Effects of rewarming therapies on outcomes in accidental hypothermia: A secondary analysis of a multicenter prospective study.

BACKGROUND: Rewarming therapies for accidental hypothermia (AH) include extracorporeal membrane oxygenation (ECMO) and non-ECMO related (conventional) therapies. However, there are limited data available to inform the selection of conventional rewarming therapy. The aim of the present study was to explore what patients' factors and which rewarming therapy predicted favorable prognosis. METHODS: This study is a secondary analysis of the Intensive Care with Extra Corporeal membrane oxygenation Rewarming in Accidentally Severe Hypothermia (ICE-CRASH) study, a multicenter prospective, observational study conducted in Japan. Enrolled in the ICE-CRASH study were patients aged ≥18 years with a core temperature of ≤32 °C who were transported to the emergency departments of 36 tertiary care hospitals in Japan between 1 December 2019 and 31 March 2022, among whom those who were rewarmed with conventional rewarming therapy were included in the present study. Logistic regression analysis was performed with 28-day survival as the objective variable; and seven factors including age, activities of daily living (ADL) independence, sequential organ failure assessment (SOFA) score, and each rewarming technique as explanatory variables. We performed linear regression analysis to identify whether each rewarming technique was associated with rewarming rate. RESULTS: Of the 499 patients enrolled in the ICE-CRASH study, 371 were eligible for this secondary analysis. The median age was 81 years, 50.9% were male, and the median initial body temperature was 28.8 °C. Age (odds ratio [OR]: 0.97, 95% confidence interval [CI]: 0.94-1.00) and SOFA score (OR: 0.73, 95% CI: 0.67-0.81) were associated with lower survival, whereas ADL independence (OR: 2.31, 95% CI: 1.15-4.63) was associated with higher survival. No conventional rewarming therapy was associated with 28-day survival. Hot bath was associated with a high rewarming rate (regression coefficient: 1.14, 95% CI: 0.75-1.53). CONCLUSION: No conventional rewarming therapy was associated with improved 28-day survival, which suggests that background factors such as age, ADL, and severity of condition contribute more to prognosis than does the selection of rewarming technique.

Multimodal Gated Mixture of Experts Using Whole Slide Image and Flow Cytometry for Multiple Instance Learning Classification of Lymphoma.

In this study, we present a deep-learning-based multimodal classification method for lymphoma diagnosis in digital pathology, which utilizes a whole slide image (WSI) as the primary image data and flow cytometry (FCM) data as auxiliary information. In pathological diagnosis of malignant lymphoma, FCM serves as valuable auxiliary information during the diagnosis process, offering useful insights into predicting the major class (superclass) of subtypes. By incorporating both images and FCM data into the classification process, we can develop a method that mimics the diagnostic process of pathologists, enhancing the explainability. In order to incorporate the hierarchical structure between superclasses and their subclasses, the proposed method utilizes a network structure that effectively combines the mixture of experts (MoE) and multiple instance learning (MIL) techniques, where MIL is widely recognized for its effectiveness in handling WSIs in digital pathology. The MoE network in the proposed method consists of a gating network for superclass classification and multiple expert networks for (sub)class classification, specialized for each superclass. To evaluate the effectiveness of our method, we conducted experiments involving a six-class classification task using 600 lymphoma cases. The proposed method achieved a classification accuracy of 72.3%, surpassing the 69.5% obtained through the straightforward combination of FCM and images, as well as the 70.2% achieved by the method using only images. Moreover, the combination of multiple weights in the MoE and MIL allows for the visualization of specific cellular and tumor regions, resulting in a highly explanatory model that cannot be attained with conventional methods. It is anticipated that by targeting a larger number of classes and increasing the number of expert networks, the proposed method could be effectively applied to the real problem of lymphoma diagnosis.

Development of an Automatic Chronological Record Creation System Using Voice AI to Facilitate Information Aggregation and Sharing in the Event of a Disaster.

OBJECTIVE: In the event of a disaster, the chain of command and communication of each relevant agency is important. In this study, a chronological record creation system using voice AI (V-CRS) was developed, and an experiment was conducted to determine whether the obtained information could be quickly and easily summarized in chronological order. METHODS: After a lecture by Japanese Disaster Medical Assistant (DMAT) Team members and 8 medical clerks on how to use the developed tool, a comparison experiment was conducted between manual input and V-CRS utilization of the time to compile disaster information. RESULTS: Results proved that V-CRS can collect information gathered at headquarters more quickly than handwriting. It was also suggested that even medical clerks who have never been trained to record information during disasters could record information at the same speed as trained DMAT personnel. CONCLUSION: V-CRS can transcribe audio information even in situations where technical terms and physical units must be recorded, such as radiation disasters. It has been proven that anyone can quickly organize information using this method, to some extent.

