Methylation entropy landscape of Chinese long-lived individuals reveals lower epigenetic noise related to human healthy aging.

The transition from ordered to noisy is a significant epigenetic signature of aging and age-related disease. As a paradigm of healthy human aging and longevity, long-lived individuals (LLI, >90 years old) may possess characteristic strategies in coping with the disordered epigenetic regulation. In this study, we constructed high-resolution blood epigenetic noise landscapes for this cohort by a methylation entropy (ME) method using whole genome bisulfite sequencing (WGBS). Although a universal increase in global ME occurred with chronological age in general control samples, this trend was suppressed in LLIs. Importantly, we identified 38,923 genomic regions with LLI-specific lower ME (LLI-specific lower entropy regions, for short, LLI-specific LERs). These regions were overrepresented in promoters, which likely function in transcriptional noise suppression. Genes associated with LLI-specific LERs have a considerable impact on SNP-based heritability of some aging-related disorders (e.g., asthma and stroke). Furthermore, neutrophil was identified as the primary cell type sustaining LLI-specific LERs. Our results highlight the stability of epigenetic order in promoters of genes involved with aging and age-related disorders within LLI epigenomes. This unique epigenetic feature reveals a previously unknown role of epigenetic order maintenance in specific genomic regions of LLIs, which helps open a new avenue on the epigenetic regulation mechanism in human healthy aging and longevity.

Small-molecule antiviral treatments for COVID-19: A systematic review and network meta-analysis.

OBJECTIVE: This study aimed to explore the efficacy and safety of small-molecule antivirals for treating coronavirus disease 2019 (COVID-19). METHODS: Seven databases were searched from their inception to 01 June 2023. The risk of bias in randomised controlled trials and retrospective studies was evaluated individually using the Cochrane risk-of-bias tool and Newcastle Ottawa Scale. RESULTS: In total, 160 studies involving 933 409 COVID-19 patients were evaluated. Compared with placebo or standard of care, proxalutamide demonstrated remarkable efficacy in reducing mortality rates, hospitalisation rates, serious adverse events, and the need for mechanical ventilation. Furthermore, it significantly enhanced both the rate of clinical improvement and expedited the duration of clinical recovery when compared with control groups. In patients with mild-to-moderate COVID-19, proxalutamide exhibited the above advantages, except for mortality reduction. Triazavirin was the most effective treatment for reducing the time required for viral clearance and improving the discharge rate. Leritrelvir and VV116 were ranked first in terms of enhancing the viral clearance rate on days 7 and 14, respectively. Molnupiravir was the most effective treatment for reducing the need for oxygen support. Overall, these findings remained consistent across the various subgroups. CONCLUSIONS: A thorough evaluation of effectiveness, applicable to both mild-to-moderate and unstratified populations, highlights the specific advantages of proxalutamide, nirmatrelvir/ritonavir, triazavirin, azvudine, molnupiravir, and VV116 in combating COVID-19. Additional clinical data are required to confirm the efficacy and safety of simnotrelvir/ritonavir and leritrelvir. The safety profiles of these antivirals were deemed acceptable.

Antiviral Treatment in Older Chinese Patient with SARS-CoV-2 and Influenza A Virus Co-Infection: A Case Series.

Coronavirus disease 2019 (COVID-19) emergence in late 2019, and wide spread quickly in the world. In China, the COVID-19 epidemic situation entered a low level now. With the arrival of flu season, the number of patients with respiratory symptoms is increasing. We reported three cases of patients who co-infected with SARS-CoV-2 and influenza A virus (IAV), and they were all treated with nirmatrelvir-ritonavir (NMV/r) and baloxavir marboxil. Due to the overlapping clinical features between the two diseases, it is important to identified them and gave the antiviral therapy timely.

Is Azvudine Comparable to Nirmatrelvir-Ritonavir in Real-World Efficacy and Safety for Hospitalized Patients with COVID-19? A Retrospective Cohort Study.

