Search

Abstract Fluorinated or fluoroalkylated alcohols are common structural motifs in biologically active molecules, natural products, and pharmaceuticals. However, pentafluorosulfanyl (SF5) alcohols, a unique class of SF5 compounds that serve as synthetically valuable building blocks, are difficult to prepare with current methodologies. In this article, we present a single-step, metal-free, and photo-induced hydroxypentafluorosulfanylation of styrenes or α,β-unsaturated esters/amide, producing a series of structurally diverse pentafluorosulfanyl alcohols with up to 89% yields. This reaction is mild and operationally simple, using molecular oxygen as the hydroxy source. The protocol is suitable for a wide range of alkenes, including natural products and drug molecule derivatives. The formed SF5 alcohol units can be readily converted into diverse functionalized SF5 compounds, such as α-SF5 ketones, SF5 diols, and SF5 cyclic carbonates. The potential applications of these SF5 compounds in pharmaceutical and material sciences are vast, making this research a step forward in the field.

Abstract Introduction There remains a critical need for precise localization of the epileptogenic foci in individuals with drug-resistant epilepsy (DRE). 18F-Fluorodeoxyglucose positron emission tomography (FDG-PET) imaging can reveal hypometabolic regions during the interval between seizures in patients with epilepsy. However, visual-based qualitative analysis is time-consuming and strongly influenced by physician experience. CortexID Suite is a quantitative analysis software that helps to evaluate PET imaging of the human brain. Therefore, we aimed to evaluate the efficacy of CortexID quantitative analysis in the localization of the epileptogenic zone in patients with temporal lobe epilepsy (TLE). Methods A total of 102 patients with epilepsy who underwent 18F-FDG-PET examinations were included in this retrospective study. The PET visual analysis was interpreted by two nuclear medicine physicians, and the quantitative analysis was performed automatically using CortexID analysis software. The assumed epileptogenic zone was evaluated comprehensively by two skilled neurologists in the preoperative assessment of epilepsy. The accuracy of epileptogenic zone localization in PET visual analysis was compared with that in CortexID quantitative analysis. Results The diagnostic threshold for the difference in the metabolic Z-score between the right and left sides of medial temporal lobe epilepsy (MTLE) was calculated as 0.87, and that for lateral temporal lobe epilepsy (LTLE) was 2.175. In patients with MTLE, the area under the curve (AUC) was 0.922 for PET visual analysis, 0.853 for CortexID quantitative analysis, and 0.971 for the combined diagnosis. In patients with LTLE, the AUC was 0.842 for PET visual analysis, 0.831 for CortexID quantitative analysis, and 0.897 for the combined diagnosis. These results indicate that the diagnostic efficacy of CortexID quantitative analysis is not inferior to PET visual analysis (p > 0.05), while combined analysis significantly increases diagnostic efficacy (p

Single-phase broken faults occur frequently, affecting the reliability of distribution network. In order to effectively identify the fault type of single-phase broken fault, this paper proposes a new identification method, which is based on the combination of variational mode decomposition and stacked auto encoder with double optimization (AO-VMD-PSO-SAE). Firstly, the zero sequence voltage, which collected in line, is decomposed into a set of variational modal components. Nextly, the stack automatic encoder is used to conduct unsupervised training on the denoised data to establish a depth learning model, and the AO optimization algorithm and the PSO optimization algorithm are used to determine the super parameters in the model.​ Finally, simulation results supported and the validity of the method was verified. What the results show is that the proposed model named AO-VMD-PSO-SAE can accurately predict the types of single-phase broken fault under noise interference.

Cutaneous hypersensitivity reactions represent the most common manifestation of drug allergy seen in the clinic, with 25% of all adverse drug reactions appearing in the skin. The severity of cutaneous eruptions can vastly differ depending on the cellular mechanisms involved from a minor, self-resolving maculopapular rash to major, life-threatening pathologies such as the T-cell mediated bullous eruptions, i.e., Stevens Johnson syndrome/toxic epidermal necrolysis. It remains a significant question as to why these reactions are so frequently associated with the skin and what factors polarise these reactions towards more serious disease states. The barrier function which the skin performs means it is constantly subject to a barrage of danger signals, creating an environment that favors elicitation. Therefore, a critical question is what drives the expansion of cutaneous lymphocyte antigen positive, skin homing, T-cell sub-populations in draining lymph nodes. One answer could be the heterologous immunity hypothesis whereby tissue resident memory T-cells that express T-cell receptors (TCRs) for pathogen derived antigens cross-react with drug antigen. A significant amount of research has been conducted on skin immunity in the context of contact allergy and the role of tissue specific antigen presenting cells in presenting drug antigen to T-cells, but it is unclear how this relates to epitopes derived from circulation. Studies have shown that the skin is a metabolically active organ, capable of generating reactive drug metabolites. However, we know that drug antigens are displayed systemically so what factors permit tolerance in one part of the body, but reactivity in the skin. Most adverse drug reactions are mild, and skin eruptions tend to be visible to the patient, whereas minor organ injury such as transient transaminase elevation is often not apparent. Systemic hypersensitivity reactions tend to have early cutaneous manifestations, the progression of which is halted by early diagnosis and treatment. It is apparent that the preference for cutaneous involvement of drug hypersensitivity reactions is multi-faceted, therefore this review aims to abridge the findings from literature on the current state of the field and provide insight into the cellular and metabolic mechanisms which may contribute to severe cutaneous adverse reactions.

