CPSF3 inhibition blocks pancreatic cancer cell proliferation through disruption of core histone mRNA processing.

Pancreatic ductal adenocarcinoma (PDAC) is a lethal disease with limited effective treatment options, potentiating the importance of uncovering novel drug targets. Here, we target cleavage and polyadenylation specificity factor 3 (CPSF3), the 3' endonuclease that catalyzes mRNA cleavage during polyadenylation and histone mRNA processing. We find that CPSF3 is highly expressed in PDAC and is associated with poor prognosis. CPSF3 knockdown blocks PDAC cell proliferation and colony formation in vitro and tumor growth in vivo. Chemical inhibition of CPSF3 by the small molecule JTE-607 also attenuates PDAC cell proliferation and colony formation, while it has no effect on cell proliferation of nontransformed immortalized control pancreatic cells. Mechanistically, JTE-607 induces transcriptional readthrough in replication-dependent histones, reduces core histone expression, destabilizes chromatin structure, and arrests cells in the S-phase of the cell cycle. Therefore, CPSF3 represents a potential therapeutic target for the treatment of PDAC.

Introducing enzymatic cleavage features and transfer learning realizes accurate peptide half-life prediction across species and organs.

Peptide drugs are becoming star drug agents with high efficiency and selectivity which open up new therapeutic avenues for various diseases. However, the sensitivity to hydrolase and the relatively short half-life have severely hindered their development. In this study, a new generation artificial intelligence-based system for accurate prediction of peptide half-life was proposed, which realized the half-life prediction of both natural and modified peptides and successfully bridged the evaluation possibility between two important species (human, mouse) and two organs (blood, intestine). To achieve this, enzymatic cleavage descriptors were integrated with traditional peptide descriptors to construct a better representation. Then, robust models with accurate performance were established by comparing traditional machine learning and transfer learning, systematically. Results indicated that enzymatic cleavage features could certainly enhance model performance. The deep learning model integrating transfer learning significantly improved predictive accuracy, achieving remarkable R2 values: 0.84 for natural peptides and 0.90 for modified peptides in human blood, 0.984 for natural peptides and 0.93 for modified peptides in mouse blood, and 0.94 for modified peptides in mouse intestine on the test set, respectively. These models not only successfully composed the above-mentioned system but also improved by approximately 15% in terms of correlation compared to related works. This study is expected to provide powerful solutions for peptide half-life evaluation and boost peptide drug development.

Droplet digital PCR: An effective method for monitoring and prognostic evaluation of minimal residual disease in JMML.

Juvenile myelomonocytic leukaemia (JMML) is a rare myeloproliferative neoplasm requiring haematopoietic stem cell transplantation (HSCT) for potential cure. Relapse poses a significant obstacle to JMML HSCT treatment, as the lack of effective minimal residual disease (MRD)-monitoring methods leads to delayed interventions. This retrospective study utilized the droplet digital PCR (ddPCR) technique, a highly sensitive nucleic acid detection and quantification technique, to monitor MRD in 32 JMML patients. The results demonstrated that ddPCR detected relapse manifestations earlier than traditional methods and uncovered molecular insights into JMML MRD dynamics. The findings emphasized a critical 1- to 3-month window post-HSCT for detecting molecular relapse, with 66.7% (8/12) of relapses occurring within this period. Slow MRD clearance post-HSCT was observed, as 65% (13/20) of non-relapse patients took over 6 months to achieve ddPCR-MRD negativity. Furthermore, bone marrow ddPCR-MRD levels at 1-month post-HSCT proved to be prognostically significant. Relapsed patients exhibited significantly elevated ddPCR-MRD levels at this time point (p = 0.026), with a cut-off of 0.465% effectively stratifying overall survival (p = 0.007), event-free survival (p = 0.035) and cumulative incidence of relapse (p = 0.035). In conclusion, this study underscored ddPCR's superiority in JMML MRD monitoring post-HSCT. It provided valuable insights into JMML MRD dynamics, offering guidance for the effective management of JMML.

