Immunomodulatory Peptides for Tumor Treatment.

Peptides exhibit various biological activities, including biorecognition, cell targeting, and tumor penetration, and can stimulate immune cells to elicit immune responses for tumor immunotherapy. Peptide self-assemblies and peptide-functionalized nanocarriers can reduce the effect of various biological barriers and the degradation by peptidases, enhancing the efficiency of peptide delivery and improving antitumor immune responses. To date, the design and development of peptides with various functionalities have been extensively reviewed for enhanced chemotherapy; however, peptide-mediated tumor immunotherapy using peptides acting on different immune cells, to the knowledge, has not yet been summarized. Thus, this work provides a review of this emerging subject of research, focusing on immunomodulatory anticancer peptides. This review introduces the role of peptides in the immunomodulation of innate and adaptive immune cells, followed by a link between peptides in the innate and adaptive immune systems. The peptides are discussed in detail, following a classification according to their effects on different innate and adaptive immune cells, as well as immune checkpoints. Subsequently, two delivery strategies for peptides as drugs are presented: peptide self-assemblies and peptide-functionalized nanocarriers. The concluding remarks regarding the challenges and potential solutions of peptides for tumor immunotherapy are presented.

Construction of site-specific magnetic Z-scheme CdS/Fe3O4@N-doped graphene aerogel microtube/N-doped TiO2 with porous structure: enhanced catalytic performance in photo-Fenton reaction.

The development of composite photocatalysts with high charge transfer efficiency, great visible light absorption, and quick recovery has aroused the interest of many researchers. Herein, based on the hydrothermal assisted vacuum freeze drying method, CdS, Fe3O4, and N-TiO2 were, respectively, fixed in the inner, middle, and outer layers of nitrogen-doped graphene aerogel for preparation of the site-specific magnetic porous Z-scheme CdS/Fe3O4@N-doped graphene aerogel microtube/N-doped TiO2 (CdS/Fe3O4@NGAM/N-TiO2) photocatalyst. For the composite, Fe3O4@NGAM carrier with porous and tubular structure not only helps the recycle and reactants/productions mass transport in the photocatalytic process but also ensures the well-steered transfer of electrons and holes from CdS and N-TiO2 in the Z-type heterojunction system, greatly improving the separation of photogenerated carriers. Besides, Fe3O4 can also work as a Fenton catalyst to activate hydrogen peroxide which is generated in situ by CdS. Thus, the CdS/Fe3O4@NGAM/N-TiO2 composite presents excellent degradation efficiencies towards methyl orange ((MO) 98% removal rate within 50 min), bisphenol A ((BPA) 96% removal rate within 50 min), tetracycline hydrochloride ((TCH) 96% removal rate within 120 min) and strong stabilities after 6 cycles. The free radical removal experiments show that ·O2- and ·OH are the main active substances of catalysis, which further confirms the synergistic effect of photocatalysis and Fenton catalysis.

Toward Highly Selective Heteroatom Dopants in Hard Carbon with Superior Lithium Storage Performance.

Hard carbons (HCs) have gained much attention for next-generation high energy density lithium-ion battery (LIB) anode candidates. However, voltage hysteresis, low rate capability, and large initial irreversible capacity severely affect their booming application. Herein, a general strategy is reported to fabricate heterogeneous atom (N/S/P/Se)-doped HC anodes with superb rate capability and cyclic stability based on a three-dimensional (3D) framework and a hierarchical porous structure. The obtained N-doped hard carbon (NHC) exhibits an excellent rate capability of 315 mA h g-1 at 10.0 A g-1 and a long-term cyclic stability of 90.3% capacity retention after 1000 cycles at 3 A g-1. Moreover, the as-constructed pouch cell delivers a high energy density of 483.8 W h kg-1 and fast charging capability. The underlying mechanisms of lithium storage are illustrated by electrochemical kinetic analysis and theoretical calculations. It is demonstrated that heteroatom doping imposes significant effects on adsorption and diffusion for Li+. The versatile strategy in this work opens an avenue for rational design of advanced carbonaceous materials with high performance for LIB applications.

Hyaluronic Acid-Based Nanocarriers for Anticancer Drug Delivery.

Hyaluronic acid (HA), a main component of the extracellular matrix, is widely utilized to deliver anticancer drugs due to its biocompatibility, biodegradability, non-toxicity, non-immunogenicity and numerous modification sites, such as carboxyl and hydroxyl groups. Moreover, HA serves as a natural ligand for tumor-targeted drug delivery systems, as it contains the endocytic HA receptor, CD44, which is overexpressed in many cancer cells. Therefore, HA-based nanocarriers have been developed to improve drug delivery efficiency and distinguish between healthy and cancerous tissues, resulting in reduced residual toxicity and off-target accumulation. This article comprehensively reviews the fabrication of anticancer drug nanocarriers based on HA in the context of prodrugs, organic carrier materials (micelles, liposomes, nanoparticles, microbubbles and hydrogels) and inorganic composite nanocarriers (gold nanoparticles, quantum dots, carbon nanotubes and silicon dioxide). Additionally, the progress achieved in the design and optimization of these nanocarriers and their effects on cancer therapy are discussed. Finally, the review provides a summary of the perspectives, the lessons learned so far and the outlook towards further developments in this field.

