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Lymph Node Yield and Lymph Node Ratio for Prognosis of Long-Term Survival in Gastric Carcinoma
Olof Jannasch,
Martin Schwanz,
Ronny Otto,
Michal Mik,
Hans Lippert,
Pawel Mroczkowski
Background: Lymphadenectomy is fundamental part of surgical strategy in patients with gastric cancer. Lymph node (LN) status is a key point in assessment of prognosis in gastric cancer. The LN ratio (LNR) - number of positive LNs / number of sampled LNs, offers a new approach for predicting survival. The aim of the study was to find factors affecting LN yield and the impact of LNR on 5-year survival. Methods: Prospective multicentre quality assurance study. Only LN-positive patients were included in the LNR calculations. Results: 4946 patients from 149 hospitals were enrolled. The inclusion criteria were met by 1884 patients. Patients were divided into two groups: Group 1 (<16 LN) 456 patients and Group 2 (≥16 LN) 1428 patients. The multivariate analysis found G2 (OR 1.98; 95%CI 1.11-3.54), G3 (OR 2.15; 95%CI 1.212-3.829), UICC-stage II (OR 1.44; 95%CI 1.01-2.06) and III (OR 1.71; 95%CI 1.14-2.57), age <70 (OR 1.818 95%CI 1.19-2.78) and female gender ( OR 1.37; 95%CI 1.00-1.86) as independent factors of ≥16 LN yield. Patients with a LNR≥0.4 have a lower probability of survival (p=0.039 and <0.001) than patients with LNR=0.1. Patients with UICC-II have a lower probability of survival than UICC-I (p=0.023). Age 70-80 (p=0.045) and >80 years (p=0.003) were negative prognostic factors for long-term survival. Conclusion: Long-term survival is directly related to adequate lymphadenectomy. LNR could be superior to pN-stage for estimating survival, and adds remarkable nuances in prognosis compared to UICC-stage. LNR also appears valid, even in the case of insufficient LN yield.
Background: Lymphadenectomy is fundamental part of surgical strategy in patients with gastric cancer. Lymph node (LN) status is a key point in assessment of prognosis in gastric cancer. The LN ratio (LNR) - number of positive LNs / number of sampled LNs, offers a new approach for predicting survival. The aim of the study was to find factors affecting LN yield and the impact of LNR on 5-year survival. Methods: Prospective multicentre quality assurance study. Only LN-positive patients were included in the LNR calculations. Results: 4946 patients from 149 hospitals were enrolled. The inclusion criteria were met by 1884 patients. Patients were divided into two groups: Group 1 (<16 LN) 456 patients and Group 2 (≥16 LN) 1428 patients. The multivariate analysis found G2 (OR 1.98; 95%CI 1.11-3.54), G3 (OR 2.15; 95%CI 1.212-3.829), UICC-stage II (OR 1.44; 95%CI 1.01-2.06) and III (OR 1.71; 95%CI 1.14-2.57), age <70 (OR 1.818 95%CI 1.19-2.78) and female gender ( OR 1.37; 95%CI 1.00-1.86) as independent factors of ≥16 LN yield. Patients with a LNR≥0.4 have a lower probability of survival (p=0.039 and <0.001) than patients with LNR=0.1. Patients with UICC-II have a lower probability of survival than UICC-I (p=0.023). Age 70-80 (p=0.045) and >80 years (p=0.003) were negative prognostic factors for long-term survival. Conclusion: Long-term survival is directly related to adequate lymphadenectomy. LNR could be superior to pN-stage for estimating survival, and adds remarkable nuances in prognosis compared to UICC-stage. LNR also appears valid, even in the case of insufficient LN yield.
