Identification of Isosteric Replacements of Glycosyl Domain of Ligands by Data Mining

Biologically equivalent replacements of key moieties in molecule rationalizes scaffold hopping, patent busting or R-group enumeration, yet heavily depending upon the expert-defined space therefore is subjective and might be biased to the chemistries they get used to. Most importantly, these explorations are often informatively incomplete since it is often confined within try-and-error cycle, only meaning what kind of substructures are suitable for the replacement occur, but fail to disclose the driving forces to support such interchanges. The Protein Data Bank (PDB) repository involving receptor-ligand interactional information reminds poorly exploited. However, manual screening the PDB become almost impossible to excavate the bioisosteric know-how with the exponentially increase of data. Therefore, a textual content parsing workflow is developed to automatedly mine local structural replacement (LSR) of specific structure. Taking the glycosyl domain for instance, a total of 41652 replacements that overlap on nucleotide ribose were identified and categorized based on their SMILE codes. Predominately ring system, such as aliphatic aromatic ring, yet amide and sulfonamide replacement also occurred. We believe these findings may enlighten medicinal chemists to design and optimize ligand structure using bioisosteric replacement strategy.

An Intelligent Caching and Replacement Strategy Based on Cache Profit Model for Space-Ground Integrated Network

Compared with the stable states of the ground networks, the space-ground integrated networks (SGIN) have limited resources, high transmission delay, and vulnerable topology, which make traditional caching strategies unable to adapt to the complex space network environment. An intelligent and efficient caching strategy is required to improve the edge service capabilities of satellites. Therefore, we investigate these problems in this paper and make the following contributions. First, the content value evaluation model based on classification and regression tree is proposed to solve the problem of “what to cache” by describing the cache value of content, which considers the multidimensional content characteristics. Second, we propose a cache decision strategy based on the node caching cost model to answer “where to cache.” This strategy modified the genetic algorithm to adapt the 0-1 knapsack problem under SDN architecture, which greatly improved the cache hit rate and the network service quality. Finally, we propose a cache replacement strategy by establishing an effective service time model between the satellite and ground transmission link, which solves the problem of “when to replace.” Numerical results demonstrate that the proposed strategy in SGIN can improve the nodes’ cache hit rate and reduce the network transmission delay and transmission hops.

A New Efficient Cache Replacement Strategy for Named Data Networking

The Information-Centric Network (ICN) is a future internet architecture with efficient content retrieval and distribution. Named Data Networking (NDN) is one of the proposed architectures for ICN. NDN’s innetwork caching improves data availability, reduce retrieval delays, network load, alleviate producer load, and limit data traffic. Despite the existence of several caching decision algorithms, the fetching and distribution of contents with minimum resource utilization remains a great challenge. In this paper, we introduce a new cache replacement strategy called Enhanced Time and Frequency Cache Replacement strategy (ETFCR) where both cache hit frequency and cache retrieval time are used to select evicted data chunks. ETFCR adds time cycles between the last two requests to adjust data chunk’s popularity and cache hits. We conducted extensive simulations using the ccnSim simulator to evaluate the performance of ETFCR and compare it to that of some well-known cache replacement strategies. Simulations results show that ETFCR outperforms the other cache replacement strategies in terms of cache hit ratio, and lower content retrieval delay.

Cefepime/Sulbactam — A New Innovative Antibiotic for In-Hospital Treatment of Severe Infections and the Implementation of Carbapenem-Replacement Strategy to Contain Antibiotic Resistance

Cefepime/sulbactam is a combined antibiotic consisting of the 4 th generation cephalosporin cefepime and the beta-lactamase inhibitor sulbactam in 1:1 ratio. Cefepime/sulbactam antibiotic was developed in Russia in 2006, it had passed preclinical and clinical studies, was approved for medical use, and has been produced in Russia since 2019. Cefepime has a wide spectrum of antimicrobial activity against gram-positive and gram-negative microorganisms, sulbactam adds two clinically important pathogens to the antimicrobial spectrum of cefepime — Acinetobacter baumannii and Bacteroides fragilis. In addition, sulbactam protects cefepime from hydrolysis by class A broad- and extended-spectrum beta-lactamases, and cefepime itself is stable against class C chromosomal beta-lactamases and partially stable to OXA-type class D carbapenemases. In vitro studies have shown that most clinical strains of ESBL-producing Klebsiella pneumoniae, Escherichia coli, Proteus spp. are sensitive to cefepime/sulbactam, as well as some strains of K.pneumoniae and A.baumannii that are resistant to carbapenems as a result of the production of class D carbapenemases. The efficacy and safety of cefepime/sulbactam have been determined in three clinical studies. Clinical and bacteriological efficacy of the drug was 97.9% and 97.6% in patients with acute community-acquired pyelonephritis. In the MAXI-19 multicenter study, the clinical efficacy of cefepime/sulbactam in patients with intra-abdominal infections, nosocomial pneumonia, and ventilator-associated pneumonia was 78.4, 90.3, and 80.7%, respectively. A comparative study examined the efficacy of cefepime/sulbactam and carbapenems in severe nosocomial infections (84% of patients had sepsis or septic shock). Clinical efficacy of cefepime/sulbactam and carbapenems was high and did not significantly differ (71% vs. 62%), as well as the bacteriological efficacy — 87% vs. 73%, while typical hospital pathogens characterized by MDR or XDR were identified in the majority of patients (most often — K.pneumoniae, A.baumannii, E.coli). During treatment with carbapenems, carbapenem-resistant bacteria were detected significantly more often (74.5%, most often A.baumannii — 44.7%, K.pneumoniae — 38.3%), compared to cefepime/sulbactam (20.0%, P.aeruginosa and K.pneumoniae, both at 15.5%), P=0.0001. The risk of superinfection was also significantly higher with carbapenems than with cefepime/sulbactam (53.3% vs. 22.2%, P=0.001). For severe infections, cefepime/sulbactam was administered at a dose of 4 g (2 g + 2 g) every 12 hours or 2 g (1 g + 1 g) every 8 hours. Currently, cefepime/sulbactam should be considered as a reliable option for the treatment of severe infections in the hospital as a carbapenem-replacement strategy to reduce the risks of selection of carbapenem-resistant gram-negative bacteria.

Modelling Hydrocortisone Pharmacokinetics on a Subcutaneous Pulsatile Infusion Replacement Strategy in Patients with Adrenocortical Insufficiency

In the context of glucocorticoid (GC) therapeutics, recent studies have utilised a subcutaneous hydrocortisone (HC) infusion pump programmed to deliver multiple HC pulses throughout the day, with the purpose of restoring normal circadian and ultradian GC rhythmicity. A key challenge for the advancement of novel HC replacement therapies is the calibration of infusion pumps against cortisol levels measured in blood. However, repeated blood sampling sessions are enormously labour-intensive for both examiners and examinees. These sessions also have a cost, are time consuming and are occasionally unfeasible. To address this, we developed a pharmacokinetic model approximating the values of plasma cortisol levels at any point of the day from a limited number of plasma cortisol measurements. The model was validated using the plasma cortisol profiles of 9 subjects with disrupted endogenous GC synthetic capacity. The model accurately predicted plasma cortisol levels (mean absolute percentage error of 14%) when only four plasma cortisol measurements were provided. Although our model did not predict GC dynamics when HC was administered in a way other than subcutaneously or in individuals whose endogenous capacity to produce GCs is intact, it was found to successfully be used to support clinical trials (or practice) involving subcutaneous HC delivery in patients with reduced endogenous capacity to synthesize GCs.