The application of artificial intelligence (AI) in diagnostic imaging has gained significant interest in recent years, particularly in lung cancer detection. This systematic review aims to assess the accuracy of machine learning (ML) AI algorithms in lung cancer detection, identify the ML architectures currently in use, and evaluate the clinical relevance of these diagnostic imaging methods. A systematic search of PubMed, Web of Science, Cochrane, and Scopus databases was conducted in February 2023, encompassing the literature published up until December 2022. The review included nine studies, comprising five case–control studies, three retrospective cohort studies, and one prospective cohort study. Various ML architectures were analyzed, including artificial neural network (ANN), entropy degradation method (EDM), probabilistic neural network (PNN), support vector machine (SVM), partially observable Markov decision process (POMDP), and random forest neural network (RFNN). The ML architectures demonstrated promising results in detecting and classifying lung cancer across different lesion types. The sensitivity of the ML algorithms ranged from 0.81 to 0.99, while the specificity varied from 0.46 to 1.00. The accuracy of the ML algorithms ranged from 77.8% to 100%. The AI architectures were successful in differentiating between malignant and benign lesions and detecting small-cell lung cancer (SCLC) and non-small-cell lung cancer (NSCLC). This systematic review highlights the potential of ML AI architectures in the detection and classification of lung cancer, with varying levels of diagnostic accuracy. Further studies are needed to optimize and validate these AI algorithms, as well as to determine their clinical relevance and applicability in routine practice.

To investigate the transmission of species of Cryptosporidium Tyzzer, 1907 in Timis County, Romania, 48 isolates of Cryptosporidium coccidia from 11 children, 29 calves and eight pigs were characterised by molecular analysis of two loci (SSU rRNA and 60-kDa glycoprotein gene). Overall, 22 isolates were amplified and sequence analyses revealed that all isolates were Cryptosporidium parvum Tyzzer, 1912. Two subtype families were identified, IIa and IId. Subtype IIdA22G1 (n = 4) was the single C. parvum subtype found in children. Subtypes found in calves included IIdA27G1 (n = 8), a novel subtype, IIdA25G1 (n = 5), IIdA22G1 (n = 2), IIdA21G1a (n = 1), and IIaA16G1R1 (n = 1). Subtype IIdA26G1 was found in a pig. These results were significantly different from previous Romanian reports, as the five subtypes of family IId identified in this study were never identified previously in this country. Thus, cattle may be a source of Cryptosporidium infections for humans and the transmission dynamics of C. parvum in Romania is more complex than previously believed.

Electrolyte imbalances are a frequently overlooked yet critical component of obesity-related metabolic dysfunction, contributing to an increased risk of cardiovascular disease, kidney impairment, and metabolic emergencies such as diabetic ketoacidosis (DKA), hyperosmolar hyperglycemic state (HHS), and acute kidney injury (AKI). These disturbances arise from insulin resistance, chronic inflammation, hormonal dysregulation, and renal dysfunction, leading to sodium retention, potassium depletion, and deficiencies in calcium and magnesium homeostasis. Managing electrolyte imbalances is essential in obesity management, as imbalances exacerbate hypertension, metabolic acidosis, neuromuscular complications, and insulin resistance. This review explores the pathophysiology of electrolyte disturbances in obesity and their impact on fluid balance, acid–base status, and metabolic health. Effective management strategies include individualized electrolyte monitoring, dietary sodium restriction, potassium supplementation, vitamin D and magnesium correction, and pharmacologic interventions targeting renin–angiotensin–aldosterone system (RAAS) activity and insulin resistance. Additionally, lifestyle interventions, including dietary modification, weight loss strategies, and hydration optimization, play a key role in preventing metabolic complications. Future research should investigate the long-term impact of electrolyte imbalances in obesity, the role of emerging therapies, and how lifestyle interventions can optimize electrolyte homeostasis and metabolic outcomes. A personalized, multidisciplinary approach integrating endocrinology, nephrology, and clinical nutrition is essential to improving the prevention and management of electrolyte imbalances in obese individuals.

This study examines the role played by individual characteristics and specific treatment methods in the evolution of hospitalized patients with coronavirus disease 2019 (COVID-19), through the lens of an observational study performed in a comparative approach between the first and second waves of coronavirus pandemic in Romania. The research endeavor is configured on a two-fold approach, including a detailed observation of the evolution of 274 hospitalized patients with COVID-19 (145 in the first wave and 129 in the second wave of infection) according to specific treatment methods applied and patients’ individual features, as well as an econometric (quantitative) analysis through structural equation modeling and Gaussian graphical models designed to acknowledge the correlations and causal relationship between all considered coordinates. The main results highlight that the specific treatment methods applied had a positive influence on the evolution of COVID-19 patients, particularly in the second wave of coronavirus pandemic. In case of the first wave of COVID-19 infection, GGM results entail that there is a strong positive correlation between the evolution of the patients and the COVID-19 disease form, which is further positively correlated with the treatment scheme. The evolution of the patients is strongly and inversely correlated with the symptomatology and the ICU hospitalization. Moreover, the disease form is strongly and inversely correlated with oxygen saturation and the residence of patients (urban/rural). The symptomatology at first appearance also strongly depends on the age of the patients (positive correlation) and of the fact that the patient is a smoker or non-smoker and has other comorbidities. Age and gender are also important credentials that shape the disease degree and patient evolution in responding to treatment as well, our study attesting strong interconnections between these coordinates, the form of disease, symptomatology and overall evolution of the patients.

Background and Objectives: Spontaneous pneumothorax (SP) and spontaneous pneumomediastinum (SPM) have frequently been cited as complications associated with coronavirus disease 2019 (COVID-19) pneumonia, with especially poor prognosis in mechanically ventilated patients. The current literature is controversial regarding the potential risk factors for developing SP or SPM (SP-SPM) in non-ventilated COVID-19 patients. Our research addressed a twofold objective: (a) to investigate the characteristics of patients with SP-SPM (both with and without COVID-19) and compare them to patients with sole COVID-19; (b) to quantify the risk of in-hospital mortality associated with SP-SPM and COVID-19. Patients and Methods: A retrospective case–control study was conducted in the emergency departments (ED) of two tertiary hospitals in Timisoara, Romania, over one year (1st April 2020‒31st March 2021; 64,845 records in total) and 70 cases of SP-SPM were identified (both SARS-CoV-2 positives and negatives). The control group comprised COVID-19 patients with no SP-SPM, included at a 2:1 ratio. Logistic regression was employed to quantify the in-hospital mortality risk associated with age, SP-SPM, and COVID-19. Results: SP-SPM and COVID-19 were connected with prolonged hospitalization, a higher percentage of intensive care admission, and a higher mortality. SP-SPM increased the odds of death by almost four times in patients of the same age, gender, smoking status, and SARS-CoV-2 infection: OR = 3.758, 95% CI (1.443– 9.792). Each additional year of age added 9.4% to the mortality risk: OR = 1.094, 95% CI (1.054– 1.135). Conclusion: ED physicians should acknowledge these potential risks when attending COVID-19 patients with SP-SPM.