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The immunoproteasome is a multicatalytic protease that is predominantly expressed in cells of hematopoietic origin. Its elevated expression has been associated with autoimmune diseases, various types of cancer, and inflammatory diseases. The development of immunoproteasome-selective inhibitors with non-peptidic scaffolds remains a challenging task. Here, we describe a focused series of psoralen-based inhibitors of the β5i subunit of the immunoproteasome with different substituents placed at position 4'. The most promising compound was further evaluated through changes at position 3 of the psoralen ring. Despite a small decrease in the inhibitory potency in comparison with the parent compound, we were able to improve the selectivity against other subunits of both the immunoproteasome and the constitutive proteasome. The most potent compounds discriminated between both proteasome types in cell lysates and also showed a decrease in cytokine secretion in peripheral blood mononuclear cells.Mycobacterium abscessus belongs to a group of rapidly growing mycobacteria (RGM) and accounts for approximately 65-80% of lung disease caused by RGM. It is highly pathogenic and is considered the prominent Mycobacterium involved in pulmonary infection in patients with cystic fibrosis and chronic pulmonary disease (CPD). FosM is a putative 134 amino acid fosfomycin resistance enzyme from M. abscessus subsp. bolletii that shares approximately 30-55% sequence identity with other vicinal oxygen chelate (VOC) fosfomycin resistance enzymes and represents the first of its type found in any Mycobacterium species. Genes encoding VOC fosfomycin resistance enzymes have been found in both Gram-positive and Gram-negative pathogens. Given that FosA enzymes from Gram-negative bacteria have evolved optimum activity towards glutathione (GSH) and FosB enzymes from Gram-positive bacteria have evolved optimum activity towards bacillithiol (BSH), it was originally suggested that FosM might represent a fourth class of enzyme that has evolved to utilize mycothiol (MSH). However, a sequence similarity network (SSN) analysis identifies FosM as a member of the FosX subfamily, indicating that it may utilize water as a substrate. Here we have synthesized MSH and characterized FosM with respect to divalent metal ion activation and nucleophile selectivity. Our results indicate that FosM is a Mn2+-dependent FosX-type hydrase with no selectivity toward MSH or other thiols as analyzed by NMR and mass spectroscopy.The CB2 receptor plays a crucial role in analgesia and anti-inflammation. To develop novel CB2 agonists with high efficacy and selectivity, a series of indole derivatives with N-ethyl morpholine moieties (compounds 1-56) were designed, synthesized and biologically evaluated. Compounds 1, 2, 3, 46 and 53 exhibited high CB2 receptor affinity at low nanomolar concentrations and good receptor selectivity (EC50(CB1)/EC50(CB2) greater than 1000). The most active compound, compound 2, was more potent than the standard drug GW405833 for in vitro agonistic action on the CB2 receptor. More importantly, in a rat model for CFA-induced inflammatory hyperalgesia, compound 2 had a potent anti-inflammatory pain effect within 12 hours after administration. At the 1 h time point, compound 2 had a dose-dependent reversal for hyperalgesia with an estimated ED50 value of 1.097 mg kg-1. Moreover, compound 2 significantly suppressed the pro-inflammatory cytokines (IL-1β, IL-6 and TNF-α) in CFA-induced lesions. These protective effects of compound 2 on inflammatory pain were superior to those of GW405833, suggesting that compound 2 may be a promising therapeutic drug that needs further validation.TOTA (trioxatriangulenium ion) is a close-shelled carbocation known to intercalate strongly with the DNA double helix (J. Reynisson, G. B. Schuster, S. B. Howerton, L. D. Williams, R. N. #link# Barnett, C. L. Cleveland, U. Landman, N. link2 Harrit, J. B. Chaires, J. Am. Chem. Soc. 2003, 125, 2072). The cytotoxicity of TOTA and its four close structural analogues, ADOTA, Pr-ADOTA, Pr-DAOTA and n-Butyl-TATA were tested against the breast cancer cell line MDA-MB-231 and colon cancer cell line HCT116. The most potent derivatives Pr-ADOTA and Pr-DAOTA had IC50 values of ∼80 nM for MDA-MB-231 but slightly higher for HCT116 in the low hundreds nM range. A 3D model assay of HCT116 spheroids was also used, mimicking a tumour environment, again both Pr-ADOTA and Pr-DAOTA were very active with IC50 values of 38 nM and 21 nM, respectively. Molecular modelling suggest that the planar derivatives intercalate between the base pairs of the DNA double helix. However, only modest DNA double stranded DNA cleavage was observed using the γH2AX assay as compared to camptothecin, a topoisomerase I poison suggesting a different mechanism. Finally, a robust density functional theory (DFT) model was built to predict the pKR+ stability values, i.e., to design derivatives, which predominantly have a non-intercalating buckled form in healthy tissues followed by a nucleophilic attach of water on the central carbon, but a planar form at relatively low pH values rendering them only cytotoxic in the interior of tumours.This review is about the significance of the use of lipidomic analysis for identifying susceptibility to skin diseases. Exactly this article describes the use of lipidomic analysis in different studies to detect abnormalities in the lipid composition of the skin to diagnose and prevent various dermatological diseases.Mobile health (m-health) is the term of monitoring the health using mobile phones and patient monitoring devices etc. It has been often deemed as the substantial breakthrough in technology in this modern era. Recently, artificial intelligence (AI) and big data analytics have been applied within the m-health for providing an effective healthcare system. Various types of data such as electronic health records (EHRs), medical images, and complicated text which are diversified, poorly interpreted, and extensively unorganized have been used in the modern medical research. This is an important reason for the cause of various unorganized and unstructured datasets due to emergence of mobile applications along with the healthcare systems. In this paper, a systematic review is carried out on application of AI and the big data analytics to improve the m-health system. Various AI-based algorithms and frameworks of big data with respect to the source of data, techniques used, and the area of application are also discussed. This paper explores the applications of AI and big data analytics for providing insights to the users and enabling them to plan, using the resources especially for the specific challenges in m-health, and proposes a model based on the AI and big data analytics for m-health. G418 of this paper will guide the development of techniques using the combination of AI and the big data as source for handling m-health data more effectively.

