Other specific DSP article suggested by Editorial Board
Integrated Prediction of Ceftriaxone Resistance in Major Salmonella Serotypes Using MALDI-TOF MS Data with Balanced Datasets and Machine Learning.
Authors: Ren J et al
Abstract
Objectives: Salmonella causes gastroenteritis and invasive infections worldwide, and rising ceftriaxone resistance complicates empirical therapy. Conventional culture-based susceptibility testing remains reliable but time-consuming. MALDI-TOF MS, widely adopted for rapid identification, offers potential for antimicrobial-resistance prediction when combined with machine learning.
Methods: 632 clinical Salmonella isolates, 536 from Zhejiang University Children’s Hospital and 96 from Wanbei Coal-Electricity Group General Hospital were analyzed. Serovar-stratified MALDI-TOF MS spectra were preprocessed and modeled with six classifiers (XGBoost, Random Forest, Logistic Regression, Naïve Bayes, Linear SVM, and RBF SVM) across five data-balancing strategies (none, SMOTE, ADASYN, undersampling, and cost-sensitive learning). Model performance was assessed by ROC-AUC, PR-AUC, sensitivity, F1 score, calibration, and decision-curve analysis. SHAP-based feature selection enhanced interpretability and reduced model complexity.
Results: Ceftriaxone resistance varied markedly by serovar, highest in S. enteritidis (46.9%) and C1 (30.9%). Multivariate analyses and visualization confirmed distinct resistance-associated spectral signatures. Serovar-specific pipelines were identified; models for C1, E1, and S. typhimurium achieved validation ROC-AUCs of 0.883 to 0.924. SHAP revealed mainly serovar-specific discriminative peaks that provided transparent sample-level explanations. Decision-curve analysis demonstrated consistent clinical net benefit compared with treat-all or treat-none strategies across a range of decision thresholds.
Conclusions: Integrating MALDI-TOF MS, serovar stratification, data-balancing strategies, and machine learning yields interpretable, serovar-specific models for rapid ceftriaxone-resistance prediction in Salmonella. Implemented as a user-friendly web application, these models could shorten time to targeted therapy and support improved antibiotic stewardship and reduce unnecessary broad-spectrum antibiotic use across clinical settings worldwide.
Other specific DSP article suggested by Editorial Board
Determinants of Antimicrobial Resistance in Acinetobacter baumannii Isolates From Intensive Care Patients in Latvia.
DOI: 10.1155/ijm/5526340
Authors: Dolgusevs M, et al
Abstract
Background: Acinetobacter baumannii is a leading nosocomial pathogen in intensive care units (ICUs), often resistant to multiple antibiotics. Data from the Baltic region remain scarce, limiting infection control and stewardship strategies.
Methods: An integrated phenotypic-genotypic analysis of A. baumannii isolates collected from ICU patients in a tertiary-care hospital in Latvia (July 2022-June 2024) was conducted. Antimicrobial susceptibility testing was performed for major antibiotic classes, and whole-genome sequencing (WGS) was used to identify genomic resistance determinants.
Results: 52 clinical isolates from 45 ICU patients were analysed. Multidrug resistance was nearly universal (98%), with complete resistance to carbapenems and fluoroquinolones and > 95% resistance to aminoglycosides and trimethoprim-sulfamethoxazole. Colistin activity was largely preserved, with resistance detected in only one isolate, despite widespread polymyxin resistance-associated mutations. Genotypic findings were mostly in line with the phenotypic results. All isolates belonged to the ST2 lineage, highlighting clonal homogeneity. No plasmid replicons were detected, suggesting chromosomal elements as the primary resistance drivers.
Conclusions: This first integrated dataset in an ICU setting from the Baltic region demonstrates alarming resistance levels and clonal dominance of ST2. The findings highlight the importance of combining WGS with susceptibility testing for accurate resistance assessment.
Other specific DSP article suggested by Editorial Board
Discriminating ST3 and non‐ST3 Staphylococcus lugdunensis using MALDI‐TOF and machine learning analysis
Authors: Yu-Hsiang Ou
Abstract
Purpose: Staphylococcus lugdunensis has gradually become an important pathogen because of its broad range of infectious symptoms, especially the high mortality associated with endocarditis. Previous epidemiological surveillance has shown that most oxacillin-resistant isolates belong to the ST3 group, the predominant population in communities. Therefore, there is a need to rapidly and efficiently evaluate antimicrobial resistance in S. lugdunensis.
Methods: To rapidly and efficiently discriminate between ST3 and non-ST3 populations, a matrix-assisted laser desorption/ionization time-of-flight (MALDITOF) platform with a machine learning approach was used to analyze 107 clinical isolates collected between 2003 and 2014.
Results: Our data showed that the signals located at both 3676 m/z and 7352 m/z in ST3 isolates varied from those of non-ST3 isolates (3683 m/z and 7366 m/z). Further, 81 isolates collected from 2016 to 2019 were used to evaluate this finding; 59 isolates were classified as ST3, and multilocus sequence typing (MLST) validation confirmed that 50 isolates belonged to ST3. Using MLST, the remaining 22 isolates classified as non-ST3 were found to be non-ST3 types. Overall, our approach had a sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of 100%, 71%, 85%, 100%, and 89%, respectively.
Conclusion: Our data demonstrate that MALDI-TOF provides a reliable way to discriminate between ST3 and non-ST3 S. lugdunensis, which is valuable for clinical identification applications.”
