THE SLC22 TRANSPORTER FAMILY: NOVEL INSIGHTS TO ROLES IN DRUG EFFICACY, DRUG-DRUG INTERACTIONS AND MOOD DISORDERS

Numerous studies have demonstrated the impact of organic cation (OCTs; SLC22 family) and anion transporters (OATs; SLC22 family) on the efficacy and safety of clinically important therapeutics. To be specific, OCTs and OATs have been identified as determinants for uptake into and secretion from enterocytes, hepatocytes and renal proximal tubular cells, and are frequent sites of drug-drug interaction (DDI). In addition, OCTs expressed in brain are components of the low-affinity, high capacity clearance pathway (uptake-2) for biogenic monoamine neurotransmitters. As a result, OCTs may represent novel targets for mood disorders. The inhibitory effects of several therapeutic agents, designed drugs and novel compounds were assessed on the function of OCTs/Octs and OATs/Oats. Among these compounds, the anthraquinone rhein showed significant inhibition on hOATs. While the antituberculosis drug ethambutol, the herbal products matrine and oxymatrine, synthetic cathinones, and all quinazoline and guanidine compounds produced significant inhibition on hOCT activity with most IC50 values in the micro- and even nanomolar ranges. Considering the clinically relevant unbound concentrations in biofluids, significant DDI potentials were found for rhein, ethambutol, matrine, oxymatrine and several synthetic cathinones affecting enterocytes, hepatocytes and/or proximal tubules. As hOCT2 and hOCT3 may participate in modulating neurotransmitter homeostasis in the CNS, these findings also suggested that the CNS pharmacological effects of synthetic cathinones, quinazoline and guanidine compounds might be due to their inhibitory effects on OCTs; although their impact may be limited solely to clearance of these compounds. Based upon their in vitro OCT/Oct inhibition profiles, three lead quinazoline and guanidine compounds were chosen for in vivo studies. Potent antidepressant-like effects of one lead hOCT-interacting compound (KEO-099) were re-confirmed in the tail suspension test. While in vivo results of the two newly identified hOCT-interacting lead compounds were somewhat less clear. Finally, homology modeling and docking studies for hOCT3 identified key amino acid residues that might be involved in interaction between hOCT3 and small molecules. Subsequent experiments confirmed a competitive mode of interaction between MPP+ and lead compounds on hOCT3. Thus, preliminary analysis indicates our hOCT3 homology model can be used to support rational drug design and high-throughput screening of novel hOCT substrates/inhibitors

A Benchmark of Dynamic Reliability Methods for Probabilistic Safety Assessment

International audienc

Alleviating the Long-Tail Problem in Conversational Recommender Systems

Conversational recommender systems (CRS) aim to provide the recommendation service via natural language conversations. To develop an effective CRS, high-quality CRS datasets are very crucial. However, existing CRS datasets suffer from the long-tail issue, \ie a large proportion of items are rarely (or even never) mentioned in the conversations, which are called long-tail items. As a result, the CRSs trained on these datasets tend to recommend frequent items, and the diversity of the recommended items would be largely reduced, making users easier to get bored. To address this issue, this paper presents \textbf{LOT-CRS}, a novel framework that focuses on simulating and utilizing a balanced CRS dataset (\ie covering all the items evenly) for improving \textbf{LO}ng-\textbf{T}ail recommendation performance of CRSs. In our approach, we design two pre-training tasks to enhance the understanding of simulated conversation for long-tail items, and adopt retrieval-augmented fine-tuning with label smoothness strategy to further improve the recommendation of long-tail items. Extensive experiments on two public CRS datasets have demonstrated the effectiveness and extensibility of our approach, especially on long-tail recommendation.Comment: work in progres

Improving Conversational Recommendation Systems via Counterfactual Data Simulation

Conversational recommender systems (CRSs) aim to provide recommendation services via natural language conversations. Although a number of approaches have been proposed for developing capable CRSs, they typically rely on sufficient training data for training. Since it is difficult to annotate recommendation-oriented dialogue datasets, existing CRS approaches often suffer from the issue of insufficient training due to the scarcity of training data. To address this issue, in this paper, we propose a CounterFactual data simulation approach for CRS, named CFCRS, to alleviate the issue of data scarcity in CRSs. Our approach is developed based on the framework of counterfactual data augmentation, which gradually incorporates the rewriting to the user preference from a real dialogue without interfering with the entire conversation flow. To develop our approach, we characterize user preference and organize the conversation flow by the entities involved in the dialogue, and design a multi-stage recommendation dialogue simulator based on a conversation flow language model. Under the guidance of the learned user preference and dialogue schema, the flow language model can produce reasonable, coherent conversation flows, which can be further realized into complete dialogues. Based on the simulator, we perform the intervention at the representations of the interacted entities of target users, and design an adversarial training method with a curriculum schedule that can gradually optimize the data augmentation strategy. Extensive experiments show that our approach can consistently boost the performance of several competitive CRSs, and outperform other data augmentation methods, especially when the training data is limited. Our code is publicly available at https://github.com/RUCAIBox/CFCRS.Comment: Accepted by KDD 2023. Code: https://github.com/RUCAIBox/CFCR

