Search Results (17)

Background: Antimicrobial resistance is a threat that increases morbidity and mortality. This cross-sectional study aimed to describe the profile of priority antimicrobial-resistant pathogens and to analyze the behavior of multidrug-resistant tuberculosis (MDR-TB) in the Santiago de Cali District, Colombia. Methods: researchers compiled information from data provided by healthcare institutions, the National Public Health Surveillance System, and laboratory-based surveillance using the World Health Organization’s WHONET v.5.6 software. Univariate statistical analysis described trends in pathogen resistance, and multivariate analysis analyzed the behavior of MDR-TB. Results: Among Gram-negative bacteria, high levels of carbapenem resistance were observed in A. baumannii (84% aztreonam resistance) and in K. pneumoniae (63%). P. aeruginosa exhibited elevated multidrug resistance, consistent with extensive antimicrobial selective pressure. MDR-TB exhibited a high burden of resistance, reaching 96%, with projections indicating a potential increase driven by monoresistance and resistance to rifampicin. Patients with drug-resistant tuberculosis who were HIV-positive or experiencing homelessness had a significantly higher likelihood of hospitalization (OR 5.59; 95% CI 3.09–10.11 and OR 2.94; 95% CI 1.48–5.81, respectively) and mortality (OR 3.34; 95% CI 1.72–6.49 and OR 2.59; 95% CI 1.16–5.79, respectively). Conclusions: The expansion of resistance mechanisms suggests sustained selective pressure, underscoring the need for strategies to optimize antibiotics. Full article

Background/Objectives: The right diagnostic tests, for the right patient, at the right time, are key to optimising antimicrobial use (AMU) and preventing antimicrobial resistance (AMR). This study evaluated diagnostic stewardship trends and AMR patterns in Zambian surveillance sentinel sites from 2020 to 2024. Methods: This descriptive, retrospective study analysed routine laboratory data from January 2020 to December 2024 at seven designated AMR surveillance sentinel sites across Zambia. Data on clinical specimens submitted for antimicrobial susceptibility testing were extracted from WHONET and analysed by year, specimen type, and antimicrobial susceptibility profile. Results: A total of 184,788 bacteriology specimens were processed over five years. Urine was the most frequently collected specimen, peaking above 20,000 in 2024. Escherichia coli was the most commonly isolated organism among 15 priority pathogens, comprising 25.9% of the 30,013 isolates. Though a statistically significant increasing trend in total organism isolations was observed annually, only Shigella sp. demonstrated a substantial increase in non-susceptibility to azithromycin (p = 0.027). High resistance was observed with doxycycline, azithromycin, clindamycin, trimethoprim/sulfamethoxazole, ciprofloxacin, and rifampicin, exhibiting resistance ranging from 50% to 80%. Critical AMR alerts included 65% for Vancomycin-Resistant Enterococcus (VRE), 72% for linezolid, and 44% for carbapenems, and possible ESBL-producing Enterobacterales showing the highest overall resistance at 35%, across sentinel sites. Conclusions: Zambia faces a concerning and significant increase in AMR, with high resistance prevalence across commonly used antibiotics. Critical resistance alerts for VRE, linezolid, carbapenems, and possible ESBL-producing Enterobacterales underscore an urgent need for robust antimicrobial stewardship and continuous diagnostic surveillance. Full article