Blood, like fresh produce, is a perishable element, with platelets having a limited lifetime of five days and red blood cells lasting 42 days. To manage the blood supply chain more effectively under demand and supply uncertainty, it is of considerable importance to developing a practical blood supply chain model. This paper proposed an essential blood supply chain model under demand and supply uncertainty.

This study focused on how to manage the blood supply chain under demand and supply uncertainty effectively. A stochastic mixed-integer linear programming (MILP) model for the blood supply chain is proposed. Furthermore, this study conducted a sensitivity analysis to examine the impacts of the coefficient of demand and supply variation and the cost parameters on the average total cost and the performance measures (units of shortage, outdated units, inventory holding units, and purchased units) for both the blood center and hospitals.

Based on the results, the hospitals and the blood center can choose tthe most efficient inventory policy with a minimum cost based on the uncertainty of blood supply and demand. The model also performs as a decision support system to help health care professionals manage and control blood inventory more effectively under blood supply and demand uncertainty, thus reducing shortage of blood and expired wastage of blood.

To establish the correlation model between Traditional Chinese Medicine (TCM) constitution and physical examination indexes by backpropagation neural network (BPNN) technology. A new method for the identification of TCM constitution in clinics is proposed, which is trying to solve the problem like shortage of TCM doctor, complicated process, low efficiency, and unfavorable application in the current TCM constitution identification methods.

The corresponding effective samples were formed by sorting out and classifying the original data which were collected from physical examination indexes and TCM constitution types of 950 physical examinees, who were examined at the affiliated hospital of Chengdu University of TCM. The BPNN algorithm was implemented using the C# programming language and Google's AI library. Then, the training group and the test (validation) group of the effective samples were, respectively, input into the algorithm, to complete the construction and validation of the target model.

For alonstitution, and it may be expected to avoid the existing problem of TCM constitution identification at present.

The more the physical examination indexes are used in training, the more accurate the network model is established to predict TCM constitution. The sample data used in this paper showed that there was a relatively strong correlation between TCM constitution and physical examination indexes. Construction of the correlation model between physical examination indexes and TCM constitution is a kind of study for the integration of Chinese and Western medicine, which provides a new approach for the identification of TCM constitution, and it may be expected to avoid the existing problem of TCM constitution identification at present.Neck injury is one of the most frequent spine injuries due to the complex structure of the cervical spine. The high incidence of neck injuries in collision accidents can bring a heavy economic burden to the society. Therefore, knowing the potential mechanisms of cervical spine injury and dysfunction is significant for improving its prevention and treatment. The research on cervical spine dynamics mainly concerns the fields of automobile safety, aeronautics, and astronautics. Numerical simulation methods are beneficial to better understand the stresses and strains developed in soft tissues with investigators and have been roundly used in cervical biomechanics. In this article, the simulation methods for the development and application of cervical spine dynamic problems in the recent years have been reviewed. The study focused mainly on multibody and finite element models. The structure, material properties, and application fields, especially the whiplash injury, were analyzed in detail. It has been shown that simulation methods have made remarkable progress in the research of cervical dynamic injury mechanisms, and some suggestions on the research of cervical dynamics in the future have been proposed.