Cost-Effective Heating Control Approaches by Demand Response and Peak Demand Limiting in an Educational Office Building with District Heating

This study examined three different approaches to reduce the heating cost while maintaining indoor thermal comfort at acceptable levels in an educational office building, including decentralized (DDRC) and centralized demand response control (CDRR) and limiting peak demand. The results showed that although all these approaches did not affect the indoor air temperature significantly, the DDRC method could adjust the heating set point to between 20–24.5 °C. The DDRC approach reached heating cost savings of up to 5% while controlling space heating temperature without sacrificing the thermal comfort. The CDRC of space heating had limited potential in heating cost savings (1.5%), while the indoor air temperature was in the acceptable range. Both the DDRC and CDRC alternatives can keep the thermal comfort at good levels during the occupied time. Depending on the district heating provider, applying peak demand limiting of 35% can not only achieve 13.6% maximum total annual district heating cost saving but also maintain the thermal comfort level, while applying that of 43% can further save 16.9% of the cost, but with sacrificing a little thermal comfort. This study shows that demand response on heating energy only benefited from the decentralized control alternative, and the district heating-based peak demand limiting has significant potential for saving heating costs

Endoribonuclease YbeY Is Essential for RNA Processing and Virulence in Pseudomonas aeruginosa

Posttranscriptional regulation plays an essential role in the quick adaptation of pathogenic bacteria to host environments, and RNases play key roles in this process by modifying small RNAs and mRNAs. We find that the Pseudomonas aeruginosa endonuclease YbeY is required for rRNA processing and the bacterial virulence in a murine acute pneumonia model. Transcriptomic analyses reveal that knocking out the ybeY gene results in downregulation of oxidative stress response genes, including the catalase genes katA and katB Consistently, the ybeY mutant is more susceptible to H2O2 and neutrophil-mediated killing. Overexpression of katA restores the bacterial tolerance to H2O2 and neutrophil killing as well as virulence. We further find that the downregulation of the oxidative stress response genes is due to defective expression of the stationary-phase sigma factor RpoS. We demonstrate an autoregulatory mechanism of RpoS and find that ybeY mutation increases the level of a small RNA, ReaL, which directly represses the translation of rpoS through the 5' UTR of its mRNA and subsequently reduces the expression of the oxidative stress response genes. In vitro assays demonstrate direct degradation of ReaL by YbeY. Deletion of reaL or overexpression of rpoS in the ybeY mutant restores the bacterial tolerance to oxidative stress and the virulence. We also demonstrate that YbeZ binds to YbeY and is involved in the 16S rRNA processing and regulation of reaL and rpoS as well as the bacterial virulence. Overall, our results reveal pleiotropic roles of YbeY and the YbeY-mediated regulation of rpoS through ReaL.IMPORTANCE The increasing bacterial antibiotic resistance imposes a severe threat to human health. For the development of effective treatment and prevention strategies, it is critical to understand the mechanisms employed by bacteria to grow in the human body. Posttranscriptional regulation plays an important role in bacterial adaptation to environmental changes. RNases and small RNAs are key players in this regulation. In this study, we demonstrate critical roles of the RNase YbeY in the virulence of the pathogenic bacterium Pseudomonas aeruginosa We further identify the small RNA ReaL as the direct target of YbeY and elucidate the YbeY-regulated pathway on the expression of bacterial virulence factors. Our results shed light on the complex regulatory network of P. aeruginosa and indicate that inference with the YbeY-mediated regulatory pathway might be a valid strategy for the development of a novel treatment strategy.</p

How early can myocardial iron overload occur in Beta thalassemia major?

BACKGROUND: Myocardial siderosis is the most common cause of death in patients with beta thalassemia major(TM). This study aimed at investigating the occurrence, prevalence and severity of cardiac iron overload in a young Chinese population with beta TM. METHODS AND RESULTS: We analyzed T2* cardiac magnetic resonance (CMR), left ventricular ejection fraction (LVEF) and serum ferritin (SF) in 201 beta TM patients. The median age was 9 years old. Patients received an average of 13 units of blood per year. The median SF level was 4536 ng/ml and 165 patients (82.1%) had SF>2500 ng/ml. Myocardial iron overload was detected in 68 patients (33.8%) and severe myocardial iron overload was detected in 26 patients (12.6%). Twenty-two patients ≤10 years old had myocardial iron overload, three of whom were only 6 years old. No myocardial iron overload was detected under the age of 6 years. Median LVEF was 64% (measured by CMR in 175 patients). Five of 6 patients with a LVEF<56% and 8 of 10 patients with cardiac disease had myocardial iron overload. CONCLUSIONS: The TM patients under follow-up at this regional centre in China patients are younger than other reported cohorts, more poorly-chelated, and have a high burden of iron overload. Myocardial siderosis occurred in patients younger than previously reported, and was strongly associated with impaired LVEF and cardiac disease. For such poorly-chelated TM patients, our data shows that the first assessment of cardiac T2* should be performed as early as 6 years old