The Hypervariable Region 1 Protein of Hepatitis C Virus Broadly Reactive with Sera of Patients with Chronic Hepatitis C Has a Similar Amino Acid Sequence with the Consensus Sequence

AbstractHypervariable region 1 (HVR1) proteins of hepatitis C virus (HCV) have been reported to react broadly with sera of patients with HCV infection. However, the variability of the broad reactivity of individual HVR1 proteins has not been elucidated. We assessed the reactivity of 25 different HVR1 proteins (genotype 1b) with sera of 81 patients with HCV infection (genotype 1b) by Western blot. HVR1 proteins reacted with 2–60 sera. The number of sera reactive with each HVR1 protein significantly correlated with the number of amino acid residues identical to the consensus sequence defined by Puntoriero et al. (G. Puntoriero, A. Lahm, S. Zucchelli, B. B. Ercole, R. Tafi, M. Penzzanera, M. U. Mondelli, R. Cortese, A. Tramontano, G. Galfre', and A. Nicosia. 1998. EMBO J. 17, 3521–3533.) (r = 0.561, P < 0.005). The most widely reactive HVR1 protein, 12-22, had a sequence similar to the consensus sequence. The peptide with C-terminal 13-amino-acids sequence of HVR1 protein 12-22 (NH2-CSFTSLFTPGPSQK) was injected into rabbits as an immunogen. The rabbit immune sera reacted with 9 of 25 HVR1 proteins of genotype 1b including HVR1 protein 12-22 and with 3 of 12 proteins of genotype 2a. These results indicate that the HVR1 protein broadly reactive with patients' sera has a sequence similar to the consensus sequence, can induce broadly reactive sera, and could be one of the candidate immunogens in a prophylactic vaccine against HCV

Optic chiasm in the species of order Clupeiformes, family Clupeidae: Optic chiasm of Spratelloides gracilis shows an opposite laterality to that of Etrumeus teres

In most teleost fishes, the optic nerves decussate completely as they project to the mesencephalic region. Examination of the decussation pattern of 25 species from 11 different orders in Pisces revealed that each species shows a specific chiasmic type. In 11 species out of the 25, laterality of the chiasmic pattern was not determined; in half of the individuals examined, the left optic nerve ran dorsally to the right optic nerve, while in the other half, the right optic nerve was dorsal. In eight other species the optic nerves from both eyes branched into several bundles at the chiasmic point, and intercalated to form a complicated decussation pattern. In the present study we report our findings that Spratelloides gracilis, of the order Clupeiformes, family Clupeidae, shows a particular laterality of decussation: the left optic nerve ran dorsally to the right (n = 200/202). In contrast, Etrumeus teres, of the same order and family, had a strong preference of the opposite (complementary) chiasmic pattern to that of S. gracilis (n = 59/59), revealing that these two species display opposite left–right optic chiasm patterning. As far as we investigated, other species of Clupeiformes have not shown left–right preference in the decussation pattern. We conclude that the opposite laterality of the optic chiasms of these two closely related species, S. gracilis and E. teres, enables investigation of species-specific laterality in fishes of symmetric shapes

The Japanese Clinical Practice Guidelines for Management of Sepsis and Septic Shock 2020 (J-SSCG 2020)

