Long-term sero-positivity for IgG, sequelae of respiratory symptoms, and abundance of malformed sperms in a patient recovered from severe COVID-19.

Patients with severe coronavirus disease in 2019 (COVID-19 pneumonia) may have many sequelae, which seriously affect their quality of life and work. Here, we report a case of infection in China, reviewed the course, treatment, and rehabilitation of a patient suffering from severe COVID-19 pneumonia, and collected his examination reports, including chest CT, laboratory examination results, lung function examination, sleep monitoring report, sex hormones, sperm morphology and activity. The patient's antiviral immunoglobulin G (IgG) continued to be positive for more than 11 months, and his small airway function was abnormal, and he suffered from respiratory problems (cough, chest pain, chest tightness, and shortness of breath), unstructured sleep apnea hypopnea syndrome, and nocturnal sleep hypoxemia. His abnormal sperm rate increased obviously, and sperm activity decreased obviously. Patients with severe COVID-19 pneumonia may have respiratory sequela, the abnormal sperm rate is obviously increased, and IgG positive can last for a long time.

Respiratory modulation of oscillometric cuff pressure pulses and Korotkoff sounds during clinical blood pressure measurement in healthy adults.

BACKGROUND: Accurate blood pressure (BP) measurement depends on the reliability of oscillometric cuff pressure pulses (OscP) and Korotkoff sounds (KorS) for automated oscillometric and manual techniques. It has been widely accepted that respiration is one of the main factors affecting BP measurement. However, little is known about how respiration affects the signals from which BP measurement is obtained. The aim was to quantify the modulation effect of respiration on oscillometric pulses and KorS during clinical BP measurement. METHODS: Systolic and diastolic BPs were measured manually from 40 healthy subjects (from 23 to 65 years old) under normal and regular deep breathing. The following signals were digitally recorded during linear cuff deflation: chest motion from a magnetometer to obtain reference respiration, cuff pressure from an electronic pressure sensor to derive OscP, and KorS from a digital stethoscope. The effects of respiration on both OscP and KorS were determined from changes in their amplitude associated with respiration between systole and diastole. These changes were normalized to the mean signal amplitude of OscP and KorS to derive the respiratory modulation depth. Reference respiration frequency, and the frequencies derived from the amplitude modulation of OscP and KorS were also calculated and compared. RESULTS: Respiratory modulation depth was 14 and 40 % for OscP and KorS respectively under normal breathing condition, with significant increases (both p < 0.05) to 16 and 49 % with deeper breathing. There was no statistically significant difference between the reference respiration frequency and those derived from the oscillometric and Korotkoff signals (both p > 0.05) during deep breathing, and for the oscillometric signal during normal breathing (p > 0.05). CONCLUSIONS: Our study confirmed and quantified the respiratory modulation effect on the oscillometric pulses and KorS during clinical BP measurement, with increased modulation depth under regular deeper breathing.

Ubiquitous Fall Hazard Identification With Smart Insole.

Falls are leading causes of nonfatal injuries in workplaces which lead to substantial injury and economic consequences. To help avoid fall injuries, safety managers usually need to inspect working areas routinely. However, it is difficult for a limited number of safety managers to inspect fall hazards instantly especially in large workplaces. To address this problem, a novel fall hazard identification method is proposed in this paper which makes it possible for all workers to report the potential hazards automatically. This method is based on the fact that people use different gaits to get across different floor surfaces. Through analyzing gait patterns, potential fall hazards could be identified automatically. In this research, Smart Insole, an insole shaped wearable system for gait analysis, was applied to measure gait patterns for fall hazard identification. Slips and trips are the focus of this study since they are two main causes of falls in workplaces. Five effective gait features were extracted to train a Support Vector Machine (SVM) model for recognizing slip hazard, trip hazard, and safe floor surfaces. Experiment results showed that fall hazards could be recognized with high accuracy (98.1%).