Exosomes-mediated delivery of miR-486-3p alleviates neuroinflammation via SIRT2-mediated inhibition of mitophagy after subarachnoid hemorrhage.

BACKGROUND: Neuroinflammation participates in the pathogenesis of subarachnoid haemorrhage (SAH); however, no effective treatments exist. MicroRNAs regulate several aspects of neuronal dysfunction. In a previous study, we found that exosomal miR-486-3p is involved in the pathophysiology of SAH. Targeted delivery of miR-486-3p without blood-brain barrier (BBB) restriction to alleviate SAH is a promising neuroinflammation approach. METHODS: In this study, we modified exosomes (Exo) to form an RVG-miR-486-3p-Exo (Exo/miR) to achieve targeted delivery of miR-486-3p to the brain. Neurological scores, brain water content, BBB damage, flow cytometry and FJC staining were used to determine the effect of miR-486-3p on SAH. Western blot analysis, ELISA and RT-qPCR were used to measure relevant protein and mRNA levels. Immunofluorescence staining and laser confocal detection were used to measure the expression of mitochondria, lysosomes and autophagosomes, and transmission electron microscopy was used to observe the level of mitophagy in the brain tissue of mice after SAH. RESULTS: Tail vein injection of Exo/miR improved targeting of miR-486-3p to the brains of SAH mice. The injection reduced levels of neuroinflammation-related factors by changing the phenotype switching of microglia, inhibiting the expression of sirtuin 2 (SIRT2) and enhancing mitophagy. miR-486-3p treatment alleviated neurobehavioral disorders, brain oedema, BBB damage and neurodegeneration. Further research found that the mechanism was achieved by regulating the acetylation level of peroxisome proliferator-activated receptor γ coactivator l alpha (PGC-1α) after SIRT2 enters the nucleus. CONCLUSION: Exo/miR treatment attenuates neuroinflammation after SAH by inhibiting SIRT2 expression and stimulating mitophagy, suggesting potential clinical applications.

Distinct mutation features and its clinical significance in myelodysplastic syndromes with normal karyotype.

Myelodysplastic syndromes (MDS) is a highly heterogeneous myeloid neoplastic disease, which needs personalized evaluation and therapy. To analyze the features and significance of gene mutations for MDS patients with normal karyotype (NK) at diagnosis, targeted sequencing was conducted on 616 MDS patients with NK, alongside 457 MDS cases with abnormal karyotype (AK). The results showed that the incidence of somatic mutation reached 70.3% and 83.8% in the NK and AK group, respectively. Initial mutation including ASXL1, DNMT3A and TET2 were common in NK group, which is the same as AK group. Some karyotype-associated gene mutations, such as TP53 and U2AF1, were relatively rare in NK group. Moreover, 34 out of 91 samples who progressed to acute myeloid leukemia (AML) underwent repeat sequencing during follow-up. 25 cases were checked out with newly emerged mutations. The AML-associated genetic alterations mainly involved with active signaling and transcription factors. In patients with NK, serial targeted sequencing was employed for minimal residual disease (MRD) monitoring, indicating the efficacy and relapse of the patients. In summary, MDS with NK showed distinct mutation features from those with AK. High-frequency gene mutations together with the mutational evolution suggested the diagnostic and monitoring significance of next generation sequencing for NK-MDS.

Elevational and temporal patterns of pollination success in distylous and homostylous buckwheats (Fagopyrum) in the Hengduan Mountains.

Reproductive strategies of sexually dimorphic plants vary in response to the environment. Here, we ask whether the sexual systems of Fagopyrum species (i.e., selfing homostylous and out-crossing distylous) represent distinct adaptive strategies to increase reproductive success in changing alpine environments. To answer this question, we determined how spatial and temporal factors (e.g., elevation and peak flowering time) affect reproductive success (i.e., stigmatic pollen load) in nine wild Fagopyrum species (seven distylous and two homostylous) among 28 populations along an elevation gradient of 1299-3315 m in the Hengduan Mountains, southwestern China. We also observed pollinators and conducted hundreds of hand pollinations to investigate inter/intra-morph compatibility, self-compatibility and pollen limitation in four Fagopyrum species (two distylous and two homostylous). We found that Fagopyrum species at higher elevation generally had bigger flowers and more stigmatic pollen loads; late-flowering individuals had smaller flowers and lower pollen deposition. Stigmatic pollen deposition was more variable in distylous species than in homostylous species. Although seed set was not pollen-limited in all species, we found that fruit set was much lower in distylous species, which rely on frequent pollinator visits, than in homostylous species capable of autonomous self-pollination. Our findings that pollination success increases at high elevations and decreases during the flowering season suggest that distylous and homostylous species have spatially and temporally distinct reproductive strategies related to environment-dependent pollinator activity.