The Japanese Clinical Practice Guidelines for Management of Sepsis and Septic Shock 2020 (J-SSCG 2020), a Japanese-specific set of clinical practice guidelines for sepsis and septic shock created as revised from J-SSCG 2016 jointly by the Japanese Society of Intensive Care Medicine and the Japanese Association for Acute Medicine, was first released in September 2020 and published in February 2021. An English-language version of these guidelines was created based on the contents of the original Japanese-language version. The purpose of this guideline is to assist medical staff in making appropriate decisions to improve the prognosis of patients undergoing treatment for sepsis and septic shock. We aimed to provide high-quality guidelines that are easy to use and understand for specialists, general clinicians, and multidisciplinary medical professionals. J-SSCG 2016 took up new subjects that were not present in SSCG 2016 (e.g., ICU-acquired weakness [ICU-AW], post-intensive care syndrome [PICS], and body temperature management). The J-SSCG 2020 covered a total of 22 areas with four additional new areas (patient- and family-centered care, sepsis treatment system, neuro-intensive treatment, and stress ulcers). A total of 118 important clinical issues (clinical questions, CQs) were extracted regardless of the presence or absence of evidence. These CQs also include those that have been given particular focus within Japan. This is a large-scale guideline covering multiple fields; thus, in addition to the 25 committee members, we had the participation and support of a total of 226 members who are professionals (physicians, nurses, physiotherapists, clinical engineers, and pharmacists) and medical workers with a history of sepsis or critical illness. The GRADE method was adopted for making recommendations, and the modified Delphi method was used to determine recommendations by voting from all committee members.As a result, 79 GRADE-based recommendations, 5 Good Practice Statements (GPS), 18 expert consensuses, 27 answers to background questions (BQs), and summaries of definitions and diagnosis of sepsis were created as responses to 118 CQs. We also incorporated visual information for each CQ according to the time course of treatment, and we will also distribute this as an app. The J-SSCG 2020 is expected to be widely used as a useful bedside guideline in the field of sepsis treatment both in Japan and overseas involving multiple disciplines.other authors: Satoru Hashimoto,Daisuke Hasegawa,Junji Hatakeyama,Naoki Hara,Naoki Higashibeppu,Nana Furushima,Hirotaka Furusono,Yujiro Matsuishi,Tasuku Matsuyama,Yusuke Minematsu,Ryoichi Miyashita,Yuji Miyatake,Megumi Moriyasu,Toru Yamada,Hiroyuki Yamada,Ryo Yamamoto,Takeshi Yoshida,Yuhei Yoshida,Jumpei Yoshimura,Ryuichi Yotsumoto,Hiroshi Yonekura,Takeshi Wada,Eizo Watanabe,Makoto Aoki,Hideki Asai,Takakuni Abe,Yutaka Igarashi,Naoya Iguchi,Masami Ishikawa,Go Ishimaru,Shutaro Isokawa,Ryuta Itakura,Hisashi Imahase,Haruki Imura,Takashi Irinoda,Kenji Uehara,Noritaka Ushio,Takeshi Umegaki,Yuko Egawa,Yuki Enomoto,Kohei Ota,Yoshifumi Ohchi,Takanori Ohno,Hiroyuki Ohbe,Kazuyuki Oka,Nobunaga Okada,Yohei Okada,Hiromu Okano,Jun Okamoto,Hiroshi Okuda,Takayuki Ogura,Yu Onodera,Yuhta Oyama,Motoshi Kainuma,Eisuke Kako,Masahiro Kashiura,Hiromi Kato,Akihiro Kanaya,Tadashi Kaneko,Keita Kanehata,Ken-ichi Kano,Hiroyuki Kawano,Kazuya Kikutani,Hitoshi Kikuchi,Takahiro Kido,Sho Kimura,Hiroyuki Koami,Daisuke Kobashi,Iwao Saiki,Masahito Sakai,Ayaka Sakamoto,Tetsuya Sato,Yasuhiro Shiga,Manabu Shimoto,Shinya Shimoyama,Tomohisa Shoko,Yoh Sugawara,Atsunori Sugita,Satoshi Suzuki,Yuji Suzuki,Tomohiro Suhara,Kenji Sonota,Shuhei Takauji,Kohei Takashima,Sho Takahashi,Yoko Takahashi,Jun Takeshita,Yuuki Tanaka,Akihito Tampo,Taichiro Tsunoyama,Kenichi Tetsuhara,Kentaro Tokunaga,Yoshihiro Tomioka,Kentaro Tomita,Naoki Tominaga,Mitsunobu Toyosaki,Yukitoshi Toyoda,Hiromichi Naito,Isao Nagata,Tadashi Nagato,Yoshimi Nakamura,Yuki Nakamori,Isao Nahara,Hiromu Naraba,Chihiro Narita,Norihiro Nishioka,Tomoya Nishimura,Kei Nishiyama,Tomohisa Nomura,Taiki Haga,Yoshihiro Hagiwara,Katsuhiko Hashimoto,Takeshi Hatachi,Toshiaki Hamasaki,Takuya Hayashi,Minoru Hayashi,Atsuki Hayamizu,Go Haraguchi,Yohei Hirano,Ryo Fujii,Motoki Fujita,Naoyuki Fujimura,Hiraku Funakoshi,Masahito Horiguchi,Jun Maki,Naohisa Masunaga,Yosuke Matsumura,Takuya Mayumi,Keisuke Minami,Yuya Miyazaki,Kazuyuki Miyamoto,Teppei Murata,Machi Yanai,Takao Yano,Kohei Yamada,Naoki Yamada,Tomonori Yamamoto,Shodai Yoshihiro,Hiroshi Tanaka,Osamu NishidaGuideline

