FEA on the biomechanical behavior of immediately loaded implants with different sizes

Objectives. The purpose of this research was to study, in the case of immediate loading, the biomechanical effect produced by the length of the implants. Material and method. The study material was a CBCT analysis performed on a patient from one dental office in Bucharest. An segment of edentulous mandibular bone was selected from the CBCT, which was processed with Mimics Innovation Suite, respectively Mimics and 3-matic. After processing the bone segment, two implants of the same manufacturer, with identical design, but different length – 10 and 13 mm respectively, were selected from the BIOMAT database. To simulate immediate loading, the bone-implant interface was not blocked and the mandible was defined with properties that characterize a bone with moderate density. A perpendicular masticatory force of 200N was applied to each of the two implants. The software ANSYS calculated the minimum, maximum values and their geometric means for the possible stresses produced on both the shorter implant (10 mm) and the longer implant (13 mm). Results. In the case of short implants, higher average stresses develop along the entire length of the implant, towards the vestibular bone plate, while in the case of long implants the higher stress seems to be cantoned towards the apical side. Conclusions. The present study shows that, in the case of immediate loading, the use of longer implants (13 mm) reduces by more than 50% the geometric mean of the stresses to which the bone-implant interface is subjected than in the case of the use of shorter implants (10 mm). In both types of implants, higher stresses occur at the level of the screw fixing the abutment in the implant

RETROSPECTIVE OBSERVATIONAL STUDY ON THE INCIDENCE OF ORAL COMPLICATIONS IN PATIENTS WITH DIABETES MELLITUS

Objectives. We performed a retrospective observational analytical study to determine the incidence of complications in the oral cavity in a group of patients diagnosed with diabetes mellitus. Material and method. We have studied 68 diabetic patients, who have been referred to dental practices seeking specialized treatment. Patient data sheets were centralized, categorized, and statistically processed with IBM SPSS Statistics 22 and Microsoft Excel 2010 depending on: sex of patients, age, type of diabetes, time elapsed from diagnosis of diabetes, the environment of origin of the patients, the level of their training, the presence or absence of oral complications and the glycosylated hemoglobin value at presentation in the dental office. Results. The study group had an average age of 56.35 years, with being the best represented in the age group 44-54. The study group was composed predominantly of female, urban patients and higher-education patients. Half of the patients in the study group were diagnosed with diabetes more than 7 years ago, and the glycosylated hemoglobin value averaged 7.309. The incidence of complications of diabetes in the oral cavity of the studied group was, in decreasing order of frequency: periodontal disease, delayed healing of oral wounds, dental caries, salivary gland dysfunctions, alteration of taste sensations, burning sensation in the oral cavity, fungal infections and traumatic ulcers. Discussions. Both types of diabetes have long-term complications that are directly proportional to the duration and value of hyperglycemia. The most common oral complications of diabetes described in the literature have been found in the complications diagnosed n the study group patients. Conclusions. In the studied group, the number of complications in the oral cavity of a patient was statistically correlated with the glycosylated hemoglobin value and with the time elapsed since the diagnosis of diabetes mellitus. There was no statistically significant correlation between the number of complications found in the oral cavity and the level of patient training

Emergency and Elective Colorectal Cancer—Relationship between Clinical Factors, Tumor Topography and Surgical Strategies: A Cohort Study

Background and Objectives: The purpose of the study was to analyze the relationships among several clinical factors and also the tumor topography and surgical strategies used in patients with colorectal cancer. Materials and Methods: We designed an analytical, observational, retrospective study that included patients admitted to our emergency surgical department and diagnosed with colorectal cancer. The study group inclusion criteria were: patients admitted during 2020–2022; patients diagnosed with colorectal cancer (including the ileocecal valve); patients who benefited from a surgical procedure, either emergency or elective. Results: In our study group, consisting of 153 patients, we accounted for 56.9% male patients and 43.1% female patients. The most common clinical manifestations were pain (73.2% of the study group), followed by abdominal distension (69.3% of the study group) and absence of intestinal transit (38.6% of the study group). A total of 69 patients had emergency surgery (45.1%), while 84 patients (54.9%) benefited from elective surgery. The most frequent topography of the tumor was the sigmoid colon, with 19.60% of the patients, followed by the colorectal junction, with 15.68% of the patients, and superior rectum and inferior rectum, with 11.11% of the patients in each subcategory. The most frequent type of procedure was right hemicolectomy (21.6% of the study group), followed by rectosigmoid resection (20.9% of the study group). The surgical procedure was finished by performing an anastomosis in 49% of the patients, and an ostomy in 43.1% of the patients, while for 7.8% of the patients, a tumoral biopsy was performed. Conclusions: Colorectal cancer remains one of the most frequent cancers in the world, with a heavy burden that involves high mortality, alterations in the quality of life of patients and their families, and also the financial costs of the medical systems