Spatiotemporal Single-Photon Airy Bullets.

Uninhibited control of the complex spatiotemporal quantum wave function of a single photon has so far remained elusive even though it can dramatically increase the encoding flexibility and thus the information capacity of a photonic quantum link. By fusing temporal waveform generation in an atomic ensemble and spatial single-photon shaping, we hereby demonstrate for the first time complete spatiotemporal control of a propagation invariant (2+1)D Airy single-photon optical bullet. These correlated photons are not only self-accelerating and impervious to spreading as their classical counterparts, but can be concealed and revealed in the presence of strong classical stray light.

Genetically proxied PCSK9 inhibition is associated with reduced psoriatic arthritis risk.

BACKGROUND: Lipid pathways play a crucial role in psoriatic arthritis development, and some lipid-lowering drugs are believed to have therapeutic benefits due to their anti-inflammatory properties. Traditional observational studies face issues with confounding factors, complicating the interpretation of causality. This study seeks to determine the genetic link between these medications and the risk of psoriatic arthritis. METHODS: This drug target study utilized the Mendelian randomization strategy. We harnessed high-quality data from population-level genome-wide association studies sourced from the UK Biobank and FinnGen databases. The inverse variance-weighted method, complemented by robust pleiotropy methods, was employed. We examined the causal relationships between three lipid-lowering agents and psoriatic arthritis to unveil the underlying mechanisms. RESULTS: A significant association was observed between genetically represented proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibition and a decreased risk of psoriatic arthritis (odds ratio [OR]: 0.51; 95% CI 0.14-0.88; P < 0.01). This association was further corroborated in an independent dataset (OR 0.60; 95% CI 0.25-0.94; P = 0.03). Sensitivity analyses affirmed the absence of statistical evidence for pleiotropic or genetic confounding biases. However, no substantial associations were identified for either 3-hydroxy-3-methylglutaryl-CoA reductase inhibitors or Niemann-Pick C1-like 1 inhibitors. CONCLUSIONS: This Mendelian randomization analysis underscores the pivotal role of PCSK9 in the etiology of psoriatic arthritis. Inhibition of PCSK9 is associated with reduced psoriatic arthritis risk, highlighting the potential therapeutic benefits of existing PCSK9 inhibitors.

In vitro and in vivo activity of ceftazidime/avibactam and aztreonam alone or in combination against mcr-9, serine- and metallo-ß-lactamases-co-producing carbapenem-resistant Enterobacter cloacae complex.

PURPOSE: Enterobacteriaceae carrying mcr-9, in particularly those also co-containing metallo-ß-lactamase (MBL) and TEM type ß-lactamase, present potential transmission risks and lack adequate clinical response methods, thereby posing a major threat to global public health. The aim of this study was to assess the antimicrobial efficacy of a combined ceftazidime/avibactam (CZA) and aztreonam (ATM) regimen against carbapenem-resistant Enterobacter cloacae complex (CRECC) co-producing mcr-9, MBL and TEM. METHODS: The in vitro antibacterial activity of CZA plus ATM was evaluated using a time-kill curve assay. Furthermore, the in vivo interaction between CZA plus ATM was confirmed using a Galleria mellonella (G. mellonella) infection model. RESULTS: All eight clinical strains of CRECC, co-carrying mcr-9, MBL and TEM, exhibited high resistance to CZA and ATM. In vitro time-kill curve analysis demonstrated that the combination therapy of CZA + ATM exerted significant bactericidal activity against mcr-9, MBL and TEM-co-producing Enterobacter cloacae complex (ECC) isolates with a 100% synergy rate observed in our study. Furthermore, in vivo survival assay using Galleria mellonella larvae infected with CRECC strains co-harboring mcr-9, MBL and TEM revealed that the CZA + ATM combination significantly improved the survival rate compared to the drug-treatment alone and untreated control groups. CONCLUSION: To our knowledge, this study represents the first report on the in vitro and in vivo antibacterial activity of CZA plus ATM against CRECC isolates co-harboring mcr-9, MBL and TEM. Our findings suggest that the combination regimen of CZA + ATM provides a valuable reference for clinicians to address the increasingly complex antibiotic resistance situation observed in clinical microorganisms.