The Japanese Clinical Practice Guidelines for Management of Sepsis and Septic Shock 2020 (J-SSCG 2020)

The Japanese Clinical Practice Guidelines for Management of Sepsis and Septic Shock 2020 (J-SSCG 2020), a Japanese-specific set of clinical practice guidelines for sepsis and septic shock created as revised from J-SSCG 2016 jointly by the Japanese Society of Intensive Care Medicine and the Japanese Association for Acute Medicine, was first released in September 2020 and published in February 2021. An English-language version of these guidelines was created based on the contents of the original Japanese-language version. The purpose of this guideline is to assist medical staff in making appropriate decisions to improve the prognosis of patients undergoing treatment for sepsis and septic shock. We aimed to provide high-quality guidelines that are easy to use and understand for specialists, general clinicians, and multidisciplinary medical professionals. J-SSCG 2016 took up new subjects that were not present in SSCG 2016 (e.g., ICU-acquired weakness [ICU-AW], post-intensive care syndrome [PICS], and body temperature management). The J-SSCG 2020 covered a total of 22 areas with four additional new areas (patient- and family-centered care, sepsis treatment system, neuro-intensive treatment, and stress ulcers). A total of 118 important clinical issues (clinical questions, CQs) were extracted regardless of the presence or absence of evidence. These CQs also include those that have been given particular focus within Japan. This is a large-scale guideline covering multiple fields; thus, in addition to the 25 committee members, we had the participation and support of a total of 226 members who are professionals (physicians, nurses, physiotherapists, clinical engineers, and pharmacists) and medical workers with a history of sepsis or critical illness. The GRADE method was adopted for making recommendations, and the modified Delphi method was used to determine recommendations by voting from all committee members.other authors: Yasuhiro Norisue, Satoru Hashimoto, Daisuke Hasegawa, Junji Hatakeyama, Naoki Hara, Naoki Higashibeppu, Nana Furushima, Hirotaka Furusono, Yujiro Matsuishi, Tasuku Matsuyama, Yusuke Minematsu, Ryoichi Miyashita, Yuji Miyatake, Megumi Moriyasu, Toru Yamada, Hiroyuki Yamada, Ryo Yamamoto, Takeshi Yoshida, Yuhei Yoshida, Jumpei Yoshimura, Ryuichi Yotsumoto, Hiroshi Yonekura, Takeshi Wada, Eizo Watanabe, Makoto Aoki, Hideki Asai, Takakuni Abe, Yutaka Igarashi, Naoya Iguchi, Masami Ishikawa, Go Ishimaru, Shutaro Isokawa, Ryuta Itakura, Hisashi Imahase, Haruki Imura, Takashi Irinoda, Kenji Uehara, Noritaka Ushio, Takeshi Umegaki, Yuko Egawa, Yuki Enomoto, Kohei Ota, Yoshifumi Ohchi, Takanori Ohno, Hiroyuki Ohbe, Kazuyuki Oka, Nobunaga Okada, Yohei Okada, Hiromu Okano, Jun Okamoto, Hiroshi Okuda, Takayuki Ogura, Yu Onodera, Yuhta Oyama, Motoshi Kainuma, Eisuke Kako, Masahiro Kashiura, Hiromi Kato, Akihiro Kanaya, Tadashi Kaneko, Keita Kanehata, Ken-ichi Kano, Hiroyuki Kawano, Kazuya Kikutani, Hitoshi Kikuchi, Takahiro Kido, Sho Kimura, Hiroyuki Koami, Daisuke Kobashi, Iwao Saiki, Masahito Sakai, Ayaka Sakamoto, Tetsuya Sato, Yasuhiro Shiga, Manabu Shimoto, Shinya Shimoyama, Tomohisa Shoko, Yoh Sugawara, Atsunori Sugita, Satoshi Suzuki, Yuji Suzuki, Tomohiro Suhara, Kenji Sonota, Shuhei Takauji, Kohei Takashima, Sho Takahashi, Yoko Takahashi, Jun Takeshita, Yuuki Tanaka, Akihito Tampo, Taichiro Tsunoyama, Kenichi Tetsuhara, Kentaro Tokunaga, Yoshihiro Tomioka, Kentaro Tomita, Naoki Tominaga, Mitsunobu Toyosaki, Yukitoshi Toyoda, Hiromichi Naito, Isao Nagata, Tadashi Nagato, Yoshimi Nakamura, Yuki Nakamori, Isao Nahara, Hiromu Naraba, Chihiro Narita, Norihiro Nishioka, Tomoya Nishimura, Kei Nishiyama, Tomohisa Nomura, Taiki Haga, Yoshihiro Hagiwara, Katsuhiko Hashimoto, Takeshi Hatachi, Toshiaki Hamasaki, Takuya Hayashi, Minoru Hayashi, Atsuki Hayamizu, Go Haraguchi, Yohei Hirano, Ryo Fujii, Motoki Fujita, Naoyuki Fujimura, Hiraku Funakoshi, Masahito Horiguchi, Jun Maki, Naohisa Masunaga, Yosuke Matsumura, Takuya Mayumi, Keisuke Minami, Yuya Miyazaki, Kazuyuki Miyamoto, Teppei Murata, Machi Yanai, Takao Yano, Kohei Yamada, Naoki Yamada, Tomonori Yamamoto, Shodai Yoshihiro, Hiroshi Tanaka & Osamu Nishid

