A Proof-of-Concept Study on the Theranostic Potential of 177 Lu-labeled Biocompatible Covalent Polymer Nanoparticles for Cancer Targeted Radionuclide Therapy.

Theranostic nanomedicine combined bioimaging and therapy probably rises more helpful and interesting opportunities for personalized medicine. In this work, 177 Lu radiolabeling and surface PEGylation of biocompatible covalent polymer nanoparticles (CPNs) have generated a new theranostic nanoformulation (177 Lu-DOTA-PEG-CPNs) for targeted diagnosis and treatment of breast cancer. The in vitro anticancer investigations demonstrate that 177 Lu-DOTA-PEG-CPNs possess excellent bonding capacity with breast cancer cells (4T1), inhibiting the cell viability, leading to cell apoptosis, arresting the cell cycle, and upregulating the reactive oxygen species (ROS), which can be attributed to the good targeting ability of the nanocarrier and the strong relative biological effect of the radionuclide labelled compound. Single photon emission computed tomography/ computed tomography (SPECT/CT) imaging and in vivo biodistribution based on 177 Lu-DOTA-PEG-CPNs reveal that notable radioactivity accumulation at tumor site in murine 4T1 models with both intravenous and intratumoral administration of the prepared radiotracer. Significant tumor inhibition has been observed in mice treated with 177 Lu-DOTA-PEG-CPNs, of which the median survival was highly extended. More strikingly, 50 % of mice intratumorally injected with 177 Lu-DOTA-PEG-CPNs was cured and showed no tumor recurrence within 90 days. The outcome of this work can provide new hints for traditional nanomedicines and promote clinical translation of 177 Lu radiolabeled compounds efficiently.

Multimodal Imaging-Guided Strategy for Developing 177Lu-Labeled Metal-Organic Framework Nanomedicine with Potential in Cancer Therapy.

Nano-metal-organic frameworks (nano-MOFs) labeled with radionuclides have shown great potential in the anticancer field. In this work, we proposed to combine fluorescence imaging (FI) with nuclear imaging to systematically evaluate the tumor inhibition of new nanomedicines from living cancer cells to the whole body, guiding the design and application of a high-performance anticancer radiopharmaceutical to glioma. An Fe-based nano-MOF vector, MIL-101(Fe)/PEG-FA, was decorated with fluorescent sulfo-cyanine7 (Cy7) to investigate the binding affinity of the targeting nanocarriers toward glioma cells in vitro, as well as possible administration modes for in vivo cancer therapy. Then, lutetium-177 (177Lu)-labeled MIL-101(Fe)/PEG-FA was prepared for high-sensitive imaging and targeted radiotherapy of glioma in vivo. It has been demonstrated that the obtained 177Lu-labeled MIL-101(Fe)/PEG-FA can work as a complementary probe to rectify the cancer binding affinity of the prepared nanocarrier given by fluorescence imaging, providing more precise biodistribution information. Besides, 177Lu-labeled MIL-101(Fe)/PEG-FA has excellent antitumor effect, leading to cell proliferation inhibition, upregulation of intracellular reactive oxygen species, tumor growth suppression, and immune response-related protein and cytokine upregulation. This work reveals that optical imaging and nuclear imaging can work complementarily as multimodal imaging in the design and evaluation of anticancer nanomedicine, offering a MIL-101(Fe)/PEG-FA-based pharmaceutical with potential in tumor endoradiotherapy.

DeepKeyGen: A Deep Learning-Based Stream Cipher Generator for Medical Image Encryption and Decryption.

The need for medical image encryption is increasingly pronounced, for example, to safeguard the privacy of the patients' medical imaging data. In this article, a novel deep learning-based key generation network (DeepKeyGen) is proposed as a stream cipher generator to generate the private key, which can then be used for encrypting and decrypting of medical images. In DeepKeyGen, the generative adversarial network (GAN) is adopted as the learning network to generate the private key. Furthermore, the transformation domain (that represents the "style" of the private key to be generated) is designed to guide the learning network to realize the private key generation process. The goal of DeepKeyGen is to learn the mapping relationship of how to transfer the initial image to the private key. We evaluate DeepKeyGen using three data sets, namely, the Montgomery County chest X-ray data set, the Ultrasonic Brachial Plexus data set, and the BraTS18 data set. The evaluation findings and security analysis show that the proposed key generation network can achieve a high-level security in generating the private key.

Using auto covariance method for functional discrimination of membrane proteins based on evolution information.

