The part played by crossed cervical pathways in determining respiratory rhythm and phrenic nerve amplitude was studied in rabbits and cats. The animals were anesthetized with halothane, paralyzed, vagotomized and mechanically ventilated. All decussating brain stem pathways were surgically interrupted. This resulted in asynchronous firing of the respiratory nerves on the left and right side of the body. However, both frequency and amplitude of the respiratory output depended on the mutual phase relationships due to neural connections crossed at the cervical level. Analysis of the mutual phase and amplitude relationships lead to the following conclusions. a. Phrenic motor neurons receive during inspiration a considerable amount of excitation via diffused pathways crossing the midline at C4-C6 level. b. Phrenic motor neurons are actively inhibited via crossed cervical pathways. c. The level of tonic bias transmitted via descending bulbo-spinal pathways significantly influences the magnitude of PhN output. d. Activity of phrenic motor neurons is transmitted back to the bulbar respiratory centers via an ascending spino-bulbar pathway.
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http://dx.doi.org/10.1016/0034-5687(90)90016-r | DOI Listing |
PLoS One
January 2025
Department of Radiation Oncology, Chulabhorn Hospital, Chulabhorn Royal Academy, Bangkok, Thailand.
Radiotherapy is the main treatment for patients with head and neck cancer (HNC) and is associated with an increased risk of ischemic cerebrovascular events (ICVE). The purpose of this cross-sectional study was to determine the incidence of ICVE and carotid artery stenosis (CAS) in patients with HNC who receive radiotherapy and the risk factors for CAS. We enrolled 907 patients with HNC who underwent radiotherapy between February 2011 and June 2022 and obtained information on their clinical and tumor characteristics and their treatment from the clinical records.
View Article and Find Full Text PDFGlobal Spine J
January 2025
Department of Neurological Surgery, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA.
Study Design: Cross-Sectional Survey.
Objective: This study aimed to assess racial disparities in self-reported barriers to care, health literacy, and health status within a large cohort of cervical stenosis patients.
Methods: This cross-sectional study used ICD-9 and ICD-10 codes to identify cervical stenosis patients recorded in the NIH All of Us Research Program between 2017 and 2022.
Pulmonology
December 2025
Department of Allergology, Institute of Allergology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
Rhinitis is a common comorbidity in patients with asthma. However, the frequency of underreported rhinitis in asthma is not known. In this study, we aimed to assess the characteristics of patients with self-reported asthma and no self-reported rhinitis, as well as the extent of the underreporting of rhinitis.
View Article and Find Full Text PDFHealth Promot Int
January 2025
School of Health Systems and Public Health, University of Pretoria, HW Snyman Building, Bophelo Road, Pretoria 0084, South Africa.
Cervical cancer is the second most common malignancy among South African women and the load of abnormal cervical smears has clinical, programmatic and policy implications. This cross-sectional study of women who presented for cervical cancer screening aimed to determine the prevalence of abnormal cervical smears and associated factors in primary health care (PHC) facilities in Gauteng-the most densely populated province in South Africa. A questionnaire collected data on socio-demography, tobacco use, sexual behaviours, HIV status, past treatment for sexually transmitted infections (STI) and cervical cancer screening in the past 10 years.
View Article and Find Full Text PDFSpine (Phila Pa 1976)
January 2025
school of Life Sciences, Beijing University of Chinese Medicine, Beijing, P.R. China.
Study Design: A cross-sectional analysis of 10,000 cervical spine X-rays.
Objective: This study investigates the variations in C6S and C7S across demographic factors (gender, age, cervical curvature, symptoms) and explores their correlation. Additionally, machine learning models are applied to improve the accuracy of C7S prediction.
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