INTRODUCTION: Azvudine and nirmatrelvir-ritonavir are more extensively used to treat COVID-19 in China due to their earlier approval by the National Medical Products Administration. However, there has been a scarcity of research directly comparing the clinical outcomes between azvudine and nirmatrelvir-ritonavir till now. We aimed to make a head-to-head comparison of the efficacy and safety of azvudine or nirmatrelvir-ritonavir in hospitalized patients with COVID-19 in China. METHODS: This retrospective cohort study was conducted using data collected from Tongde Hospital of Zhejiang Province between December 2022 and January 2023. All-cause mortality, risk of progressing to a critical condition, proportion with nucleic-acid negative conversion (PNANC), time to first nucleic-acid negative conversion (TFNANC), length of hospital stay and incidence of adverse events were systematically assessed as outcomes. Multi-model regression analysis, propensity-score-matching analysis, subgroup analysis and several sensitivity analyses were applied to compare these outcomes. RESULTS: This study included a total of 1571 hospitalized patients with COVID-19, among whom 272 received nirmatrelvir-ritonavir and 156 received azvudine. We found no significant differences in all-cause mortality (HR 1.41; 95% CI 0.56-3.56; P = 0.471), risk of progressing to critical COVID-19 (HR 1.67; 95% CI 0.78-3.60; P = 0.189), PNANC (HR 0.87; 95% CI 0.69-1.09; P = 0.220), length of stay (ß - 0.82; 95% CI - 2.78 to 1.15; P = 0.414) and adverse event rate (3.21% vs. 4.41%, P = 0.538) between the two groups, although azvudine was slightly less effective than nirmatrelvir-ritonavir. Meanwhile, the azvudine group exhibited a significantly longer TFNANC (ß 2.53; 95% CI 0.76-4.29; P = 0.005) than the nirmatrelvir-ritonavir group. Results were similar for propensity-score matching and multiple sensitivity analyses. CONCLUSION: Azvudine probably possessed comparable efficacy and safety to nirmatrelvir-ritonavir, although it was less effective than nirmatrelvir-ritonavir for some outcomes.

Metabolic Modeling Identifies a Novel Molecular Type of Glioblastoma Associated with Good Prognosis.

Glioblastoma (GBM) is one of the most aggressive forms of cancer. Although IDH1 mutation indicates a good prognosis and a potential target for treatment, most GBMs are IDH1 wild-type. Identifying additional molecular markers would help to generate personalized therapies and improve patient outcomes. Here, we used our recently developed metabolic modeling method (genome-wide precision metabolic modeling, GPMM) to investigate the metabolic profiles of GBM, aiming to identify additional novel molecular markers for this disease. We systematically analyzed the metabolic reaction profiles of 149 GBM samples lacking IDH1 mutation. Forty-eight reactions showing significant association with prognosis were identified. Further analysis indicated that the purine recycling, nucleotide interconversion, and folate metabolism pathways were the most robust modules related to prognosis. Considering the three pathways, we then identified the most significant GBM type for a better prognosis, namely N+P-. This type presented high nucleotide interconversion (N+) and low purine recycling (P-). N+P--type exhibited a significantly better outcome (log-rank p = 4.7 × 10-7) than that of N-P+. GBM patients with the N+P--type had a median survival time of 19.6 months and lived 65% longer than other GBM patients. Our results highlighted a novel molecular type of GBM, which showed relatively high frequency (26%) in GBM patients lacking the IDH1 mutation, and therefore exhibits potential in GBM prognostic assessment and personalized therapy.

Bone Marrow Mesenchymal Stem Cells Derived from Juvenile Macaques Reversed the Serum Protein Expression Profile in Aged Macaques.

OBJECTIVE: The aim of the study was to reveal the changes in serum protein composition and content in macaques during the process of ageing, and explore the effect of bone marrow mesenchymal stem cell (BMMSC) on the serum protein expression profile in elderly macaques. METHODS: Naturally ageing macaques were assessed according to age. BMMSCs were intravenously infused into aged macaques. In addition, peripheral blood was collected to obtain serum for dataindependent acquisition (DIA) protein sequencing to identify aging-related indicators. One hundred eighty days after macaques received BMMSC treatment, haemoxylin and eosin (HE) staining was performed to observe the morphology and structure of aortic arches. RESULTS: Compared to infant and young control macaques, aged macaques showed erythema on the face, dry skin, reduced amounts of hair on the head and back, and paleness. Cultured BMMSCs from the 4th passage (P4 BMMSCs) were grown in accordance with standards used to culture mesenchymal stem cells. After BMMSC treatment, the assessed aortic arches showed no calcium salt deposition or cell necrosis, and the characteristics of the serum protein expression profile tended to be similar to that of the infant and young groups, with the expression of 41 proteins upregulated with age and that of 30 proteins downregulated with age but upregulated after BMMSC treatment. Moreover, we identified 44 significantly differentially expressed proteins between the aged model and treatment groups; 11 of the upregulated proteins were related to vascular ageing, neuronal ageing and haematopoiesis, and 33 of the downregulated proteins were associated with neuronal ageing, cardiovascular disease, and tumours. Interestingly, S100 expression in serum was significantly decreased, COMP expression was significantly increased, NKAP expression reappeared, and LCN2, CSF1R, CORO1C, CSTB and RSU-1 expression disappeared after BMMSC treatment. CONCLUSION: BMMSCs can reverse ageing-related serum protein expression.

dNAGLU Extends Life Span and Promotes Fitness and Stress Resistance in Drosophila.