Membrane transporters are critical in living cells. Therefore, the discrimination of the types of membrane proteins based on their functions is of great importance both for helping genome annotation and providing a supplementary role to experimental researchers to gain insight into membrane proteins' function. There are a lot of computational methods to facilitate the identification of the functional types of membrane proteins. However, in these methods, the local sequence environment was not integrated into the constructed model. In this study, we described a new strategy to predict the functional types of membrane proteins using a model based on auto covariance and position-specific scoring matrix. The novelty of the presented approach is considering the distribution of different positions of functional conservation sites in protein sequences. Thereby, this model adequately takes into account the long-range correlation between such sites during sequential evolution. Fivefold cross-validation test shows that this method greatly improves the prediction accuracy and achieves an acceptable prediction accuracy of 87.51%. The result indicates that the current approach might be an effective tool for predicting the functional types of membrane proteins only using the primary sequences. The code and dataset used in this article are freely available at http://cic.scu.edu.cn/bioinformatics/predict_membrane.zip.

Effects of neighboring sequence environment in predicting cleavage sites of signal peptides.

Signal peptide has a pivotal role in the translocation of secretory protein. Some models have been designed to predict its cleavage site. It is reported that the cleavage site has relationship with the neighboring sequence environment, i.e., hydrophobic core h-region, and the specific patterns in c-region. In some studies, this finding does facilitate the prediction of cleavage site. However, in these models, sequence environment information is merely taken account of as model inputs and no detailed investigation into its effect on the prediction of cleavage site has been made. In this work, we analyze the constraint on cleave site placed by the hydrophobic core of signal peptide and then use it to improve the performance of the signal peptide cleavage site prediction. Our model is designed as follows: firstly, a sliding window is used to scan sample and artificial neural network (ANN) is employed to give cleavage site/non-cleavage site scores. Then, based on an estimated hydrophobic h-region a correcting function is proposed to improve the prediction result, in which the sequence environment is taken into account. A trend of cleavage site is indicated by our analysis for each position, which is consistent with experimental findings. Through this correcting step, the improvement of prediction accuracy is over 7%. It therefore demonstrates the neighboring sequence environment is helpful for determination of cleavage site. Program written in Matlab can be downloaded from http://www.scucic.cn/combined model/source code.html.

Simultaneous determination of free amino acid content in tea infusions by using high-performance liquid chromatography with fluorescence detection coupled with alternating penalty trilinear decomposition algorithm.

In this paper, a novel application of alternating penalty trilinear decomposition (APTLD) for high-performance liquid chromatography with fluorescence detection (HPLC-FLD) has been developed to simultaneously determine the contents of free amino acids in tea. Although the spectra of amino acid derivatives were similar and a large number of water-soluble compounds are coextracted, APTLD could predict the accurate concentrations together with reasonable resolution of chromatographic and spectral profiles for the amino acids of interest owing to its "second-order advantage". An additional advantage of the proposed method is lower cost than traditional methods. The results indicate that it is an attractive alternative strategy for the routine resolution and quantification of amino acids in the presence of unknown interferences or when complete separation is not easily achieved.

Construction and application of a novel library: Fourier transform infrared wavelet coefficients library.

This article aims at designing a wavelet alternative to Fourier transform infrared spectra (FTIR). In order to select the most suitable wavelet parameters to perform, several decomposition levels and 53 wavelets were tested by trial and error approach, respectively. The result indicated that discrete meyer wavelet (dmey) associated with its third decomposition level was very efficient for this purpose. On the base of it, a novel library named as Fourier transform infrared wavelet coefficients library (FTIR-WC) has been constructed. Finally, two tools such as library search and structure elucidation were developed to evaluate the capability of the new library system. The results obtained were also compared with those from FTIR library by a variety of indices. The results suggested that the new library performed better but with less volume. This work is expected to propose a novel and practical strategy in infrared spectroscopic analysis.

Local sequence information-based support vector machine to classify voltage-gated potassium channels.

In our previous work, we developed a computational tool, PreK-ClassK-ClassKv, to predict and classify potassium (K+) channels. For K+ channel prediction (PreK) and classification at family level (ClassK), this method performs well. However, it does not perform so well in classifying voltage-gated potassium (Kv) channels (ClassKv). In this paper, a new method based on the local sequence information of Kv channels is introduced to classify Kv channels. Six transmembrane domains of a Kv channel protein are used to define a protein, and the dipeptide composition technique is used to transform an amino acid sequence to a numerical sequence. A Kv channel protein is represented by a vector with 2000 elements, and a support vector machine algorithm is applied to classify Kv channels. This method shows good performance with averages of total accuracy (Acc), sensitivity (SE), specificity (SP), reliability (R) and Matthews correlation coefficient (MCC) of 98.0%, 89.9%, 100%, 0.95 and 0.94 respectively. The results indicate that the local sequence information-based method is better than the global sequence information-based method to classify Kv channels.