Artificial intelligence-based prediction of molecular and genetic markers for hepatitis C-related hepatocellular carcinoma.

Background: Hepatocellular carcinoma (HCC) is the main cause of mortality from cancer globally. This paper intends to classify public gene expression data of patients with Hepatitis C virus-related HCC (HCV+HCC) and chronic HCV without HCC (HCV alone) through the XGboost approach and to identify key genes that may be responsible for HCC. Methods: The current research is a retrospective case-control study. Public data from 17 patients with HCV+HCC and 35 patients with HCV-alone samples were used in this study. An XGboost model was established for the classification by 10-fold cross-validation. Accuracy (AC), balanced accuracy (BAC), sensitivity, specificity, positive predictive value, negative predictive value, and F1 score were utilized for performance assessment. Results: AC, BAC, sensitivity, specificity, positive predictive value, negative predictive value, and F1 scores from the XGboost model were 98.1, 97.1, 100, 94.1, 97.2, 100, and 98.6%, respectively. According to the variable importance values from the XGboost, the HAO2, TOMM20, GPC3, and PSMB4 genes can be considered potential biomarkers for HCV-related HCC. Conclusion: A machine learning-based prediction method discovered genes that potentially serve as biomarkers for HCV-related HCC. After clinical confirmation of the acquired genes in the following medical study, their therapeutic use can be established. Additionally, more detailed clinical works are needed to substantiate the significant conclusions in the current study.

Predicting Duodenal Cancer Risk in Patients with Familial Adenomatous Polyposis Using Machine Learning Model.

BACKGROUND/AIMS: The aim of this study was to both classify data of familial adenomatous polyposis patients with and without duode- nal cancer and to identify important genes that may be related to duodenal cancer by XGboost model. MATERIALS AND METHODS: The current study was performed using expression profile data from a series of duodenal samples from familial adenomatous polyposis patients to explore variations in the familial adenomatous polyposis duodenal adenoma-carcinoma sequence. The expression profiles obtained from cancerous, adenomatous, and normal tissues of 12 familial adenomatous polyposis patients with duodenal cancer and the tissues of 12 familial adenomatous polyposis patients without duodenal cancer were compared. The ElasticNet approach was utilized for the feature selection. Using 5-fold cross-validation, one of the machine learning approaches, XGboost, was utilized to classify duodenal cancer. Accuracy, balanced accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score performance metrics were assessed for model performance. RESULTS: According to the variable importance obtained from the modeling, ADH1C, DEFA5, CPS1, SPP1, DMBT1, VCAN-AS1, APOB genes (cancer vs. adenoma); LOC399753, APOA4, MIR548X, and ADH1C genes (adenoma vs. adenoma); SNORD123, CEACAM6, SNORD78, ANXA10, SPINK1, and CPS1 (normal vs. adenoma) genes can be used as predictive biomarkers. CONCLUSIONS: The proposed model used in this study shows that the aforementioned genes can forecast the risk of duodenal cancer in patients with familial adenomatous polyposis. More comprehensive analyses should be performed in the future to assess the reliability of the genes determined.

Value of fecal calprotectin in prediction of acute appendicitis based on a proposed model of machine learning.

BACKGROUND: The aim of this study is to apply random forest (RF), one of the machine learning (ML) algorithms, to a dataset consisting of patients with a presumed diagnosis of acute appendicitis (AAp) and to reveal the most important factors associated with the diagnosis of AAp based on the variable importance. METHODS: An open-access dataset comparing two patient groups with (n=40) and without (n=44) AAp to predict biomarkers for AAp was used for this case-control study. RF was used for modeling the data set. The data were divided into two training and test dataset (80: 20). Accuracy, balanced accuracy (BC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) performance metrics were appraised for model performance. RESULTS: Accuracy, BC, sensitivity, specificity, PPV, NPV, and F1 scores pertaining to the RF model were 93.8%, 93.8%, 87.5%, 100%, 100%, 88.9%, and 93.3%, respectively. Following the variable importance values regarding the model, the variables most associated with the diagnosis and prediction of AAp were fecal calprotectin (100 %), radiological imaging (89.9%), white blood test (51.8%), C-reactive protein (47.1%), from symptoms onset to the hospital visit (19.3%), patients age (18.4%), alanine aminotransferase levels >40 (<1%), fever (<1%), and nausea/vomiting (<1%), respectively. CONCLUSION: A prediction model was developed for AAp with the ML method in this study. Thanks to this model, biomarkers that predict AAp with high accuracy were determined. Thus, the decision-making process of clinicians for diagnosing AAp will be facilitated, and the risks of perforation and unnecessary operations will be minimized thanks to the timely diagnosis with high accuracy.

