Abdominopelvic actinomycosis is a rare chronic or subacute bacterial infection caused by Actinomyces israelii, a Gram-positive anaerobic bacterium that normally colonizes the digestive and genital tracts, clinically presented as an inflammatory mass or abscess formation. Methods: We reviewed the medical records of the patients from our clinic with abdominopelvic actinomycosis who underwent surgery between 2002 and 2022. In this period, 28 cases (9 men and 19 women) were treated. The mean age was 43.36 years and they were hospitalized for abdominopelvic tumors or inflammatory tumors in 15 cases and inflammatory disease in 13 cases. Results: Causes of actinomycosis in the studied group were an intra-uterine contraceptive device in 17 cases, foreign bodies in 2 cases, diabetes in 4 cases, stenting of the bile duct in 1 case, and immunodepression. For 6 patients, we performed surgery by open approach and for 21 patients by a laparoscopic approach. For nine patients, abdominopelvic actinomycosis had been mimicking a colon malignancy (cecum and ascending colon, four cases; transverse colon, two cases; and on the greater omentum, three cases) and for six patients, a pelvic tumor (advanced ovarian cancer). After surgery the patients underwent specific treatment with antibiotics, with good results. In two cases we discovered and treated hepatic actinomycosis, one case by a laparoscopic approach and one case by a percutaneous approach. In our lot we noticed three recurrences that required reintervention in patients who had had short-term antibiotics due to non-compliance with treatment out of four such cases. Conclusions: For abdominopelvic malignancies, actinomycosis should be included in the differential diagnosis, as well as for inflammatory bowel diseases and bowel obstructions. We have a wide range of patients considering the rarity of this condition. Long-term antibiotics are necessary to prevent recurrence.

Nontrombotic pulmonary embolism represents the embolization of different types of materials (cells, organisms, gas, foreign material) into pulmonary circulation. The disease is uncommon, and clinical presentation together with laboratory findings are nonspecific. Its pathology is usually misdiagnosed based on imaging findings as pulmonary thromboembolism, but the correct diagnosis is essential because different therapeutic approaches are required. In this context, knowledge of the risk factors associated with nontrombotic pulmonary embolism and specific clinical symptoms is fundamental. Our objective was to discuss the specific features of the most common etiologies of nontrombotic pulmonary embolism, gas, fat, amniotic fluid, sepsis and tumors, to provide assistance for a rapid and correct diagnosis. Because the most common etiologies are iatrogenic, knowledge of the risk factors could be an important tool for prevention or rapid treatment if the disease develops during different procedures. The diagnosis of nontrombotic pulmonary embolisms represent a laborious challenge, and endeavors should be made to prevent development and increase awareness of this disease.

Background and Objectives: Postoperative pancreatic fistula after cephalic pancreatoduodenectomy (CPD) is still the leading cause of postoperative morbidity, entailing long hospital stay and costs or even death. The aim of this study was to propose the use of morphologic parameters based on a preoperative multisequence computer tomography (CT) scan in predicting the clinically relevant postoperative pancreatic fistula (CRPF) and a risk score based on a multiple regression analysis. Materials and Methods: For 78 consecutive patients with CPD, we measured the following parameters on the preoperative CT scans: the density of the pancreas on the unenhanced, arterial, portal and delayed phases; the unenhanced density of the liver; the caliber of the main pancreatic duct (MPD); the preoperatively estimated pancreatic remnant volume (ERPV) and the total pancreatic volume. We assessed the correlation of the parameters with the clinically relevant pancreatic fistula using a univariate analysis and formulated a score using the strongest correlated parameters; the validity of the score was appreciated using logistic regression models and an ROC analysis. Results: When comparing the CRPF group (28.2%) to the non-CRPF group, we found significant differences of the values of unenhanced pancreatic density (UPD) (44.09 ± 6.8 HU vs. 50.4 ± 6.31 HU, p = 0.008), delayed density of the pancreas (48.67 ± 18.05 HU vs. 61.28 ± 16.55, p = 0.045), unenhanced density of the liver (UDL) (44.09 ± 6.8 HU vs. 50.54 ± 6.31 HU, p = 0.008), MPD (0.93 ± 0.35 mm vs. 3.14 ± 2.95 mm, p = 0.02) and ERPV (46.37 ± 10.39 cm³ vs. 34.87 ± 12.35 cm³, p = 0.01). Based on the odds ratio from the multiple regression analysis and after calculating the optimum cut-off values of the variables, we proposed two scores that both used the MPD and the ERPV and differing in the third variable, either including the UPD or the UDL, producing values for the area under the receiver operating characteristic curve (AUC) of 0.846 (95% CI 0.694–0.941) and 0.774 (95% CI 0.599–0.850), respectively. Conclusions: A preoperative CT scan can be a useful tool in predicting the risk of clinically relevant pancreatic fistula.

