Background: CTX-M betalactamases have shown a rapid spread in the recent years among Enterobacteriaceae and have become the most prevalent Extended Spectrum Beta-Lactamases (ESBLs) in many parts of the world. The introduction and dissemination of antibiotic-resistant genes limits options for treatment, increases mortality and morbidity in patients, and leads to longer hospitalization and expensive costs. We aimed to identify the beta-lactamases circulating encoded by the genes bla_CTX-M-15, bla_SHV-1 and bla_TEM-1 in Escherichia coli (E. coli) and Klebsiella pneumoniae (K. pneumoniae) strains. Furthermore, we established the associated resistance phenotypes among patients hospitalized in the Intensive Care Unit (ICU) from County Clinical Emergency Hospital of Craiova, Romania. Methods: A total of 46 non-duplicated bacterial strains (14 strains of E. coli and 32 strains of K. pneumoniae), which were resistant to ceftazidime (CAZ) and cefotaxime (CTX) by Kirby–Bauer disk diffusion method, were identified using the automated VITEK2 system. Detection of ESBL-encoding genes and other resistance genes was carried out by PCR. Results. E. coli strains were resistant to 3rd generation cephalosporins and moderately resistant to quinolones, whereas K. pneumoniae strains were resistant to penicillins, cephalosporins, and sulfamides, and moderately resistant to quinolones and carbapenems. Most E. coli strains harbored bla_CTX-M-15 gene (13/14 strains), a single strain had the bla_SHV-1 gene, but 11 strains harbored bla_TEM-1 gene. The mcr-1 gene was not detected. We detected tet(A) gene in six strains and tet(B) in one strain. In K. pneumoniae strains we detected bla_CTX-M-15 in 23 strains, bla_SHV-₁ in all strains and bla_TEM-1 in 14 strains. The colistin resistance gene mcr-1 was not detected. The tetracycline gene tet(A) was detected in 11 strains, but the gene tet(B) was not detected in any strains. Conclusions. The development in antibiotic resistance highlights the importance of establishing policies to reduce antibiotic use and improving the national resistance surveillance system in order to create local antibiotic therapy guidelines.

The study evaluated the evolution of the incidence of infections with Klebsiella in the County Clinical Emergency Hospital of Craiova (SCJUC), Romania. Also, we monitored antibiotic resistance over more than two years and detected changes in resistance to various antimicrobial agents. Our study included 2062 patients (823 women and 1239 men) hospitalised in SCJUC during the period 1st of September 2017 to 30 June 2019. In 458 patients (22.21%) from the 2062 total patients, the collected samples (1116) were positive and from those, we isolated 251 strains of Klebsiella spp. We conducted a longitudinal analysis of the prevalence of Klebsiella spp. over calendar months, which showed a prevalence in surgical wards that ranged between 5.25% and 19.49% in June 2018, while in medical wards the variation was much wider, between 5.15% and 17.36% in April 2018. Klebsiella spp. strains showed significant resistance to Amoxicillin/Clavulanate, Aztreonam and Cephalosporins such as Ceftriaxone, Ceftazidime and Cefepime. We examined the possible link with the consumption of antibiotics in the same month by performing a multiple linear regression analysis. The evolution of antibiotic resistance in Klebsiella was correlated with the variation of resistance in other bacteria, which suggests common resistance mechanisms in the hospital environment. By performing the regression for dependency between antibiotic resistance and antibiotic consumption, we observed some correlations between antibiotic consumption and the development of antibiotic resistance after 1, 2 and even 3 months (e.g., resistance to meropenem was influenced by the consumption in the hospital ward of imipenem 1 month and two months before, but only 1 month before by the consumption of meropenem). The clustering of strains showed filiation between multiresistant Klebsiella spp. strains isolated from specific patients from the ICU. The evolution of prevalence and antibiotic resistance in Klebsiella correlated with the resistance in other bacteria, which suggest common resistance mechanisms in the hospital environment, and also with the consumption of antibiotics.

(1) Background: The new SARS-COV-2 pandemic overwhelmed intensive care units, clinicians, and radiologists, so the development of methods to forecast the diagnosis’ severity became a necessity and a helpful tool. (2) Methods: In this paper, we proposed an artificial intelligence-based multimodal approach to forecast the future diagnosis’ severity of patients with laboratory-confirmed cases of SARS-CoV-2 infection. At hospital admission, we collected 46 clinical and biological variables with chest X-ray scans from 475 COVID-19 positively tested patients. An ensemble of machine learning algorithms (AI-Score) was developed to predict the future severity score as mild, moderate, and severe for COVID-19-infected patients. Additionally, a deep learning module (CXR-Score) was developed to automatically classify the chest X-ray images and integrate them into AI-Score. (3) Results: The AI-Score predicted the COVID-19 diagnosis’ severity on the testing/control dataset (95 patients) with an average accuracy of 98.59%, average specificity of 98.97%, and average sensitivity of 97.93%. The CXR-Score module graded the severity of chest X-ray images with an average accuracy of 99.08% on the testing/control dataset (95 chest X-ray images). (4) Conclusions: Our study demonstrated that the deep learning methods based on the integration of clinical and biological data with chest X-ray images accurately predicted the COVID-19 severity score of positive-tested patients.

