Zobrazeno 1 - 10
of 324
pro vyhledávání: '"Taka- aki Nakada"'
Autor:
Yuriko Yamazaki, Tomoka Ito, Seitaro Nakagawa, Takashi Sugihira, Chinami Kurita-Tachibana, Amer E. Villaruz, Kensuke Ishiguro, Barbora Salcman, Shuo Li, Sanami Takada, Naohiro Inohara, Yoko Kusuya, Aki Shibata, Masakazu Tamai, Reika Aoyama, Kanako Inoue, Shota Murata, Kazuyuki Matsushita, Akiko Miyabe, Toshibumi Taniguchi, Hidetoshi Igari, Naruhiko Ishiwada, Masateru Taniguchi, Taka-Aki Nakada, Hiroyuki Matsue, Manabu Fujimoto, Haruka Hishiki, Yoshiteru Osone, Hiromichi Hamada, Naoki Shimojo, Tsutomu Suzuki, Michael Otto, Gabriel Núñez, Hiroki Takahashi, Akiko Takaya, Yuumi Nakamura
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-15 (2024)
Abstract Staphylococcus aureus can cause outbreaks and becomes multi-drug resistant through gene mutations and acquiring resistance genes. However, why S. aureus easily adapts to hospital environments, promoting resistance and recurrent infections, r
Externí odkaz:
https://doaj.org/article/786e7ed982d340fb8bdfc8014ce47951
Publikováno v:
Journal of Epidemiology, Vol 34, Iss 8, Pp 380-386 (2024)
Background: We evaluated the applicability of automated citation screening in developing clinical practice guidelines. Methods: We prospectively compared the efficiency of citation screening between the conventional (Rayyan) and semi-automated (ASRev
Externí odkaz:
https://doaj.org/article/b50b1e6cbb43477aae1e68363dacf8b2
Autor:
Nozomi Takahashi, Taro Imaeda, Takehiko Oami, Toshikazu Abe, Nobuaki Shime, Kosaku Komiya, Hideki Kawamura, Yasuo Yamao, Kiyohide Fushimi, Taka‑aki Nakada
Publikováno v:
BMC Infectious Diseases, Vol 24, Iss 1, Pp 1-9 (2024)
Abstract Background It is important to determine the prevalence and prognosis of community-acquired infection (CAI) and nosocomial infection (NI) to develop treatment strategies and appropriate medical policies in aging society. Methods Patients hosp
Externí odkaz:
https://doaj.org/article/363d155865a0419c93a1539cd700393c
Autor:
Masayoshi Shinozaki, Daiki Saito, Keisuke Tomita, Taka-aki Nakada, Yukihiro Nomura, Toshiya Nakaguchi
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-11 (2024)
Abstract To efficiently allocate medical resources at disaster sites, medical workers perform triage to prioritize medical treatments based on the severity of the wounded or sick. In such instances, evaluators often assess the severity status of the
Externí odkaz:
https://doaj.org/article/9ba9e8b777374076a88bf54c72623e04
Autor:
Naoki Ikezawa, Takayuki Okamoto, Yoichi Yoshida, Satoru Kurihara, Nozomi Takahashi, Taka-aki Nakada, Hideaki Haneishi
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-11 (2024)
Abstract A stroke is a medical emergency and thus requires immediate treatment. Paramedics should accurately assess suspected stroke patients and promptly transport them to a hospital with stroke care facilities; however, current assessment procedure
Externí odkaz:
https://doaj.org/article/002ec9bfd1b84742859525a64b3f7e1a
Autor:
Takeo Kurita, Takehiko Oami, Yoko Tochigi, Keisuke Tomita, Takaki Naito, Kazuaki Atagi, Shigeki Fujitani, Taka-aki Nakada
Publikováno v:
Heliyon, Vol 10, Iss 11, Pp e32655- (2024)
This study investigated the accuracy of a machine learning algorithm for predicting mortality in patients receiving rapid response system (RRS) activation. This retrospective cohort study used data from the In-Hospital Emergency Registry in Japan, wh
Externí odkaz:
https://doaj.org/article/9d2bdb6c65ce432c96fa039ef15b69a6
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-9 (2024)
Abstract This retrospective cohort study aimed to develop and evaluate a machine-learning algorithm for predicting oliguria, a sign of acute kidney injury (AKI). To this end, electronic health record data from consecutive patients admitted to the int
Externí odkaz:
https://doaj.org/article/381bbdcacbac482f8eecaa873c54e413
Autor:
Nozomi Takahashi, Yutaka Kondo, Kenji Kubo, Moritoki Egi, Ken-ichi Kano, Yoshiyasu Ohshima, Taka-aki Nakada
Publikováno v:
Journal of Intensive Care, Vol 11, Iss 1, Pp 1-11 (2023)
Abstract Background The efficacy of therapeutic drug monitoring (TDM)-based antimicrobial dosing optimization strategies on pharmacokinetics/pharmacodynamics and specific drug properties for critically ill patients is unclear. Here, we conducted a sy
Externí odkaz:
https://doaj.org/article/ab248293ce4d43acaae11e1426c426ee
Autor:
Yosuke Hayashi, Takashi Shimazui, Keisuke Tomita, Tadanaga Shimada, Rie E. Miura, Taka-aki Nakada
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-8 (2023)
Abstract Increased fluid overload (FO) is associated with poor outcomes in critically ill patients, especially in acute kidney injury (AKI). However, the exact timing from when FO influences outcomes remains unclear. We retrospectively screened inten
Externí odkaz:
https://doaj.org/article/91d5c7feb6294565a801a7682711cb4c
Machine learning algorithms for predicting days of high incidence for out-of-hospital cardiac arrest
Autor:
Kaoru Shimada-Sammori, Tadanaga Shimada, Rie E. Miura, Rui Kawaguchi, Yasuo Yamao, Taku Oshima, Takehiko Oami, Keisuke Tomita, Koichiro Shinozaki, Taka-aki Nakada
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-8 (2023)
Abstract Predicting out-of-hospital cardiac arrest (OHCA) events might improve outcomes of OHCA patients. We hypothesized that machine learning algorithms using meteorological information would predict OHCA incidences. We used the Japanese population
Externí odkaz:
https://doaj.org/article/06db531055b84a178b9099bfada32b25