FraHMT: A Fragment-Oriented Heterogeneous Graph Molecular Generation Model for Target Proteins.

The molecular generation task stands as a pivotal step in the domains of computational chemistry and drug discovery, aiming to computationally generate molecular structures for specific properties. In contrast to previous models that focused primarily on SMILES strings or molecular graphs, our model placed a special emphasis on the substructure information on molecules, enabling the model to learn richer chemical rules and structure features from fragments and chemical reaction information on molecules. To accomplish this, we fragmented the molecules to construct heterogeneous graph representations based on atom and fragment information. Then our model mapped the heterogeneous graph data into a latent vector space by using an encoder and employed a self-regressive generative model as a decoder for molecular generation. Additionally, we performed transfer learning on the model using a small set of ligand molecules known to be active against the target protein to generate molecules that bind better to the target protein. Experimental results demonstrate that our model is highly competitive with state-of-the-art models. It can generate valid and diverse molecules with favorable physicochemical properties and drug-likeness. Importantly, they produce novel molecules with high docking scores against the target proteins.

Local Scaffold Diversity-Contributed Generator for Discovering Potential NLRP3 Inhibitors.

Deep generative models have become crucial tools in de novo drug design. In current models for multiobjective optimization in molecular generation, the scaffold diversity is limited when multiple constraints are introduced. To enhance scaffold diversity, we herein propose a local scaffold diversity-contributed generator (LSDC), which can be utilized to generate diverse lead compounds capable of satisfying multiple constraints. Compared to the state-of-the-art methods, molecules generated by LSDC exhibit greater diversity when applied to the generation of inhibitors targeting the NOD-like receptor (NLR) family, pyrin domain-containing protein 3 (NLRP3). We present 12 molecules, some of which feature previously unreported scaffolds, and demonstrate their reasonable docking binding modes. Consequently, the modification of selected scaffolds and subsequent bioactivity evaluation lead to the discovery of two potent NLRP3 inhibitors, A22 and A14, with IC50 values of 38.1 nM and 44.43 nM, respectively. And the oral bioavailability of compound A14 is very high (F is 83.09% in mice). This work contributes to the discovery of novel NLRP3 inhibitors and provides a reference for integrating AI-based generation with wet experiments.

2,2- dimethylbenzopyran derivatives containing pyridone structural fragments as selective dual-targeting inhibitors of HIF-1α and EZH2 for the treatment of lung cancer.

We formerly reported that EZH2 inhibitors sensitized HIF-1 inhibitor-resistant cells and inhibited HIF-1α to promote SUZ12 transcription, leading to enhanced EZH2 enzyme activity and elevated H3K27me3 levels, and conversely, inhibition of EZH2 promoted HIF-1α transcription. HIF-1α and EZH2 interacted to form a negative feedback loop that reinforced each other's activity. In this paper, a series of 2,2- dimethylbenzopyran derivatives containing pyridone structural fragments were designed and synthesized with DYB-03, a HIF-1α inhibitor previously reported by our group, and Tazemetostat, an EZH2 inhibitor approved by FDA, as lead compounds. Among these compounds, D-01 had significant inhibitory activities on HIF-1α and EZH2. In vitro experiments showed that D-01 significantly inhibited the migration of A549 cells, clone, invasion and angiogenesis. Moreover, D-01 had good pharmacokinetic profiles. All the results about compound D-01 could lay a foundation for the research and development of HIF-1α and EZH2 dual-targeting compounds.

Leveraging the Fragment Molecular Orbital and MM-GBSA Methods in Virtual Screening for the Discovery of Novel Non-Covalent Inhibitors Targeting the TEAD Lipid Binding Pocket.