Reverse network diffusion to remove indirect noise for better inference of gene regulatory networks.

MOTIVATION: Gene regulatory networks (GRNs) are vital tools for delineating regulatory relationships between transcription factors and their target genes. The boom in computational biology and various biotechnologies has made inferring GRNs from multi-omics data a hot topic. However, when networks are constructed from gene expression data, they often suffer from false-positive problem due to the transitive effects of correlation. The presence of spurious noise edges obscures the real gene interactions, which makes downstream analyses, such as detecting gene function modules and predicting disease-related genes, difficult and inefficient. Therefore, there is an urgent and compelling need to develop network denoising methods to improve the accuracy of GRN inference. RESULTS: In this study, we proposed a novel network denoising method named REverse Network Diffusion On Random walks (RENDOR). RENDOR is designed to enhance the accuracy of GRNs afflicted by indirect effects. RENDOR takes noisy networks as input, models higher-order indirect interactions between genes by transitive closure, eliminates false-positive effects using the inverse network diffusion method, and produces refined networks as output. We conducted a comparative assessment of GRN inference accuracy before and after denoising on simulated networks and real GRNs. Our results emphasized that the network derived from RENDOR more accurately and effectively captures gene interactions. This study demonstrates the significance of removing network indirect noise and highlights the effectiveness of the proposed method in enhancing the signal-to-noise ratio of noisy networks. AVAILABILITY AND IMPLEMENTATION: The R package RENDOR is provided at https://github.com/Wu-Lab/RENDOR and other source code and data are available at https://github.com/Wu-Lab/RENDOR-reproduce.

Effects of antibiotic cocktail on the fecal microbiota and their potential correlation of local immune response.

BACKGROUND: The guts of mammals are home to trillions of microbes, forming a complex and dynamic ecosystem. Gut microbiota is an important biological barrier for maintaining immune homeostasis. Recently, the use of antibiotics to clear gut microbiota has gained popularity as a low cost and easy-to-use alternative to germ-free animals. However, the effect of the duration of the antibiotic cocktail on the gut microbiome is unclear, and more importantly, the effect of dramatic changes in the gut microbiota on intestinal tissue morphology and local immune response is rarely reported. RESULTS: We observed a significant reduction in fecal microbiota species and abundance after 1 week of exposure to an antibiotic cocktail, gavage twice daily by intragastric administration. In terms of composition, Bacteroidetes and Firmicutes were replaced by Proteobacteria. Extending antibiotic exposure to 2-3 weeks did not significantly improve the overall efficiency of microbiotal consumption. No significant histomorphological changes were observed in the first 2 weeks of antibiotic cocktail exposure, but the expression of inflammatory mediators in intestinal tissue was increased after 3 weeks of antibiotic cocktail exposure. Mendelian randomization analysis showed that Actinobacteria had a significant causal association with the increase of IL-1ß (OR = 1.65, 95% CI = 1.23 to 2.21, P = 0.007) and TNF-α (OR = 1.81, 95% CI = 1.26 to 2.61, P = 0.001). CONCLUSIONS: Our data suggest that treatment with an antibiotic cocktail lasting 1 week is sufficient to induce a significant reduction in gut microbes. 3 weeks of antibiotic exposure can lead to the colonization of persistant microbiota and cause changes in intestinal tissue and local immune responses.

The Structural and Molecular Mechanisms of Mycobacterium tuberculosis Translational Elongation Factor Proteins.

Targeting translation factor proteins holds promise for developing innovative anti-tuberculosis drugs. During protein translation, many factors cause ribosomes to stall at messenger RNA (mRNA). To maintain protein homeostasis, bacteria have evolved various ribosome rescue mechanisms, including the predominant trans-translation process, to release stalled ribosomes and remove aberrant mRNAs. The rescue systems require the participation of translation elongation factor proteins (EFs) and are essential for bacterial physiology and reproduction. However, they disappear during eukaryotic evolution, which makes the essential proteins and translation elongation factors promising antimicrobial drug targets. Here, we review the structural and molecular mechanisms of the translation elongation factors EF-Tu, EF-Ts, and EF-G, which play essential roles in the normal translation and ribosome rescue mechanisms of Mycobacterium tuberculosis (Mtb). We also briefly describe the structure-based, computer-assisted study of anti-tuberculosis drugs.