Background/Objectives: Antimicrobial resistance (AMR) is a major global health threat that undermines treatment in humans and animals. In Zambia, where livestock production underpins food security and livelihoods, AMR challenges are aggravated by limited surveillance, weak diagnostics, and poor regulatory enforcement, facilitating the spread of resistant pathogens across the human–animal–environment interface. This study aims to analyse AMR patterns of bacterial isolates collected from Zambia’s animal health sector between 2020 and 2024, to generate evidence that informs national AMR surveillance, supports antimicrobial stewardship (AMS) interventions, and strengthens One Health strategies to mitigate the spread of resistant pathogens. Methods: We conducted a retrospective descriptive analysis of previously collected routine laboratory data from five well-established animal health AMR surveillance sentinel sites between January 2020 and December 2024. Data were analysed by year, sample type, and antimicrobial susceptibility testing (AST) profiles using WHONET. Results: A total of 1688 samples were processed, with faecal samples accounting for 87.6%. Animal environmental samples (feed, manure, litter, abattoir/meat processing floor, wall, and equipment surface swabs) (collected from abattoirs, water, and farms) increased significantly over time (p = 0.027). Overall, Escherichia coli (E. coli) (50.4%) and Enterococcus spp. (30%) were the most frequently isolated bacteria. E. coli exhibited high resistance to tetracycline (74%) and ampicillin (72%) but remained susceptible to aztreonam (98%), nitrofurantoin (95%), and imipenem (93%). Enterococcus spp. were susceptible to penicillin (84%) and ampicillin (89%) but showed borderline resistance to vancomycin (53%) and linezolid (50%). Klebsiella spp. demonstrated resistance to ciprofloxacin (52%) and gentamicin (40%), whereas Salmonella spp. remained highly susceptible. Notably, resistance to amoxicillin/clavulanic acid rose sharply from 22.2% to 81.8% (p = 0.027). Across 1416 isolates, high levels of multidrug resistance (MDR) were observed, particularly in E. coli (48.4%) and K. pneumoniae (18.6%), with notable proportions progressing toward possible Extensively Drug-Resistant (XDR) and Pan-Drug-Resistant (PDR) states. Conclusions: The findings of this study reveal rising resistance to commonly used antibiotics in the animal health sector. Despite the lack of molecular analysis, our findings underscore the urgent need for AMS programs and integrated AMR surveillance under Zambia’s One Health strategy. Full article

Antimicrobial resistance is a global health issue, and the WHO has made significant efforts in the development of tools for its monitoring. However, such tools are underutilized, due to limited knowledge, technical capacity, and scarcity of economic resources. AMR surveillance can be conducted using WHOnet and R, two free-of-charge software tools widely adopted in both clinical practice and scientific research. WHOnet is designed for managing laboratory data and antimicrobial susceptibility test results, while R is a programming language dedicated to statistical computing and data visualization. The combined use of these tools enables a reproducible workflow for retrospective AMR trend analysis. This paper provides step-by-step instructions on how to perform such analysis and also provides the respective R code. The described code and software results are shown using real-world data from an Italian hospital as an example. The standardization of the analysis process and the rapid availability of data on antimicrobial resistance are critical for both clinicians and public health professionals. They would allow for empirical decisions on antimicrobial treatment based on the specific epidemiological characteristics of the hospital or community setting. Full article

Background: The increase in antimicrobial resistance (AMR) is a growing concern for global health. Understanding longitudinal trends in pathogen prevalence and resistance patterns is essential for guiding clinical management and antibiotic stewardship. This retrospective observational study analyzed respiratory microbial isolates collected from 2018 to 2023 in Tor Vergata University Hospital, Rome, Italy. Methods: The data were analyzed through WHOnet 2025 software, and the breakpoint references used are those of EUCAST 2025. The data analyzed included pathogen identification, antibiotic resistance rates, and specimen types. Time-trend analyses were conducted to assess changes in pathogen prevalence and antibiotic resistance rates over time, using the Pearson correlation coefficient and linear regression model. Results: More than 54,000 unique microorganism/drug associations were analyzed, with the majority of them relative to inpatients (over 90%). A. baumannii showed persistently high prevalence and drug resistance to multiple antibiotics. Significant upward resistance trends of K. pneumoniae to multiple antibiotics were observed. Approximately 20% of clinical isolates were fungi, also including some non-albicans Candida (NAC) species, which exhibit intrinsic resistance to azoles. Other microorganisms displayed variable trends in prevalence and resistance profiles. Conclusions: These findings underscore the dynamism of changing patterns of prevalence of microorganisms and their resistance to antimicrobials. They underscore the importance of continuous microbiological surveillance to optimize empirical therapies and guide infection control policies. Full article