The Hippo pathway controls organ size and homeostasis and is linked to numerous diseases, including cancer. The transcriptional enhanced associate domain (TEAD) family of transcription factors acts as a receptor for downstream effectors, namely yes-associated protein (YAP) and transcriptional co-activator with PDZ-binding motif (TAZ), which binds to various transcription factors and is essential for stimulated gene transcription. YAP/TAZ-TEAD facilitates the upregulation of multiple genes involved in evolutionary cell proliferation and survival. TEAD1-4 overexpression has been observed in different cancers in various tissues, making TEAD an attractive target for drug development. The central drug-accessible pocket of TEAD is crucial because it undergoes a post-translational modification called auto-palmitoylation. Crystal structures of the C-terminal TEAD complex with small molecules are available in the Protein Data Bank, aiding structure-based drug design. In this study, we utilized the fragment molecular orbital (FMO) method, molecular dynamics (MD) simulations, shape-based screening, and molecular mechanics-generalized Born surface area (MM-GBSA) calculations for virtual screening, and we identified a novel non-covalent inhibitor-BC-001-with IC50 = 3.7 µM in a reporter assay. Subsequently, we optimized several analogs of BC-001 and found that the optimized compound BC-011 exhibited an IC50 of 72.43 nM. These findings can be used to design effective TEAD modulators with anticancer therapeutic implications.

MEF2D Functions as a Tumor Suppressor in Breast Cancer.

The myocyte enhancer factor 2 (MEF2) gene family play fundamental roles in the genetic programs that control cell differentiation, morphogenesis, proliferation, and survival in a wide range of cell types. More recently, these genes have also been implicated as drivers of carcinogenesis, by acting as oncogenes or tumor suppressors depending on the biological context. Nonetheless, the molecular programs they regulate and their roles in tumor development and progression remain incompletely understood. The present study evaluated whether the MEF2D transcription factor functions as a tumor suppressor in breast cancer. The knockout of the MEF2D gene in mouse mammary epithelial cells resulted in phenotypic changes characteristic of neoplastic transformation. These changes included enhanced cell proliferation, a loss of contact inhibition, and anchorage-independent growth in soft agar, as well as the capacity for tumor development in mice. Mechanistically, the knockout of MEF2D induced the epithelial-to-mesenchymal transition (EMT) and activated several oncogenic signaling pathways, including AKT, ERK, and Hippo-YAP. Correspondingly, a reduced expression of MEF2D was observed in human triple-negative breast cancer cell lines, and a low MEF2D expression in tissue samples was found to be correlated with a worse overall survival and relapse-free survival in breast cancer patients. MEF2D may, thus, be a putative tumor suppressor, acting through selective gene regulatory programs that have clinical and therapeutic significance.

Effect of an extraoral scavenging device on eliminating droplets and aerosols generated in ultrasonic supragingival scaling: A randomised clinical trial.

OBJECTIVES: Ultrasonic scaling is extensively applied as part of the initial therapy for periodontal diseases, which has been restricted since the outbreak of the COVID-19 pandemic due to droplets and aerosols generated by ultrasonic devices. An extraoral scavenging device (EOS) was designed for diminishing droplets and aerosols in dental clinics. The objective of this study is to evaluate the effect of EOS on eliminating droplets and aerosols during ultrasonic supragingival scaling. METHODS: This single-blinded, randomised controlled clinical trial enrolled 45 patients with generalised periodontitis (stage I or II, grade A or B) or plaque-induced gingivitis. The patients were randomly allocated and received ultrasonic supragingival scaling under three different intervention measures: only saliva ejector (SE), SE plus EOS and SE plus high-volume evacuation (HVE). The natural sedimentation method was applied to sample droplets and aerosols before or during supragingival scaling. After aerobic culturing, colony-forming units (CFUs) were counted and analysed. RESULTS: Compared with the level before treatment, more CFUs of samples throughout treatment could be obtained at the operator's chest and the patient's chest and the table surface when using SE alone (p < 0.05). Compared with the SE group, the SE + EOS group and the SE + HVE group obtained decreasing CFUs at the operator's chest and the patient's chest (p < 0.05), while no significant difference was determined between these two groups. CONCLUSIONS: The EOS effectively eliminated splatter contamination from ultrasonic supragingival scaling, which was an alternative precaution for nosocomial contamination in dental clinics.