Background: Implant-prosthetic therapy requires a detailed assessment of the bone structure before designing a personalized treatment plan. Tooth extraction at the molar level is followed by a series of bone changes dependent on the patient’s general condition and age and the area in which it was performed. The fractal analysis of cone beam computed tomography (CBCT) represents a way to assess the quality of post-extraction bone regeneration. The purpose of this study was to analyze the alveolar bone changes after tooth extraction using fractal analysis on CBCT images. Methods: This retrospective study included pre- and post-extraction CBCTs at 3 months of 60 patients who underwent 100 extractions of first and/or second molars. Fractal analysis on CBCT images was performed using ImageJ, and the data obtained from the measurements were statistically processed. A multiple regression model was used to assess factors influencing bone remodeling. Results: Fractal analysis performed on CBCT images showed that most patients experienced advanced bone remodeling, this being more pronounced in those from rural areas, in the vertical plane at the mandible and at the second molar. The multiple regression model showed that the factors that play an important role in predicting bone resorption are represented by age group (age above 56 years old is associated with less bone resorption), location (bone resorption is more pronounced at the mandible level), and molar (bone resorption for the second molar is higher). Conclusions: Post-extraction bone changes were influenced by the age of the patient and by the location of the extraction, with the maxilla and first molar having better fractal analysis values compared to the mandible and second molar. These results emphasize the importance of training implantologists in CBCT evaluation to improve personalized implant-prosthetic treatment decisions.

Background and Objectives: Hepatitis B virus infection remains a major public health concern. The interaction between hepatitis B virus (HBV) hepatitis B virus and the host inflammatory response is an important contributing factor driving liver damage and diseases outcomes. The management of chronic hepatitis B virus infection is an area of massive unmet clinical need worldwide. Our primary aim for this study was to evaluate biological response rates and sustained virological response in patients with chronic hepatitis B treated with Peg-IFN α-2a/b. The second aim of the study was the identification of metabolic changes and insulin resistance. Materials and Methods: We enrolled in this study 166 patients who fulfilled all inclusion and exclusion criteria. These treatment-naive patients with chronic HBV were treated with Pegylated Interferon α-2a/b. HBV infection was defined by the presence of HBV serological markers (HBsAg, anti-HBsAb, anti-HBcAb, HBeAg, anti HBeAb) by Enzyme-Linked Immuno Sorbent Assay (ELISA) and serum HBV-DNA levels were estimated by a commercially available quantitative polymerase chain reaction (PCR) assay. Results: Patients’ recovery progress has been evaluated by determining the following: age, gender; biochemical tests; alanine aminotransferase, aspartate aminotransferase; serological assays for HBV serological markers (HBsAg, anti-HBsAc/Ab, anti-HBcAc/Ab, HBeAg, anti HBeAc/Ab); molecular tests to detect viral particles, testing for HBV DNA (PCR) to confirm the diagnosis and quantify the number of viral copies in the blood (viremia); liver ultrasound—performed through epigastric and intercostal approach (transversal and longitudinal sections). Conclusions: Our results indicated that only HOMA index values, that of fasting insulin, together with baseline HBV DNA, alanine aminotransferase values, mean blood glucose at the beginning of treatment may be predictive of the early viral response in chronic hepatitis B.

Background: Osseointegration is the main factor that ensures the long-term success of implant-prosthetic therapy, but besides this, there are other important factors, such as the quality of the alveolar bone and the time of placement of dental implants. The study aimed to analyze changes in the alveolar bone following tooth extraction, comparing natural healing with immediate implant placement, using fractal analysis on OPG images. Methods: This retrospective study included OPG images obtained before tooth extraction and 3 months after surgery in 91 patients who underwent maxillary and mandibular molar extractions and opted for either natural healing or immediate dental implant placement. Fractal analysis of OPG images was performed using Image J software, and the resulting measurements were subsequently statistically analyzed. Results: Most extractions were performed in the maxilla, and most were at the level of the first molar. The study group showed a faster healing process following immediate placement of dental implants, regardless of location, and a similar distribution of bone resorption and healing, with clear differences in location: the mandible had a faster healing process than the maxilla. Conclusions: Fractal analysis showed a better and quicker bone healing of the alveolar bone in immediate implant placement in molar areas compared with post-extraction natural healing, especially in the lower jaw.