Background: Osteoporosis and bone fractures affect health and quality of life. Since bone disease is multifactorial, identifying risk factors is key in prevention. There are multiple reports on how viral hepatitis especially chronic hepatitis B (CHB) and chronic hepatitis C (CHC) are affecting bone disease, but results vary. Here, we analyzed the potential association between CHB/CHC and osteoporosis or bone fractures in a large outpatient cohort in Germany. Methods: We included 3,136 outpatients with CHB and 15,608 matched non-hepatitis individuals as well as 2,867 outpatients with CHC and 14,335 matched non-hepatitis individuals from the Disease Analyzer Database between 2005 and 2022. The main outcome was the 5-year cumulative incidence of osteoporosis and bone fractures as a function of either CHB or CHC. Results: Within 5 years of the index date, 2.9% vs. 1.6% of patients with and without CHB were diagnosed with osteoporosis (p=0.001) and 1.0% vs. 0.4% were diagnosed with bone fractures (p<0.001). Moreover, 3.3% of CHC patients and 2.2% of individuals without hepatitis were diagnosed with osteoporosis (p=0.002). In Cox-regression analyses, CHB was significantly associated with an increased risk for osteoporosis (HR: 1.76) and fractures (HR:2.43) and CHC with osteoporosis (HR: 1.54). For both CHB and CHC, the association with osteoporosis was restricted to the female subgroup. Conclusion: CHB and CHC are associated with osteoporosis in women. CHB in male patients is associated with a higher risk of fractures. More research is are needed to understand the underlying pathophysiological mechanisms.

Electroencephalography (EEG) is essential for tracking brain activity and identifying seizure effects. However, epileptic behaviour can only be detected after a specialist has carefully analysed all EEG recordings along with a proper history of the patient. A skilled physician is required for the right epilepsy diagnosis and therapy. But most of the time, patients visit the clinician in the interictal stage with no proper history documented. Therefore, it was essential to the automatic prediction of stages of seizure. K nearest neighbours (KNN) and random forest (RF) models using raw EEG signals, preictal, ictal, postictal, and interictal stages were identified in this study. The possibility of these characteristics is explored by examining how well time-domain signals work in the prediction of epileptic stages using intracranial EEG datasets from Freiburg Hospital (FH), Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT), and Temple University Hospital (TUHEEG). To test the viability of this approach, two different types of simulations were carried out on three binary classifications (interictal vs. preictal, interictal vs. ictal, preictal vs. postictal, and interictal vs. postictal), and one four-class problem (interictal vs. preictal vs. ictal vs. postictal) was performed for each model. The average accuracy when using time-domain signals in the FH database was 90.5% and 75.0%; CHB-MIT was 92.87% and 75.9%; and TUHEEG was 94.46% and 76.8%, respectively, for the KNN and RF models.