Efficient model selection for predictive pattern mining model by safe pattern pruning.

Predictive pattern mining is an approach used to construct prediction models when the input is represented by structured data, such as sets, graphs, and sequences. The main idea behind predictive pattern mining is to build a prediction model by considering unified inconsistent notation sub-structures, such as subsets, subgraphs, and subsequences (referred to as patterns), present in the structured data as features of the model. The primary challenge in predictive pattern mining lies in the exponential growth of the number of patterns with the complexity of the structured data. In this study, we propose the safe pattern pruning method to address the explosion of pattern numbers in predictive pattern mining. We also discuss how it can be effectively employed throughout the entire model building process in practical data analysis. To demonstrate the effectiveness of the proposed method, we conduct numerical experiments on regression and classification problems involving sets, graphs, and sequences.

Are Online Simulations for Radiation Emergency Medical Preparedness Less Effective in Teaching Than Face-to-Face Simulations?

OBJECTIVE: The study clarified differences in understanding and satisfaction between face-to-face and online training on radiation emergency medical preparedness (REMP) training. METHODS: The training was held at Hirosaki University between 2018 and 2022, with 46 face-to-face participants and 25 online participants. RESULTS: Face-to-face training was significantly more understandable than online for the use of the Geiger counter (P < 0.05), but the educational effect of virtual reality (VR) was not significantly different from the actual practice. For the team exercise of taking care of the victims, online resulted in a significantly higher understanding (P < 0.05). CONCLUSIONS: Interactive exercises can be done online with equipment sent to learners, and VR is also as effective. The use of videos was more effective for first-timers to learn the practical process from a bird's-eye view, especially for team-based medical procedures.

Generalized Low-Rank Update: Model Parameter Bounds for Low-Rank Training Data Modifications.

In this study, we have developed an incremental machine learning (ML) method that efficiently obtains the optimal model when a small number of instances or features are added or removed. This problem holds practical importance in model selection, such as cross-validation (CV) and feature selection. Among the class of ML methods known as linear estimators, there exists an efficient model update framework, the low-rank update, that can effectively handle changes in a small number of rows and columns within the data matrix. However, for ML methods beyond linear estimators, there is currently no comprehensive framework available to obtain knowledge about the updated solution within a specific computational complexity. In light of this, our study introduces a the generalized low-rank update (GLRU) method, which extends the low-rank update framework of linear estimators to ML methods formulated as a certain class of regularized empirical risk minimization, including commonly used methods such as support vector machines and logistic regression. The proposed GLRU method not only expands the range of its applicability but also provides information about the updated solutions with a computational complexity proportional to the number of data set changes. To demonstrate the effectiveness of the GLRU method, we conduct experiments showcasing its efficiency in performing cross-validation and feature selection compared to other baseline methods.

Novel machine learning method allerStat identifies statistically significant allergen-specific patterns in protein sequences.

Cutting-edge technologies such as genome editing and synthetic biology allow us to produce novel foods and functional proteins. However, their toxicity and allergenicity must be accurately evaluated. It is known that specific amino acid sequences in proteins make some proteins allergic, but many of these sequences remain uncharacterized. In this study, we introduce a data-driven approach and a machine-learning method to find undiscovered allergen-specific patterns (ASPs) among amino acid sequences. The proposed method enables an exhaustive search for amino acid subsequences whose frequencies are statistically significantly higher in allergenic proteins. As a proof-of-concept, we created a database containing 21,154 proteins of which the presence or absence of allergic reactions are already known and applied the proposed method to the database. The detected ASPs in this proof-of-concept study were consistent with known biological findings, and the allergenicity prediction performance using the detected ASPs was higher than extant approaches, indicating this method may be useful in evaluating the utility of synthetic foods and proteins.

Safe RuleFit: Learning Optimal Sparse Rule Model by Meta Safe Screening.

We consider the problem of learning a sparse rule model, a prediction model in the form of a sparse linear combination of rules, where a rule is an indicator function defined over a hyper-rectangle in the input space. Since the number of all possible such rules is extremely large, it has been computationally intractable to select the optimal set of active rules. In this paper, to solve this difficulty for learning the optimal sparse rule model, we propose Safe RuleFit (SRF). Our basic idea is to develop meta safe screening (mSS), which is a non-trivial extension of well-known safe screening (SS) techniques. While SS is used for screening out one feature, mSS can be used for screening out multiple features by exploiting the inclusion-relations of hyper-rectangles in the input space. SRF provides a general framework for fitting sparse rule models for regression and classification, and it can be extended to handle more general sparse regularizations such as group regularization. We demonstrate the advantages of SRF through intensive numerical experiments.

