Archives of Clinical Infectious Diseases
Volume:20 Issue: 2, Apr 2025

There are ongoing controversies regarding pregnancy outcomes during the COVID-19 era.

This observational study with a retrospective design was conducted to investigate the maternal and neonatal impacts of COVID-19 on pregnancy outcomes.

An observational study with a retrospective design was carried out using census sampling from February 2020 to July 2021. The study population included pregnant women who were referred to Asalian Hospital in Khorramabad city, western Iran. A total of 769 mothers (107 with COVID-19 and 662 without COVID-19) were included in the study.

The most common symptoms reported by infected mothers were body pain/myalgia (68, 72.3%), fatigue (57, 60.6%), headache (47, 50%), and shivering (46, 48.9%). After adjusting for covariates, the risk of caesarean section (CS) in pregnant women with COVID-19 was found to be 3.2 times higher (95% confidence interval: 1.90 - 5.50). In contrast, the risks of newborn hospitalization, prematurity, low APGAR scores, respiratory distress, and smaller head circumference were higher among newborns of non-COVID-19-infected mothers than those of COVID-19-infected mothers (P < 0.05).

While it is generally expected that COVID-19-infected mothers would experience adverse maternal and neonatal outcomes, this retrospective study demonstrated that COVID-19 infection does not result in adverse outcomes for mothers or newborns, apart from a higher rate of CS.

Bacillary dysentery is an invasive bacterial gastroenteritis that damages the colon epithelium and leads to bloody diarrhea. Enteroinvasive Escherichia coli (EIEC) and Shigella flexner i are two major etiologic agents of the disease. The virF gene is a transcriptional regulator of the virulence genes involved in the invasion of these bacteria. Previous studies have shown that sub-minimum inhibitory concentrations (sub-MICs) of antibiotics have significant effects on bacterial virulence.

This study aimed to evaluate the effects of sub-MICs of ciprofloxacin and azithromycin on S. flexneri and EIEC.

Prototype strains of both bacteria were treated with sub-MICs of 1/2, 1/4, and 1/8 of ciprofloxacin and azithromycin antibiotics. Changes in the expression of the virF gene in antibiotic-treated samples compared to control samples were analyzed using relative real-time polymerase chain reaction (PCR).

The mean expression of the virF gene in all sub-MICs of ciprofloxacin was increased in both S. flexneri and EIEC, while a down-regulation was observed in sub-MICs of azithromycin. These gene expression changes were dose-dependent.

The results demonstrated that the virulence of S. flexneri and EIEC is affected by sub-MICs of azithromycin and ciprofloxacin. Given that azithromycin, unlike ciprofloxacin, reduces the severity of infection at sub-MICs, it is a more appropriate choice with a lower risk for treating acute infections caused by these bacteria.

Antimicrobial resistance (AMR) has been a concerning public health issue even before the coronavirus disease 2019 (COVID-19) outbreak. However, the impact of the COVID-19 pandemic on AMR has not been comprehensively investigated.

The main objective of this study was to investigate the patterns of AMR before, during, and after the COVID-19 pandemic.

Data from hospital records of cancer patients in the hematology ward were obtained for this cross-sectional study using a census sampling method from January 2018 to July 2023. Clinical specimens were collected, including urine, stool, cerebrospinal fluid, ascites, pleural fluid, oropharynx, blood, and synovial fluid. All specimens were sent to the central laboratory of the hospital. The obtained samples were cultured on blood agar (Merck) and MacConkey agar (Merck) media and incubated for 24 hours. The classification of strains as resistant, intermediate, or susceptible was determined according to the Clinical and Laboratory Standards Institute (CLSI) guidelines.

In the present study, 382 isolates were obtained from 186 (48.7%) males and 196 (51.3%) females admitted to the hematology ward at Taleqani Hospital. Among the 382 isolates, 102 were Escherichia coli , 97 were Klebsiella pneumoniae , 51 were Pseudomonas aeruginosa , 49 were coagulase-negative Staphylococcus , 30 were S. aureus , and 53 were other species. There was a changing trend in the antibiotic susceptibility patterns of ceftazidime for coagulase-negative Staphylococcus , as well as in multiple agents including meropenem, amikacin, and piperacillin-tazobactam for P. aeruginosa . For K. pneumoniae , there was a significant change in the antibiotic susceptibility patterns for amikacin, piperacillin, and ceftriaxone. For S. aureus , a significant difference was observed in the antibiotic susceptibility patterns of meropenem and clindamycin. For E. coli , significant differences were found in antibiotic susceptibility patterns for imipenem, amikacin, and cefazolin.

Although we have navigated through the COVID-19 pandemic, its consequences, such as altered trends in AMR, continue to pose challenges to the healthcare system. Coronavirus disease 2019has influenced the resistance patterns of both gram-negative and gram-positive bacteria. Monitoring adherence to guidelines is crucial to prevent further spread of resistant strains, particularly in developing countries where improvements in attitudes toward antibiotic prescription and hygiene standards are needed.