Incorporating network diffusion and peak location information for better single-cell ATAC-seq data analysis.

Single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) data provided new insights into the understanding of epigenetic heterogeneity and transcriptional regulation. With the increasing abundance of dataset resources, there is an urgent need to extract more useful information through high-quality data analysis methods specifically designed for scATAC-seq. However, analyzing scATAC-seq data poses challenges due to its near binarization, high sparsity and ultra-high dimensionality properties. Here, we proposed a novel network diffusion-based computational method to comprehensively analyze scATAC-seq data, named Single-Cell ATAC-seq Analysis via Network Refinement with Peaks Location Information (SCARP). SCARP formulates the Network Refinement diffusion method under the graph theory framework to aggregate information from different network orders, effectively compensating for missing signals in the scATAC-seq data. By incorporating distance information between adjacent peaks on the genome, SCARP also contributes to depicting the co-accessibility of peaks. These two innovations empower SCARP to obtain lower-dimensional representations for both cells and peaks more effectively. We have demonstrated through sufficient experiments that SCARP facilitated superior analyses of scATAC-seq data. Specifically, SCARP exhibited outstanding cell clustering performance, enabling better elucidation of cell heterogeneity and the discovery of new biologically significant cell subpopulations. Additionally, SCARP was also instrumental in portraying co-accessibility relationships of accessible regions and providing new insight into transcriptional regulation. Consequently, SCARP identified genes that were involved in key Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways related to diseases and predicted reliable cis-regulatory interactions. To sum up, our studies suggested that SCARP is a promising tool to comprehensively analyze the scATAC-seq data.

Raman lidar at 355 nm using low dead time photon counting for atmospheric aerosol measurements.

Photon counting is an effective way to enhance the dynamic range of the data acquisition system (DAQ) in Raman lidars. However, there exists a deficiency of relatively high dead times among current options, which necessitates an additional calibration procedure for the nonlinearity of the photon counting signal, thus leading to unanticipated errors. A field programmable gate array (FPGA)-based photon counting module has been proposed and implemented in a Raman lidar, offering two operational channels. Through observational experiments, it was determined that this module has an overall dead time of 1.13 ns taking advantage of the high-speed amplifier/discriminator pair and the logic design, a significant improvement compared to the 4.35 ns of a commercially used Licel transient recorder within the same counting rate range. This notably low dead time implies that its output maintains sufficient linearity even at substantially high counting rates. As a result, the need for a dead time calibration procedure prior to signal integration with the analog signal is eliminated, reducing uncertainty in the final integrated signal, and even in the retrieval result. The backscattering result of the comparison between this module and a transient recorder indicates that a more precise performance can be acquired benefiting from this hardware upgrading.

Dynamics of SARS-CoV-2 IgG antibodies and neutralising antibodies in rheumatic and musculoskeletal diseases patients with COVID-19.

OBJECTIVES: Rheumatic and musculoskeletal diseases (RMD) may exhibit different immune responses to novel coronavirus (COVID-19) infection compared to healthy individuals. While previous studies have primarily investigated changes in COVID-19-related antibodies post-vaccination for RMD patients, this study sought to explore the dynamics of SARS-CoV-2 IgG antibodies and neutralising antibodies (NAb) in RMD patients after COVID-19 infection. METHODS: In this longitudinal study, we monitored the SARS-CoV-2 IgG antibodies and NAb levels in RMD patients and healthy controls (HC) at 60 and 90 days post-COVID-19 infection. Chemiluminescent immunoassay was used to detect the levels of novel coronavirus-specific IgG (anti-S1/S2 IgG) antibodies and NAb. RESULTS: A total of 292 RMD patients and 104 HC were enrolled in the study. At both the 60-day and 90-day post-COVID-19 infection, RMD patients exhibited significantly lower levels of anti-S1/S2 IgG and NAb than those in the HC group (p<0.001). The anti-S1/S2 IgG antibody levels remained relatively stable, while the NAb levels in RMD patients could vary greatly between the 60th and 90th days. A logistic regression analysis revealed that the prior administration of glucocorticoids (GC), immunosuppressants, and b/tsDMARDs stood out as independent risk factors associated with reduced anti-S1/S2 IgG and NAb levels, irrespective of the specific RMD subtypes. CONCLUSIONS: GC and anti-rheumatic medications can potentially alter the production of specific antibodies, especially NAb, in RMD patients post-COVID-19 infection. These findings emphasise the importance of continuous monitoring for NAb fluctuations in RMD patients following a COVID-19 infection.

