Healing Richard Nixon: A Doctor\u27s Memoir

Richard M. Nixon remains an enigma even thirty years after his resignation. Of the many portraits of this complex man, none have been more intimate or revealing than this memoir from his personal physician, friend, and confidante of more than forty years, John C. Lungren, M.D. Dr. Lungren, with his son and co-author John C. Lungren Jr., portrays Nixon as a paradoxical man—intense, compassionate, guarded, intelligent, resilient, deeply religious, enormously successful but ultimately tragic. Lungren describes his battle to restore the president’s health after his resignation and reveals previously unknown details about Nixon’s two intensive hospitalizations, his near fatal vascular collapse, and his depression. Lungren experienced firsthand Nixon’s thoughts and feelings during the public scrutiny of federal prosecution for his role in the Watergate break-in. Accused of shielding his friend, Lungren himself came under fire; his private office was even burgled in an apparent attempt to copy Nixon’s private medical records. Using previously unpublished sources, original correspondence, and private photographs, Healing Richard Nixon places Nixon in a new light. No future research or conclusions about Nixon—the man or the president—will be complete without consulting this fascinating memoir. The late John C. Lungren M.D. became Richard Nixon\u27s personal physician in 1952. John C Lungren Jr. is a former reporter for Knight-Ridder and author of Hesburgh of Notre Dame:Priest, Educator, Public Servant. Lungren describes the real Nixon, as he struggled with and ultimately overcame near-fatal illness, depression, and political ridicule to become an ‘elder statesman’ constructively advancing international stability. —Choice Rather than retelling the familiar facts, the authors provide a fresh perspective: that of Nixon’s personal physician, a post the senior Lungren held from 1952 until the early 1980s. —ForeWord Out of all the books written about President Richard Nixon, the Lungrens have authored one of the few chronicles I find to be totally accurate. Brings the reader a personal view of Nixon which only would be available to a long-time personal friend and a doctor who stayed within his code of ethics even under the pressure of Watergate. —Herbert G. Klein, Former Nixon Administration White House Director of Communications Essential reading for anyone wishing to understand all the dimensions of a great architect of peace. —John H. Taylor, Director, Richard Nixon Library & Birthplace Valuable for its conversation about Nixon\u27s medical ordeals. Dr. Lungren admires Nixon but recognized some of his faults. —Library Journalhttps://uknowledge.uky.edu/upk_political_history/1023/thumbnail.jp

Exploring the Boundaries of GPT-4 in Radiology

The recent success of general-domain large language models (LLMs) has significantly changed the natural language processing paradigm towards a unified foundation model across domains and applications. In this paper, we focus on assessing the performance of GPT-4, the most capable LLM so far, on the text-based applications for radiology reports, comparing against state-of-the-art (SOTA) radiology-specific models. Exploring various prompting strategies, we evaluated GPT-4 on a diverse range of common radiology tasks and we found GPT-4 either outperforms or is on par with current SOTA radiology models. With zero-shot prompting, GPT-4 already obtains substantial gains ($\approx$ 10% absolute improvement) over radiology models in temporal sentence similarity classification (accuracy) and natural language inference ($F_1$). For tasks that require learning dataset-specific style or schema (e.g. findings summarisation), GPT-4 improves with example-based prompting and matches supervised SOTA. Our extensive error analysis with a board-certified radiologist shows GPT-4 has a sufficient level of radiology knowledge with only occasional errors in complex context that require nuanced domain knowledge. For findings summarisation, GPT-4 outputs are found to be overall comparable with existing manually-written impressions.Comment: EMNLP 2023 mai

High-Throughput Precision Phenotyping of Left Ventricular Hypertrophy with Cardiovascular Deep Learning

Left ventricular hypertrophy (LVH) results from chronic remodeling caused by a broad range of systemic and cardiovascular disease including hypertension, aortic stenosis, hypertrophic cardiomyopathy, and cardiac amyloidosis. Early detection and characterization of LVH can significantly impact patient care but is limited by under-recognition of hypertrophy, measurement error and variability, and difficulty differentiating etiologies of LVH. To overcome this challenge, we present EchoNet-LVH - a deep learning workflow that automatically quantifies ventricular hypertrophy with precision equal to human experts and predicts etiology of LVH. Trained on 28,201 echocardiogram videos, our model accurately measures intraventricular wall thickness (mean absolute error [MAE] 1.4mm, 95% CI 1.2-1.5mm), left ventricular diameter (MAE 2.4mm, 95% CI 2.2-2.6mm), and posterior wall thickness (MAE 1.2mm, 95% CI 1.1-1.3mm) and classifies cardiac amyloidosis (area under the curve of 0.83) and hypertrophic cardiomyopathy (AUC 0.98) from other etiologies of LVH. In external datasets from independent domestic and international healthcare systems, EchoNet-LVH accurately quantified ventricular parameters (R2 of 0.96 and 0.90 respectively) and detected cardiac amyloidosis (AUC 0.79) and hypertrophic cardiomyopathy (AUC 0.89) on the domestic external validation site. Leveraging measurements across multiple heart beats, our model can more accurately identify subtle changes in LV geometry and its causal etiologies. Compared to human experts, EchoNet-LVH is fully automated, allowing for reproducible, precise measurements, and lays the foundation for precision diagnosis of cardiac hypertrophy. As a resource to promote further innovation, we also make publicly available a large dataset of 23,212 annotated echocardiogram videos

Equilibrium and dynamical properties of two dimensional self-gravitating systems

A system of N classical particles in a 2D periodic cell interacting via long-range attractive potential is studied. For low energy density $U$ a collapsed phase is identified, while in the high energy limit the particles are homogeneously distributed. A phase transition from the collapsed to the homogeneous state occurs at critical energy U_c. A theoretical analysis within the canonical ensemble identifies such a transition as first order. But microcanonical simulations reveal a negative specific heat regime near $U_c$. The dynamical behaviour of the system is affected by this transition : below U_c anomalous diffusion is observed, while for U > U_c the motion of the particles is almost ballistic. In the collapsed phase, finite $N$-effects act like a noise source of variance O(1/N), that restores normal diffusion on a time scale diverging with N. As a consequence, the asymptotic diffusion coefficient will also diverge algebraically with N and superdiffusion will be observable at any time in the limit N \to \infty. A Lyapunov analysis reveals that for U > U_c the maximal exponent \lambda decreases proportionally to N^{-1/3} and vanishes in the mean-field limit. For sufficiently small energy, in spite of a clear non ergodicity of the system, a common scaling law \lambda \propto U^{1/2} is observed for any initial conditions.Comment: 17 pages, Revtex - 15 PS Figs - Subimitted to Physical Review E - Two column version with included figures : less paper waste

Impact of Anaerobic Growth Conditions on Toxic Shock Syndrome Toxin-I Production by Staphylococcus aureus

Objective: The impact of anaerobic growth conditions on the Staphylococcus aureus toxic shock syndrome toxin-1 (TSST-1) production was studied