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Modeling and simulation of distribution and drug resistance of major pathogens in patients with respiratory system infections.

BMC Infect Dis

January 2025

Department of Respiratory Medicine, Anting Hospital of Jiading District, 1060 Hejing Road, Anting Town, Jiading District, Shanghai, 201805, China.

Background: Respiratory tract infections (RTIs) are one of the leading causes of morbidity and mortality worldwide. The increase in antimicrobial resistance in respiratory pathogens poses a major challenge to the effective management of these infections.

Objective: To investigate the distribution of major pathogens of RTIs and their antimicrobial resistance patterns in a tertiary care hospital and to develop a mathematical model to explore the relationship between pathogen distribution and antimicrobial resistance.

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Background: People with malignancy of undefined primary origin (MUO) have a poor prognosis and may undergo a protracted diagnostic workup causing patient distress and high cancer related costs. Not having a primary diagnosis limits timely site-specific treatment and access to precision medicine. There is a need to improve the diagnostic process, and healthcare delivery and support for these patients.

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New applications such as the Internet of Things, autonomous driving, Industry X.0 and many more will transmit sensitive information via fibers and over the air with envisioned data rates beyond terabits per second. Therefore, the encryption has to be simple, fast and spectrally efficient, so that the power consumption and latency are low and the scarce bandwidth is not wasted.

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Air pollution in cities, especially NO, is linked to numerous health problems, ranging from mortality to mental health challenges and attention deficits in children. While cities globally have initiated policies to curtail emissions, real-time monitoring remains challenging due to limited environmental sensors and their inconsistent distribution. This gap hinders the creation of adaptive urban policies that respond to the sequence of events and daily activities affecting pollution in cities.

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Background And Objective: To assess whether conventional brightness-mode (B-mode) transrectal ultrasound images of the prostate reveal clinically significant cancers with the help of artificial intelligence methods.

Methods: This study included 2986 men who underwent biopsies at two institutions. We trained the PROstate Cancer detection on B-mode transrectal UltraSound images NETwork (ProCUSNet) to determine whether ultrasound can reliably detect cancer.

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