The clinical characteristics, gene mutations and outcomes of myelodysplastic syndromes with diabetes mellitus.

PURPOSE: Diabetes mellitus (DM) is the second most common comorbidity in myelodysplastic syndromes (MDS). The purpose of the study was to investigate the clinical characteristics of MDS patients with DM. METHODS: A retrospective analysis was performed on the clinical data of 890 MDS patients with or without DM. Clinical data, including genetic changes, overall survival (OS), leukemia-free survival (LFS) and infection, were analyzed. RESULTS: Among 890 patients, 184 (20.7%) had DM. TET2 and SF3B1 mutations occurred more frequently in the DM group than those in the non-DM group (p = 0.0092 and p = 0.0004, respectively). Besides, DM was an independent risk factor for infection (HR 2.135 CI 1.451-3.110, p = 0.000) in MDS. Compared to non-DM patients, MDS patients with DM had poor OS and LFS (p = 0.0002 and p = 0.0017, respectively), especially in the lower-risk group. While in multivariate analysis, DM did not retain its prognostic significance and the prognostic significance of infection was maintained (HR 2.488 CI 1.749-3.538, p = 0.000). CONCLUSIONS: MDS patients with DM have an inferior prognosis which may due to higher infection incidence, with TET2 and SF3B1 mutations being more frequent in those cases.

Pattern-Aware Transformer: Hierarchical Pattern Propagation in Sequential Medical Images.

This paper investigates how to effectively mine contextual information among sequential images and jointly model them in medical imaging tasks. Different from state-of-the-art methods that model sequential correlations via point-wise token encoding, this paper develops a novel hierarchical pattern-aware tokenization strategy. It handles distinct visual patterns independently and hierarchically, which not only ensures the full flexibility of attention aggregation under different pattern representations but also preserves both local and global information simultaneously. Based on this strategy, we propose a Pattern-Aware Transformer (PATrans) featuring a global-local dual-path pattern-aware cross-attention mechanism to achieve hierarchical pattern matching and propagation among sequential images. Furthermore, PATrans is plug-and-play and can be seamlessly integrated into various backbone networks for diverse downstream sequence modeling tasks. We demonstrate its general application paradigm across four domains and five benchmarks in video object detection and 3D volumetric semantic segmentation tasks, respectively. Impressively, PATrans sets new state-of-the-art across all these benchmarks, i.e., CVC-Video (92.3% detection F1), ASU-Mayo (99.1% localization F1), Lung Tumor (78.59% DSC), Nasopharynx Tumor (75.50% DSC), and Kidney Tumor (87.53% DSC). Codes and models are available at https://github.com/GGaoxiang/PATrans.

Osteopontin modulates microglial activation states and attenuates inflammatory responses after subarachnoid hemorrhage in rats.

AIMS: Osteopontin (OPN) has demonstrated neuroprotective effects in various stroke models. Its role in neuroinflammation after brain injury remains to be elucidated. This study aims to clarify the effect of OPN on neuroinflammation, particularly on the functional states of microglia after subarachnoid hemorrhage (SAH). METHODS: 77 rats were randomly divided into the following groups: Sham, SAH 24 h, SAH + rOPN, SAH + Vehicle (PBS), SAH + OPN siRNA, and SAH + Scr siRNA, SAH + rOPN+Fib-14 and SAH + rOPN+DMSO. Modified Garcia and beam balance tests were used to evaluate neurobehavioral outcomes. Semi-quantitative immunofluorescence staining was performed to measure expression of myeloperoxidase (MPO) and microglia activation state markers CD16, CD206 after SAH and recombinant OPN treatment. The quantification of microglia activation and functional markers CD16, CD206, TNF-α and IL-10 were further evaluated using Western-blotting. RESULTS: Nasal administration of rOPN improved neurological dysfunction, attenuated neutrophil infiltration, and decreased expression of phenotypic and functional markers of pro-inflammatory microglia CD16 and TNF-α. It also promoted an anti-inflammatory microglial state, as evidenced by increased expression of CD206 and IL-10. Furthermore, after blocking the phosphorylation of FAK signaling, the effects of rOPN on microglial activation states were partially reversed. The downstream pathways of STAT3 and NF-κB also exhibited consistent changes, suggesting the involvement of the STAT3 and NF-κB pathways in OPN's modulation of microglial activation via integrin-FAK signaling. CONCLUSION: OPN attenuates inflammatory responses after SAH by promoting an anti-inflammatory microglial state, potentially mediated through the integrin-FAK-STAT3 and NF-κB signaling pathways.