Lyme borreliosis is a significant zoonotic disease with global prevalence, and due to its severe complications, early diagnosis is crucial. Limited information is available about the disease in Iran.

Therefore, this study aimed to determine the frequency of IgG antibodies against Borrelia burgdorferi among high-risk personnel.

In this cross-sectional study, serum samples were collected from 91 out of 450 employees working at the main industrial slaughterhouse in Mashhad, northeast Iran. Relevant information from the participants was recorded using a pre-prepared checklist. The presence of anti- B. burgdorferi IgG antibodies was evaluated using the EUROIMMUN ELISA kit, following the manufacturer’s instructions. The data were analyzed using SPSS software.

According to ELISA results, 10 out of 91 individuals (11%) tested positive for IgG antibodies against B. burgdorferi . Based on optical density (OD) values, 9 participants (9.9%) were categorized as borderline, while 72 workers tested negative. The rate of positive cases was significantly higher among workers who had contact with sheep compared to those who worked with cattle (chi-square test: P = 0.03; OR = 8.97; 95% CI: 1.06 - 75.56).

Based on the ELISA technique, 11% of slaughterhouse workers tested positive for anti- B. burgdorferi antibodies, with the majority of these individuals working with sheep. While the possibility of cross-reactivity should be considered, this study highlights a health concern for high-risk occupations. Comprehensive diagnostic evaluations should be performed in cases of clinical suspicion.

The COVID-19 pandemic, which occurred between 2019 and 2023, posed a significant threat to global health. Its high transmissibility, the emergence of new variants, and the novel nature of the disease made treatment and control highly challenging.

This study aimed to develop an algorithm for predicting the mortality of hospitalized COVID-19 patients using machine learning methods.

This cross-sectional study was conducted on 581 hospitalized COVID-19 patients. The approach integrated multi-model features derived from computed tomography (CT) scans and electronic health record (EHR) data. High-resolution computed tomography (HRCT) images were initially processed using the Pulmonary Toolkit package in MATLAB software. Subsequently, the extracted variables were entered into the model as predictive factors, alongside demographic characteristics, underlying conditions, and laboratory results of the patients. The machine learning model was developed using the AdaBoost method by incorporating demographic and laboratory data with HRCT features.

In this study, 581 hospitalized COVID-19 patients were included. Among them, 199 (34.25%) patients died, while 382 (65.75%) recovered. According to the machine learning algorithm, the most effective variables for predicting COVID-19 mortality were lymphocyte variables, CRP, age, mean lung density, lung tissue percentage, RBC count, D-dimer levels, and emphysema. The MCC Index in this study was 0.73, and the area under the ROC curve was 0.96.

According to our results, the three variables with the greatest impact on predicting mortality in COVID-19 patients were related to HRCT findings, laboratory results, and patient age. Therefore, it is recommended that, given the high cost of HRCT, this diagnostic test should only be performed if other risk factors are identified in laboratory results. If necessary, HRCT should be conducted promptly.

In recent years, resistant infections and antibiotic resistance in livestock have been increasing, and it seems that these issues will cause more problems in the future.

This study investigates the association between the pattern of Escherichia coli antibiotic resistance (ECAR) and the identification of the mobilized colistin resistance ( mcr-1 gene) among clinical and non-clinical samples.

In this study, 265 samples were collected from clinical (human urine, N = 79) and non-clinical sources (animal feces from poultry and livestock farms, N = 186). All samples were processed, and E. coli bacteria were isolated and identified. The agar dilution method was used to evaluate colistin resistance, and the presence of the mcr-1 gene was characterized among E. coli isolates.

Out of 265 samples (clinical and fecal), 37.97% (30/79) of clinical samples and 37.63% (70/186) of animal feces showed growth of E. coli . The highest number of E. coli isolates was found in feces from livestock farms (71.48%; 50/70). Antimicrobial susceptibility testing (AST) of E. coli isolated from clinical samples showed that the highest resistance (46.66%) was to ciprofloxacin, and the lowest resistance (16.66%) was to colistin . Analysis of polymerase chain reaction (PCR) data also showed that 60% of colistin -resistant isolates from clinical urine samples and 70% from animal feces samples were positive for the plasmid-mediated mcr-1 gene.

The findings indicate that the high frequency of the mcr-1 gene among both clinical and animal-collected isolates may be a significant factor in the development of colistin resistance among circulating E. coli isolates.

Urinary tract infections (UTIs) are among the most prevalent infectious diseases in children and are a leading cause of antibiotic use and pediatric hospitalization.

This study aimed to investigate the most common specimens and determine the frequency of Multiple Antibiotic Resistance Indexes (MARI) for predominant bacteria in the pediatric hospital of Kirkuk.

A total of 299 different samples were collected from the pediatric hospital in Kirkuk province between May 1st and October 1st. The samples were cultured on blood agar, mannitol agar, and MacConkey agar and incubated for 24 hours at 37°C. Identification was based on morphological and microscopic examination, as well as the API kit.