To identify new factors that promote longevity and healthy aging, we studied Drosophila CG13397, an ortholog of the human NAGLU gene, a lysosomal enzyme overexpressed in centenarians. We found that the overexpression of CG13397 (dNAGLU) ubiquitously, or tissue specifically, in the nervous system or fat body could extend fly life span. It also extended the life span of flies overexpressing human Aß42, in a Drosophila Alzheimer's disease (AD) model. To investigate whether dNAGLU could influence health span, we analyzed the effect of its overexpression on AD flies and found that it improved the climbing ability and stress resistance, including desiccation and hunger, suggesting that dNAGLU improved fly health span. We found that the deposition of Aß42 in the mushroom body, which is the fly central nervous system, was reduced, and the lysosomal activity in the intestine was increased in dNAGLU over-expressing flies. When NAGLU was overexpressed in human U251-APP cells, which expresses a mutant form of the Aß-precursor protein (APP), APP-p.M671L, these cells exhibited stronger lysosomal activity and and enhanced expression of lysosomal pathway genes. The concentration of Aß42 in the cell supernatant was reduced, and the growth arrest caused by APP expression was reversed, suggesting that NAGLU could play a wider role beyond its catalytic activity to enhance lysosomal activity. These results also suggest that NAGLU overexpression could be explored to promote healthy aging and to prevent the onset of neurodegenerative diseases, including AD.

Multiple time-series expression trajectories imply dynamic functional changes during cellular senescence.

Cellular senescence is a dynamic process driven by epigenetic and genetic changes. Although some transcriptomic signatures of senescent cells have been discovered, how these senescence-related signals change over time remains largely unclear. Here, we profiled the transcriptome dynamics of human dermal fibroblast (HDF) cells in successive stages of growth from proliferation to senescence. Based on time-series expression profile analysis, we discovered four trajectories (C1, C2, C3, C4) that are dynamically expressed as senescence progresses. While some genes were continuously up-regulated (C4) or down-regulated (C2) with aging, other genes did not change linearly with cell proliferation, but remained stable until entering the senescent state (C1, C3). Further analysis revealed that the four modes were enriched in different biological pathways, including regulation of cellular senescence. These findings provide a new perspective on understanding the dynamic regulatory mechanism of cellular senescence.

Mechanism of Thymus Rejuvenation in Elderly Macaques.

Senile thymus atrophy is an important factor leading to decreased immune function. Repairing the atrophic thymus tissue structure, rebuilding immune function, and replenishing the number of exogenous stem cells may be ideal methods. In this study, bone marrow mesenchymal stem cells were intravenously infused into elderly macaques. We found that thymus volume was substantially increased, some thymus tissue regeneration was observed, the degree of thymus tissue fibrosis decreased, collagen fiber deposition decreased, cortical and medulla structures emerged gradually, the number of apoptotic cells decreased significantly, and the expression of apoptosis-related proteins decreased. For the effects of stem cell therapy on aging-related genes, we performed transcriptomic analysis of thymus tissue. The results show the expression pattern of the tissue transcriptome tended to be similar to the thymus expression pattern in young macaques compared with the elderly group, reverse aging-related proteins. Based on the results, it is suggested that stem cell therapy is an ideal method to prevent or reverse the aging of the thymus.

Specific Gain and Loss of Co-Expression Modules in Long-Lived Individuals Indicate a Role of circRNAs in Human Longevity.

Deep RNA sequencing of 164 blood samples collected from long-lived families was performed to investigate the expression patterns of circular RNAs (circRNAs). Unlike that observed in previous studies, circRNA expression in long-lived elderly individuals (98.3 ± 3.4 year) did not exhibit an age-accumulating pattern. Based on weighted circRNA co-expression network analysis, we found that longevous elders specifically gained eight but lost seven conserved circRNA-circRNA co-expression modules (c-CCMs) compared with normal elder controls (spouses of offspring of long-lived individuals, age = 59.3 ± 5.8 year). Further analysis showed that these modules were associated with healthy aging-related pathways. These results together suggest an important role of circRNAs in regulating human lifespan extension.

