A series of 4-substituted methoxylbenzoyl-aryl-thiazoles (SMART) have been discovered and synthesized as a result of structural modifications of the lead compound 2-arylthiazolidine-4-carboxylic acid amides (ATCAA). The antiproliferative activity of the SMART agents against melanoma and prostate cancer cells was improved from μM to low nM range compared with ATCAA series. The structure-activity relationship was discussed from modifications of "A", "B" "C" rings and the linker. Preliminary mechanism of action studies indicated that these compounds exert their anticancer activity through inhibition of tubulin polymerization.

Barnes et al. show that a bioactive lipid, lysophosphatidylserine, negatively influences T reg cell accumulation and activity through one of its receptors, GPR174. The authors speculate that GPR174 antagonism may be therapeutic for autoimmune diseases.

Previous work demonstrated that both the opioid antagonist (−)-naloxone and the nonopioid (+)-naloxone inhibit toll-like receptor 4 (TLR4) signaling and reverse neuropathic pain expressed shortly after chronic constriction injury. The present studies reveal that the TLR4 contributes to neuropathic pain in another major model (spinal nerve ligation) and to long established (2–4 mon) neuropathic pain, not just to pain shortly after nerve damage. Additionally, analyses of plasma levels of (+)-naloxone after subcutaneous administration indicate that (+)-naloxone has comparable pharmacokinetics to (−)-naloxone with a relatively short half-life. This finding accounts for the rapid onset and short duration of allodynia reversal produced by subcutaneous (+)-naloxone. Given that TLR2 has also recently been implicated in neuropathic pain, cell lines transfected with either TLR4 or TLR2, necessary co-signaling molecules, and a reporter gene were used to define whether (+)-naloxone effects could be accounted for by actions at TLR2 in addition to TLR4. (+)-Naloxone inhibited signaling by TLR4 but not TLR2. These studies provide evidence for broad involvement of TLR4 in neuropathic pain, both early after nerve damage and months later. Additional, they provide further support for the TLR4 inhibitor (+)-naloxone as a novel candidate for the treatment of neuropathic pain.

Target identification is one of the most critical steps following cell-based phenotypic chemical screens aimed at identifying compounds with potential uses in cell biology and for developing novel disease therapies. Current in silico target identification methods, including chemical similarity database searches, are limited to single or sequential ligand analysis that have limited capabilities for accurate deconvolution of a large number of compounds with diverse chemical structures. Here, we present CSNAP (Chemical Similarity Network Analysis Pulldown), a new computational target identification method that utilizes chemical similarity networks for large-scale chemotype (consensus chemical pattern) recognition and drug target profiling. Our benchmark study showed that CSNAP can achieve an overall higher accuracy (>80%) of target prediction with respect to representative chemotypes in large (>200) compound sets, in comparison to the SEA approach (60–70%). Additionally, CSNAP is capable of integrating with biological knowledge-based databases (Uniprot, GO) and high-throughput biology platforms (proteomic, genetic, etc) for system-wise drug target validation. To demonstrate the utility of the CSNAP approach, we combined CSNAP's target prediction with experimental ligand evaluation to identify the major mitotic targets of hit compounds from a cell-based chemical screen and we highlight novel compounds targeting microtubules, an important cancer therapeutic target. The CSNAP method is freely available and can be accessed from the CSNAP web server (http://services.mbi.ucla.edu/CSNAP/).