Ebselen is an organoselenium drug that inhibits the SARS-CoV-2 main protease (Mpro). Here, the authors co-crystallised Mpro with ebselen and an ebselen derivative and observed an enzyme bound organoselenium covalent adduct in the crystal structures, which was also confirmed by mass spectrometry analysis.

Abstract The simulation of complex power grid adopts the method of model segmentation. The system is divided into two or more subsystems. Each subsystem is calculated and solved in different computing units. Because the simulation model runs on different hardware, the time synchronization between subsystems may bring errors and stability problems. In this paper, a time synchronization method based on virtual power is proposed. It is assumed that there is a virtual load composed of resistance and inductance between different simulation subsystems, and two identical voltage sources are set at both terminals of the virtual load. When there is no time error between the subsystems, the power consumed by the virtual load should be zero, otherwise virtual power will be generated. The virtual power is sent to the PI regulator as the error signal, and the output of the regulator is used as the time error correction signal of the subsystem until the virtual power is zero, so as to realize the clock synchronization between multiple computers. In this paper, the theoretical analysis and implementation method of this time synchronization scheme are given in detail, and the simulation results show that the method is correct.

Purpose: The prognosis of breast cancer (BC) patients who develop into brain metastases (BMs) is very poor. Thus, it is of great significance to explore the etiology of BMs in BC and identify the key genes involved in this process to improve the survival of BC patients with BMs. Patients and methods: The gene expression data and the clinical information of BC patients were downloaded from TCGA and GEO database. Differentially expressed genes (DEGs) in TCGA-BRCA and GSE12276 were overlapped to find differentially expressed metastatic genes (DEMGs). The protein-protein interaction (PPI) network of DEMGs was constructed via STRING database. ClusterProfiler R package was applied to perform the gene ontology (GO) enrichment analysis of DEMGs. The univariate Cox regression analysis and the Kaplan-Meier (K-M) curves were plotted to screen DEMGs associated with the overall survival and the metastatic recurrence survival, which were identified as the key genes associated with the BMs in BC. The immune infiltration and the expressions of immune checkpoints for BC patients with brain relapses and BC patients with other relapses were analyzed respectively. The correlations among the expressions of key genes and the differently infiltrated immune cells or the differentially expressed immune checkpoints were calculated. The gene set enrichment analysis (GSEA) of each key gene was conducted to investigate the potential mechanisms of key genes involved in BC patients with BMs. Moreover, CTD database was used to predict the drug-gene interaction network of key genes. Results: A total of 154 DEGs were identified in BC patients at M0 and M1 in TCGA database. A total of 667 DEGs were identified in BC patients with brain relapses and with other relapses. By overlapping these DEGs, 17 DEMGs were identified, which were enriched in the cell proliferation related biological processes and the immune related molecular functions. The univariate Cox regression analysis and the Kaplan-Meier curves revealed that CXCL9 and GPR171 were closely associated with the overall survival and the metastatic recurrence survival and were identified as key genes associated with BMs in BC. The analyses of immune infiltration and immune checkpoint expressions showed that there was a significant difference of the immune microenvironment between brain relapses and other relapses in BC. GSEA indicated that CXCL9 and GPR171 may regulate BMs in BC via the immune-related pathways. Conclusion: Our study identified the key genes associated with BMs in BC patients and explore the underlying mechanisms involved in the etiology of BMs in BC. These findings may provide a promising approach for the treatments of BC patients with BMs.