Background/Objectives: Streptococcus agalactiae (Group B Streptococci, GBS) is Gram-positive, beta-hemolytic coccus known to be transmitted by vertical transmission in neonates during birth with neonatal sepsis, pneumonia, and meningitis. In adults, particularly the elderly and those with diabetes mellitus, GBS can also cause pneumonia and sepsis. Penicillin is the drug of choice, and GBS is generally susceptible to this antibiotic. This study investigates trends in GBS isolation rates and penicillin non-susceptibility over time at a university hospital. Methods: We retrospectively analyzed 24 years (2000–2023) of microbiological data from Ilsan Paik Hospital to investigate trends in GBS isolation and penicillin susceptibility. Isolates were identified and tested using the Vitek 2 system, following CLSI guidelines. WHONET 2023 was used for data aggregation and analysis. Trends were analyzed by dividing the study period into three intervals: Period 1 (2000–2009), Period 2 (2010–2019), and Period 3 (2020–2023). Antimicrobial susceptibility rates for total GBS and PCN-NS GBS (penicillin non-susceptible group B Streptococcus) were compared using chi-square tests. Results: Among 257,884 total isolates, 3003 (1.16%) were GBS, and 29 (0.97%) were PCN-NS. GBS and PCN-NS isolation rates increased significantly across the three periods (p = 0.0001 and p = 0.009, respectively). PCN-NS GBS showed reduced susceptibility to all tested antimicrobials, with no drug showing higher susceptibility compared to total GBS. Conclusions: This study demonstrates a statistically significant rise in both GBS isolation rate and penicillin non-susceptibility over time. Given the emergence of multidrug-resistant GBS strains, susceptibility testing and interdisciplinary collaboration between microbiologists and clinicians are critical to guiding effective antimicrobial therapy and preventing neonatal and adult GBS infections. Full article

Urogenital infections contribute greatly to both hospital- and community-acquired infections. In Ghana, the prevalence of resistance to commonly used antibiotics is relatively high. This study sought to evaluate the antibiotic sensitivity of bacterial urogenital pathogens from patient samples in a regional and district hospital in the Volta Region of Ghana. A retrospective cross-sectional study was conducted using data obtained between January and December 2023 from Volta Regional Hospital and Margret Marquart Catholic Hospital. Bacteria were isolated from urine, urethral swabs, and vaginal swabs from 204 patients. Data on culture and sensitivity assays performed using the Kirby–Bauer disc diffusion method were extracted and analyzed using WHONET. The most prevalent organisms isolated from the samples from both facilities were Escherichia coli (24.9%), Staphylococcus aureus (21.5%), and Klebsiella oxytoca (8.8%). The isolates were mostly resistant to amoxicillin/clavulanic acid (n = 75, 95% CI [91.8–99.9]), meropenem (n = 61, 95% CI [87.6–99.4]), cefuroxime (n = 54, 95% CI [78.9–96.5]), ampicillin (n = 124, 95% CI [61.2–77.9]), and piperacillin (n = 43, 95% CI [82.9–99.2]). Multidrug-resistant (MDR, 70 (34.1%)), extensively drug-resistant (XDR, 63 (30.7%)), and pandrug-resistant (PDR, 9 (4.3%)) strains of S. aureus, E. coli, and Pseudomonas aeruginosa were identified from the patient samples. The study highlights the presence of high-priority resistant urogenital pathogens of public health significance to varied antibiotic groups. Full article