Thrombocytopenia and hyperprogression after radiotherapy and camrelizumab treatment in an esophageal cancer patient with increased JAK2 gene copies: a case report.

Radiotherapy (RT) and immune checkpoint inhibitor (ICI) are important treatments for esophageal cancer. Some studies have confirmed the safety and effectiveness of using RT in combination with ICI, while serious side effects have been exhibited by some patients. We report a patient with metastatic esophageal cancer who received RT combined with ICI. The patient experienced severe thrombocytopenia, and treatment with thrombopoietin and corticosteroids were ineffective. Finally, the patient developed abscopal hyperprogression outside the radiation field. Interestingly, next-generation sequencing revealed increased JAK2 gene copies in the surgical slices. The JAK2/STAT3 pathway is involved in the regulation of megakaryocyte development. Recurrent thrombocytopenia may activate the JAK2/STAT3 pathway, leading to megakaryocyte differentiation and platelet biogenesis. However, persistent activation of the JAK2/STAT3 pathway has been associated with immune ICI resistance and tumor progression. This case indicates that thrombocytopenia and increased JAK2 gene copies may be risk factors for poor prognosis after ICI and RT treatment.

One Fits Many: Class Confusion Loss for Versatile Domain Adaptation.

In the open world, various label sets and domain configurations give rise to a variety of Domain Adaptation (DA) setups, including closed-set, partial-set, open-set, and universal DA, as well as multi-source and multi-target DA. It is notable that existing DA methods are generally designed only for a specific setup, and may under-perform in setups they are not tailored to. This paper shifts the common paradigm of DA to Versatile Domain Adaptation (VDA), where one method can handle several different DA setups without any modification. Towards this goal, we first delve into a general inductive bias: class confusion, and then uncover that reducing such pairwise class confusion leads to significant transfer gains. With this insight, we propose one general class confusion loss (CC-Loss) to learn many setups. We estimate class confusion based only on classifier predictions and minimize the class confusion to enable accurate target predictions. Further, we improve the loss by enforcing the consistency of confusion matrices under different data augmentations to encourage its invariance to distribution perturbations. Experiments on 2D vision and 3D vision benchmarks show that the CC-Loss performs competitively in different mainstream DA setups. Code is available at https://github.com/thuml/Transfer-Learning-Library.

Predictive value of glycoprotein DKK3 for early neurological deterioration after ischemic stroke.

OBJECTIVE: To explore the value of serum Dickkopf-3 (sDKK3) in predicting Early Neurological Deterioration (END) and in-hospital adverse outcomes in acute ischemic stroke (AIS) patients. METHODS: AIS patients (n = 200) were included and assessed by the National Institutes of Health Stroke Rating Scale. Serum Dkk3 levels were assessed by ELISA. END was defined as an increase of ≥ 4 points in NIHSS score within 72h. The biological threshold of sDKK3 level and END occurrence were predicted based on X-tile software. Primary outcomes were END and all-cause death, and the secondary outcome was ICU admission during hospitalization. The logistic regression model and Cox risk regression model were applied to evaluate the relationship between DKK3 level and END incidence, all-cause in-hospital mortality, and in-hospital adverse outcomes (ICU admission). RESULTS: During hospitalization, the incidence of END in patients with AIS was 13.0 %, and the mortality rate within 7 days after END was 11.54 % (3/26). In patients below the serum DKK3 cutoff (93.0 pg/mL), the incidence of END was 43.5 % (20/48). Patients with lower sDKK3 levels were associated with a 1.188-fold increased risk of developing END (OR = 1.188, 95 % CI 1.055‒1.369, p < 0.0001). However, there was no significant association with admission to the ICU. sDKK3 below the threshold (93.0 pg/mL) was a risk factor for death. CONCLUSION: Predictive threshold levels of serum DKK3 based on X-tile software may be a potential predictive biomarker of in-hospital END in patients with AIS, and low levels of DKK3 are independently associated with increased in-hospital mortality.