The Use of Artificial Neural Network Analysis to Improve the Predictive Accuracy of Prostate Biopsy in the Japanese Population

Objective: We examined the efficacy of an artificial neural network analysis (ANNA) based on parameters available from previously existing examinations for improving the predictive accuracy of prostate cancer screening in the Japanese population. Methods: Two hundred and twenty-eight patients with prostate-specific antigen (PSA) o

Comparison of CO-RADS Scores Based on Visual and Artificial Intelligence Assessments in a Non-Endemic Area

In this study, we first developed an artificial intelligence (AI)-based algorithm for classifying chest computed tomography (CT) images using the coronavirus disease 2019 Reporting and Data System (CO-RADS). Subsequently, we evaluated its accuracy by comparing the calculated scores with those assigned by radiologists with varying levels of experience. This study included patients with suspected SARS-CoV-2 infection who underwent chest CT imaging between February and October 2020 in Japan, a non-endemic area. For each chest CT, the CO-RADS scores, determined by consensus among three experienced chest radiologists, were used as the gold standard. Images from 412 patients were used to train the model, whereas images from 83 patients were tested to obtain AI-based CO-RADS scores for each image. Six independent raters (one medical student, two residents, and three board-certified radiologists) evaluated the test images. Intraclass correlation coefficients (ICC) and weighted kappa values were calculated to determine the inter-rater agreement with the gold standard. The mean ICC and weighted kappa were 0.754 and 0.752 for the medical student and residents (taken together), 0.851 and 0.850 for the diagnostic radiologists, and 0.913 and 0.912 for AI, respectively. The CO-RADS scores calculated using our AI-based algorithm were comparable to those assigned by radiologists, indicating the accuracy and high reproducibility of our model. Our study findings would enable accurate reading, particularly in areas where radiologists are unavailable, and contribute to improvements in patient management and workflow