Investigation of Bacterial Persistence and Filaments Formation in Clinical Klebsiella pneumoniae: First Report from Iraq

Bacterial persistence is recognized as a major cause of antibiotic therapy failure, causing biofilms, and chronic intractable infections. The emergence of persisters in Klebsiella pneumoniae isolates has become a worldwide public health concern. The goal of the present study is to investigate the formation of persister cells beside filaments in Iraqi K. pneumoniae isolates. A total of fifty clinical K. pneumoniae isolates were collected from different clinical specimens and identified using the genotypic identification by using specific primer (rpoB gene) from housekeeping genes. Persister cells investigation is performed by exposure of stationary phase K. pneumoniae isolates to a high concentration of ciprofloxacin (×10 MIC) and counting the number of viable persister cells by CFU counts. Bacterial filament formation is detected and measured by light microscope scanning electron microscope. The results show the  bility of these pathogenic bacteria to form persister cells to survive the bactericidal antibiotics and to cause chronic infection.Furthermore, persistent isolates have the ability to change in shape and size extensively, about 4 times increase in cell length than their normal length. These phenomena are possibly the initial stages of bacterial resistance prevalence

Artificial Intelligence and the Silent Pandemic of Antimicrobial Resistance: A Comprehensive Exploration

The rise of antimicrobial resistance (AMR) in the 21st century has made it a worldwide disaster. Due to the fast spread of AMR illnesses and the lack of novel antimicrobials, the silent pandemic is well known. This issue requires a fast and meaningful response, not just speculation. To address this dilemma, deep learning (DL) and machine learning (ML) have become essential in many sectors. As a cornerstone of modern research, machine learning helps handle the many aspects of AMR. AI helps researchers construct clinical decision-support systems by collecting clinical data. These methods enable antimicrobial resistance monitoring and wise use. Additionally, AI applications help research new drugs. AI also excels at synergistic medicine combinations, providing new treatment methods. This paper summarizes our extensive study of AI and the silent epidemic of antibiotic resistance. Through deep learning and machine learning applications across multiple dimensions, we hope to contribute to the proactive management of AMR, moving away from its presentation as a future problem to present-day solutions

Unveiling the High Prevalence of Antibiotic Resistance and Quorum Sensing Genes in Uropathogenic Escherichia coli

Escherichia coli is considered one of the uropathogenic bacteria with different infection symptoms representing mild illness to acute sepsis. This study aims to detect E. coli in patients with urinary infection and investigate quorum sensing genes (lux S and motA) in multi-drug resistant isolates of E. coli.  200 urine samples were collected from patients with urinary tract infections from several hospitals in Baghdad. The antibiotics sensitivity test showed high resistance of isolates for Ampicillin (100%), Cefazolin (97%), Trimethoprim/ Sulfamethoxazole (83%), Ceftriaxone (77%), Ceftazidime and Ciprofloxacin (70% each of them), and moderate resistance of isolates for Levofloxacin (50%), Gentamicin (47%), Cefepime (40%), while low resistance Piperacillin/ Tazobactam (33%), Cefoxitin (30%), Nitrofurantoin (17%), Imipenem (10%), Ertapenem and Amikacin (7% each of them), and Tigecycline (3%). The results showed an increase percentage of infection in females was 30% in the ages 30-44 years, whereas in ages 15-29 and more than 45 years was 17%.  There was a high percentage (57.11%) of resistant isolates in females which are ages 30-44 years. While the ages more than 45 years were 66.4% and ages 15-29 were 34%. While, in males, the percentage was high in ages more than 45 years (35.25%) followed by age groups 30-44 years (31.5%) and 15-29 years (31%).  The prevalence percentage for luxS and motA genes in E. coli was 100%. In conclusion, E. coli isolates were multi-drug resistant due to all isolates had quorum sensing genes. Moreover, uropathogenic of E. coli in females was more frequent than in males due to the resistance of bacteria to antibiotics.    