Posted: 30 December 2024
Optimizing Mandibular Reconstruction Through Surgeon-Bioengineer Collaboration: Proposed Protocol for Virtual Surgical Planning
Dong-Ho Shin,
Hyo-Joon Kim,
Ji-Su Oh,
Seong-Yong Moon
Posted: 30 December 2024
Long-Term Outcomes and Toxicity of Salvage Radiation Therapy Using TomoTherapy with Image-Guided Radiation Therapy for Postoperative Prostate Cancer Patients
Yuki Mukai,
Motoko Omura,
Yumiko Minagawa,
Misato Mase,
Yuta Nishikawa,
Ichiro Miura,
Masaharu Hata
Posted: 30 December 2024
Presumed Immune-Mediated Hemolytic Anemia in Foal with Rhodococcus equi Infection- Case Report
Raimonda Tamulionytė-Skėrė,
Indrė Mickevičienė,
Renata Gruodytė,
Barbora Paulavičiūtė,
Jūratė Hinksman,
Marija Uršulė Driukė,
Aistė Urbonavičiūtė
Posted: 30 December 2024
Topic Modeling the Academic Discourse on Critical Incident Stress Debriefing and Management (CISD/CISM) for First Responders
Robert Lundblad,
Saul Jaeger,
Jennifer Moreno,
Charles Silber,
Matthew Rensi,
Cass Dykeman
Background/Objectives: This study examines the academic discourse surrounding Critical Incident Stress Debriefing (CISD) and Critical Incident Stress Management (CISM) for first responders using Latent Dirichlet Allocation (LDA) topic modeling. Its aim is to uncover latent topical structures within the literature and critically evaluate underlying assumptions to identify gaps and limitations. Method: A corpus of 214 research article abstracts related to CISD/M was gathered from the Web of Science Core Collection. After preprocessing, we used Orange Data Mining software’s LDA tool to analyze the corpus. Models ranging from 2 to 10 topics were tested for log perplexity and topic coherence, with LDAvis visualizations guiding interpretation and labeling. A four-topic model offered the best balance of detail and interpretability. Results: Four topics emerged: (1) Critical Incident Stress Management in medical and emergency settings, (2) Psychological and group-based interventions for PTSD and trauma, (3) Peer support and experiences of emergency and military personnel, and (4) Mental health interventions for first responders. Key gaps included limited focus on cumulative trauma, insufficient longitudinal research, and variability in procedural adherence affecting outcomes. Conclusions: The findings highlight the need for CISD/M protocols to move beyond event-specific interventions and address cumulative stressors. Recommendations include incorporating holistic, proactive mental health strategies and conducting longitudinal studies to evaluate long-term effectiveness. These insights can help refine CISD/M approaches and enhance their impact on first responders working in high-stress environments.
Background/Objectives: This study examines the academic discourse surrounding Critical Incident Stress Debriefing (CISD) and Critical Incident Stress Management (CISM) for first responders using Latent Dirichlet Allocation (LDA) topic modeling. Its aim is to uncover latent topical structures within the literature and critically evaluate underlying assumptions to identify gaps and limitations. Method: A corpus of 214 research article abstracts related to CISD/M was gathered from the Web of Science Core Collection. After preprocessing, we used Orange Data Mining software’s LDA tool to analyze the corpus. Models ranging from 2 to 10 topics were tested for log perplexity and topic coherence, with LDAvis visualizations guiding interpretation and labeling. A four-topic model offered the best balance of detail and interpretability. Results: Four topics emerged: (1) Critical Incident Stress Management in medical and emergency settings, (2) Psychological and group-based interventions for PTSD and trauma, (3) Peer support and experiences of emergency and military personnel, and (4) Mental health interventions for first responders. Key gaps included limited focus on cumulative trauma, insufficient longitudinal research, and variability in procedural adherence affecting outcomes. Conclusions: The findings highlight the need for CISD/M protocols to move beyond event-specific interventions and address cumulative stressors. Recommendations include incorporating holistic, proactive mental health strategies and conducting longitudinal studies to evaluate long-term effectiveness. These insights can help refine CISD/M approaches and enhance their impact on first responders working in high-stress environments.
Posted: 30 December 2024
The Effect of Cardiometabolic Drugs on Non-Alcoholic Fatty Liver Disease (NAFLD), Metabolic Dysfunction-Associated Fatty Liver Disease (MAFLD), and Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD)
Marios Zisis,
Maria Eleni Chondrogianni,
Theodoros Androutsakos,
Ilias Rantos,
Evangelos Oikonomou,
Antonios Chatzigeorgiou,
Eva Kassi
Posted: 30 December 2024
Analyzing the Transitional Zone of Peri-Implant Soft Tissue in Subcrestally Placed Implants: A 3D CBCT-Based Model for Esthetics and Biological Stability in Molar Cases
Chiyun Won
Posted: 30 December 2024
Real-World Setting of Efficacy and Safety of 3 Years of Rifaximin Administration in Japanese Patients with Hepatic Encephalopathy: A Multicenter Retrospective Study
Hideto Kawaratani,
Tadashi Namisaki,
Yasuteru Kondo,
Ryoji Tatsumi,
Naoto Kawabe,
Norikazu Tanabe,
Akira Sakamaki,
Kyoko Hoshikawa,
Yoshihito Uchida,
Kei Endo
Background/Objectives: Rifaximin is commonly used for patients with hepatic encephalopathy (HE); however, there is little data on the effects of its long-term (>1 year) administration in Jap-anese patients with cirrhosis. Therefore, we examined the effects and safety of three-year rifaxi-min treatment on HE in Japan. Methods: A total of 190 patients with cirrhosis who were contin-uously administered rifaximin for more than one year developed overt or covert HE, which was diagnosed by a physician. Laboratory data were collected at baseline, 3, 6, 12, 18, 24, 30, and 36 months following rifaximin administration. We examined the cumulative OHE incidences, overall survival rates, and hepatic functional reserves following rifaximin treatment. The occur-rence of adverse events was also assessed. Results: Ammonia levels improved significantly after 3 months of rifaximin administration, which continued for three years. Serum albumin and pro-thrombin activity also significantly improved after three years of rifaximin administration. Cumulative overt HE incidences were 12.1%, 19.7%, and 24.9% at 1, 2, and 3 years, respectively. The survival rates following rifaximin treatment were 100%, 88.9%, and 77.8% at 1, 2, and 3 years, respectively. In contrast, renal function and electrolytes did not change following rifaximin ad-ministration. Only three (1.6%) patients discontinued RFX therapy because of severe diarrhea after a year of RFX administration. No other serious adverse events were observed. Conclusion: Long-term rifaximin treatment (three years) is effective and safe for patients with HE and im-proves the hepatic function reserve and overall survival.