SMG6 regulates DNA damage and cell survival in Hippo pathway kinase LATS2-inactivated malignant mesothelioma.

Many genes responsible for Malignant mesothelioma (MM) have been identified as tumor suppressor genes and it is difficult to target these genes directly at a molecular level. We searched for the gene which showed synthetic lethal phenotype with LATS2, one of the MM causative genes and one of the kinases in the Hippo pathway. Here we showed that knockdown of SMG6 results in synthetic lethality in LATS2-inactivated cells. We found that this synthetic lethality required the nuclear translocation of YAP1 and TAZ. Both are downstream factors of the Hippo pathway. We also demonstrated that this synthetic lethality did not require SMG6 in nonsense-mediated mRNA decay (NMD) but in regulating telomerase reverse transcriptase (TERT) activity. In addition, the RNA-dependent DNA polymerase (RdDP) activity of TERT was required for this synthetic lethal phenotype. We confirmed the inhibitory effects of LATS2 and SMG6 on cell proliferation in vivo. The result suggests an interaction between the Hippo and TERT signaling pathways. We also propose that SMG6 and TERT are novel molecular target candidates for LATS2-inactivated cancers such as MM.

Early Initiation of Venovenous Extracorporeal Membrane Oxygenation for Critically Ill COVID-19 Patients.

The optimal timing for initiating extracorporeal membrane oxygenation (ECMO) after starting mechanical ventilation has yet to be clarified. We report herein the cases of two patients with coronavirus disease 2019 (COVID-19) acute respiratory distress syndrome (ARDS) who were successfully managed with an early ECMO induction strategy. Case 1 involved a 64-year-old man admitted in respiratory distress with polymerase chain reaction-confirmed COVID-19. On day 5 at hospital, he was intubated, but oxygenation remained unimproved despite mechanical ventilation treatment with high positive end-expiratory pressure (PEEP) (PaO2/FiO2 [P/F] ratio, 127; Respiratory ECMO Survival Prediction [RESP] score, 4). ECMO was initiated 4 hours after intubation, and stopped on day 16 at hospital. The patient was discharged from hospital on day 36. Case 2 involved a 49-year-old man who had been admitted 8 days prior. He was intubated on hospital on day 2. High PEEP mechanical ventilation did not improve oxygenation (P/F ratio, 93; RESP score, 7). ECMO was stopped on hospital on day 7 and he was discharged from hospital on day 21. The strategy of early initiation of ECMO in these two cases may have minimized the risk of ventilation-related lung injury and contributed to the achievement of favorable outcomes.

Development, implementation, and refinement of a comprehensive postcardiac arrest care training course in Japan.

Aim: In Japan, no training course is dedicated to postcardiac arrest care (PCAC), including venoarterial extracorporeal membrane oxygenation (VA-ECMO); thus, faculty members of the Japanese Circulation Society developed an original, comprehensive PCAC training course. This report reviews the development, implementation, and refinement of this PCAC training course. Methods: We examined the preserved data from the Japanese Circulation Society PCAC training courses between 2014 and 2020. Data related to the learning content and number of the attendees and instructors were collected and summarized. Results: Sixteen courses were held between August 2014 and February 2020, before the coronavirus disease 2019 (COVID-19) pandemic. A total of 677 health care providers participated: 351 doctors, 225 nurses, 62 perfusionists, five emergency medical professionals, and two pharmacists. Thirty-two attendees' data were missing. The core learning contents of all the courses included a standardized postcardiac arrest algorithm, targeted temperature management, VA-ECMO cannulation skills, and postcannulation management. Concerning curriculum evolution, extracorporeal cardiopulmonary resuscitation simulation, postarrest neurological examination and monitoring, and ultrasound-guided Seldinger technique training were added in the 4th, 5th, and 13th courses, respectively. Conclusion: The Japanese Circulation Society PCAC training course has been developed and refined to provide an organized, comprehensive opportunity for health care providers to acquire specific knowledge and skills in PCAC and VA-ECMO.

Prehospital Administration of Aspirin and Nitroglycerin for Patients With Suspected Acute Coronary Syndrome　- A Systematic Review.