Effects of technology-assisted rehabilitation for patients with hip arthroplasty: A meta-analysis.

BACKGROUND: To investigate the efficacy of technology-assisted rehabilitation compared to that of usual care programs after total hip arthroplasty (THA) through randomized controlled trials (RCTs). METHODS: The Medline (PubMed), Cochrane Library, Embase and Web of Science databases were searched for RCTs regarding the efficacy of technology-assisted rehabilitation following THA. Data were analyzed using Stata 12.0 software. RESULTS: Eleven RCTs involving 1327 patients were included in the meta-analysis. The pooled effect size showed that compared to usual care, telerehabilitation significantly improved the Harris score (standardized mean difference [SMD] 0.74, 95% confidence interval [CI] 0.58 to 0.90) and functional independence measure (FIM) score (SMD 1.26, 95% CI 0.48 to 2.03). In addition, video-based therapy could significantly improve walk test results (SMD 0.43, 95% CI 0.11 to 0.75). CONCLUSION: The findings suggest that technology-assisted rehabilitation, especially telerehabilitation, have been shown to improve the physical function of patients following THA compared to conventional rehabilitation. More robust studies are needed to validate the long-term efficacy and safety of innovative technology-assisted training strategies.

PathExpSurv: pathway expansion for explainable survival analysis and disease gene discovery.

BACKGROUND: In the field of biology and medicine, the interpretability and accuracy are both important when designing predictive models. The interpretability of many machine learning models such as neural networks is still a challenge. Recently, many researchers utilized prior information such as biological pathways to develop neural networks-based methods, so as to provide some insights and interpretability for the models. However, the prior biological knowledge may be incomplete and there still exists some unknown information to be explored. RESULTS: We proposed a novel method, named PathExpSurv, to gain an insight into the black-box model of neural network for cancer survival analysis. We demonstrated that PathExpSurv could not only incorporate the known prior information into the model, but also explore the unknown possible expansion to the existing pathways. We performed downstream analyses based on the expanded pathways and successfully identified some key genes associated with the diseases and original pathways. CONCLUSIONS: Our proposed PathExpSurv is a novel, effective and interpretable method for survival analysis. It has great utility and value in medical diagnosis and offers a promising framework for biological research.

ROBO1 deficiency impairs HSPC homeostasis and erythropoiesis via CDC42 and predicts poor prognosis in MDS.

Myelodysplastic syndrome (MDS) is a group of clonal hematopoietic neoplasms originating from hematopoietic stem progenitor cells (HSPCs). We previously identified frequent roundabout guidance receptor 1 (ROBO1) mutations in patients with MDS, while the exact role of ROBO1 in hematopoiesis remains poorly delineated. Here, we report that ROBO1 deficiency confers MDS-like disease with anemia and multilineage dysplasia in mice and predicts poor prognosis in patients with MDS. More specifically, Robo1 deficiency impairs HSPC homeostasis and disrupts HSPC pool, especially the reduction of megakaryocyte erythroid progenitors, which causes a blockage in the early stages of erythropoiesis in mice. Mechanistically, transcriptional profiling indicates that Cdc42, a member of the Rho-guanosine triphosphatase family, acts as a downstream target gene for Robo1 in HSPCs. Overexpression of Cdc42 partially restores the self-renewal and erythropoiesis of HSPCs in Robo1-deficient mice. Collectively, our result implicates the essential role of ROBO1 in maintaining HSPC homeostasis and erythropoiesis via CDC42.