Antimicrobial resistance (AMR) is a critical public health issue in Bangladesh, where antibiotic use is widespread but often unregulated. This nationwide cross-sectional study (2017–2020) analyzed data from 26 public and private laboratories across all divisions of the country. Standardized data on antimicrobial susceptibility testing (AST) were collected, curated, and analyzed using WHONET, QAAPT, and R software to assess resistance patterns in 232,329 bacterial isolates from various clinical specimens. Escherichia coli was the most common pathogen (32.5%), followed by Klebsiella sp. (15.5%) and Pseudomonas sp. (10.6%). Urine specimens comprised 50.3% of the tested samples, while blood and soft tissue/body fluids accounted for 12.1% and 24.8%, respectively. Patients aged 55 years and older represented the largest group (36.3%), highlighting their vulnerability to drug-resistant infections. Resistance to third-generation cephalosporins was alarmingly high in Escherichia coli (62.9% resistant to ceftriaxone), whereas carbapenem resistance remained relatively low (5.3% and 6.8% to imipenem and meropenem, respectively). Klebsiella sp. showed widespread resistance, though carbapenems remained relatively effective (imipenem resistance 20.3%, meropenem 21.7%). In contrast, Salmonella sp. remained largely sensitive to third-generation cephalosporins. However, 42% of Staphylococcus aureus isolates were methicillin-resistant (MRSA). This study underscores the urgent need for improved antibiotic stewardship, enhanced diagnostic capacity, and strengthened AMR surveillance to preserve treatment options in Bangladesh. Full article

Introduction: Antimicrobial resistance (AMR) is a critical global public health issue, leading to prolonged illness, increased morbidity and mortality, and rising healthcare costs. The effectiveness of antibiotics is diminishing due to the emergence of resistant bacterial strains. This study aimed to determine the bacterial profile and AMR patterns of clinical isolates at Uttara Adhunik Medical College Hospital (UAMCH), Dhaka. Methods: A retrospective study was conducted at UAMCH from January 2017 to December 2019. A total of 32,187 clinical specimens (urine, blood, stool, wound swabs/pus, and sputum) were processed, of which 4232 yielded positive cultures. Bacterial identification followed standard bacteriological methods, and antibiotic susceptibility was assessed using the Kirby–Bauer disc diffusion method per CLSI guidelines. Data analysis was conducted using WHONET and QAAPT. Results: The highest proportion of positive cultures was from urine (47.5%), followed by blood (35.0%) and wound swabs/pus (10.1%). The most common isolates were Escherichia coli (37.2%), Salmonella typhi (25.7%), and Klebsiella sp. (11.5%). Gram-negative bacteria showed high resistance to commonly used antibiotics such as amoxicillin/clavulanic acid, cefixime, and ceftriaxone, while the resistance rates were lower for gentamicin, amikacin, and meropenem. However, Acinetobacter sp. exhibited alarming resistance to all tested antibiotics. Conclusions: This study highlights concerning resistance patterns among bacterial isolates, emphasizing the need for ongoing AMR surveillance to inform treatment strategies and improve patient care in Bangladesh. Full article

Introduction: Antimicrobial resistance (AMR) is a global public health issue, particularly in resource-limited, low- to middle-income countries like Bangladesh. In this study, we analyze and present four years of data on AMR from a tertiary care hospital in Bangladesh to inform policymakers and the wider community. Methods: In a retrospective cross-sectional study, we collected data for 4403 bacterial isolates reported between January 2017 and February 2020 at Mymensingh Medical College Hospital (MMCH), Bangladesh. All data were entered, cleaned, and analyzed using the software Stata Version-16.0, WHONET, a microbiology laboratory data management solution, and Quick Analysis of Antimicrobial Patterns and Trends (QAAPT), an AMR data visualization platform. Results: The bacteria were most commonly isolated from urine (71.66%, n = 3155), followed by pus (11.63%, n = 512), sputum (6.70%, n = 295), wound swabs (6.70%, n = 295), stool (1.91%, n = 84), endotracheal aspirate (1.20%, n = 53), and blood (0.20%, n = 9). Gram-negative bacteria predominated in all samples. Escherichia coli was the most common Gram-negative bacterium (31.30%, n = 1378), while Staphylococcus aureus was the most common Gram-positive bacterium (4.38%, n = 193). Antimicrobial susceptibility testing (AST) showed that multidrug resistance in Gram-negative bacteria such as E. coli, Klebsiella sp., and Acinetobacter sp. was common. S. aureus exhibited high resistance rates for beta-lactams, macrolides, and quinolones. In the urine samples, E. coli demonstrated high resistance to antibiotics like amoxicillin/clavulanic acid, ciprofloxacin, ceftriaxone, and cefuroxime (60–100%). Critical and high-priority pathogens as listed by the WHO constituted approximately 60% of the isolates. The AMR trends over three months showed increased resistance to amoxicillin/clavulanic acid for E. coli and to cefuroxime for Klebsiella sp. For S. aureus, the resistance to ciprofloxacin increased over three years, while the resistance to azithromycin decreased. Conclusions: There is a rise in bacterial resistance to the available antibiotics, with a significant prevalence of critical and high-priority pathogens in Bangladesh. We recommend vigilant AMR surveillance and stewardship programs to control the AMR in this country. Full article