Background: Recent guidelines for acute coronary syndrome (ACS) recommend prehospital administration of aspirin and nitroglycerin for ACS patients. However, there is no clear evidence to support this. We investigated the benefits and harms of prehospital administration of aspirin and nitroglycerin by non-physician healthcare professionals in patients with suspected ACS. Methods and Results: We searched the PubMed database and used the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) approach to assess the certainty of evidence. Three retrospective studies for aspirin and 1 for nitroglycerin administered in the prehospital setting to patients with acute myocardial infarction were included. Prehospital aspirin administration was associated with significantly lower 30-day and 1-year mortality compared with aspirin administration after arrival at hospital, with odds ratios (OR) of 0.59 (95% confidence interval [CI] 0.35-0.99) and 0.47 (95% CI 0.36-0.62), respectively. Prehospital nitroglycerin administration was also associated with significantly lower 30-day and 1-year mortality compared with no prehospital administration (OR 0.34 [95% CI 0.24-0.50] and 0.38 [95% CI 0.29-0.50], respectively). The certainty of evidence was very low in both systematic reviews. Conclusions: Our systematic reviews suggest that prehospital administration of aspirin and nitroglycerin by non-physician healthcare professionals is beneficial for patients with suspected ACS, although the certainty of evidence is very low. Further investigation is needed to determine the benefit of the prehospital administration of these agents.

Wood-Splitter-Related Upper-Limb Injuries: A Single-Centered Case-Series Study.

(1) Background: Injuries to the upper limbs during wood splitting can affect social and economic life. We aimed to describe the clinical information concerning these injuries in Japan. (2) Methods: We identified patients from our patient database from April 2015-November 2021 and extracted data from their medical records, which includes age, gender, occupation, month, time and location of the injury, diagnosis, duration of hospitalization, ICU admission, treatment interventions including surgery, outcome, and medical costs. (3) Result: Seventeen cases were identified. Most of the patients were male (n = 15), with median age being 68 years old. Regarding the patients' backgrounds, six were apple farmers and three were unemployed. Injuries to the index finger was most common (n = 9), followed by injuries to the thumb in five cases (n = 5). Most of the incidents occurred at home or on the patient's farm estate. No injuries were due to incidents at work. (4) Conclusion: The wood splitter-related injuries required long-term treatment and frequently damaged the thumb, a functionally important digit. All the injuries were sustained during non-occupational use of a wood splitter. Therefore, we suggest that safety training should be provided to prevent traumatic injuries when these products are being sold.

Prehospital Activation of the Catheterization Laboratory Among Patients With Suspected ST-Elevation Myocardial Infarction Outside of a Hospital　- Systematic Review and Meta-Analysis.

Background: In the management of patients with ST-elevation myocardial infarction (STEMI), system delays for reperfusion therapy are still a matter of concern. We investigated the impact of prehospital activation of the catheterization laboratory in the management of STEMI patients. Methods and Results: This is a systematic review of observational studies. A search was conducted of the PubMed database from inception to July 2020 to identify articles for inclusion in the study. The critical outcomes were short- and long-term mortality. The important outcome was door-to-balloon time. The GRADE approach was used to assess the certainty of the evidence. Seven studies assessed short-term mortality; 1,541 were assigned to the prehospital activation (PH) group and 1,191 were assigned to the emergency department activation (ED) group. There were 26 fewer deaths per 1,000 patients in the PH group. Three studies assessed long-term mortality; 713 patients were assigned to the PH group and 1,026 were assigned to the ED group. There were 54 fewer deaths per 1,000 patients among the PH group. Five studies assessed door-to-balloon time; 959 were assigned to the PH group and 631 to the ED group. Door-to-balloon time was 33.1 min shorter in the PH group. Conclusions: Prehospital activation of the catheterization laboratory resulted in lower mortality and shorter door-to-balloon time for patients with suspected STEMI outside of a hospital.

Supplemental Oxygen and Acute Myocardial Infarction　- A Systematic Review and Meta-Analysis.

Background: In Japan, oxygen is commonly administered during the acute phase of myocardial infarction (MI) to patients without oxygen saturation monitoring. In this study we assessed the effects of supplemental oxygen therapy, compared with ambient air, on mortality and cardiac events by synthesizing evidence from randomized controlled trials (RCTs) of patients with suspected or confirmed acute MI. Methods and Results: PubMed was systematically searched for full-text RCTs published in English before June 21, 2020. Two reviewers independently screened the search results and appraised the risk of bias. The estimates for each outcome were pooled using a random-effects model. In all, 2,086 studies retrieved from PubMed were screened. Finally, 7,322 patients from 9 studies derived from 4 RCTs were analyzed. In-hospital mortality in the oxygen and ambient air groups was 1.8% and 1.6%, respectively (risk ratio [RR] 0.90; 95% confidence interval [CI] 0.38-2.10]); 0.8% and 0.5% of patients, respectively, experienced recurrent MI (RR 0.44; 95% CI 0.12-1.54), 1.5% and 1.6% of patients, respectively, experienced cardiac shock (RR 1.10; 95% CI 0.77-1.59]), and 2.4% and 2.0% of patients, respectively, experienced cardiac arrest (RR 0.91; 95% CI 0.43-1.94). Conclusions: Routine supplemental oxygen administration may not be beneficial or harmful, and high-flow oxygen may be unnecessary in normoxic patients in the acute phase of MI.

