Pegylated interferon alpha-2a once weekly provides more effective and safer therapy than standard interferon alpha-2a thrice weekly for treatment-naive dialysis patients with chronic hepatitis C.

BackgroundEpilepsy is a common chronic neurological disorder characterized by recurrent unprovoked seizures. Electroencephalogram (EEG) signals play a critical role in the diagnosis of epilepsy. Multichannel EEGs contain more information than do single-channel EEGs. Automatic detection algorithms for spikes or seizures have traditionally been implemented on single-channel EEG, and algorithms for multichannel EEG are unavailable.MethodologyThis study proposes a physiology-based detection system for epileptic seizures that uses multichannel EEG signals. The proposed technique was tested on two EEG data sets acquired from 18 patients. Both unipolar and bipolar EEG signals were analyzed. We employed sample entropy (SampEn), statistical values, and concepts used in clinical neurophysiology (e.g., phase reversals and potential fields of a bipolar EEG) to extract the features. We further tested the performance of a genetic algorithm cascaded with a support vector machine and post-classification spike matching.Principal FindingsWe obtained 86.69% spike detection and 99.77% seizure detection for Data Set I. The detection system was further validated using the model trained by Data Set I on Data Set II. The system again showed high performance, with 91.18% detection of spikes and 99.22% seizure detection.ConclusionWe report a de novo EEG classification system for seizure and spike detection on multichannel EEG that includes physiology-based knowledge to enhance the performance of this type of system.

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Real-world data regarding the effectiveness and safety of generic sofosbuvir (SOF)-based interferonfree direct acting antiviral agents (DAAs) for patients with chronic hepatitis C virus (HCV) infection remain limited. A total of 517 chronic HCV-infected patients receiving 12 or 24 weeks of SOF-based therapies were retrospectively enrolled in 4 academic centers in Taiwan. The rate of sustained virologic response at week 12 off-therapy (SVR 12) and that of treatment completion were assessed. The baseline characteristics and on-treatment HCV viral kinetics to predict SVR 12 were analyzed. By evaluable population (EP) analysis, the SVR 12 rate was 95.4% (95% confidence interval [CI]: 93.2-96.9%). The SVR 12 was achieved in 29 of 34 patients (85.3%, 95% CI: 69.6-93.6%), 130 of 139 patients (93.5%, 95% CI: 88.2-96.6%), 119 of 124 patients (96.0%, 95% CI: 90.9-98.3%) and 215 of 220 patients (97.7%, 95% CI: 94.8-99.0%) who received SOF in combination with ribavirin (RBV), ledipasvir (LDV), daclatasvir (DCV) and velpatasvir (VEL), respectively. Of 517 patients, 514 (99.4%) completed the scheduled treatment. All 15 patients with true virologic failures were relapsers. Two decompensated cirrhotic patients had on-treatment deaths which were not related to DAAs. All 7 patients who were lost to follow-up had undetectable HCV RNA level at the last visit. The SVR 12 rates were comparable in terms of baseline patient characteristics and viral decline at week 4 of treatment. In conclusion, generic SOFbased regimens are well tolerated and provide high SVR 12 rates in patients with chronic HCV infection.

The increasing dengue burden and epidemic severity worldwide have highlighted the need to improve surveillance. In non-endemic areas such as Taiwan, where outbreaks start mostly with imported cases from Southeast Asia, a closer examination of surveillance dynamics to detect cases early is necessary. To evaluate problems with dengue surveillance and investigate the involvement of different factors at various epidemic stages, we investigated 632 laboratory-confirmed indigenous dengue cases in Kaohsiung City, Taiwan during 2009–2010. The estimated sensitivity of clinical surveillance was 82.4% (521/632). Initially, the modified serological surveillance (targeting only the contacts of laboratory-confirmed dengue cases) identified clinically unrecognized afebrile cases in younger patients who visited private clinics and accounted for 30.4% (35/115) of the early-stage cases. Multivariate regression indicated that hospital/medical center visits [Adjusted Odds Ratio (aOR): 11.6, 95% confidence interval (CI): 6.3–21.4], middle epidemic stage [aOR: 2.4 (1.2–4.7)], fever [aOR: 2.3 (2.3–12.9)], and musculo-articular pain [aOR: 1.9 (1.05–3.3)] were significantly associated with clinical reporting. However, cases with pruritus/rash [aOR: 0.47 (0.26–0.83)] and diarrhea [aOR: 0.47 (0.26–0.85)] were underreported. In conclusion, multiple factors contributed to dengue surveillance problems. To prevent a large-scale epidemic and minimize severe dengue cases, there is a need for integrated surveillance incorporating entomological, clinical, serological, and virological surveillance systems to detect early cases, followed by immediate prevention and control measures and continuous evaluation to ensure effectiveness. This effort will be particularly important for an arbovirus, such as Zika virus, with a high asymptomatic infection ratio. For dengue- non-endemic countries, we recommend serological surveillance be implemented in areas with high Aedes mosquito indices or many breeding sites. Syndromic surveillance, spatial analysis and monitoring changes in epidemiological characteristics using a geographical information system, as well as epidemic prediction models involving epidemiological, meteorological and environmental variables will be helpful for early risk communication to increase awareness.