Bacterial Filaments Induced by Antibiotic Minimal Inhibitory Concentrations in Persister Cells

Background: The ability of minor subpopulations among clonal populations to survive antibiotics is referred to as bacterial persistence. It is believed that persisters come from latent cells, where antibiotic target areas are less active and incapable of being affected. Objective: 112 clinical Escherichia coli isolates were acquired out of diverse medical samples and genetically identified using the uspA gene, which is part of the housekeeping genes. Methods: The examination of persister cells was carried out by subjecting isolates of E. coli in the exponential phase with high dose of ciprofloxacin (20 fold MIC) and calculating the surviving persister cells using CFU (colony forming units) counts. The detection and measurement of bacterial filament production was done using scanning electron and light microscopy. Results: Results showed that the bacterial filament cells kept on lengthen but cease to divide (no septa formation) at sub-minimal inhibitory doses of ciprofloxacin. Persistent isolates were shown to exhibit a wide range of form and size variations, with cells up to 4.5 times longer than usual. Conclusions: The results showed the importance of antibiotic stress on persisted cells that result in the production of filaments as a means of survival and the need to examine these rare phenotypic variations. These occurrences may be the beginning of the spread of bacterial resistance

The Effect of pH Variation on Antibiotic Susceptibility of MDR Klebsiella pneumoniae Isolates

Background: Klebsiella pneumoniae is a significant opportunistic pathogen responsible for various nosocomial infections in humans. The emergence of multi-drug resistant strains poses a significant challenge in clinical settings, necessitating a deeper understanding of factors influencing antimicrobial resistance. Objective: This research aimed to investigate the impact of pH variation on the resistance patterns of multi-resistant K. pneumoniae isolated from Iraqi patients with urinary tract infections and wound infections against different antibiotics. Methods: Forty K. pneumoniae isolates were obtained from urine samples and wound swabs, and their identification was confirmed using the VITEK ® 2 compact system and molecular identification of the rpoB housekeeping gene. Antibiotic susceptibility testing was performed using the Kirby Bauer’s disk diffusion method under varying pH conditions (pH 5, 7, 9, and 11) at 37°C for 18 to 24 hours. Results: The study findings indicated that K. pneumoniae isolates exhibited differential susceptibility to antibiotics based on pH conditions. Cefotaxime demonstrated increased efficacy under alkaline pH, while tetracycline showed optimal efficacy under acidic conditions. However, ciprofloxacin displayed resistant phenotypes at acidic pH 5 and either resistant or intermediate phenotypes at alkaline pH 9. Conclusions: The results suggest a potential influence of pH on the antibiotic susceptibility profiles of K. pneumoniae isolates. Understanding the role of pH in antimicrobial resistance can inform strategies for better managing infections caused by multi-resistant pathogens. Further research is warranted to elucidate the underlying mechanisms and implications for clinical practice

Using the Lactobacillus gasseri filtrate to protect the mice from the pathogenic bacteria Aeromonas spp.

The protective effect of the Probiotic bacteria (Lactobacillus gasseri) against the pathogenic Bacteria (Aeromonas spp) was studied in vivo and in vitro, the inhibitory effect of the Probiotic bacteria filtrate was tested with the Well diffusion method, the filtrate showed a clear inhibiting efficiency toward the pathogenic bacteria and the diameter of the inhibition zone was 16 mm. A group of mice were injected intraperitoneally with (0.25 ml) of the filtrate for 10 days, then they were injected with (0.2 ml) of the Aeromonas spp bacteria living cells ( cell\ml) intraperitoneally, while the control group were injected with 0.25 ml of PBS. The mice were killed after 12 hours of injection with the pathogenic bacteria, they were injected with (5ml) of PBS intraperitoneally, the contents of the periton, Liver and the Spleen were taken after killing the mice, then a dilutions of the Periton contents were made and 0.1 ml was streaking on the media to calculate the number of the growing bacterial colonies, then a part of the Spleen was homogenized and streaking on the media, then the growing colonies were calculated and compared with the control mice, also the macrophage in the contents of the periton were counted, and a samples of the organs were taken directly after the killing and were putted in the formalin solution (10%) for study the histopathological changes. The results shows that the mice who were injected with the probiotic bacteria wasn't effected when they were exposed to the pathogenic bacteria unlike the group that were only exposed to the pathogenic bacteria without being injected with the probiotoic bacteria, and that shows the protective effect of the (Lactobacillus gasseri) against the pathogenic bacteria (Aeromonas spp), and the pathogenic changes shows an inflammatory response and that the tissues were appeared in their natural form

The Prevalence of Microorganisms in H1N1 Patients Compared to Seasonal Influenza in a Sample of Iraqi Patients