Privacy-Preserving and Explainable AI in Industrial Applications

The industrial environment has gone through the fourth revolution, also called “Industry 4.0”, where the main aspect is digitalization. Each device employed in an industrial process is connected to a network called the industrial Internet of things (IIOT). With IIOT manufacturers being capable of tracking every device, it has become easier to prevent or quickly solve failures. Specifically, the large amount of available data has allowed the use of artificial intelligence (AI) algorithms to improve industrial applications in many ways (e.g., failure detection, process optimization, and abnormality detection). Although data are abundant, their access has raised problems due to privacy concerns of manufacturers. Censoring sensitive information is not a desired approach because it negatively impacts the AI performance. To increase trust, there is also the need to understand how AI algorithms make choices, i.e., to no longer regard them as black boxes. This paper focuses on recent advancements related to the challenges mentioned above, discusses the industrial impact of proposed solutions, and identifies challenges for future research. It also presents examples related to privacy-preserving and explainable AI solutions, and comments on the interaction between the identified challenges in the conclusions

Developing a Novel Murine Meningococcal Meningitis Model Using a Capsule-Null Bacterial Strain

Background: Neisseria meningitidis (meningococcus) is a Gram-negative bacterium that colonises the nasopharynx of about 10% of the healthy human population. Under certain conditions, it spreads into the body, causing infections with high morbidity and mortality rates. Although the capsule is the key virulence factor, unencapsulated strains have proved to possess significant clinical implications as well. Meningococcal meningitis is a primarily human infection, with limited animal models that are dependent on a variety of parameters such as bacterial virulence and mouse strain. In this study, we aimed to develop a murine Neisseria meningitidis meningitis model to be used in the study of various antimicrobial compounds. Method: We used a capsule-deficient Neisseria meningitidis strain that was thoroughly analysed through various methods. The bacterial strain was incubated for 48 h in brain–heart infusion (BHI) broth before being concentrated and injected intracisternally to bypass the blood–brain barrier in CD-1 mice. This prolonged incubation time was a key factor in increasing the virulence of the bacterial strain. A total of three more differently prepared inoculums were tested to further solidify the importance of the protocol (a 24-h incubated inoculum, a diluted inoculum, and an inactivated inoculum). Antibiotic treatment groups were also established. The clinical parameters and number of deaths were recorded over a period of 5 days, and comatose mice with no chance of recovery were euthanised. Results: The bacterial strain was confirmed to have no capsule but was found to harbour a total of 56 genes coding virulence factors, and its antibiotic susceptibility was established. Meningitis was confirmed through positive tissue culture and histological evaluation, where specific lesions were observed, such as perivascular sheaths with inflammatory infiltrate. In the treatment groups, survival rates were significantly higher (up to 81.25% in one of the treatment groups compared to 18.75% in the control group). Conclusion: We managed to successfully develop a cost-efficient murine (using simple CD-1 mice instead of expensive transgenic mice) meningococcal meningitis model using an unencapsulated strain with a novel method of preparation

The Value of Early and Follow-Up Elevated Scores Based on Peripheral Complete Blood Cell Count for Predicting Adverse Outcomes in COVID-19 Patients

