Background: Although most common adverse events associated with dry needling can be considered minor, serious adverse events including induced pneumothorax cannot be excluded, and safety instructions for reducing the risk of pleura puncture are needed.
Objective: To investigate if anthropometric features can predict the rhomboid major muscle and pleura depth in a sample of healthy subjects to avoid the risk of pneumothorax during dry needling.
Methods: A diagnostic study was conducted on 59 healthy subjects (52.5% male) involving a total of 236 measurements (both sides in maximum inspiration and expiration), to calculate the accuracy of a prediction model for both pleura and rhomboid depth, as assessed with ultrasound imaging, based on sex, age, height, weight, body mass index (BMI), breathing and chest circumference. A correlation matrix and a multiple linear regression analyses were used to detect those variables contributing significantly to the variance in both locations.
Results: Men showed greater height, weight, BMI, thorax circumference and skin-to-rhomboid, rhomboid-to-pleura and skin-to-pleura distances (P < .001). Sex, BMI, and thorax circumference explained 51.5% of the variance of the rhomboid (P < .001) and 69.7% of pleura (P < .001) depth limit. In general, inserting a maximum length of 19 mm is recommended to reach the deep limit of rhomboid major decreasing the risk of passing through the pleura.
Conclusion: This study identified that gender, BMI and thorax circumference can predict both rhomboid and pleura depth, as assessed with ultrasonography, in healthy subjects. Our findings could assist clinicians in the needle length election in avoiding the risk of induced pneumothorax during dry needling.
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http://dx.doi.org/10.1111/ijcp.14176 | DOI Listing |
J Med Internet Res
January 2025
Department of Anesthesiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
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Methods: In this retrospective study, data from 958 patients who underwent LT between January 2015 and January 2020 were extracted from the Third Affiliated Hospital of Sun Yat-sen University.
ACS Biomater Sci Eng
January 2025
Mechanical Engineering Department, Worcester Polytechnic Institute, Worcester, Massachusetts 01609, United States.
Mechanical properties of engineered connective tissues are critical for their success, yet modern sensors that measure physical qualities of tissues for quality control are invasive and destructive. The goal of this work was to develop a noncontact, nondestructive method to measure mechanical attributes of engineered skin substitutes during production without disturbing the sterile culture packaging. We optimized a digital holographic vibrometry (DHV) system to measure the mechanical behavior of Apligraf living cellular skin substitute through the clear packaging in multiple conditions: resting on solid agar as when the tissue is shipped, on liquid media in which it is grown, and freely suspended in air as occurs when the media is removed for feeding.
View Article and Find Full Text PDFJAMA Cardiol
January 2025
Program of Medical and Population Genetics, Broad Institute of MIT (Massachusetts Institute of Technology) and Harvard, Cambridge, Massachusetts.
Importance: Treatment to lower high levels of low-density lipoprotein cholesterol (LDL-C) reduces incident coronary artery disease (CAD) risk but modestly increases the risk for incident type 2 diabetes (T2D). The extent to which genetic factors across the cholesterol spectrum are associated with incident T2D is not well understood.
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Cell Biochem Biophys
January 2025
Department of Otolaryngology, Head and Neck Surgery, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430000, China.
Sensorineural hearing loss (SNHL) is an increasingly prevalent sensory disorder, but the underlying mechanisms remain poorly understood. Adaptor related protein complex 2 subunit beta 1 (AP2B1) has been indicated to be detectable in mature cochleae. Nonetheless, it is unclear whether AP2B1 is implicated in the progression of SNHL.
View Article and Find Full Text PDFClin Exp Nephrol
January 2025
Kawasaki Medical School, Department of Nephrology and Hypertension, Kurashiki, Japan.
Background: Chronic kidney disease (CKD) represents a significant public health challenge, with rates consistently on the rise. Enhancing kidney function prediction could contribute to the early detection, prevention, and management of CKD in clinical practice. We aimed to investigate whether deep learning techniques, especially those suitable for processing missing values, can improve the accuracy of predicting future renal function compared to traditional statistical method, using the Japan Chronic Kidney Disease Database (J-CKD-DB), a nationwide multicenter CKD registry.
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