This study provides valuable information on secondary microbial infections in H1N1 patients compared to Seasonal Influenza in Iraqi Patients. Nasopharynx  swabs were collected from  (12 ) patients  infected with Seasonal influenza (11  from Baghdad  and 1 Patient from south of Iraq) ,and ( 22 ) samples from patients with 2009 H1N1 ( 20 from Baghdad and  2 from  south of Iraq). The results show that the patients infected with 2009 H1N1 Virus were younger than healthy subjects and those infected with seasonal influenza. And the difference reached to the level of significance     (p< 0.01) compared with healthy subjects.Two cases infected with 2009 H1N1 virus (9.1%) were from south of the Iraq and remaining 20 cases were from Baghdad . Polymicrobial isolates from nasopharynx swab were observed in patients infected with 2009 H1N1 virus. Polybacterial infections (2-7 microorganisms) and fungal infection were reported in 21 out of 22 patients (95.5%) and 5 out of 22 (22.7 %) respectively.The predominant isolated microorganisms were Streptococcus pyogenes , Staphylococcus aureus and Streptococcus pneumoniae  were found in 95.2 %  , 95.2 % and 90.5 %  respectively .The results also show  that seven microorganisms were isolated from 10 (47.6 %) patients infected with 2009 H1N1 , no microorganism was isolated from patients infected with seasonal influenza or healthy persons. Key words: Seasonal Influenza , 2009 H1N1, Nasopharynx  swab

Autoantibodies against type I IFNs in patients with life-threatening COVID-19

Interindividual clinical variability in the course of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is vast. We report that at least 101 of 987 patients with life-threatening coronavirus disease 2019 (COVID-19) pneumonia had neutralizing immunoglobulin G (IgG) autoantibodies (auto-Abs) against interferon-w (IFN-w) (13 patients), against the 13 types of IFN-a (36), or against both (52) at the onset of critical disease; a few also had auto-Abs against the other three type I IFNs. The auto-Abs neutralize the ability of the corresponding type I IFNs to block SARS-CoV-2 infection in vitro. These auto-Abs were not found in 663 individuals with asymptomatic or mild SARS-CoV-2 infection and were present in only 4 of 1227 healthy individuals. Patients with auto-Abs were aged 25 to 87 years and 95 of the 101 were men. A B cell autoimmune phenocopy of inborn errors of type I IFN immunity accounts for life-threatening COVID-19 pneumonia in at least 2.6% of women and 12.5% of men

Burden of disease scenarios for 204 countries and territories, 2022–2050: a forecasting analysis for the Global Burden of Disease Study 2021