(1) Background: Numerous variables could influence the risk of rectal cancer recurrence or metastasis, and machine learning (ML)-based algorithms can help us refine the risk stratification process of these patients and choose the best therapeutic approach. The aim of this study was to assess the predictive performance of 4 ML-based models for the prediction of local recurrence or distant metastasis in patients with locally advanced low rectal adenocarcinomas who underwent neoadjuvant chemoradiotherapy and surgical treatment; (2) Methods: Patients who were admitted at the first Oncologic Surgical Clinic from the Regional Institute of Oncology, Iasi, Romania were retrospectively included in this study between November 2019 and July 2023. Decision tree (DT), naïve Bayes (NB), support vector machine (SVM), and random forest (RF) were used to analyze imagistic, surgical, and pathological data retrieved from the medical files, and their predictive performance was assessed; (3) Results: The best predictive performance was achieved by RF when used to predict disease recurrence (accuracy: 90.85%) or distant metastasis (accuracy: 89.63%). RF was closely followed by SVM (accuracy for recurrence 87.8%; accuracy for metastasis: 87.2%) in terms of predictive performance. NB and DT achieved moderate predictive power for the evaluated outcomes; (4) Conclusions: Complex algorithms such as RF and SVM could be useful for improving the prediction of adverse oncological outcomes in patients with low rectal adenocarcinoma.

Machine learning is a branch of artificial intelligence that allows computer systems to learn directly from examples, data, and experience. Statistical modeling is more about finding connections between variables and consequently the impact of these relationships, while also catering for prediction. It should be clear that these two methodologies are different in terms of their purpose, despite the fact that they use similar means to get there. The evaluation of the machine learning algorithm uses a set of tests to validate its accuracy. Although, for a statistical model, the analysis of regression parameters by confidence intervals, significance tests and other tests can be used to assess the legitimacy of the model. To demonstrate the applications and usefulness of this theory, an experimental study was conducted on zebrafish exposed to mycotoxin. Methods: Patulin (70 µg/L) and kojic acid (100 mg/L, 204 mg/L, and 284 mg/L) were administered by immersion to zebrafish once daily for a period of 7 days before the behavior testing. The following behavioral tests were performed: a novel tank test (NTT) (to assess the explorative behavior and anxiety); and a Y-maze test (which measures the spontaneous explorative behavior). Behavioral tests were performed on separate days. For the behavior tests, the statistical analysis was performed using ANOVA variation analysis (two-way ANOVA). All results are expressed as the mean ± standard error of the mean. The values of the general index F for which p < 0.05 were considered statistically significant. Results: Y-maze—patulin exposure led to an intensification of the locomotor activity and an increased traveled distance and number of arm entries. By increasing the spontaneous alternation between the aquarium’s arms, patulin has shown a stimulating effect on spatial memory. In the case of zebrafish exposed to 100 mg/L kojic acid, the traveled distance was shorter by 27% than the distance attained by those in the control group. The higher doses of kojic acid (204 mg/L and 284 mg/L) led to an increased locomotor activity, distance traveled, number of arm entries, and the spontaneous alternation. The increase in spontaneous alternation demonstrates that 204 mg/L and 284 mg/L kojic acid doses had a stimulating effect on spatial memory. Novel tank test—compared to the control group, the traveled distance of the patulin-exposed fish is slightly reduced. Compared to the control group, the traveled distance of the kojic acid-exposed fish is reduced, due to a shorter mobile time (by 25–27% in the case of fish exposed to 204 mg/L and 284 mg/L kojic acid). Patulin and kojic acid exhibit toxic effects on zebrafish liver, kidney, and myocardium and leads to severe alteration. We continued the analysis by trying some machine learning algorithms on the classification problems in the case of the two behavioral tests MAZE and NTT, after which we concluded that the results were better in the case of the NTT test relative to the MAZE test and that the use of decision tree algorithms leads to amazing results, knowing that their hierarchical structure allows them to learn signals from both classes. Conclusions: The groups exposed to patulin and kojic acid show histological changes in the liver, kidneys, and myocardial muscle tissue. The novel tank test, which assesses exploratory behavior, has been shown to be conclusive in the behavioral analysis of fish that have been given toxins, demonstrating that the intoxicated fish had a decreased explorative behavior and increased anxiety. We were able to detect a machine learning algorithm in the category of decision trees, which can be trained to classify the behavior of fish that were given a toxin in the category of those used in the experiment, only by analyzing the characteristic features of the NTT Behavior Test.

Non-alcoholic fatty liver disease (NAFLD) has gained attention in the last few years due to its increasing prevalence worldwide becoming a global epidemic. The increasing incidence of NAFLD and the concurrent increase in the number of hepatocellular carcinoma (HCC) cases at a global level is a matter of concern. HCC has several risk factors, of which NAFLD and its associated metabolic disturbances—type 2 diabetes mellitus, obesity, and dyslipidemia—are of great interest due to their accelerating rise in incidence worldwide. There is a high amount of data derived from basic and clinical studies that reveal the molecular pathways that drive NAFLD-associated HCC. Based on these findings, new prevention, surveillance, and treatment strategies are emerging. However, current data on treatment modalities in NAFLD-associated HCC are still scarce, though the results from non-NAFLD HCC studies are promising and could provide a basis for a future research agenda to address NAFLD/NASH patients. Clinicians should carefully assess all the clinical and radiological parameters and establish a prognosis based on the Barcelona Clinic Liver Cancer classification and discuss in a multidisciplinary team the treatment strategy. The specific factors associated with NAFLD-associated HCC which can have a negative impact on survival even in patients with early HCC, such as cardiovascular disease, type 2 diabetes, and obesity, should be taken into consideration. This review aims to discuss the latest recommendations regarding the diagnosis and treatment of NAFLD-associated HCC and the remaining challenges.