EMS3: An Improved Algorithm for Finding Edit-Distance Based Motifs.

Discovering patterns in biological sequences is a crucial step to extract useful information from them. Motifs can be viewed as patterns that occur exactly or with minor changes across some or all of the biological sequences. Motif search has numerous applications including the identification of transcription factors and their binding sites, composite regulatory patterns, similarity among families of proteins, etc. The general problem of motif search is intractable. One of the most studied models of motif search proposed in literature is Edit-distance based Motif Search (EMS). In EMS, the goal is to find all the patterns of length l that occur with an edit-distance of at most d in each of the input sequences. EMS algorithms existing in the literature do not scale well on challenging instances and large datasets. In this paper, the current state-of-the-art EMS solver is advanced by exploiting the idea of dimension reduction. A novel idea to reduce the cardinality of the alphabet is proposed. The algorithm we propose, EMS3, is an exact algorithm. I.e., it finds all the motifs present in the input sequences. EMS3 can be also viewed as a divide and conquer algorithm. In this paper, we provide theoretical analyses to establish the efficiency of EMS3. Extensive experiments on standard benchmark datasets (synthetic and real-world) show that the proposed algorithm outperforms the existing state-of-the-art algorithm (EMS2).

Asynchronous Parallel Stochastic Quasi-Newton Methods.

Although first-order stochastic algorithms, such as stochastic gradient descent, have been the main force to scale up machine learning models, such as deep neural nets, the second-order quasi-Newton methods start to draw attention due to their effectiveness in dealing with ill-conditioned optimization problems. The L-BFGS method is one of the most widely used quasi-Newton methods. We propose an asynchronous parallel algorithm for stochastic quasi-Newton (AsySQN) method. Unlike prior attempts, which parallelize only the calculation for gradient or the two-loop recursion of L-BFGS, our algorithm is the first one that truly parallelizes L-BFGS with a convergence guarantee. Adopting the variance reduction technique, a prior stochastic L-BFGS, which has not been designed for parallel computing, reaches a linear convergence rate. We prove that our asynchronous parallel scheme maintains the same linear convergence rate but achieves significant speedup. Empirical evaluations in both simulations and benchmark datasets demonstrate the speedup in comparison with the non-parallel stochastic L-BFGS, as well as the better performance than first-order methods in solving ill-conditioned problems.

Efficient Algorithms for Finding the Closest l-mers in Biological Data.

With the advances in the next generation sequencing technology, huge amounts of data have been and get generated in biology. A bottleneck in dealing with such datasets lies in developing effective algorithms for extracting useful information from them. Algorithms for finding patterns in biological data pave the way for extracting crucial information from the voluminous datasets. In this paper we focus on a fundamental pattern, namely, the closest l-mers. Given a set of m biological strings S1,S2,…,Sm and an integer l, the problem of interest is that of finding an l-mer from each string such that the distance among them is the least. I.e., we want to find m l-mers X1,X2,…,Xm such that Xi is an l-mer in Si (for 1 ≤ i ≤ m) and the Hamming distance among these m l-mers is the least (from among all such possible l-mers). This problem has many applications including motif search. Algorithms for finding the closest l-mers have been used in solving the (l,d)-motif search problem (see e.g., \cite{PeSz00,DBR07}). In this paper novel algorithms are proposed for this problem for the case of . A comprehensive experimental evaluation is performed for m=3, along with a further empirical study of m=4 and 5.

Minimotif Miner 4: a million peptide minimotifs and counting.

Minimotif Miner (MnM) is a database and web system for analyzing short functional peptide motifs, termed minimotifs. We present an update to MnM growing the database from ∼300 000 to >1 000 000 minimotif consensus sequences and instances. This growth comes largely from updating data from existing databases and annotation of articles with high-throughput approaches analyzing different types of post-translational modifications. Another update is mapping human proteins and their minimotifs to know human variants from the dbSNP, build 150. Now MnM 4 can be used to generate mechanistic hypotheses about how human genetic variation affect minimotifs and outcomes. One example of the utility of the combined minimotif/SNP tool identifies a loss of function missense SNP in a ubiquitylation minimotif encoded in the excision repair cross-complementing 2 (ERCC2) nucleotide excision repair gene. This SNP reaches genome wide significance for many types of cancer and the variant identified with MnM 4 reveals a more detailed mechanistic hypothesis concerning the role of ERCC2 in cancer. Other updates to the web system include a new architecture with migration of the web system and database to Docker containers for better performance and management. Weblinks:minimotifminer.org and mnm.engr.uconn.edu.

Bull serum albumin coated Au@Agnanorods as SERS probes for ultrasensitive osteosarcoma cell detection.

Surface-Enhanced Raman scattering (SERS) has been widely used for imaging and sensing. However, limited reports are currently available on SERS-based cancer cell targeting strategy due to the challenge of synthesizing highly sensitive, reproducible and biocompatible SERS probe. Herein, we developed novel SERS probes, based on BSA (Bull Serum Albumin) coated gold-silver core-shell nanorods modified with Raman reporter 5,5-dithiobis 2-nitrobenzoic acid (DTNB) (Au@AgNRs@BSA@Anti-MICA), for in vitro cancer cell detection. Our results demonstrate that the SERS probe is very robust for cancer cell ultrasensitive detection with good biocompatibility and strong SERS signal.