Background: Bacterial infections constitute a large portion of infectious disorders. The location of culture-positive specimens and profiles of antibiotic resistance for common pathogens have been the focal points of subsequent investigations. Methodology: The diagnosis of microbiology was carried out using traditional culture techniques. In accordance with Clinical and Laboratory Standards Institute guidelines, the Kirby–Bauer disk diffusion method was employed for antimicrobial susceptibility analysis. The data were extracted from WHONET 2022 software version 22.5.5 and analyzed using SPSS software. Results: In total, 2489 pathogens were isolated from 2073 patients in three consecutive years. About 768 (34.9%) of the isolates were from the neonatal intensive care unit and the pediatric wards, and 63.2% isolates were from blood specimens. The ESKAPE pathogens were predominant (67%). About 100 Enterobacteriaceae family member bacteria were resistant to carbapenem drugs, and 320 isolates of this family were expected to be beta lactamase producers. A total of 120 methicillin-resistant S. aureus isolates were also identified. Conclusions: Among the isolates, ESKAPE pathogens accounted for the greatest proportion. Most isolates were from the neonatal intensive care unit. A significant number of multidrug-resistant, extreme drug-resistant, and pandrug-resistant isolates were identified in the present study. Full article

Bacterial and fungal secondary and co-infections are commonly identified with viral respiratory infections. This study was undertaken to determine the incidence and factors associated with bacterial and fungal infections in patients with COVID-19 as well as antibiotics prescription patterns within the first and second waves of the outbreak in Malaysia. Clinical records of 3532 COVID-19 patients admitted to hospitals in Malaysia between 4 February and 4 August 2020 were analyzed. Co-morbidities, clinical features, investigations, treatment, and complications were captured using the REDCap database. Culture and sensitivity test results were retrieved from the WHONET database. Univariate and multivariate regression analyses were used to identify associated determinants. A total of 161 types of bacterial and fungal infections were found in 81 patients, i.e., 2.3%. The most common bacterial cultures were Gram-negative, i.e., Pseudomonas aeruginosa (15.3%) and Klebsiella pneumoniae (13.9%). The most common fungal isolate was Candida albicans (41.2%). Augmentin, ceftriaxone, tazocin, meropenem, and azithromycin were the five most frequently prescribed antibiotics. The latter four were classified under the “Watch” category in the WHO AwaRe list. Our data showed that bacterial and fungal secondary and co-infections were frequently found in severely ill COVID-19 patients and were associated with a higher mortality rate. Full article

Wheel odometry is a simple and low-cost localization technique that can be used for localization in GNSS-deprived environments; however, its measurement accuracy is affected by many factors, such as wheel slip, wear, and tire pressure changes, resulting in unpredictable and variable errors, which in turn affect positioning performance. To improve the localization performance of wheel odometry, this study developed a wheel odometry error prediction model based on a transformer neural network to learn the measurement uncertainty of wheel odometry and accurately predict the odometry error. Driving condition characteristics including features describing road types, road conditions, and vehicle driving operations were considered, and models both with and without driving condition characteristics were compared and analyzed. Tests were performed on a public dataset and an experimental vehicle. The experimental results demonstrate that the proposed model can predict the odometry error with higher accuracy, stability, and reliability than the LSTM and WhONet models under multiple challenging and longer GNSS outage driving conditions. At the same time, the transformer model’s overall performance can be improved in longer GNSS outage driving conditions by considering the driving condition characteristics. Tests on the experimental vehicle demonstrate the model’s generalization capability and the improved positioning performance of dead reckoning when using the proposed model. This study explored the possibility of applying a transformer model to wheel odometry and provides a new solution for using deep learning in localization. Full article