System-level metabolic modeling facilitates unveiling metabolic signature in exceptional longevity.

Although it is well known that metabolic control plays a crucial role in regulating the health span and life span of various organisms, little is known for the systems metabolic profile of centenarians, the paradigm of human healthy aging and longevity. Meanwhile, how to well characterize the system-level metabolic states in an organism of interest remains to be a major challenge in systems metabolism research. To address this challenge and better understand the metabolic mechanisms of healthy aging, we developed a method of genome-wide precision metabolic modeling (GPMM) which is able to quantitatively integrate transcriptome, proteome and kinetome data in predictive modeling of metabolic networks. Benchmarking analysis showed that GPMM successfully characterized metabolic reprogramming in the NCI-60 cancer cell lines; it dramatically improved the performance of the modeling with an R2 of 0.86 between the predicted and experimental measurements over the performance of existing methods. Using this approach, we examined the metabolic networks of a Chinese centenarian cohort and identified the elevated fatty acid oxidation (FAO) as the most significant metabolic feature in these long-lived individuals. Evidence from serum metabolomics supports this observation. Given that FAO declines with normal aging and is impaired in many age-related diseases, our study suggests that the elevated FAO has potential to be a novel signature of healthy aging of humans.

Screening of antibacterial compounds with novel structure from the FDA approved drugs using machine learning methods.

Bacterial infection is one of the most important factors affecting the human life span. Elderly people are more harmed by bacterial infections due to their deficits in immunity. Because of the lack of new antibiotics in recent years, bacterial resistance has increasingly become a serious problem globally. In this study, an antibacterial compound predictor was constructed using the support vector machines and random forest methods and the data of the active and inactive antibacterial compounds from the ChEMBL database. The results showed that both models have excellent prediction performance (mean accuracy >0.9 and mean AUC >0.9 for the two models). We used the predictor to screen potential antibacterial compounds from FDA-approved drugs in the DrugBank database. The screening results showed that 1087 small-molecule drugs have potential antibacterial activity and 154 of them are FDA-approved antibacterial drugs, which accounts for 76.2% of the approved antibacterial drugs collected in this study. Through molecular fingerprint similarity analysis and common substructure analysis, we screened 8 predicted antibacterial small-molecule compounds with novel structures compared with known antibacterial drugs, and 5 of them are widely used in the treatment of various tumors. This study provides a new insight for predicting antibacterial compounds by using approved drugs, the predicted compounds might be used to treat bacterial infections and extend lifespan.

Prediction of allergic reaction risk of Shenmai injection based on real-world evidence coupled with UPLC-Q-TOF-MS.

The allergic reaction (AR) of Chinese herbal injection (CHI) has become one of the most noticeable focuses of public health in China. However, it still remains a considerable controversy as to whether low-molecular-weight components in CHI have potential sensitization. In this study, the relationship between AR and low-molecular-weight component profile of Shenmai injection was explored by an interdisciplinary technology integrating real-world evidence and ultra-performance liquid chromatography-quadrupole time-of-flight mass spectroscopy (UPLC-Q-TOF-MS). The AR information of hospitalized patients was obtained by comprehensively analyzing real-world evidence from January 2015 to June 2019 at two Chinese hospitals. The UPLC-Q-TOF-MS was exploited to systematically investigate the low-molecular-weight component profile with 50-1500 m/z mass range, and 3725 MS1 peaks were detected. The optimized partial least squares discriminant analysis model was established to map the influence of low-molecular-weight components on AR. The results of this study showed that high levels of organic acids administered intravenously might be a potential risk factor for inducing AR. By using this method, Shenmai injection with high AR risk could be recognized precisely with 100% accuracy before clinical use.

Identification of hub ubiquitin ligase genes affecting Alzheimer's disease by analyzing transcriptome data from multiple brain regions.