Background/Objectives: Rifaximin is commonly used for patients with hepatic encephalopathy (HE); however, there is little data on the effects of its long-term (>1 year) administration in Jap-anese patients with cirrhosis. Therefore, we examined the effects and safety of three-year rifaxi-min treatment on HE in Japan. Methods: A total of 190 patients with cirrhosis who were contin-uously administered rifaximin for more than one year developed overt or covert HE, which was diagnosed by a physician. Laboratory data were collected at baseline, 3, 6, 12, 18, 24, 30, and 36 months following rifaximin administration. We examined the cumulative OHE incidences, overall survival rates, and hepatic functional reserves following rifaximin treatment. The occur-rence of adverse events was also assessed. Results: Ammonia levels improved significantly after 3 months of rifaximin administration, which continued for three years. Serum albumin and pro-thrombin activity also significantly improved after three years of rifaximin administration. Cumulative overt HE incidences were 12.1%, 19.7%, and 24.9% at 1, 2, and 3 years, respectively. The survival rates following rifaximin treatment were 100%, 88.9%, and 77.8% at 1, 2, and 3 years, respectively. In contrast, renal function and electrolytes did not change following rifaximin ad-ministration. Only three (1.6%) patients discontinued RFX therapy because of severe diarrhea after a year of RFX administration. No other serious adverse events were observed. Conclusion: Long-term rifaximin treatment (three years) is effective and safe for patients with HE and im-proves the hepatic function reserve and overall survival.
Posted: 30 December 2024
Supra-Normal Ejection Fraction at Hospital Admission Stratifies Mortality Risk in HFpEF Patients Aged ≥70 Years
Andrea Sonaglioni,
Chiara Lonati,
Valentina Scime’,
Gian Luigi Nicolosi,
Antonino Bruno,
Michele Lombardo,
Sergio Harari
Posted: 30 December 2024
Transfer Learning and Neural Network-Based Approach on Structural MRI Data for Prediction and Classification of Alzheimer’s Disease
Farideh Momeni,
Daryoush Shahbazi-Gahrouei,
Tahereh Mahmoudi,
Alireza Mehdizadeh
Alzheimer's disease (AD) is a neurodegenerative condition that has no definitive treatment and its early diagnosis can help to prevent or slow down its progress. Neuroimaging in particular, structural magnetic resonance imaging (sMRI) and the progress of artificial intelligence (AI) have significant attention in AD detection. In this study, 398 participants were used from the ADNI and OASIS global database of sMRI including 98 individuals with AD, 102 with early mild cognitive impairment (EMCI), 98 with late mild cognitive impairment (LMCI), and 100 normal controls (NC). The proposed model achieved high area under the curve (AUC) values and an accuracy of 99.7%, which is very remarkable for all four classes: NC vs. AD: AUC = [0.985], EMCI vs. NC: AUC = [0.961], LMCI vs. NC: AUC = [0.951], LMCI vs. AD: AUC = [0.989], and EMCI vs. LMCI: AUC = [1.000]. The results reveal that this model incorporates DenseNet169, transfer learning, and class decomposition to classify AD stages, particularly in differentiating EMCI from LMCI. Overall, this model performs well with high accuracy and area under the curve for AD diagnostics at early stages.
Alzheimer's disease (AD) is a neurodegenerative condition that has no definitive treatment and its early diagnosis can help to prevent or slow down its progress. Neuroimaging in particular, structural magnetic resonance imaging (sMRI) and the progress of artificial intelligence (AI) have significant attention in AD detection. In this study, 398 participants were used from the ADNI and OASIS global database of sMRI including 98 individuals with AD, 102 with early mild cognitive impairment (EMCI), 98 with late mild cognitive impairment (LMCI), and 100 normal controls (NC). The proposed model achieved high area under the curve (AUC) values and an accuracy of 99.7%, which is very remarkable for all four classes: NC vs. AD: AUC = [0.985], EMCI vs. NC: AUC = [0.961], LMCI vs. NC: AUC = [0.951], LMCI vs. AD: AUC = [0.989], and EMCI vs. LMCI: AUC = [1.000]. The results reveal that this model incorporates DenseNet169, transfer learning, and class decomposition to classify AD stages, particularly in differentiating EMCI from LMCI. Overall, this model performs well with high accuracy and area under the curve for AD diagnostics at early stages.
Posted: 30 December 2024
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