Background: The ongoing COVID-19 pandemic has put a constant strain on hospital resources, so there is a dire need for investigation methods that are widely available and that can predict mortality and the need for critical care. Hematological indices, which can be easily calculated from a complete blood count (CBC), are useful in determining a patient’s inflammatory response to infectious diseases. Aim: This was a prospective cohort study that aimed to assess the prognostic value of scores based on CBCs in hospitalized patients with mild or moderate COVID-19 and medical comorbidities regarding the need for intensive care unit (ICU) therapy and short-term mortality. Methods: We included 607 patients with confirmed COVID-19, followed up for the need for ICU admission (15.5%) and 30 day mortality post-discharge (21.7%). CBC-derived scores were tested upon emergency department (ED) admission and after a median of 8 days. Results: In a multivariate model, elevated followed-up neutrophil-to-lymphocyte ratio (NLR) predicted increased odds for ICU admission (OR: 1.14 [95%CI: 1.06–1.22], p < 0.001) and short-term mortality (OR: 1.30 [95%CI: 1.09–1.57], p = 0.005). Monocyte-to-lymphocyte ratio (MLR) predicted 2.5-fold increased odds for ICU admission and 2.2-fold increased odds for mortality. Conclusion: NLR and MLR followed up 8 days post-admission are predictive for adverse outcomes in mild or moderate COVID-19 patients

Inflammatory and Cardiac Biomarkers in Relation with Post-Acute COVID-19 and Mortality: What We Know after Successive Pandemic Waves

Background: Biomarkers were correlated with mortality in critically ill COVID-19 patients. No prediction tools exist for noncritically ill COVID-19 patients. We aimed to compare the independent prognostic value of inflammation and cardiac biomarkers for post-acute COVID-19 patients and the 30-day mortality rate in noncritically ill COVID-19 patients, as well as the relation with the virus variant involved. Methods: This observational cohort study was conducted at an emergency clinical hospital between 1 October 2020 and 31 December 2021. We included consecutive patients with biomarkers determined within 24 h of presentation, followed up at least 30 days postdischarge. Results: Post-acute COVID-19 was diagnosed in 20.3% of the cases and the all-cause 30-day mortality rate was 35.1% among 978 patients infected with variants of concern. Neutrophil-to-lymphocyte ratio (1.06 [95%CI, 1.01–1.11], p = 0.015) and NT-pro BNP were correlated with 30-daymortality, while the monocyte-to-lymphocyte ratio (2.77 [95%CI, 1.10–6.94], p = 0.03) and NT-pro BNP (1.68 [95%CI, 1.00–2.84], p = 0.05) were correlated with post-acute COVID-19. High-sensitivity to troponin was associated with 30-day mortality (1.55 [95%CI, 1.00–2.42], p = 0.05). A Cox proportional-hazards model confirmed that NT-pro BNP was independently associated with mortality. NT-pro BNP remained independently associated with 30-day mortality during follow-up (1.29 [95%CI, 1.07–1.56], p = 0.007) after adjustment for confounders. Conclusion: Inflammation and cardiac biomarkers, determined upon admission and predischarge, in a cohort of hospitalized noncritically ill COVID-19 patients throughout successive pandemic waves, showed a predictive value for post-acute COVID-19 and 30-day mortality

A framework for validating AI in precision medicine: considerations from the European ITFoC consortium

International audienceBackground: Artificial intelligence (AI) has the potential to transform our healthcare systems significantly. New AI technologies based on machine learning approaches should play a key role in clinical decision-making in the future. However, their implementation in health care settings remains limited, mostly due to a lack of robust validation procedures. There is a need to develop reliable assessment frameworks for the clinical validation of AI. We present here an approach for assessing AI for predicting treatment response in triple-negative breast cancer (TNBC), using real-world data and molecular-omics data from clinical data warehouses and biobanks. Methods: The European "ITFoC (Information Technology for the Future Of Cancer)" consortium designed a framework for the clinical validation of AI technologies for predicting treatment response in oncology. Results: This framework is based on seven key steps specifying: (1) the intended use of AI, (2) the target population, (3) the timing of AI evaluation, (4) the datasets used for evaluation, (5) the procedures used for ensuring data safety (including data quality, privacy and security), (6) the metrics used for measuring performance, and (7) the procedures used to ensure that the AI is explainable. This framework forms the basis of a validation platform that we are building for the "ITFoC Challenge". This community-wide competition will make it possible to assess and compare AI algorithms for predicting the response to TNBC treatments with external real-world datasets. Conclusions: The predictive performance and safety of AI technologies must be assessed in a robust, unbiased and transparent manner before their implementation in healthcare settings. We believe that the consideration of the ITFoC consortium will contribute to the safe transfer and implementation of AI in clinical settings, in the context of precision oncology and personalized care