Evaluation of Bone Mineral Metabolism After Liver Transplantation by Bone Mineral Densitometry and Biochemical Markers.

AIM: This study aimed to evaluate the course of bone and mineral metabolism after liver transplantation (LT) in patients with chronic liver disease. METHODS: One hundred four patients who had undergone LT and had a minimum of 6 months of follow-up after LT were included in this prospective cohort study. The following parameters were evaluated for each patient: preoperative and postoperative (postoperative day [POD]30, POD90, POD180) osteocalcin, bone-specific alkaline phosphatase (BALP), type 1 collagen, beta-C-terminal end telopeptide (ß-CTx), vitamin D, parathyroid hormone (PTH), ALP, calcium, phosphate, sedimentation, and bone mineral densitometer scores (L2, L4, L total, and F total). The parameters were compared in terms of sex, presence of liver tumor (hepatocellular carcinoma [HCC; n = 19] vs non-HCC [n = 85]), and presence of autoimmune liver disease (autoimmune liver disease [ALD; n = 8] vs non-ALD [n = 96]). RESULTS: The median age of the patients (n = 81 men and n = 23 women) was 52 years (95% CI, 50-56). There was a significant change in the defined time intervals in parameters such as osteocalcin (P < .001), BALP (P < .001), ß-CTx (P < .001), vitamin D (P < .001), PTH (P < .001), ALP (P = .001), calcium (P < .001), phosphate (P = .001), L2 (P = .038), L total (P = .026), and F total (P < .001) scores. There was a significant difference in POD90 ALP (P = .033), POD180 calcium (P = .011), POD180 phosphate (P = .011), preoperative sedimentation (P = .032), and POD180 F total (P = .013) scores between both sexes. There was a significant difference in POD180 osteocalcin (P = .023), POD180 ß-CTx (P = .017), and preOP calcium (P = .003) among the HCC and non-HCC groups. Furthermore, we found significant differences in preoperative ALP (P = .008), preoperative sedimentation (P = .019), POD90 (P = .037) and POD180 L2 (P = .005) scores, preoperative (P = .049) and POD180 L4 (P = .017), and POD180 L total (P = .010) and F total (P = .022) scores between the patients with and without ALD. CONCLUSION: This study shows that the bone and mineral metabolism of the LT recipients was negatively affected after LT. In addition, we showed that bone and mineral metabolism was more prominent in patients with HCC, and bone mineral density scores were higher in patients with ALD.

Effect of the COVID-19 Pandemic on the Psychological Health of Patients Who Underwent Liver Transplantation Due to Hepatocellular Carcinoma.

BACKGROUND: The primary aim of this study was to compare liver transplant (LT) recipients with and without hepatocellular carcinoma (HCC) in terms of COVID-19-related depression, anxiety, and stress. METHOD: A total of 504 LT recipients with (HCC group; n = 252) and without HCC (non-HCC group; n = 252) were included in the present case-control study. Depression Anxiety Stress Scales (DASS-21) and Coronavirus Anxiety Scale (CAS) were used to evaluate the depression, stress, and anxiety levels of LT patients. DASS-21 total and CAS-SF scores were determined as the primary outcomes of the study. Poisson regression and negative binomial regression models were used to predict the DASS and CAS scores. The incidence rate ratio (IRR) was used as a coefficient. Both groups were also compared in terms of awareness of the COVID-19 vaccine. RESULTS: Poisson regression and negative binomial regression analyses for DASS-21 total and CAS-SF scales showed that the negative binomial regression method was the appropriate model for both scales. According to this model, it was determined that the following independent variables increased the DASS-21 total score: non-HCC (IRR: 1.26; p = 0.031), female gender (IRR: 1.29; p = 0.036), presence of chronic disease (IRR: 1.65; p < 0.001), exposure to COVID-19 (IRR: 1.63; p < 0.001), and nonvaccination (IRR: 1.50; p = 0.002). On the other hand, it was determined that the following independent variables increased the CAS score: female gender (IRR:1.75; p = 0.014) and exposure to COVID-19 (IRR: 1.51; p = 0.048). Significant differences were found between the HCC and non-HCC groups in terms of median DASS-21 total (p < 0.001) and CAS-SF (p = 0.002) scores. Cronbach's alpha internal consistency coefficients of DASS-21 total and CAS-SF scales were calculated to be 0.823 and 0.783, respectively. CONCLUSION: This study showed that the variables including patients without HCC, female gender, having a chronic disease, being exposed to COVID-19, and not being vaccinated against COVID-19 increased anxiety, depression, and stress. High internal consistency coefficients obtained from both scales indicate that these results are reliable.