The bcl6 corepressor mutation regulates the progression and transformation of myelodysplastic syndromes by repressing the autophagy flux.

The occurrence of autophagy dysregulation is vital in the development of myelodysplastic syndrome and its transformation to acute myeloid leukemia. However, the mechanisms are largely unknown. Here, we have investigated the mechanism of the bcl6 corepressor mutation in myelodysplastic syndrome development and its transformation to acute myeloid leukemia. We identified a novel pathway involving histone deacetylase 6 and forkhead box protein O1, which leads to autophagy defects following the bcl6 corepressor mutation. And this further causes apoptosis and cell cycle arrest. The bcl6 corepressor-mutation-repressed autophagy resulted in the accumulation of damaged mitochondria, DNA, and reactive oxygen species in myelodysplastic syndrome cells, which could then lead to genomic instability and spontaneous mutation. Our results suggest that the bcl6 corepressor inactivating mutations exert pro-carcinogenic effects through survival strike, which is only an intermediate process. These findings provide mechanistic insights into the role of the bcl6 corepressor gene in myelodysplastic syndrome.

Comparison of Prognostic Models for Functional Outcome in Aneurysmal Subarachnoid Hemorrhage Based on Machine Learning.

BACKGROUND: Controversy exists regarding the superiority of the performance of prognostic tools based on advanced machine learning (ML) algorithms for patients with aneurysmal subarachnoid hemorrhage (aSAH). However, it is unclear whether ML prognostic models will benefit patients due to the lack of a comprehensive assessment. We aimed to develop and evaluate ML models for predicting unfavorable functional outcomes for aSAH patients and identify the model with the greatest performance. METHODS: In this retrospective study, a dataset of 955 patients with aSAH was used to construct and validate prognostic models for functional outcomes assessed using the modified Rankin scale during a follow-up period of 3-6 months. Clinical scores and clinical and radiological features on admission and secondary complications were used to construct models based on 5 ML algorithms (i.e., logistic regression [LR], k-nearest neighbor, extreme gradient boosting, random forest, and artificial neural network). For evaluation among the models, the area under the receiver operating characteristic curve, area under the precision-recall curve, calibration curve, and decision curve analysis were used. RESULTS: Composite models had significantly higher area under the receiver operating characteristic curves than did simple models in predicting unfavorable functional outcomes. Compared with other composite models (random forest and extreme gradient boosting) with good calibration, LR had the highest area under the precision-recall score and showed the greatest benefit in decision curve analysis. CONCLUSIONS: Of the 5 studied ML models, the conventional LR model outperformed the advanced algorithms in predicting the prognosis and could be a useful tool for health care professionals.

Effectiveness of decision support tools on reducing antibiotic use for respiratory tract infections: a systematic review and meta-analysis.

Background: Clinical decision support tools (CDSs) have been demonstrated to enhance the accuracy of antibiotic prescribing among physicians. However, their effectiveness in reducing inappropriate antibiotic use for respiratory tract infections (RTI) is controversial. Methods: A literature search in 3 international databases (Medline, Web of science and Embase) was conducted before 31 May 2023. Relative risk (RR) and corresponding 95% confidence intervals (CI) were pooled to evaluate the effectiveness of intervention. Summary effect sizes were calculated using a random-effects model due to the expected heterogeneity (I 2 over 50%). Results: A total of 11 cluster randomized clinical trials (RCTs) and 5 before-after studies were included in this meta-analysis, involving 900,804 patients met full inclusion criteria. Among these studies, 11 reported positive effects, 1 reported negative results, and 4 reported non-significant findings. Overall, the pooled effect size revealed that CDSs significantly reduced antibiotic use for RTIs (RR = 0.90, 95% CI = 0.85 to 0.95, I 2 = 96.10%). Subgroup analysis indicated that the intervention duration may serve as a potential source of heterogeneity. Studies with interventions duration more than 2 years were found to have non-significant effects (RR = 1.00, 95% CI = 0.96 to 1.04, I 2 = 0.00%). Egger's test results indicated no evidence of potential publication bias (p = 0.287). Conclusion: This study suggests that CDSs effectively reduce inappropriate antibiotic use for RTIs among physicians. However, subgroup analysis revealed that interventions lasting more than 2 years did not yield significant effects. These findings highlight the importance of considering intervention duration when implementing CDSs. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023432584, Identifier: PROSPERO (CRD42023432584).