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ML algorithms are used to develop prognostic and diagnostic models and so to support clinical decision-making. This study uses eight supervised ML algorithms to predict the need for intensive care, intubation, and mortality risk for COVID-19 patients. The study uses two datasets: (1) patient demographics and clinical data (n = 11,712), and (2) patient demographics, clinical data, and blood test results (n = 602) for developing the prediction models, understanding the most significant features, and comparing the performances of eight different ML algorithms. Experimental findings showed that all prognostic prediction models reported an AUROC value of over 0.92, in which extra tree and CatBoost classifiers were often outperformed (AUROC over 0.94). The findings revealed that the features of C-reactive protein, the ratio of lymphocytes, lactic acid, and serum calcium have a substantial impact on COVID-19 prognostic predictions. This study provides evidence of the value of tree-based supervised ML algorithms for predicting prognosis in health care.

Delftia acidovorans is an aerobic, nonfermenting Gram‐negative bacillus. It is usually a nonpathogenic environmental organism and is rarely clinically significant. Although D acidovorans infection most commonly occurs in hospitalized or immunocompromised patients, there are also several reports documenting the infection in immunocompetent patients. The present article describes a B cell lymphoblastic leukemia patient with D acidovorans pneumonia who was successfully treated with antibiotic therapy. The present report indicates that unusual pathogens may be clinically significant in both immunocompromised and immunocompetent patients. D acidovorans is often resistant to aminoglycosides; therefore, rapid detection of this microorganism is important.

Urinary tract infections (UTIs) are among the most common bacterial infections worldwide, with and being the predominant pathogens. Diagnostic delays necessitate the use of broad-spectrum antibiotics, which fuels antimicrobial resistance. This study aimed to develop and validate an artificial intelligence (AI) model for the rapid identification of and colonies from urine culture images. We analyzed 1547 chromogenic agar urine culture images (850 , 697 ) obtained using an automated Copan WASP system. A YOLOv12 deep learning model was trained on expert-labeled colonies. The reference standard comprised MALDI-TOF Mass Spectrometry (MS) for and a characteristic chromogenic morphology for . This phenotypic method is standard in clinical practice, with validation studies for the specific agar used reporting a sensitivity of 98.1-99.0% and a specificity of 99.1% for identification. Performance was assessed on an internal test set and via external validation using 91 independent images. The model achieved 99% accuracy (precision, recall, F1-score: 0.99) on internal testing. External validation yielded 100% accuracy, with the critical note that species labels in this independent set were inferred from colony colour. Rare errors involved atypical (eg, gold-pigmented) colonies. YOLOv12 outperformed five benchmark deep learning models. This AI model enables rapid (sub-second), accurate phenotypic classification of the most common UTI pathogens directly from routine culture plates. Integration into automated systems could reduce the diagnostic timeline by approximately 18 hours, facilitating earlier targeted therapy and supporting antimicrobial stewardship. A key consideration for implementation is the model's basis in phenotypic identification, which aligns with standard laboratory workflow but requires awareness of rare chromogenic variants. The reliance on chromogenic morphology for ground truth has been a key limitation of the study.

[LANGUAGE= \"English\"] INTRODUCTION: Urinary tract infections (UTIs) are one of the most common community-acquired infectious diseases globally. This study was conducted to contribute to the data of our country by examining the distribution of UTI agents isolated from outpatients and their antibiotic susceptibility results.METHODS: The positive urine cultures of 24,917 outpatients aged 18 years and older, which were sent to the Istanbul Public Hospitals Services Presidency-2 Central Laboratory between January 2016 and December 2019, and their antibiotic susceptibility results were retrospectively evaluated.RESULTS: Of the 24,917 uropathogens, 87% were Gram-negative bacteria and 13% were Gram-positive bacteria. The most commonly isolated organisms were Escherichia coli (57%), Klebsiella pneumoniae (15%), and Enterococcus spp (12%). E. coli showed high resistance to all antibiotics tested except for aminoglycoside group, carbapenem group, nitrofurantoin, and fosfomycin, while K. pneumoniae showed high resistance to all antibiotics except for aminoglycoside group and carbapenem group. In enterococci, high-level resistance was determined only to gentamicin and ciprofloxacin.DISCUSSION AND CONCLUSION: In our study, it was determined that most of the antibiotics used for the treatment of community-acquired UTIs had a higher resistance rate than the recommended 10–20% value for empirical treatment. We think that it is very important to follow region-specific epidemiological data, take the necessary measures, and use antibiotics rationally.

Lactococcus lactis is a gram-positive, facultative anaerobic coccus that is occasionally isolated from human mucocutaneous surfaces such as the intestines. It is used in the dairy industry for milk acidification and is mostly nonpathogenic in immunocompetent humans, however a number of cases of infection with L. lactis have been reported in recent years. In this article, we describe two cases of infection due to L. lactis in patients with chronic diarrhea. The first case is a five-month-old boy who was operated on for volvulus on his first day of life and had ileostomy with subsequent diagnosis of chronic diarrhea and bacteremia due to L. Lactis. The second case is a six-month-old girl with the diagnosis of chronic diarrhea that developed after a catheter-related bloodstream infection. Both of the infections due to L. Lactis spp lactis were successfully treated with intravenous vancomycin therapy. Although Lactococcus species is mostly known as nonpathogenic, it should be kept in mind as a potential pathogen, especially in patients with gastrointestinal disorders.

