A case report of Cogan's syndrome with recurrent coronary stenosis.

Cogan's syndrome (CS) is recognized as a form of variable vasculitis. This report presents the case of a middle-aged woman experiencing recurrent coronary artery stenosis, accompanied by a history of non-syphilis keratitis, vestibular auditory symptoms, and venous thrombosis. Positron emission tomography/computed tomography revealed an elevated uptake of (18)F-fluorodeoxyglucose in the subclavian artery, common carotid artery, aortic arch, and thoracic aorta. A diagnosis of Cogan's syndrome was made. The aim of this study was to increase clinicians' awareness of the vascular manifestations in CS and to emphasize the importance of thorough history taking. CS should be included in the differential diagnosis when patients present with recurrent coronary artery stenosis.

Effects of airborne pollen on allergic rhinitis and asthma across different age groups in Beijing, China.

In the context of global warming and rapid urbanization, pollen has become a significant public health concern for Chinese citizens. However, there is a paucity of epidemiological research on the impact of pollen on allergen-linked diseases, such as allergic rhinitis and asthma, in China. Using data from the Beijing Chaoyang Hospital between 2013 and 2019, which included allergic rhinitis and asthma incidence, meteorological records, and air pollution data, we employed a Generalized Additive Model (GAM) to examine the relationship between overall and type-specific pollen concentrations in relation to varying population exposures. We found that increased overall pollen concentrations significantly increased the risks of allergic rhinitis and asthma in diverse populations. Notably, the risk of allergic rhinitis was higher than that of asthma at equivalent pollen concentrations. Seasonal trends indicated that spring pollen peaks, primarily from trees, were associated with a lower risk of both allergic rhinitis and asthma than autumn peaks, predominantly from weeds. This study underscores the importance of identifying pollen species that pose heightened risks to different demographic groups across seasons, thereby providing targeted interventions for public health agencies.

Phase Engineering of Molybdenum Carbide/Oxide for Highly-Efficient Electrocatalytic Hydrogen Production.

Molybdenum carbide is one of non-precious metal materials and it is a promising electrocatalyst for hydrogen production. Synthesis and improvement of molybdenum carbide's catalytic performance is of great significance. Herein, carbon-based Mo2 C with hybrid phase of MoO2 was fabricated by matching the composition of Mo and C derived from pomelo peel and optimizing raw carbon amount and prolysis temperature. The resulting MoO2 /Mo2 C@C is composed of loose microspheres with mesoporous microstructure and is self-assembled from nanoparticles. The results show that MoO2 /Mo2 C@C2 material exhibits excellent HER performance and only a low overpotentials of 143 mV is needed to drive the current density of 10 mA cm-2 in alkaline solution, and Tafel slopes are only 63.4 mV dec-1 , respectively. Notably, it has potential application at low overpotentials to drive large current density and this strategy also provides a universal route to prepare non-noble metal catalyst with hybrid phase of oxide/carbide.

Feasible pickup from intact ossicular chain with floating piezoelectric microphone.

OBJECTIVES: Many microphones have been developed to meet with the implantable requirement of totally implantable cochlear implant (TICI). However, a biocompatible one without destroying the intactness of the ossicular chain still remains under investigation. Such an implantable floating piezoelectric microphone (FPM) has been manufactured and shows an efficient electroacoustic performance in vitro test at our lab. We examined whether it pick up sensitively from the intact ossicular chain and postulated whether it be an optimal implantable one. METHODS: Animal controlled experiment: five adult cats (eight ears) were sacrificed as the model to test the electroacoustic performance of the FPM. Three groups were studied: (1) the experiment group (on malleus): the FPM glued onto the handle of the malleus of the intact ossicular chains; (2) negative control group (in vivo): the FPM only hung into the tympanic cavity; (3) positive control group (Hy-M30): a HiFi commercial microphone placed close to the site of the experiment ear. The testing speaker played pure tones orderly ranged from 0.25 to 8.0 kHz. The FPM inside the ear and the HiFi microphone simultaneously picked up acoustic vibration which recorded as .wav files to analyze. RESULTS: The FPM transformed acoustic vibration sensitively and flatly as did the in vitro test across the frequencies above 2.0 kHz, whereas inefficiently below 1.0 kHz for its overloading mass. Although the HiFi microphone presented more efficiently than the FPM did, there was no significant difference at 3.0 kHz and 8.0 kHz. CONCLUSIONS: It is feasible to develop such an implantable FPM for future TICIs and TIHAs system on condition that the improvement of Micro Electromechanical System and piezoelectric ceramic material technology would be applied to reduce its weight and minimize its size.

A study of feature extraction for Alzheimer's disease based on resting-state fMRI.

The Alzheimer's Disease (AD) has become a major threat of human health with its incidence rate ascending year by year. Early diagnosis of AD is very important for AD patients to keep life quality. The resting-state fMRI (rs-fMRI) which precisely reflects the brain changes on the resting state of individuals provides a quantitative approach, which has been introduced to distinguish AD patients from normal population. In this study, we proposed a method to find the most distinctive features identifying AD patients from rs-fMRI images. The ALFF and ReHo parameters based on pre-processed rs-fMRI data were extracted, and some key parameters of the brain functional network based on graph theory were calculated. Then we tested the recognition performance of different classifiers, and the best classification algorithm, that is, Support Vector Machine (SVM) with linear-kernel are selected. Finally through a recursive feature selection procedure, we got the most distinctive feature set. Additionally, this study also implies that there may be several changes in some particular ROIs of the brain during the AD development.