The ubiquitin-proteasome system (UPS) plays crucial roles in numerous cellular functions. Dysfunction of the UPS shows certain correlations with the pathological changes in Alzheimer's disease (AD). This study aimed to explore the different impairments of the UPS in multiple brain regions and identify hub ubiquitin ligase (E3) genes in AD. The brain transcriptome, blood transcriptome and proteome data of AD were downloaded from a public database. The UPS genes were collected from the Ubiquitin and Ubiquitin-like Conjugation Database. The hub E3 genes were defined as the differentially expressed E3 genes shared by more than three brain regions. E3Miner and UbiBrowser were used to predict the substrate of hub E3. This study shows varied impairment of the UPS in different brain regions in AD. Furthermore, we identify seven hub E3 genes (CUL1, CUL3, EIF3I, NSMCE1, PAFAH1B1, RNF175, and UCHL1) that are downregulated in more than three brain regions. Three of these genes (CUL1, EIF3I, and NSMCE1) showed consistent low expression in blood. Most of these genes have been reported to promote AD, whereas the impact of RNF175 on AD is not yet reported. Further analysis revealed a potential regulatory mechanism by which hub E3 and its substrate genes may affect transcription functions and then exacerbate AD. This study identified seven hub E3 genes and their substrate genes affect transcription functions and then exacerbate AD. These findings may be helpful for the development of diagnostic biomarkers and therapeutic targets for AD.

Correction to: FastMM: an efficient toolbox for personalized constraint-based metabolic modeling.

An amendment to this paper has been published and can be accessed via the original article.

Systematic metabolic analysis of potential target, therapeutic drug, diagnostic method and animal model applicability in three neurodegenerative diseases.

Considerable evidence suggests that metabolic abnormalities are associated with neurodegenerative diseases. This study aimed to conduct a systematic metabolic analysis of Alzheimer's disease (AD), Parkinson's disease (PD) and Huntington's disease (HD). Human and mouse model microarray datasets were downloaded from the Gene Expression Omnibus database. The metabolic genes and pathways were collected from the Recon 3D human metabolic model. Drug and target information was obtained from the DrugBank database. This study identified ATP1A1, ATP6V1G2, GOT1, HPRT1, MAP2K1, PCMT1 and PLK2 as key metabolic genes that were downregulated in AD, PD and HD. We screened 57 drugs that target these genes, such as digoxin, ouabain and diazoxide. This study constructed multigene diagnostic models for AD, PD and HD by using metabolic gene expression profiles in blood, all models showed high accuracy (AUC > 0.8) both in the experimental and validation sets. Furthermore, analysis of animal models showed that there was almost no consistency among the metabolic changes between mouse models and human diseases. This study systematically revealed the metabolic damage among AD, PD, and HD and uncovered the differences between animal models and human diseases. This information may be helpful for understanding the metabolic mechanisms and drug development for neurodegenerative diseases.

FastMM: an efficient toolbox for personalized constraint-based metabolic modeling.

BACKGROUND: Constraint-based metabolic modeling has been applied to understand metabolism related disease mechanisms, to predict potential new drug targets and anti-metabolites, and to identify biomarkers of complex diseases. Although the state-of-art modeling toolbox, COBRA 3.0, is powerful, it requires substantial computing time conducting flux balance analysis, knockout analysis, and Markov Chain Monte Carlo (MCMC) sampling, which may limit its application in large scale genome-wide analysis. RESULTS: Here, we rewrote the underlying code of COBRA 3.0 using C/C++, and developed a toolbox, termed FastMM, to effectively conduct constraint-based metabolic modeling. The results showed that FastMM is 2~400 times faster than COBRA 3.0 in performing flux balance analysis and knockout analysis and returns consistent outputs. When applied to MCMC sampling, FastMM is 8 times faster than COBRA 3.0. FastMM is also faster than some efficient metabolic modeling applications, such as Cobrapy and Fast-SL. In addition, we developed a Matlab/Octave interface for fast metabolic modeling. This interface was fully compatible with COBRA 3.0, enabling users to easily perform complex applications for metabolic modeling. For example, users who do not have deep constraint-based metabolic model knowledge can just type one command in Matlab/Octave to perform personalized metabolic modeling. Users can also use the advance and multiple threading parameters for complex metabolic modeling. Thus, we provided an efficient and user-friendly solution to perform large scale genome-wide metabolic modeling. For example, FastMM can be applied to the modeling of individual cancer metabolic profiles of hundreds to thousands of samples in the Cancer Genome Atlas (TCGA). CONCLUSION: FastMM is an efficient and user-friendly toolbox for large-scale personalized constraint-based metabolic modeling. It can serve as a complementary and invaluable improvement to the existing functionalities in COBRA 3.0. FastMM is under GPL license and can be freely available at GitHub site: https://github.com/GonghuaLi/FastMM.

Identification of DNA N6-methyladenine sites by integration of sequence features.