Polychlorinated dibenzo-p-dioxins (PCDDs, dioxins), polychlorinated dibenzofurans (PCDFs), and polychlorinated biphenyls (PCBs) are environmental endocrine disruptors that have half-lives of 7–10 years in the human body and have toxicities that probably include carcinogenesis. A high ratio of 4-hydroxyl estradiol (4-OH-E2) to 2-hydroxyl estradiol (2-OH-E2) has been suggested as a potential biomarker for estrogen-dependent neoplasms. In this cohort study of maternal–fetal pairs, we examined the relationship of PCDD/PCDF and PCB exposure to levels of estrogen metabolites in the sera of 50 pregnant women 25–34 years of age from central Taiwan. Maternal blood was collected during the third trimester, and the placenta was collected at delivery. We measured 17 dioxin congeners, 12 dioxin-like PCBs, and 6 indicator PCBs in placenta using gas chromatography coupled with high-resolution mass spectrometry. Estrogen metabolites in maternal serum were analyzed by liquid chromatography tandem mass spectrometry. The ratio of 4-OH-E2:2-OH-E2 decreased with increasing exposure to 2,3,7,8-tetrachlorodibenzo-p-dioxin (β = −0.124, p = 0.004 by the general linear regression model, R = 0.4). Meanwhile, serum levels of 4-OH-E2 increased with increasing concentrations of high-chlorinated PCDFs (i.e., 1,2,3,4,6,7,8-hepta-CDF: β = 0.454, p = 0.03, R = 0.30). Altered estrogen catabolism might be associated with body burdens of PCDDs/PCDFs. Our study suggests that exposure to PCDDs/PCDFs significantly affects estrogen metabolism. Therefore, PCDD/PCDF exposure must be considered when using the OH-E2 ratio as a breast cancer marker.

Group B streptococcus (GBS) is a common asymptomatic colonizer in acidic vagina of pregnant women and can transmit to newborns, causing neonatal pneumonia and meningitis. Biofilm formation is often associated with bacterial colonization and pathogenesis. Little is known about GBS biofilm and the effect of environmental stimuli on their growth along with biofilm formation. The objective of this study was to investigate the survival and biofilm formation of GBS, isolated from pregnant women, in nutrient-limited medium under various pH conditions. Growth and survival experiments were determined by optical density and viable counts. Crystal violet staining, scanning electron microscopy, and atomic force microscopy (AFM) were used to analyze the capacity of biofilm production. Our results showed that GBS isolates proliferated with increasing pH with highest maximum specific growth rate (μmax) at pH 6.5, but survived at pH 4.5 for longer than 48 h. Biofilm formation of the 80 GBS isolates at pH 4.5 was significantly higher than at pH 7.0. This difference was confirmed by two other methods. The low elastic modulus obtained from samples at pH 4.5 by AFM revealed the softness of biofilm; in contrast, little or no biofilm was measured at pH 7.0. Under acidic pH, the capability of biofilm formation of serotypes III and V showed statistically significant difference from serotypes Ia and Ib. Our finding suggested that survival and enhanced biofilm formation at vaginal pH are potentially advantageous for GBS in colonizing vagina and increase the risk of vaginosis and neonatal infection.

No abstract

Digital physiological signals in telecare medicine information systems have been widely applied in remote medical applications, such as telecare, tele-examination, and telediagnosis, via computer networking transmission or wireless communication. However, these medical records need to ensure authorization demands in the channel model for human body communication and remote medical servers and enhance the confidentiality, recoverability, and availability of transmission data. Hence, this study proposes a symmetric cryptography scheme with a chaotic map and a multilayer machine learning network (MMLN) to achieve physiological signal infosecurity. A chaotic pseudorandom number generator within specific control parameters can dynamically produce unordered sequence numbers to set the secret keys for a regular secret key update, thereby improving the security of private cipher codes. The chaotic map is quickly iterated to produce a pseudorandom key stream for real-time applications, and the private cipher codes are selected using the initial and specific control parameters at the data emitter and receiver ends. A general regression neural network is used to map the highdimensional input-output pair of cipher codes for substitution and permutation processes. Its adaptive MMLN with an optimization algorithm can rapidly train the random cipher code protocol to achieve an encryptor and a decryptor for a regular encrypted communication. Using the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) Arrhythmia Database, 100 electrocardiogram fragments are used to verify the proposed model, and the peak signal-to-noise ratio (PSNR) as a quantitative quality metric is used to evaluate the visual quality after encryption and decryption processes for further diagnosis applications. Experimental results show that the proposed scheme has a higher mean PSNR (35.26  3.77 dB) and shorter mean executing time (0.16  0.01 s) compared with traditional cryptography protocol schemes.