The results showed that most of the samples were urine, with 221 samples collected. Of these, 66 (28.50%) showed positive growth, and 155 (71.49%) showed negative growth. Blood samples were the second most common, with 51 samples collected, of which 4 (9.61%) were positive, and 47 (90.38%) were negative. Stool samples amounted to 13, with 2 (15.38%) showing positive growth, and 11 (84.61%) showing negative growth. Cerebrospinal fluid (CSF), throat swab, and vaginal swab samples amounted to 11, 2, and 1, respectively, with no bacterial growth observed in them. The majority of isolates were from urine, with Escherichia coli being the most common species (28 isolates, 42.42%). Other species isolated included Klebsiella pneumoniae (13 isolates, 19.69%), Staphylococcus spp. (12 isolates, 19.05%), Enterobacter spp. (7 isolates, 10.61%), and Pseudomonas spp. (4 isolates, 6.06%). Proteus spp. and Streptococcus spp. each accounted for 1 isolate (1.25%). The bacteria isolated from blood were Acinetobacter spp., Enterobacter spp., Pseudomonas spp., and Klebsiella spp., each representing 1 isolate (1.25%) of the total. Two (100%) E. coli isolates were obtained from stool samples. E. coli isolates from urine were resistant to ampicillin (Am), tetracycline (Te), amoxicillin/clavulanic acid (AMC), and doxycycline (Do). Klebsiella spp. were resistant to Am, Staphylococcus spp. to erythromycin (E) and azithromycin (AZM), Enterobacter spp. to amikacin (AK) and AMC, and Pseudomonas aeruginosa to gentamicin (CN), all exhibiting 100% resistance. The MARI was greater than 0.2 in 27 isolates of E. coli (96.42%), 13 isolates of Klebsiella spp. (100%), 11 isolates of Staphylococcus spp. (91.66%), 7 isolates of Enterobacter spp. (100%), and 4 isolates of P. aeruginosa (100%).

The current study concludes that urinary tract infection is the most common pathological condition among the young age group. Urine is one of the most frequently examined samples, with E. coli being the most predominant pathogen. The study also highlights the prevalence of antibiotic-resistant bacteria, as evidenced by the elevated MARI in isolates of E. coli , K. pneumoniae , Enterobacter spp., and Pseudomonas spp.

Prophylactic antibiotics can reduce post-surgical infection rates, but their improper use in surgery remains a significant concern. This misuse leads to adverse drug reactions, increased bacterial resistance, and unnecessary hospital costs.

This study aimed to examine and compare prophylactic antibiotic prescribing practices in general surgery departments at two Tehran university hospitals, in relation to established guidelines.

A cross-sectional study was conducted on 194 surgical patients. Data collection included demographic details, surgery type, and antibiotic prophylaxis protocols, covering medication type, dosage, pre-surgical timing, and prophylaxis duration.

The study included 27.8% clean surgeries and 72.2% clean-contaminated surgeries. Cefazolin was the primary antibiotic prescribed. According to the American Society of Health-System Pharmacists (ASHP) treatment guidelines, 10% of patients received antibiotics without proper indication. Among the 90 cases requiring antibiotic prophylaxis, appropriate antibiotic selection occurred in 39.0% of cases at Rasul Akram Hospital and 41.5% at Firouzgar Hospital. In these cases, errors were noted in dosing (79.8%), pre-operative timing (97.4%), and administration protocol (100%). Only 33% of cases followed the optimal prophylaxis duration.

These hospitals require an evidence-based antibiotic prescription program that adheres to clinical guidelines and includes close monitoring of implementation. Regular training and review of clinical guidelines are essential for all healthcare professionals.

MicroRNAs (miRNAs) regulate the immune response to infectious diseases. They exhibit alterations during the early stages of disease development, even before the pathogen is detectable.

The objective of this study was to analyze the expression levels of miR-1307-3p and miR-3613-5p at the onset of COVID-19, aiming to identify miRNAs that could function as early predictive biomarkers.

This case-control study analyzed the expression levels of miR-1307-3p and miR-3613-5p at the onset of COVID-19 to identify early predictive biomarkers. Plasma miRNAs from 26 COVID-19 patients hospitalized in the ICU ward were sequenced and compared to a healthy control group of 26 individuals. Additionally, plasma biomarkers associated with the immune system were evaluated. Total RNA was extracted using an RNX solution and subjected to cDNA synthesis with a specific loop sequence for each miRNA. The samples were subsequently analyzed using relative real-time PCR.

RT-qPCR analysis revealed a significant reduction in the detection rate of miR-1307-3p (P < 0.0001) in the patient group compared to the control group. Conversely, no significant differences were observed in the levels of miR-3613-5p between the patient and control groups.

miR-1307-3p shows potential as a biomarker for the diagnosis of COVID-19.