Other specific DSP article suggested by Editorial Board
The implementation of the Infection Control Map—an integrated, visual online information system—enhanced infection control practices and reduced the incidence of multidrug-resistant organisms in a medical intensive care unit
Authors: Chih-Hao Chen
Abstract
Background: Fragmented and siloed nature of healthcare data leads to insufficient recognition of infection control priorities and poor adherence to preventive measures. We evaluated the impact of a visualized and integrated online information system on the infection control practice and incidence of multidrug-resistant organisms (MDROs), antimicrobial consumptions, and clinical outcomes in a medical intensive care unit (ICU).
Methods: Patients hospitalized in one medical ICU during July 1, 2023, to November 30, 2024, were collected, and were analyzed by clinical outcomes, the incidence rate of healthcare-associated MDROs, and the antimicrobial consumptions before and after the implementation of Infection Control Map (ICM). Another ICU was selected as comparison.
Results: A total of 775 patients were included and divided into two periods: Pre-intervention period (n = 262), and Intervention period (n = 513). There were no statistical differences among demographics except the Acute Physiologic Assessment and Chronic Health Evaluation II (APACHE II) score, which was higher in the Intervention period than that of Pre-intervention period (30.0 vs 25.0, p = 0.000). The incidence rate of MDROs decreased over time (tau = −0.53, p = 0.019), especially the methicillin-resistant Staphylococcus aureus (1.5 vs 0.3 per 1000 patient-days, incidence rate ratio = 0.2, p = 0.012), as well as the consumption of meropenem (tau = −0.53, p = 0.003). There was significant decrease in 30-day all-cause mortality after the ICM use by multivariate analysis.
Conclusion: The implementation of the ICM significantly strengthened infection control practices, resulting in a marked reduction in healthcare-associated MRSA incidence, targeted antimicrobial usage, and 30-day all-cause mortality.”
Other specific DSP article suggested by Editorial Board
Attitudes to cross infection, nebuliser hygiene and antimicrobial resistance in people with cystic fibrosis: Results of an international survey
Authors: Beverley Cherie Millar
Abstract
Background : Respiratory infection is a major cause of disease severity in people with cystic fibrosis (PwCF). This project aimed to establish the CF community’s opinion regarding cross infection (CI), nebuliser hygiene, antimicrobial resistance, personal impact of microbiological findings and the role of the microbiology laboratory.
Methods: A questionnaire was completed anonymously (n = 280; PwCF (n = 128), parents (n = 123); friends/family/carers/charity personnel (n = 29)) from 13 countries. Readability scores (Flesch Reading Ease (FRE), Flesch Kincaid Grade Level (FKGL)) were determined for CI/IP&C information from six national CF charities and 21 scientific abstracts.
Results: Respondents (72.5%) indicated knowledge of laboratory aspects of CF microbiology was important, however implications of microbiological findings on personal health/well-being were of higher importance (p < 0.0001). Cross infection/infection prevention & control (CI/IP&C) was of highest importance (95.6% respondents) with 27.3% indicating they were not given adequate information, particularly in older respondents (50 y+) (p = 0.006) versus young adults (16-29 y) and respondents from the Middle East versus N. America (p = 0.022) and Europe (p = 0.045). Responses highlighted how CI/IP&C health literacy could be enhanced. Respondents (77.3%), particularly females (p < 0.0001), indicated they would increase the frequency of nebuliser disinfection following guidance on infection risks/best practice, therefore an educational video was prepared. CI/IP&C readability scores (mean ± sd) from CF charities (FRE 52.5 ± 10.8; FKGL 9.7 ± 2.3) were more readable (p < 0.0001) than scientific abstracts (FRE 13.3 ± 11.1; FKGL 16.9 ± 2.3), however not meeting the targets (FRE≥60 and FKGL≤8).
Conclusion:
There is a requirement for further CI/IP&C evidence-based guidance, policies/guidelines, education awareness, best practice in the home environment and multi-modal communication, enabling the CF community to make informed choices on lifestyle behaviours.”
Other specific DSP article suggested by Editorial Board
Contribution of weekly ward rounds led by pediatric infectious diseases specialists in a pediatric intensive care unit.
Authors: de Gregorio M et al
Abstract
Background: Laminar airflow filters have been suggested as a potential preventive factor for surgical site infections, given their ability to reduce the airborne microbiological load. However, their role is still unclear, and evidence regarding vascular surgery patients is scarce. Our aim was to assess the impact of laminar-airflow filters on surgical site infections.
Methods: This single-centre retrospective cohort study was conducted with vascular surgery patients who underwent arterial vascular intervention through a groin incision between July 2018 and July 2019 (turbulent airflow cohort) and July 2020 and July 2021 (laminar airflow cohort). Data were prospectively collected from electronic medical files. We estimated the cumulative incidence of surgical site infections and its 95% confident interval (95%CI). A propensity score matching analysis was performed.
Results: We included 200 patients, 78 in the turbulent airflow cohort and 122 in the laminar airflow cohort. The cumulative incidence was 15.4% (12/78; 95%CI: 9.0–25.0%) in the turbulent-airflow cohort and 14.8% (18/122; 95%CI: 9.5 –22.1%) in the laminar-airflow cohort (p-value: 1.00). The propensity score matching yielded a cumulative incidence of surgical site infection of 13.9% (10/72) with turbulent airflow and 12.5% (9/72) with laminar airflow (p-value: 1.00). Risk factors associated with infection were chronic kidney disease (OR 2.70; 95%CI: 1.14–6.21) and a greater body mass index (OR 1.47; 95%CI: 1.01–2.14).
Conclusion: Laminar airflow filters were associated with a non-significant reduction of surgical site infections. Further research is needed to determine its usefulness and cost-effectiveness. Surgical site infection incidence was associated with chronic kidney disease and a greater body mass index. Hence, efforts should be made to optimize the body mass index before surgery and prevent chronic kidney disease in patients with known arterial disease.”