BACKGROUND: An increasing number of nucleic acid modifications have been profiled with the development of sequencing technologies. DNA N6-methyladenine (6mA), which is a prevalent epigenetic modification, plays important roles in a series of biological processes. So far, identification of DNA 6mA relies primarily on time-consuming and expensive experimental approaches. However, in silico methods can be implemented to conduct preliminary screening to save experimental resources and time, especially given the rapid accumulation of sequencing data. RESULTS: In this study, we constructed a 6mA predictor, p6mA, from a series of sequence-based features, including physicochemical properties, position-specific triple-nucleotide propensity (PSTNP), and electron-ion interaction pseudopotential (EIIP). We performed maximum relevance maximum distance (MRMD) analysis to select key features and used the Extreme Gradient Boosting (XGBoost) algorithm to build our predictor. Results demonstrated that p6mA outperformed other existing predictors using different datasets. CONCLUSIONS: p6mA can predict the methylation status of DNA adenines, using only sequence files. It may be used as a tool to help the study of 6mA distribution pattern. Users can download it from https://github.com/Konglab404/p6mA.

Glycerophosphodiester phosphodiesterase 1 (GDE1) acts as a potential tumor suppressor and is a novel therapeutic target for non-mucin-producing colon adenocarcinoma.

Colon adenocarcinoma (COAD) represents a major public health issue due to its high incidence and mortality. As different histological subtypes of COAD are related to various survival outcomes and different therapies, finding specific targets and treatments for different subtypes is one of the major demands of individual disease therapy. Interestingly, as these different subtypes show distinct metabolic profiles, it may be possible to find specific targets related to histological typing by targeting COAD metabolism. In this study, the differential expression patterns of metabolism-related genes between COAD (n = 289) and adjacent normal tissue (n = 41) were analyzed by one-way ANOVA. We then used weighted gene co-expression network analysis (WGCNA) to further identify metabolism-related gene connections. To determine the critical genes related to COAD metabolism, we obtained 2,114 significantly differentially expressed genes (DEGs) and 12 modules. Among them, we found the hub module to be significantly associated with histological typing, including non-mucin-producing colon adenocarcinoma and mucin-producing colon adenocarcinoma. Combining survival analysis, we identified glycerophosphodiester phosphodiesterase 1 (GDE1) as the most significant gene associated with histological typing and prognosis. This gene displayed significantly lower expression in COAD compared with normal tissues and was significantly correlated with the prognosis of non-mucin-producing colon adenocarcinoma (p = 0.0017). Taken together, our study showed that GDE1 exhibits considerable potential as a novel therapeutic target for non-mucin-producing colon adenocarcinoma.

The Impact Of Pharmaceutical Interventions On The Use Of Carbapenems In A Chinese Hospital: A Pre-Post Study.

BACKGROUND: The challenge of drug resistance to carbapenems is of international concern with leading to increased hospital lengths of stay, costs, and mortality rates. How to get rid of the vicious cycle of drug resistance, new drugs, and re-resistance, and even the emergence of all-drug-resistant bacteria that humans cannot cope with, are the major challenges we face. To date, data about pharmaceutical interventions on the use of carbapenems are currently limited. PATIENTS AND METHODS: A retrospective cohort study was conducted to compare pre- and post-intervention in Tongde Hospital of Zhejiang Province. Pharmaceutical interventions were performed in the post-intervention group, including real time monitoring of medication orders, educative group activities, and making interventions to physicians. Intervention acceptance and outcomes, including the length of hospital stay, readmission rates, 30-day mortality, and utilization of carbapenems, which was evaluated by the daily defined doses (DDDs), the days of therapy (DOTs), and the cost of carbapenems, were reviewed. RESULTS: During the study, 593 interventions were provided by clinical pharmacists with an average acceptance rate of 82.79%. Compared with the pre-intervention group, prescriptions of carbapenems for pathogen-directed therapy were improved significantly in the post-intervention group (59.27% vs 21.74%, p=0.022). The DDDs decreased from 281.96 to 174.28 and DOTs decreased from 9.19 to 5.18 after pharmaceutical intervention, and the pharmaceutical interventions had significantly lower mean total cost of carbapenems ($13,828.8 vs $8137.1, p=0.004) and length of hospital stay (9.3±1.5 vs 15.9±2.2, p=0.014). There was a significant reduction in 30-day mortality in the post-intervention group (9.46% vs 17.86%, p=0.013) while there were no differences found in the 30-day readmission (20.19% vs 20.66%, p=0.99). CONCLUSION: Implementation of pharmaceutical interventions in our hospital successfully improved the appropriateness of carbapenem prescribing overall, and reduced the DDDs, DOTs, length of hospital day, and cost of carbapenems.