Mitigating the risk of drug hypersensitivity reactions is an important facet of a given pharmaceutical, with poor performance in this area of safety often leading to warnings, restrictions and withdrawals. In the last 50 years, efforts to diagnose, manage, and circumvent these obscure, iatrogenic diseases have resulted in the development of assays at all stages of a drugs lifespan. Indeed, this begins with intelligent lead compound selection/design to minimize the existence of deleterious chemical reactivity through exclusion of ominous structural moieties. Preclinical studies then investigate how compounds interact with biological systems, with emphasis placed on modeling immunological/toxicological liabilities. During clinical use, competent and accurate diagnoses are sought to effectively manage patients with such ailments, and pharmacovigilance datasets can be used for stratification of patient populations in order to optimise safety profiles. Herein, an overview of some of the in-vitro approaches to predict intrinsic immunogenicity of drugs and diagnose culprit drugs in allergic patients after exposure is detailed, with current perspectives and opportunities provided.

Object: Skull base meningiomas with extracranial extensions are rarely described. This study describes the clinical features, surgical management and clinical outcomes of these rare tumors and investigates risk factors associated with progression-free survival (PFS).Methods: The clinical data of 34 consecutive patients who underwent surgery for skull base meningiomas with extracranial extensions from 2007 to 2018 were retrospectively collected and analyzed.Results: The mean patient age was 47.9 ± 13.9 years; 50.0% were male. The most common symptoms on admission were ophthalmic. All patients underwent a multidisciplinary consultation before surgery, and received individualized surgical management. The gross total resection (GTR) rate was 55.9% (19/34). Twelve patients received post-operative adjuvant radiotherapy (RT). Twelve patients experienced tumor recurrence during the follow-up period. The median PFS duration was 54 months. The mean overall survival (OS) duration was 111 months. By univariate analysis, a higher histological grade (WHO grade II and III), Ki-67 LI ≥ 5 and the extent of resection (EOR) were significantly associated with tumor recurrence. Multivariate analysis revealed Ki-67 LI ≥ 5, the EOR and adjuvant RT as prognostic factor of PFS.Conclusions: These relatively rare meningiomas are difficult to resect and have a poor prognosis; they are more common in males and have a higher histological grade than intracranial meningiomas. Multidisciplinary collaboration and individualized surgical strategies are crucial for surgically managing these complex tumors. Total removal of the tumor remains challenging. Subtotal resection (STR) or partial resection (PR) followed by RT is a reasonable strategy when radical resection is infeasible. Adjuvant RT should be recommended especially for tumors with histopathological risk factors (Ki-67 LI ≥ 5 or high histological grade).

In order to apply the cyber-physical system (CPS) co-simulation method based on state cache to a complex distribution network (CDN), a physical model corresponding to multiple operating states should be proposed. As the different spatial resolution of the physical model leads to different performances of the cyber-physical models, a multi-resolution model based on time-state-machine is presented for CDN here. First, the operation process of CDN is divided into five operating states by using time-state-machine method, and the boundary conditions between states are qualitatively discussed. Second, three different spatial resolution physical models of CDN are built, and their simulation performances under different operating states are quantitatively analysed to form a multi-resolution physical model of CDN. Finally, the multi-resolution model of CDN is validated by use of a cyber-physical co-simulation example. Simulation results show that the multi-resolution model is capable to automatically transmission from anyone operating state to another.

The coming interaction between a growing electrified vehicle fleet and the desired growth in renewable energy provides new insights into the economic dispatch (ED) problem. This paper presents an economic dispatch model that considers electric vehicle charging, battery exchange stations, and wind farms. This ED model is a high-dimensional, non-linear, and stochastic problem and its solution requires powerful methods. A new finite action-set learning automata (FALA)-based approach that has the ability to adapt to a stochastic environment is proposed. The feasibility of the proposed approach is demonstrated in a modified IEEE 30 bus system. It is compared with continuous action-set learning automata and particle swarm optimization-based approaches in terms of convergence characteristics, computational efficiency, and solution quality. Simulation results show that the proposed FALA-based approach was indeed capable of more efficiently obtaining the approximately optimal solution. In addition, by using an optimal dispatch schedule for the interaction between electric vehicle stations and power systems, it is possible to reduce the gap between demand and power generation at different times of the day.

Precise and robust localization in a large-scale outdoor environment is essential for an autonomous vehicle. In order to improve the performance of the fusion of GNSS (Global Navigation Satellite System)/IMU (Inertial Measurement Unit)/DMI (Distance-Measuring Instruments), a multi-constraint fault detection approach is proposed to smooth the vehicle locations in spite of GNSS jumps. Furthermore, the lateral localization error is compensated by the point cloud-based lateral localization method proposed in this paper. Experiment results have verified the algorithms proposed in this paper, which shows that the algorithms proposed in this paper are capable of providing precise and robust vehicle localization.