Construction of diagnostic models for the progression of hepatocellular carcinoma using machine learning.

Liver cancer is one of the most prevalent forms of cancer worldwide. A significant proportion of patients with hepatocellular carcinoma (HCC) are diagnosed at advanced stages, leading to unfavorable treatment outcomes. Generally, the development of HCC occurs in distinct stages. However, the diagnostic and intervention markers for each stage remain unclear. Therefore, there is an urgent need to explore precise grading methods for HCC. Machine learning has emerged as an effective technique for studying precise tumor diagnosis. In this research, we employed random forest and LightGBM machine learning algorithms for the first time to construct diagnostic models for HCC at various stages of progression. We categorized 118 samples from GSE114564 into three groups: normal liver, precancerous lesion (including chronic hepatitis, liver cirrhosis, dysplastic nodule), and HCC (including early stage HCC and advanced HCC). The LightGBM model exhibited outstanding performance (accuracy = 0.96, precision = 0.96, recall = 0.96, F1-score = 0.95). Similarly, the random forest model also demonstrated good performance (accuracy = 0.83, precision = 0.83, recall = 0.83, F1-score = 0.83). When the progression of HCC was categorized into the most refined six stages: normal liver, chronic hepatitis, liver cirrhosis, dysplastic nodule, early stage HCC, and advanced HCC, the diagnostic model still exhibited high efficacy. Among them, the LightGBM model exhibited good performance (accuracy = 0.71, precision = 0.71, recall = 0.71, F1-score = 0.72). Also, performance of the LightGBM model was superior to that of the random forest model. Overall, we have constructed a diagnostic model for the progression of HCC and identified potential diagnostic characteristic gene for the progression of HCC.

MOASL: Predicting drug mechanism of actions through similarity learning with transcriptomic signature.

Understanding the mechanisms of actions (MOAs) of compounds is crucial in drug discovery. A common step in drug MOAs annotation is to query the dysregulated gene signatures induced by drugs in a reference library of pre-defined signatures. However, traditional similarity-based computational strategies face challenges when dealing with high-dimensional and noisy transcriptional signature data. To address this issue, we introduce MOASL (MOAs prediction via Similarity Learning), a novel approach that contrastive to learn similarity embeddings among signatures with shared MOAs automatically. We evaluated the accuracy of signature matching on various transcriptional activity score (TAS) datasets and individual cell lines by using MOASL. The results show MOASL achieved higher performance over several statistical and machine learning methods. Furthermore, we provided the rationale of our model by visualizing the signature annotation procedure. Using MOASL, the MOAs label of query signature could be conveniently defined by calculating the similarity between the query embedding and the reference embeddings. Finally, we applied MOASL to repurpose thousands of compounds as glucocorticoid receptor (GR) agonists, accurately identifying 8 out of the top 10 compounds. MOASL is conveniently accessible on GitHub at https://github.com/jianglikun/MOASL, empowering researchers and practitioners in the field of drug discovery to predict the MOAs of drug.

Comparative metabolomics reveals serum metabolites changes in goats during different developmental stages.

Goats can provide meat, milk and skins for humans and are livestock with high economic benefits. However, despite their economic significance, the comprehensive analysis of goats' serum metabolic profile and its intricate alterations throughout their developmental journey remains conspicuously absent. To investigate the stage-specificity and dynamic change characteristics of metabolites during the growth and development of goats, this study compared the alterations in serum hormone levels and serum biochemical markers across different developmental stages of female goats (1, 60, 120 and 180 days old; n = 5). Additionally, a serum untargeted LC-MS metabolomics analysis was conducted. A total of 504 DAMs were identified with age. The results indicated that PE, PC, Lyso-PE, Lyso-PC and FAFHA may play important roles in lipid metabolism in goats after birth. Weighted gene co-expression network analysis (WGCNA) identified two metabolite modules (Turquoise and Yellow) and key metabolites within these modules that were significantly associated with phenotypic features. L-carnitine may be a metabolite related to muscle development in goats. The findings of this study demonstrate notable variations in serum metabolites across distinct developmental phases in goats. Lipids and organic acids play important roles in different developmental stages of goats.