Modeling Based on Ensemble Learning Methods for Detection of Diagnostic Biomarkers from LncRNA Data in Rats Treated with Cis-Platinum-Induced Hepatotoxicity.

BACKGROUND: The first aim of this study is to perform bioinformatic analysis of lncRNAs obtained from liver tissue samples from rats treated with cisplatin hepatotoxicity and without pathology. Another aim is to identify possible biomarkers for the diagnosis/early diagnosis of hepatotoxicity by modeling the data obtained from bioinformatics analysis with ensemble learning methods. METHODS: In the study, 20 female Sprague-Dawley rats were divided into a control group and a hepatotoxicity group. Liver samples were taken from rats, and transcriptomic and histopathological analyses were performed. The dataset achieved from the transcriptomic analysis was modeled with ensemble learning methods (stacking, bagging, and boosting). Modeling results were evaluated with accuracy (Acc), balanced accuracy (B-Acc), sensitivity (Se), specificity (Sp), positive predictive value (Ppv), negative predictive value (Npv), and F1 score performance metrics. As a result of the modeling, lncRNAs that could be biomarkers were evaluated with variable importance values. RESULTS: According to histopathological and immunohistochemical analyses, a significant increase was observed in the sinusoidal dilatation and Hsp60 immunoreactivity values in the hepatotoxicity group compared to the control group (p < 0.0001). According to the results of the bioinformatics analysis, 589 lncRNAs showed different expressions in the groups. The stacking model had the best classification performance among the applied ensemble learning models. The Acc, B-Acc, Se, Sp, Ppv, Npv, and F1-score values obtained from this model were 90%, 90%, 80%, 100%, 100%, 83.3%, and 88.9%, respectively. lncRNAs with id rna-XR_005492522.1, rna-XR_005492536.1, and rna-XR_005505831.1 with the highest three values according to the variable importance obtained as a result of stacking modeling can be used as predictive biomarker candidates for hepatotoxicity. CONCLUSIONS: Among the ensemble algorithms, the stacking technique yielded higher performance results as compared to the bagging and boosting methods on the transcriptomic data. More comprehensive studies can support the possible biomarkers determined due to the research and the decisive results for the diagnosis of drug-induced hepatotoxicity.

Immunosuppressive Medication Adherence in Patients With Hepatocellular Cancer Who Have Undergo Liver Transplantation: A Case Control Study.

BACKGROUND: We aimed to compare the adherence to immunosuppressive medication use in patients who underwent liver transplantation (LT) due to hepatocellular carcinoma (HCC) and non-HCC reasons. METHODS: The study population was determined as 242 patients with HCC and 1290 patients with non-HCC who had LT performed in our institute between March 2002 and November 2021; all these patients were contacted by phone in March 2022. The sample size was calculated using the MedCalc software program, and the number of patients required in each group was determined as 111 patients. Furthermore, we used the sample.int function, a random integer generator in the R (version 4.1.2) software program. Whereas demographic and clinical parameters were determined as independent variables, the immunosuppressive medication adherence scale (IMAS) score was determined as a dependent variable. Patients were evaluated by the IMAS. This 11-item IMAS scale evaluates the lowest compliance score as 11 and the highest as 55. RESULTS: Out of a total number of 221 patients, 161 (72%) were men and 60 (27.1%) were women, with a median age of 58 years (IQR: 14); one patient in the non-HCC group was excluded due to lack of data. Among the HCC and non-HCC groups, significant differences were found in terms of the variables of age (P = .003), IMAS score (P < .001), sex (P = .001), working status (P = .004), chronic diseases (P = .008), tacrolimus alone (P < .001), tacrolimus plus everolimus (P < .001), and often medication changes (P < .001). A statistically significant correlation was found between the IMAS score and whether the patients had HCC (P < .001) and frequently changing immunosuppressive drugs (P = .023). CONCLUSION: This study showed that patients with frequent drug changes or non-HCC etiology had better adherence to immunosuppressive drug use.

Assessment of Liver Regeneration in Patients Who Have Undergone Living Donor Hepatectomy for Living Donor Liver Transplantation.

BACKGROUND: Inflammation and the associated immune pathways are among the most important factors in liver regeneration after living donor hepatectomy. Various biomarkers, especially liver function tests, are used to show liver regeneration. The aim of this study was to evaluate the course of liver regeneration following donor hepatectomy (LDH) by routine and regeneration-related biomarkers. METHOD: Data from 63 living liver donors (LLDs) who underwent LDH in Inonu University Liver Transplant Institute were prospectively analyzed. Serum samples were obtained on the preoperative day and postoperative days (POD) 1, 3, 5, 10, and 21. Regenerative markers including alfa-fetoprotein (AFP), des carboxy prothrombin (DCP), ornithine decarboxylase (ODC), retinol-binding protein 4 (RBP4), and angiotensin-converting enzyme isotype II (ACEII) and liver function tests including alanine aminotransferase (ALT), aspartate aminotransferase (AST), gamma-glutamyl transferase (GGT), alkaline phosphatase (ALP) and total bilirubin levels were all analyzed. RESULTS: The median age of the LLDs was 29.7 years and 28 LLDs were female. Eight LLDs developed postoperative complications requiring relaparotomy. The routine laboratory parameters including AST (<0.001), ALT (<0.001), ALP (<0.001), and total bilirubin (<0.001) showed a significant increase over time until postoperative day (POD) 3. For the regeneration-related parameters, except for the RBP4, all parameters including ACEII (p = 0.006), AFP (p = 0.002), DCP (p = 0.007), and ODC (p = 0.002) showed a significant increase in POD3. The regeneration parameters showed a different pattern of change. In right-lobe liver grafts, ACEII (p = 0.002), AFP (p = 0.035), and ODC (p = 0.001) showed a significant increase over time. DCP (p = 0.129) and RBP4 (p = 0.335) showed no significant changes in right-lobe liver grafts. CONCLUSIONS: Regenerative markers are increased in a sustained fashion following LDH. This is more prominent following right-lobe grafts which are indicative of progenitor-associated liver regeneration.

Histopathological Features of Gallbladder Specimens Obtained From Liver Recipients.

BACKGROUND: To evaluate the histopathological features of gallbladder specimens obtained from liver transplantation (LT) recipients and to find the correlation between these findings with the clinical features of patients. METHODS: The 1985 patients who underwent LT in our institute between March 2002 and January 2021 and whose data regarding pathologic analysis of gallbladder could retrospectively be obtained were included in the study. The data of the patients including age, gender, the reason for LT (fulminant or nonfulminant), presence of ascites, macroscopic characteristics of gallbladder (the length, diameter, and wall thickness), and microscopic findings were all obtained and analyzed in the present study. RESULTS: A total of 1985 patients (men = 1300 and women = 685) with a median age of 39.4 years were included in this study. LT was performed in 249 patients because of fulminant liver failure, and abdominal ascites were detected in 933 patients during LT. There were statistical differences in terms of age (P < .001), gallbladder length (P < .001). and width (P < .001) among the both gender, but there was no difference in terms of histopathologic characteristics and presence of gallstones. On the other hand, there were significant differences in terms of age (P < .001), gallbladder length (P < .001), width (P < .001), wall thickness (P = .021), presence of gallstones (P < .001), and histopathologic characteristics (P < .001) between the patients with fulminant and nonfulminant liver failure etiologies. Similar results were obtained when characteristics of patients with and without ascites were compared. CONCLUSIONS: This the first study analyzing the histopathological analysis of gallbladder specimens in LT recipients. Chronic liver disease, presence of ascites and gender are the factors affecting the macroscopic and microscopic features of the gallbladder.

Measurement of Heavy Metal and Antioxidant-Oxidant Levels in Tissues Obtained From Three Different Localizations of Explant Hepatectomy of Patients With Hepatocellular Carcinoma.

BACKGROUND: To reveal any difference in terms of heavy metal and antioxidant/oxidant levels of liver tissues obtained from 3 different locations of hepatectomy specimens of patients with hepatocellular carcinoma (HCC). METHODS: Total hepatectomy materials of patients who underwent liver transplantation for HCC were objects of this study. Three liver tissue samples were obtained from each material, one from HCC tissue, one adjacent from the border of HCC, and one at least 3 cm distant from HCC, each 10 × 10 mm in diameter. Samples are preserved at -70°C. Levels of heavy metals (As, Cd, Cu, Mn, Pb, Se, and Zn) and oxidant-antioxidant parameters (catalase, glutathione peroxidase [GSHPx], superoxide dismutase [SOD], nitric oxide, prolidase, glutathione, malondialdehyde, total oxidant status, antioxidant status, oxidative stress index, total-thiol, native thiol, and disulphid) are measured. RESULTS: This study included 22 patients (18 men, 4 women with an age range of 3 to 66 years. There were significant differences in terms of Cd, Pb, Zn, GSHPx, SOD, nitric oxide, and native thiol levels between liver tissues derived from 3 different locations. Cd, Pb, and Zn levels were significantly different in tumor tissues, whereas GSHPx and SOD levels were significantly different in tumor and neighboring tissues. Nitric oxide levels were relatively different in tumor tissues compared with tumor-neighboring tissues. Native thiol levels differed significantly in tumor tissues compared with tissues distant from tumor. CONCLUSIONS: The aim of this study is unique in medical literature, which reveals that the amount of heavy metals and antioxidant/oxidant accumulation are variable in the same liver tissue in different locations because of multiple and yet unknown factors.

Machine Learning-based Prediction of HBV-related Hepatocellular Carcinoma and Detection of Key Candidate Biomarkers.

Objective: This study aimed to classify open-access gene expression data of patients with hepatitis B virus-related hepatocellular carcinoma (HBV + HCC) and chronic HBV without HCC (HBV alone) using the XGBoost method, one of the machine learning methods, and reveal important genes that may cause HCC. Methods: This case-control study used the open-access gene expression data of patients with HBV + HCC and HBV alone. Data from 17 patients with HBV + HCC and 36 patients with HBV were included. XGBoost was constructed for the classification via 10-fold cross-validation. Accuracy, balanced accuracy, sensitivity, selectivity, positive-predictive value, and negative-predictive value performance metrics were evaluated for model performance. Results: According to the feature-selection method, 18 genes were selected, and modeling was performed with these input variables. Accuracy, balanced accuracy, sensitivity, specificity, positive-predictive value, negative-predictive value, and F1 score obtained from XGBoost model were 98.1%, 98.6%, 100%, 97.2%, 94.4%, 100%, and 97.1%, respectively. Based on the predictor importance findings acquired from XGBoost, the RNF26, FLJ10233, ACBD6, RBM12, PFAS, H3C11, and GKP5 can be employed as potential biomarkers of HBV-related HCC. Conclusions: In this study, genes that may be possible biomarkers of HBV-related HCC were determined using a machine learning-based prediction approach. After the reliability of the obtained genes are clinically verified in subsequent research, therapeutic procedures can be established based on these genes, and their usefulness in clinical practice may be documented.

Opinions of nursing and theology faculty students on Xenotransplantation.

BACKGROUND: It is mentioned that students' opinions about xenotransplantation (XTx) have been explored in a limited manner. In particular, there is no literature in Turkey on Nursing and Theology students' perspectives on XTx. This research aimed to find out what Nursing and Theology students thought about XTx. METHODS: This descriptive and cross-sectional study was conducted on students studying at the Theology and Nursing faculties. The study population consisted of 2.581 students educated in these faculties. Without using any sampling method, it was aimed to reach all students, and 1.780 (70%) students were reached. Data were collected using a participant identification form and questionnaire form, which the researchers developed. RESULTS: The difference between the answers given by the Nursing and Theology students to the information statements about XTx was statistically significant (p < .001). Nursing and Theology students' attitudes to organ or tissue Tx from halal animals in case of necessity were positive (p < .001). While the nursing students' attitude toward organ or tissue Tx from non-helal animals in case of necessity was negative, Theology students had no idea (p < .001). In other attitude statements, while nursing students responded positively, Theology students responded as "I have no idea" (p ≤ .001). CONCLUSION: Theology students tended to have the question about XTx and only positive attitude towards XTx from halal animals. Nursing students mostly had positive attitude, but negative when XTx is practiced out of necessity.

Can Temperament and Character Traits Be Used in the Diagnostic Differentiation of Children With ADHD?

ABSTRACT: In this study, it was aimed to determine the contributions of temperament and character traits to the diagnosis of attention deficit hyperactivity disorder (ADHD) in children. Thirty-six patients between the ages of 9 and 14 with a diagnosis of combined type ADHD and 39 healthy children were included in the study. The Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version-Turkish Version and the Turgay DSM-IV Disruptive Behavior Disorders Rating Scale parent form were used to assess hyperactivity/impulsivity and inattentiveness, and comorbid disorders. The Junior Temperament and Character Inventory-Revised form was used to evaluate temperament-character traits. The classification-based association rules (CBARs) method was used for finding rules predicting ADHD accurately. Low persistence and self-directedness scores, and higher disorderliness and fatigability subgroup scores were found in the ADHD group. In CBARs, the separation of children with ADHD from healthy controls could be made with 0.83 accuracy, 0.80 sensitivity, and 0.86 specificity. The results of our study support the view that temperament-character traits can help clinical diagnosis of ADHD.