This paper proposed an entropy weight optimum seeking method (EWOSM) based on the typical scenarios partitioning and load distribution matching, to solve the reactive power optimization problem in distribution network under the background of big data. Firstly, the mathematic model of reactive power optimization is provided to analyze the relationship between the data source and the optimization schemes in distribution network, which illustrate the feasibility of using large amount of historical data to solve reactive power optimization. Then, the typical scenarios partitioning method and load distribution matching method are presented, which can select out some loads that have the same or similar distributions with the load to be optimized from historical database rapidly, and the corresponding historical optimization schemes are used as the alternatives. As the reactive power optimization is a multi-objective problem, the multi-attribute decision making method based on entropy weight method is used to select out the optimal scheme from the alternatives. The objective weights of evaluation indexes are determined by entropy weight method, and then the multi-attribute decision making problem is transformed to a single attribute decision making problem. Finally, the proposed method is tested on several systems with different scales and compared with existing methods to prove the validity and superiority.

This work presents a method for distribution network reconfiguration with the simultaneous consideration of distributed generation (DG) allocation. The uncertainties of load fluctuation before the network reconfiguration are also considered. Three optimal objectives, including minimal line loss cost, minimum Expected Energy Not Supplied, and minimum switch operation cost, are investigated. The multi-objective optimization problem is further transformed into a single-objective optimization problem by utilizing weighting factors. The proposed network reconfiguration method includes two periods. The first period is to create a feasible topology network by using binary particle swarm optimization (BPSO). Then the DG allocation problem is solved by utilizing sensitivity analysis and a Harmony Search algorithm (HSA). In the meanwhile, interval analysis is applied to deal with the uncertainties of load and devices parameters. Test cases are studied using the standard IEEE 33-bus and PG&E 69-bus systems. Different scenarios and comparisons are analyzed in the experiments. The results show the applicability of the proposed method. The performance analysis of the proposed method is also investigated. The computational results indicate that the proposed network reconfiguration algorithm is feasible.

A distribution generation (DG) multiobjective optimization method based on an improved Pareto evolutionary algorithm is investigated in this paper. The improved Pareto evolutionary algorithm, which introduces a penalty factor in the objective function constraints, uses an adaptive crossover and a mutation operator in the evolutionary process and combines a simulated annealing iterative process. The proposed algorithm is utilized to the optimize DG injection models to maximize DG utilization while minimizing system loss and environmental pollution. A revised IEEE 33-bus system with multiple DG units was used to test the multiobjective optimization algorithm in a distribution power system. The proposed algorithm was implemented and compared with the strength Pareto evolutionary algorithm 2 (SPEA2), a particle swarm optimization (PSO) algorithm, and nondominated sorting genetic algorithm II (NGSA-II). The comparison of the results demonstrates the validity and practicality of utilizing DG units in terms of economic dispatch and optimal operation in a distribution power system.

In order to reduce the error of automatic frequency control of transmission machinery under different disturbances, an automatic frequency control system of transmission machinery based on back-propagation (BP) neural network algorithm is designed. The gradient descent method and Newton method are combined to optimize the BP neural network. Aiming at the periodicity and trend of the actual mechanical vibration frequency time series, the first order backward difference is processed by the difference method, and the autoregressive sequence is derived to obtain the control model. The experimental results show that the system can realize the automatic control of transmission mechanical frequency with small error under different types of interference, and the control effect is ideal. [ABSTRACT FROM AUTHOR]

Epidermal growth factor receptor inhibitors (EGFRIs) are widely used to treat various types of malignancies. One of the common adverse reactions is cutaneous toxicity, mostly presenting as acneiform eruptions, paronychia and xerosis. Erosive pustular dermatosis of the scalp (EPDS) is a rare cutaneous adverse reaction that develops during treatment with EGFRIs. The pathogenesis of EGFRI-induced EPDS is poorly understood. Here we present three cases of EPDS induced by EGFRIs. The proteins LTA4H (leukotriene A-4 hydrolase), METAP1 (methionine aminopeptidase 1), BID (BH3-interacting domain death agonist), SMAD1 (mothers against decapentaplegic homologue), PRKRA (interferon-inducible double-stranded RNA-dependent protein kinase activator A), YES1 (tyrosine-protein kinase Yes) and EGFL7 (epidermal growth factor-like protein 7) were significantly upregulated in EGFRI-stimulated peripheral blood mononuclear cell cultures, and validated in the lesions. All of the proteins colocalized with CD4+ and CD8+ T-cell expression. Next-generation-based human leucocyte antigen (HLA) typing showed all patients carried HLA-C*15:02, and modelling studies showed that afatinib and erlotinib bound well within the E/F binding pockets of HLA-C*15:02. Moreover, T cells were preferentially activated by EGFRIs in individuals carrying HLA-C*15:02. The case series revealed that EGFRI-induced EPDS may be mediated by drug-specific T cells. [ABSTRACT FROM AUTHOR]