Transcriptome analysis revealed the characteristics and functions of long non-coding RNAs in the hypothalamus during sexual maturation in goats.

The hypothalamus is an essential neuroendocrine area in animals that regulates sexual development. Long non-coding RNAs (lncRNAs) are hypothesized to regulate physiological processes related to animal reproduction. However, the regulatory mechanism by which lncRNAs participate in sexual maturity in goats is poorly known, particularly from birth to sexual maturation. In this study, RNAseq analysis was conducted on the hypothalamus of four developmental stages (1day (D1, n = 5), 2 months (M2, n = 5), 4 months (M4, n = 5), and 6 months (M6, n = 5)) of Jining grey goats. The results showed that a total of 237 differentially expressed lncRNAs (DELs) were identified in the hypothalamus. Among these, 221 DELs exhibited cis-regulatory effects on 693 target genes, while 24 DELs demonstrated trans-regulatory effects on 63 target genes. The target genes of these DELs are mainly involved in biological processes related to energy metabolism, signal transduction and hormone secretion, such as sphingolipid signaling pathway, adipocytokine signaling pathway, neurotrophic signaling pathway, glutamatergic synapse, P53 signaling pathway and GnRH signaling pathway. In addition, XR_001918477.1, TCONS_00077463, XR_001918760.1, and TCONS_00029048 and their potential target genes may play a crucial role in the process of goat sexual maturation. This study advances our understanding of lncRNA in hypothalamic tissue during sexual maturation in goats and will give a theoretical foundation for improving goat reproductive features.

Activities of aztreonam in combination with several novel ß-lactam-ß-lactamase inhibitor combinations against carbapenem-resistant Klebsiella pneumoniae strains coproducing KPC and NDM.

Isolates coproducing serine/metallo-carbapenems are a serious emerging public health threat, given their rapid dissemination and the limited number of treatment options. The purposes of this study were to evaluate the in vitro antibacterial activity of novel ß-lactam-ß-lactamase inhibitor combinations (BLBLIs) against carbapenem-resistant Klebsiella pneumoniae (CRKP) coproducing metallo-ß-lactamase and serine-ß-lactamase, and to explore their effects in combination with aztreonam, meropenem, or polymyxin in order to identify the best therapeutic options. Four CRKP isolates coproducing K. pneumoniae carbapenemase (KPC) and New Delhi metallo-ß-lactamase (NDM) were selected, and a microdilution broth method was used to determine their susceptibility to antibiotics. Time-kill assay was used to detect the bactericidal effects of the combinations of antibiotics. The minimum inhibitory concentration (MIC) values for imipenem and meropenem in three isolates did not decrease after the addition of relebactam or varbobactam, but the addition of avibactam to aztreonam reduced the MIC by more than 64-fold. Time-kill assay demonstrated that imipenem-cilastatin/relebactam (ICR) alone exerted a bacteriostatic effect against three isolates (average reduction: 1.88 log10 CFU/mL) and ICR combined with aztreonam exerted an additive effect. Aztreonam combined with meropenem/varbobactam (MEV) or ceftazidime/avibactam (CZA) showed synergistic effects, while the effect of aztreonam combined with CZA was inferior to that of MEV. Compared with the same concentration of aztreonam plus CZA combination, aztreonam/avibactam had a better bactericidal effect (24 h bacterial count reduction >3 log10CFU/mL). These data indicate that the combination of ATM with several new BLBLIs exerts powerful bactericidal activity, which suggests that these double ß-lactam combinations might provide potential alternative treatments for infections caused by pathogens coproducing-serine/metallo-carbapenems.