescues, and intelligent homecare.

Currently, despite the advances in individualized treatment, breast cancer still remains the deadliest form of cancer in women. Diagnostic, prognostic, and therapy-predictive methods are mainly based on the evaluation of tumor tissue samples and are aimed to improve the overall therapeutic level. Therefore, the exploration of a series of circulating biomarkers, which serve as the information source of tumors and could be obtained by peripheral blood samples, represents a high field of interest. Apart from classical biomarkers, exosomes, which are nanovesicles, are emerging as an accessible and efficient source of cell information. The purpose of this review is to summarize the peculiarities of the presently available breast cancer exosomal biomarkers; the review also provides the prediction of a multitude of potential target genes of exosomal microRNAs using 4 databases.

Cell-based immunotherapy holds promise in the quest for the treatment of cancer, having potential synergy with surgery, chemotherapy and radiotherapy. As a novel approach for adoptive cell-based immunotherapy, cytokine-induced killer (CIK) cells have moved from the 'bench to bedside'. CIK cells are a heterogeneous subset of ex-vitro expanded, polyclonal T-effector cells with both natural killer (NK) and Tcell properties, which present potent non-major histocompatibility complex-restricted cytotoxicity against a variety of tumor target cells. Initial clinical studies on CIK cell therapy have provided encouraging results and revealed synergistic antitumor effects when combined with standard therapeutic procedures. At the same time, issues such as inadequate quality control and quantity of CIK cells as well as exaggerated propaganda were continuously emerging. Thus, the Ministry of Health in China stopped CIK cell therapy in May 2016, which was a major setback for the innovation of CIK cell-based immunotherapy. Thus, it is very important to modify technical criteria to develop a standardized operation procedure (SOP) and standardized system for evaluating antitumor efficacy in a safe way.

Radar has been widely applied in many scenarios as a critical remote sensing tool for non-contact vital sign monitoring, particularly for sleep monitoring and heart rate measurement within the home environment. For non-contact monitoring with radar, interference from house pets is an important issue that has been neglected in the past. Many animals have respiratory frequencies similar to those of humans, and they are easily mistaken for human targets in non-contact monitoring, which would trigger a false alarm because of incorrect physiological parameters from the animal. In this study, humans and common pets in families, such as dogs, cats, and rabbits, were detected using an impulse radio ultrawideband (IR-UWB) radar, and the echo signals were analyzed in the time and frequency domains. Subsequently, based on the distinct in-body structure between humans and animals, we propose a parameter, the respiratory and heartbeat energy ratio (RHER), which reflects the contribution rate of breathing and heartbeat in the detected vital signs. Combining this parameter with the energy index, we developed a novel scheme to distinguish between humans and animals. In the developed scheme, after background noise removal and direct-current component suppression, an energy indicator is used to initially identify the target. The signal is then decomposed using a variational mode decomposition algorithm, and the variational intrinsic mode functions that represent human respiration and heartbeat components are obtained and utilized to calculate the RHER parameter. Finally, the RHER index is applied to rapidly distinguish between humans and animals. Our experimental results demonstrate that the proposed approach more effectively distinguishes between humans and animals in terms of monitoring vital signs than the existing methods. Furthermore, its rapidity and need for only minimal calculation resources enable it to meet the needs of real-time monitoring.