Antimicrobial resistance is a global health threat and efforts to mitigate it is warranted, thus the need for local antibiograms to improve stewardship. This study highlights the process that was used to develop an antibiogram to monitor resistance at a secondary-level health facility to aid empirical clinical decision making in a sub-Saharan African county. This retrospective cross-sectional descriptive study used 3 years of cumulative data from January 2016 to December 2018. Phenotypic data was manually imputed into WHONET and the cumulative antibiogram constructed using standardized methodologies according to CLSI M39-A4 guidelines. Pathogens were identified by standard manual microbiological methods and antimicrobial susceptibility testing was performed using Kirby-Bauer disc diffusion method according to CLSI M100 guidelines. A total of 14,776 non-duplicate samples were processed of which 1163 (7.9%) were positive for clinically significant pathogens. Among the 1163 pathogens, E. coli (n = 315) S. aureus (n = 232), and K. pneumoniae (n = 96) were the leading cause of disease. Overall, the susceptibility for E. coli and K. pneumoniae from all samples were: trimethoprim-sulfamethoxazole (17% and 28%), tetracycline (26% and 33%), gentamicin (72% and 46%), chloramphenicol (76 and 60%), and ciprofloxacin (69% and 59%), and amoxicillin/clavulanic (77% and 54%) respectively. Extended spectrum beta-lactamase (ESBL) resistance was present in 23% (71/315) vs. 35% (34/96) respectively. S. aureus susceptibility for methicillin was 99%. This antibiogram has shown that improvement in combination therapy is warranted in The Gambia. Full article

The aim of the current study was to analyse the distribution of antimicrobial drug resistance (AMR) among Pseudomonas aeruginosa (P. aeruginosa, PA) isolates from Guangdong Provincial People’s Hospital (GDPH) from 2017 to 2021, and the impact of the COVID-19 outbreak on changes in the clinical distribution and drug resistance rate of P. aeruginosa to establish guidelines for empiric therapy. Electronic clinical data registry records from 2017 to 2021 were retrospectively analysed to study the AMR among P. aeruginosa strains from GDPH. The strains were identified by VITEK 2 Compact and MALDI-TOF MS, MIC method or Kirby–Bauer method for antibiotic susceptibility testing. The results were interpreted according to the CLSI 2020 standard, and the data were analysed using WHONET 5.6 and SPSS 23.0 software. A total of 3036 P. aeruginosa strains were detected in the hospital from 2017 to 2021, and they were primarily distributed in the ICU (n = 1207, 39.8%). The most frequent specimens were respiratory tract samples (59.6%). The detection rate for P. aeruginosa in 5 years was highest in September, and the population distribution was primarily male(68.2%). For the trend in the drug resistance rate, the 5-year drug resistance rate of imipenem (22.4%), aztreonam (21.5%) and meropenem (19.3%) remained at high levels. The resistance rate of cefepime decreased from 9.4% to 4.8%, showing a decreasing trend year by year (p < 0.001). The antibiotics with low resistance rates were aminoglycoside antibiotics, which were gentamicin (4.4%), tobramycin (4.3%), and amikacin (1.4%), but amikacin showed an increasing trend year by year (p = 0.008). Our analysis indicated that the detection rate of clinically resistant P. aeruginosa strains showed an upwards trend, and the number of multidrug-resistant (MDR) strains increased year by year, which will lead to stronger pathogenicity and mortality. However, after the outbreak of COVID-19 in 2020, the growth trend in the number of MDR bacteria slowed, presumably due to the strict epidemic prevention and control measures in China. This observation suggests that we should reasonably use antibiotics and treatment programs in the prevention and control of P. aeruginosa infection. Additionally, health prevention and control after the outbreak of the COVID-19 epidemic (such as wearing masks, washing hands with disinfectant, etc., which reduced the prevalence of drug resistance) led to a slowdown in the growth of the drug resistance rate of P. aeruginosa in hospitals, effectively reducing the occurrence and development of drug resistance, and saving patient’s treatment costs and time. Full article