poses a global public health threat due to its multidrug resistance and association with severe invasive infections. With limited treatment options, combination therapies are being explored to enhance antifungal efficacy. This study aimed to evaluate the in vitro synergistic activity of anidulafungin combined with isavuconazole, voriconazole, posaconazole, and amphotericin B against clinical isolates. Fifty clinical isolates were identified via MALDI-TOF MS. Antifungal susceptibility testing was performed using the EUCAST broth microdilution method to determine Minimum Inhibitory Concentrations (MICs). Subsequently, the in vitro interactions of anidulafungin with four other antifungals were assessed using a checkerboard assay. Drug interactions were classified by calculating the Fractional Inhibitory Concentration Index (FICI). High resistance rates were observed for fluconazole (72%) and amphotericin B (48%), whereas no isolates were resistant to echinocandins. In combination tests, interactions were predominantly indifferent (56-84%), followed by partial synergy (12-40%). A single instance of true synergy (FICI = 0.49) was noted with the anidulafungin-posaconazole combination in one isolate. Synergy rates (synergy + partial synergy) varied significantly across combinations (Fisher's exact test, p = 0.0044), with anidulafungin-azole combinations showing the highest rates (36-40%) compared to anidulafungin-amphotericin B (12%). No antagonism (FICI ≥ 4) was detected in any combination. The predominance of indifferent interactions, with some partial synergy, suggests that combination therapy may not consistently offer a strong synergistic benefit for these isolates. This exploratory study characterizes the in vitro interaction landscape in our isolate collection, providing foundational data to guide future research and inform clinical decisions where treatment options are scarce.

PurposeThis comprehensive prospective study aimed to investigate the bacterial contamination of antibiotic steroid eye ointments and drops frequently used by eye patients.MethodIn this comprehensive prospective study, a total of 410 multi-use topical eye medications containing 15 different ingredients from 22 pharmaceutical companies used by 185 patients were analyzed. Four groups were formed as follows: group 1: antibiotic ointments (n: 109); group 2: antibiotic drops (n: 103); group 3: steroid ointments (n: 67); and group 4: steroid drops (n: 131). Topical multi-use eye drops and ointments used by patients at home for at least 1 week were randomly collected. The caps and contents were separately bacteriologically examined in a chocolate agar medium.ResultsOur study detected bacterial contamination in 23 containers (5.6%) of the total 410 topical drugs. According to the groups, bacterial contamination was detected in 10 of 67 (14.9%) steroid ointments, 6 of 109 (5.5%) antibiotic ointments, 4 of 131(3.1%) steroid drops, and 3 of 103 (2.9%) antibiotic drops. While the bacterial contamination rate in ointments was 9.1%, this rate was 3% in drops. The difference between them was statistically significant (p = 0.015). According to the post-hoc pairwise comparisons, the difference between steroid drops and steroid ointment (p = 0.0023) was statistically significant. Among all drugs, contamination was detected in 12 of the 93 (12.9%) containers used after keratitis, conjunctivitis, and inflammatory conditions. It was determined that preservatives statistically reduced bacterial growth on the cap. The preservatives did not have a statistically significant effect on the bacterial contamination of the contents compared to the caps. While all contaminations were detected in illiterate and primary school graduates, no contamination was seen in the drugs used by any secondary school or university graduate.ConclusionOur study detected contamination in all topical ophthalmic drug groups. Contamination rates were found to be higher in ointments and steroids. Bacterial contamination was also seen in drugs containing preservatives. We should be careful in the use of topical medications. We do not recommend the bilateral use of ointments and drops in infected eyes, such as those with keratitis, or after intraocular surgeries, such as those for cataracts.

Objective: Quick diagnosis of COVID-19 has been an important factor to manage the ongoing pandemic at hospitals and other health facilities. We aimed to investigate the effects of PCR test on hemogram parameters in COVID-19 patients. Materials and Methods: We collected hemogram data of 120 nasopharyngeal and oropharyngeal combo swab PCR positive and 119 PCR negative patients admitted to our hospital’s COVID-19 clinics with COVID-19 symptoms between 1 April 2020 and 24 June2020. Results: Age, MPV and NLR were found to be higher; hemoglobin, neutrophil, lymphocytes, basophil, platelet, PCT, WBC levels were lower in PCR positive cases. The highest sensitivity, 75 % is found on WBC count with cut off 7.15. Conclusion:Lower leukocyte count than 7.15, lower neutrophil count than 4.91, greater NLR than 2.95, lower platelet than 221.5 may give an idea about the diagnosis of SARS-CoV-2 infection. Bangladesh Journal of Medical Science Vol. 21 No. 02 April’22 Page : 391-397

Objective: Quick diagnosis of COVID-19 has been an important factor to manage the ongoing pandemic at hospitals and other health facilities.We aimed to investigate the effects of PCR test on hemogram parameters in COVID-19 patients. Materials and Methods: We collected hemogram data of 120 nasopharyngeal and oropharyngeal combo swab PCR positive and 119 PCR negative patients admitted to our hospital’s COVID-19 clinics with COVID-19 symptoms between 1 April 2020 and 24 June2020. Results: Age, MPV and NLR were found to be higher; hemoglobin, neutrophil, lymphocytes, basophil, platelet, PCT, WBC levels were lower in PCR positive cases.The highest sensitivity, 75 % is found on WBC count with cut off 7.15. Conclusion: Lower leukocyte count than 7.15, lower neutrophil count than 4.91, greater NLR than 2.95, lower platelet than 221.5 may give an idea about the diagnosis of SARS-CoV-2 infection. Bangladesh Journal of Medical Science Vol.20(5) 2021 p.118-124