Systematic Review and Meta-Analysis of Diagnostic Accuracy to Identify ST-Segment Elevation Myocardial Infarction on Interpretations of Prehospital Electrocardiograms.

Background: The aim of this study was to assess and discuss the diagnostic accuracy of prehospital ECG interpretation through systematic review and meta-analyses. Methods and Results: Relevant literature published up to July 2020 was identified using PubMed. All human studies of prehospital adult patients suspected of ST-segment elevation myocardial infarction in which prehospital electrocardiogram (ECG) interpretation by paramedics or computers was evaluated and reporting all 4 (true-positive, false-positive, false-negative, and true-negative) values were included. Meta-analyses were conducted separately for the diagnostic accuracy of prehospital ECG interpretation by paramedics (Clinical Question [CQ] 1) and computers (CQ2). After screening, 4 studies for CQ1 and 6 studies for CQ2 were finally included in the meta-analysis. Regarding CQ1, the pooled sensitivity and specificity were 95.5% (95% confidence interval [CI] 82.5-99.0%) and 95.8% (95% CI 82.3-99.1%), respectively. Regarding CQ2, the pooled sensitivity and specificity were 85.4% (95% CI 74.1-92.3%) and 95.4% (95% CI 87.3-98.4%), respectively. Conclusions: This meta-analysis suggests that the diagnostic accuracy of paramedic prehospital ECG interpretations is favorable, with high pooled sensitivity and specificity, with an acceptable estimated number of false positives and false negatives. Computer-assisted ECG interpretation showed high pooled specificity with an acceptable estimated number of false positives, whereas the pooled sensitivity was relatively low.

Performance of the 0-Hour/1-Hour Algorithm for Diagnosing Myocardial Infarction in Patients With Chest Pain in the Emergency Department　- A Systematic Review and Meta-Analysis.

Background: This study assessed the diagnostic performance of the 0-hour/1-hour (0/1-h) algorithm to rule in and rule out acute myocardial infarction (MI) in patients presenting to the emergency department (ED) for suspected acute coronary syndrome without ST-segment elevation, as recommended in the 2015 European Society of Cardiology (ESC) guideline. Methods and Results: Following the Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy (PRISMA-DTA) guidelines, a systematic review was conducted using the PubMed database from inception to March 31, 2020. We included any article published in English investigating the diagnostic performance of the ESC 0/1-h algorithm for diagnosing MI in patients with chest pain visiting the ED. Of 651 studies identified as potentially available for the study, 7 studies including 16 databases were analyzed. A meta-analysis of the diagnostic accuracy of the 0/1-h algorithm using high-sensitivity cardiac troponin I (hs-cTn) with 6 observational databases showed a pooled sensitivity of 99.3% (95% confidence interval [CI] 98.5-99.7%) and a pooled specificity of 90.1% (95% CI 80.7-95.2%). A meta-analysis of the diagnostic accuracy of 10 observational databases of the ESC 0/1-h algorithm using hs-cTn revealed a pooled sensitivity of 99.3% (95% CI 96.9-99.9%) and a pooled specificity of 91.7% (95% CI 83.5-96.1%). Conclusions: Our results demonstrate that the ESC 0/1-h algorithm can effectively rule in and rule out patients with non-ST-segment elevation MI.

Short chain fatty acids-producing and mucin-degrading intestinal bacteria predict the progression of early Parkinson's disease.

To elucidate the relevance of gut dysbiosis in Parkinson's disease (PD) in disease progression, we made random forest models to predict the progression of PD in two years by gut microbiota in 165 PD patients. The area under the receiver operating characteristic curves (AUROCs) of gut microbiota-based models for Hoehn & Yahr (HY) stages 1 and 2 were 0.799 and 0.705, respectively. Similarly, gut microbiota predicted the progression of Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) III scores in an early stage of PD with AUROC = 0.728. Decreases of short-chain fatty acid-producing genera, Fusicatenibacter, Faecalibacterium, and Blautia, as well as an increase of mucin-degrading genus Akkermansia, predicted accelerated disease progression. The four genera remained unchanged in two years in PD, indicating that the taxonomic changes were not the consequences of disease progression. PD patients with marked gut dysbiosis may thus be destined to progress faster than those without gut dysbiosis.