Objectives: To analyze the validity of the ratio between the second and fourth finger (digit ratio; 2D/4D) of the left hand as a predictor for prostate cancer (PCa) in a group of men undergoing prostate biopsy.
Methods: We prospectively recruited 204 consecutive patients referred for transrectal prostate biopsy due to PSA elevation or abnormal digital rectal examination between January 2008 and June 2009. The same physician performed all clinical examinations, digit ratio measurements and transrectal biopsy in all cases. Digit ratio determination was done with a Vernier caliper in the left hand. Patients underwent determination of hormone profile (testosterone and sexual hormone binding globulin (SHBG)) between 7:00AM and 11:00AM. Age, digital rectal examination, PSA, free PSA, PSA density, testosterone and SHBG, pathological report and D2 and D4 measurements were recorded prospectively.
Results: Variables age and SHBG were directly related to PCa. Prostate volume was inversely related to neoplasia. 2D/4D ratio >0,95 (OR (CI 95%) 4,4 (1,491-13,107) was related to neoplasia. No differences in PCa were seen regarding PSA, free PSA, PSA density, digital rectal examination and testosterone.
Conclusion: High digit ratio predicts PCa in men undergoing prostate biopsy. Digit ratio >0,95 has 4-fold risk of PCa compared to men with digit ratio ≤0.95.
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Drug Des Devel Ther
January 2025
Department of Anesthesiology, Ningbo No. 2 hospital, Ningbo, 315010, People's Republic of China.
Objective: This study aims to compare the recovery profiles of remimazolam combined with flumazenil against those of propofol in patients undergoing painless surgical abortion, focusing on psychomotor function and emergence. Rapid recovery and restoration of psychomotor function are critical for enhancing patient safety and satisfaction in outpatient procedures like surgical abortion.
Methods: A total of 110 patients scheduled for surgical abortion were randomly assigned to either the remimazolam group (Group R) or the propofol group (Group P) in a 1:1 ratio.
Small
January 2025
School of Advanced Materials Science and Engineering, Sungkyunkwan University, Suwon, 16419, Republic of Korea.
Homeostasis is essential in biological neural networks, optimizing information processing and experience-dependent learning by maintaining the balance of neuronal activity. However, conventional two-terminal memristors have limitations in implementing homeostatic functions due to the absence of global regulation ability. Here, three-terminal oxide memtransistor-based homeostatic synapses are demonstrated to perform highly linear synaptic weight update and enhanced accuracy in neuromorphic computing.
View Article and Find Full Text PDFNat Commun
January 2025
School of Applied and Engineering Physics, Cornell University, Ithaca, NY, USA.
Energy efficiency in computation is ultimately limited by noise, with quantum limits setting the fundamental noise floor. Analog physical neural networks hold promise for improved energy efficiency compared to digital electronic neural networks. However, they are typically operated in a relatively high-power regime so that the signal-to-noise ratio (SNR) is large (>10), and the noise can be treated as a perturbation.
View Article and Find Full Text PDFAlzheimers Dement (N Y)
November 2024
Department of Psychiatry, Harvard Medical School Massachusetts General Hospital Boston Massachusetts USA.
Objective: Physical activity (PA) has been linked to reduced Alzheimer's disease (AD) risk. However, less is known about its effects in the AD preclinical stage. We aimed to investigate whether greater PA was associated with lower plasma biomarkers of AD pathology, neural injury, reactive astrocytes, and better cognition in individuals with autosomal-dominant AD due to the presenilin-1 E280A mutation who are virtually guaranteed to develop dementia.
View Article and Find Full Text PDFNPJ Digit Med
January 2025
College of Medicine, Chang Gung University, Taoyuan, Taiwan.
Deep learning analysis of electrocardiography (ECG) may predict cardiovascular outcomes. We present a novel multi-task deep learning model, the ECG-MACE, which predicts the one-year first-ever major adverse cardiovascular events (MACE) using 2,821,889 standard 12-lead ECGs, including training (n = 984,895), validation (n = 422,061), and test (n = 1,414,933) sets, from Chang Gung Memorial Hospital database in Taiwan. Data from another independent medical center (n = 113,224) was retrieved for external validation.
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