Background: Future trends in disease burden and drivers of health are of great interest to policy makers and the public at large. This information can be used for policy and long-term health investment, planning, and prioritisation. We have expanded and improved upon previous forecasts produced as part of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) and provide a reference forecast (the most likely future), and alternative scenarios assessing disease burden trajectories if selected sets of risk factors were eliminated from current levels by 2050. Methods: Using forecasts of major drivers of health such as the Socio-demographic Index (SDI; a composite measure of lag-distributed income per capita, mean years of education, and total fertility under 25 years of age) and the full set of risk factor exposures captured by GBD, we provide cause-specific forecasts of mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) by age and sex from 2022 to 2050 for 204 countries and territories, 21 GBD regions, seven super-regions, and the world. All analyses were done at the cause-specific level so that only risk factors deemed causal by the GBD comparative risk assessment influenced future trajectories of mortality for each disease. Cause-specific mortality was modelled using mixed-effects models with SDI and time as the main covariates, and the combined impact of causal risk factors as an offset in the model. At the all-cause mortality level, we captured unexplained variation by modelling residuals with an autoregressive integrated moving average model with drift attenuation. These all-cause forecasts constrained the cause-specific forecasts at successively deeper levels of the GBD cause hierarchy using cascading mortality models, thus ensuring a robust estimate of cause-specific mortality. For non-fatal measures (eg, low back pain), incidence and prevalence were forecasted from mixed-effects models with SDI as the main covariate, and YLDs were computed from the resulting prevalence forecasts and average disability weights from GBD. Alternative future scenarios were constructed by replacing appropriate reference trajectories for risk factors with hypothetical trajectories of gradual elimination of risk factor exposure from current levels to 2050. The scenarios were constructed from various sets of risk factors: environmental risks (Safer Environment scenario), risks associated with communicable, maternal, neonatal, and nutritional diseases (CMNNs; Improved Childhood Nutrition and Vaccination scenario), risks associated with major non-communicable diseases (NCDs; Improved Behavioural and Metabolic Risks scenario), and the combined effects of these three scenarios. Using the Shared Socioeconomic Pathways climate scenarios SSP2-4.5 as reference and SSP1-1.9 as an optimistic alternative in the Safer Environment scenario, we accounted for climate change impact on health by using the most recent Intergovernmental Panel on Climate Change temperature forecasts and published trajectories of ambient air pollution for the same two scenarios. Life expectancy and healthy life expectancy were computed using standard methods. The forecasting framework includes computing the age-sex-specific future population for each location and separately for each scenario. 95% uncertainty intervals (UIs) for each individual future estimate were derived from the 2·5th and 97·5th percentiles of distributions generated from propagating 500 draws through the multistage computational pipeline. Findings: In the reference scenario forecast, global and super-regional life expectancy increased from 2022 to 2050, but improvement was at a slower pace than in the three decades preceding the COVID-19 pandemic (beginning in 2020). Gains in future life expectancy were forecasted to be greatest in super-regions with comparatively low life expectancies (such as sub-Saharan Africa) compared with super-regions with higher life expectancies (such as the high-income super-region), leading to a trend towards convergence in life expectancy across locations between now and 2050. At the super-region level, forecasted healthy life expectancy patterns were similar to those of life expectancies. Forecasts for the reference scenario found that health will improve in the coming decades, with all-cause age-standardised DALY rates decreasing in every GBD super-region. The total DALY burden measured in counts, however, will increase in every super-region, largely a function of population ageing and growth. We also forecasted that both DALY counts and age-standardised DALY rates will continue to shift from CMNNs to NCDs, with the most pronounced shifts occurring in sub-Saharan Africa (60·1% [95% UI 56·8–63·1] of DALYs were from CMNNs in 2022 compared with 35·8% [31·0–45·0] in 2050) and south Asia (31·7% [29·2–34·1] to 15·5% [13·7–17·5]). This shift is reflected in the leading global causes of DALYs, with the top four causes in 2050 being ischaemic heart disease, stroke, diabetes, and chronic obstructive pulmonary disease, compared with 2022, with ischaemic heart disease, neonatal disorders, stroke, and lower respiratory infections at the top. The global proportion of DALYs due to YLDs likewise increased from 33·8% (27·4–40·3) to 41·1% (33·9–48·1) from 2022 to 2050, demonstrating an important shift in overall disease burden towards morbidity and away from premature death. The largest shift of this kind was forecasted for sub-Saharan Africa, from 20·1% (15·6–25·3) of DALYs due to YLDs in 2022 to 35·6% (26·5–43·0) in 2050. In the assessment of alternative future scenarios, the combined effects of the scenarios (Safer Environment, Improved Childhood Nutrition and Vaccination, and Improved Behavioural and Metabolic Risks scenarios) demonstrated an important decrease in the global burden of DALYs in 2050 of 15·4% (13·5–17·5) compared with the reference scenario, with decreases across super-regions ranging from 10·4% (9·7–11·3) in the high-income super-region to 23·9% (20·7–27·3) in north Africa and the Middle East. The Safer Environment scenario had its largest decrease in sub-Saharan Africa (5·2% [3·5–6·8]), the Improved Behavioural and Metabolic Risks scenario in north Africa and the Middle East (23·2% [20·2–26·5]), and the Improved Nutrition and Vaccination scenario in sub-Saharan Africa (2·0% [–0·6 to 3·6]). Interpretation: Globally, life expectancy and age-standardised disease burden were forecasted to improve between 2022 and 2050, with the majority of the burden continuing to shift from CMNNs to NCDs. That said, continued progress on reducing the CMNN disease burden will be dependent on maintaining investment in and policy emphasis on CMNN disease prevention and treatment. Mostly due to growth and ageing of populations, the number of deaths and DALYs due to all causes combined will generally increase. By constructing alternative future scenarios wherein certain risk exposures are eliminated by 2050, we have shown that opportunities exist to substantially improve health outcomes in the future through concerted efforts to prevent exposure to well established risk factors and to expand access to key health interventions

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

Background: Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period. Methods: 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. Findings: Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. Interpretation: Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic