Background & ObjectiveCurrently, a major clinical challenge is to distinguish between chronic liver disease caused by metabolic syndrome (non-alcoholic fatty liver disease, NAFLD) from that caused by long term or excessive alcohol consumption (ALD). The etiology of severe liver disease affects treatment options and priorities for liver transplantation and organ allocation. Thus we compared physiologically similar NAFLD and ALD patients to detect biochemical differences for improved separation of these mechanistically overlapping etiologies.MethodsIn a cohort of 31 NAFLD patients with BMI below 30 and a cohort of ALD patient with (ALDC n = 51) or without cirrhosis (ALDNC n = 51) serum transaminases, cell death markers and (adipo-)cytokines were assessed. Groups were compared with One-way ANOVA and Tukey's correction. Predictive models were built by machine learning techniques.ResultsNAFLD, ALDNC or ALDC patients did not differ in demographic parameters. The ratio of alanine aminotransferase/aspartate aminotransferase - common serum parameters for liver damage - was significantly higher in the NAFLD group compared to both ALD groups (each p<0.0001). Adiponectin and tumor necrosis factor(TNF)-alpha were significantly lower in NAFLD than in ALDNC (p<0.05) or ALDC patients (p<0.0001). Significantly higher serum concentrations of cell death markers, hyaluronic acid, adiponectin, and TNF-alpha (each p<0.0001) were found in ALDC compared to ALDNC. Using machine learning techniques we were able to discern NAFLD and ALDNC (up to an AUC of 0.9118±0.0056) or ALDC and ALDNC (up to an AUC of 0.9846±0.0018), respectively.ConclusionsMachine learning techniques relying on ALT/AST ratio, adipokines and cytokines distinguish NAFLD and ALD. In addition, severity of ALD may be non-invasively diagnosed via serum cytokine concentrations.

BackgroundAs findings regarding predictors for good outcome after total joint arthroplasty are highly inconsistent, aim of this study was to investigate the influence of the psychosocial variables sense of coherence and social support as well as mental distress on physical outcome after surgery. It should be investigated if different predictors are important in patients after total hip arthroplasty (THA) compared to patients after total knee arthroplasty (TKA).MethodsIn a prospective design, 44 patients undergoing THA and 61 patients undergoing TKA were examined presurgery and 6 and 12 weeks after surgery using WOMAC (disease-specific outcome), SF-36 (health-related quality of life), BSI (psychological distress), SOC-13 (sense of coherence), and F-SozU (social support). Changes over time were calculated by analyses of variance with repeated measures. Stepwise multiple linear regression analyses were computed for each group to predict scores of WOMAC total and all WOMAC subscales 12 weeks postoperatively.ResultsTHA as well as TKA patients experienced improvements in all parameters (effect sizes for WOMAC scores between η2 = .387 and η2 = .631) with THA patients showing even better results than TKA patients. WOMAC scores 12 weeks after surgery were predicted predominantly by WOMAC baseline scores in TKA with an amount of explained variance between 9.6 and 19.5%. In THA, 12-weeks WOMAC scores were predicted by baseline measures of psychosocial aspects (anxiety, sense of coherence, social support). In this group, predictors accounted for 17.1 to 31.6% of the variance.ConclusionsDifferent predictors for outcome after total joint arthroplasty were obtained for THA and TKA patients. Although psychosocial aspects seemed to be less important in TKA patients, preoperatively, distressed patients of both groups should be offered interventions to reduce psychological distress to obtain better outcomes after surgery.

The results suggest that implementation of an addiction therapy program during the waiting time might help to limit the frequency of drinking. These patients appeared often to under-report their alcohol consumption; including a biomarker such as urinary EtG in such settings is recommended.

Introduction: Neurofeedback (NF) or electroencephalogram (EEG)-Biofeedback is a drug-free form of brain training to directly alter the underlying neural mechanisms of cognition and behavior. It is a technique that measures a subject’s EEG signal, processes it in real time, with the goal to enable a behavioral modification by modulating brain activity. The most common application of the NF technology is in epilepsies, migraine, attention-deficit/hyperactivity disorder, autism spectrum disorder, affective disorders, and psychotic disorders. Few studies have investigated the use of NF in context of psychosomatic illnesses. Little is known about the use in cancer patients or postcancer survivors despite the high number of this patient group. Objectives: We here provide a systematic review of the use and effect of NF on symptoms and burden in cancer patients and long-term cancer survivors. Methods: In conducting this systematic review, we followed the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) Statement. Results: Our search resulted in only 3 experimental studies, 1 observational study, and 2 case reports. Given the heterogeneity of the intervention systems and protocols, no meta-analysis was conducted. Conclusion: Altogether, there is initial evidence that NF is a complementary, drug-free, and noninvasive therapy that has the potential to ameliorate symptoms in this patient group, such as pain, fatigue, depression, and sleep. Further studies are highly needed.

This study provides a comprehensive assessment of own body representation and linguistic representation of bodies in general in women with typical and atypical anorexia nervosa (AN). Methods: In a series of desktop experiments, participants rated a set of adjectives according to their match with a series of computer generated bodies varying in body mass index, and generated prototypic body shapes for the same set of adjectives. We analysed how body mass index of the bodies was associated with positive or negative valence of the adjectives in the different groups. Further, body image and own body perception were assessed. Results: In a German-Italian sample comprising 39 women with AN, 20 women with atypical AN and 40 age matched control participants, we observed effects indicative of weight stigmatization, but no significant differences between the groups. Generally, positive adjectives were associated with lean bodies, whereas negative adjectives were associated with obese bodies. Discussion: Our observations suggest that patients with both typical and atypical AN affectively and visually represent body descriptions not differently from healthy women. We conclude that overvaluation of low body weight and fear of weight gain cannot be explained by generally distorted perception or cognition, but require individual consideration.