Background: Protracted withdrawal syndrome (PWS) after stopping antidepressants (frequently also referred to as post-acute withdrawal syndrome or PAWS) has been described in a few case reports. However, a detailed quantitative analysis of specific symptom manifestations in antidepressant PWS is still lacking. Methods: We extracted patient narratives from a large English-language internet forum SurvivingAntidepressants.org , a peer support site concerned about withdrawal from antidepressants. PWS was ascertained based on diagnostic criteria proposed by Chouinard and Chouinard, specifically ⩾6 months of continuous antidepressant use, with emergence of new and/or more intense symptoms after discontinuation that last beyond the initial 6 weeks of acute withdrawal. We assessed medication history, outcome of PWS, and the prevalence of specific symptoms. Results: In total, n = 69 individual reports of protracted withdrawal were selected for analysis. At time of the subjects’ most recent reports, duration of PWS ranged from 5 to 166 months, mean = 37 months, median = 26 months. Length of time on the antidepressant causing protracted withdrawal ranged from 6 to 278 months, mean = 96 months, and median = 79 months. Throughout the withdrawal experience, affective symptoms, mostly anxiety, depression, emerging suicidality and agitation, were reported by 81%. Somatic symptoms, mostly headache, fatigue, dizziness, brain zaps, visual changes, muscle aches, tremor, diarrhea, and nausea were reported by 75%. Sleep problems (44%) and cognitive impairments (32%) were mentioned less frequently. These broad symptom domains were largely uncorrelated. Conclusion: PWS or PAWS from antidepressants can be severe and long-lasting, and its manifestations clinically heterogeneous. Long-term antidepressant exposure may cause multiple body system impairments. Although both somatic and affective symptoms are frequent, they are mostly unrelated in terms of occurrence. Proper recognition and detection of PWS thus requires a comprehensive assessment of medication history, duration of the withdrawal syndrome, and its various somatic, affective, sleep, and cognitive symptoms.
The novel COVID-19 disease has been declared a pandemic event. Early detection of infection symptoms and contact tracing are playing a vital role in containing COVID-19 spread. As demonstrated by recent literature, multi-sensor and connected wearable devices might enable symptom detection and help tracing contacts, while also acquiring useful epidemiological information. This paper presents the design and implementation of a fully open-source wearable platform called H-Watch. It has been designed to include several sensors for COVID-19 early detection, multi-radio for wireless transmission and tracking, a microcontroller for processing data on-board, and finally, an energy harvester to extend the battery lifetime. Experimental results demonstrated only 5.9 mW of average power consumption, leading to a lifetime of 9 days on a small watch battery. Finally, all the hardware and the software, including a machine learning on MCU toolkit, are provided open-source, allowing the research community to build and use the H-Watch.
Radio Frequency (RF) wireless power transfer is a promising technology that has the potential to constantly power small Internet of Things (IoT) devices, enabling even battery-less systems and reducing their maintenance requirements. However, to achieve this ambitious goal, carefully designed RF energy harvesting (EH) systems are needed to minimize the conversion losses and the conversion efficiency of the limited power. For intelligent internet of things sensors and devices, which often have non-constant power requirements, an additional power management stage with energy storage is needed to temporarily provide a higher power output than the power being harvested. This paper proposes an RF wireless power energy conversion system for miniaturized IoT composed of an impedance matching network, a rectifier, and power management with energy storage. The proposed sub-system has been experimentally validated and achieved an overall power conversion efficiency (PCE) of over 30 % for an input power of −10 dBm and a peak efficiency of 57 % at 3 dBm.
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