Safety and utility of Endoscopic Ultrasound with Bronchoscope-guided Fine Needle Aspiration (EUS-B-FNA) in suspected lung cancer patients with poor respiratory or general conditions: a prospective three-center observational study.

BACKGROUND: Although transbronchial diagnostic procedures are sometimes difficult to perform because of the patient's respiratory or general conditions, endoscopic ultrasound with bronchoscope-guided fine-needle aspiration (EUS-B-FNA), a known transesophageal diagnostic procedure, might be useful for such cases. We conducted this prospective three-center observational study to evaluate the safety and efficacy of EUS-B-FNA in suspected lung cancer patients with poor respiratory or general conditions. METHODS: Patients with suspected lung cancer with respiratory failure, Eastern Cooperative Oncology Group performance status of 2 or higher, or severe respiratory symptoms, were enrolled. The primary endpoints were the diagnostic yield of lung cancer and its safety, and the secondary endpoints were the success rate of molecular and programmed death ligand 1 (PD-L1) analyses, and the 6-month survival rate in patients with lung cancer. RESULTS: We enrolled 30 patients, of which 29 were included in the analysis. Among them, 26 were eventually diagnosed with lung cancer. The diagnostic yield for lung cancer was 100% (26/26). There were no adverse events associated with EUS-B-FNA requiring procedure discontinuation. The success rates of molecular analysis for EGFR, ALK, ROS-1, and BRAF were 100% (14/14), 100% (11/11), 100% (9/9), and 75% (6/8), respectively. The success rate of the PD-L1 analysis was 100% (15/15). The 6-month survival rate in patients with lung cancer was 53.8% (95% confidence interval [CI]: 33.4-76.4), and the median overall survival (OS) was 196 days (95% CI: 142-446). CONCLUSIONS: EUS-B-FNA is a safe and effective diagnostic method, even in patients with suspected lung cancer with poor respiratory or general conditions. TRIAL REGISTRATION: This clinical trial was registered at https://www.umin.ac.jp/ctr/index.htm (UMIN000041235, approved on 28/07/2020).

Extracting clinical terms from radiology reports with deep learning.

Extracting clinical terms from free-text format radiology reports is a first important step toward their secondary use. However, there is no general consensus on the kind of terms to be extracted. In this paper, we propose an information model comprising three types of clinical entities: observations, clinical findings, and modifiers. Furthermore, to determine its applicability for in-house radiology reports, we extracted clinical terms with state-of-the-art deep learning models and compared the results. We trained and evaluated models using 540 in-house chest computed tomography (CT) reports annotated by multiple medical experts. Two deep learning models were compared, and the effect of pre-training was explored. To investigate the generalizability of the model, we evaluated the use of other institutional chest CT reports. The micro F1-score of our best performance model using in-house and external datasets were 95.36% and 94.62%, respectively. Our results indicated that entities defined in our information model were suitable for extracting clinical terms from radiology reports, and the model was sufficiently generalizable to be used with dataset from other institutions.

Mitochondrial DNA that escapes from autophagy causes inflammation and heart failure.

Heart failure is a leading cause of morbidity and mortality in industrialized countries. Although infection with microorganisms is not involved in the development of heart failure in most cases, inflammation has been implicated in the pathogenesis of heart failure. However, the mechanisms responsible for initiating and integrating inflammatory responses within the heart remain poorly defined. Mitochondria are evolutionary endosymbionts derived from bacteria and contain DNA similar to bacterial DNA. Mitochondria damaged by external haemodynamic stress are degraded by the autophagy/lysosome system in cardiomyocytes. Here we show that mitochondrial DNA that escapes from autophagy cell-autonomously leads to Toll-like receptor (TLR) 9-mediated inflammatory responses in cardiomyocytes and is capable of inducing myocarditis and dilated cardiomyopathy. Cardiac-specific deletion of lysosomal deoxyribonuclease (DNase) II showed no cardiac phenotypes under baseline conditions, but increased mortality and caused severe myocarditis and dilated cardiomyopathy 10 days after treatment with pressure overload. Early in the pathogenesis, DNase II-deficient hearts showed infiltration of inflammatory cells and increased messenger RNA expression of inflammatory cytokines, with accumulation of mitochondrial DNA deposits in autolysosomes in the myocardium. Administration of inhibitory oligodeoxynucleotides against TLR9, which is known to be activated by bacterial DNA, or ablation of Tlr9 attenuated the development of cardiomyopathy in DNase II-deficient mice. Furthermore, Tlr9 ablation improved pressure overload-induced cardiac dysfunction and inflammation even in mice with wild-type Dnase2a alleles. These data provide new perspectives on the mechanism of genesis of chronic inflammation in failing hearts.

[Monitored Anesthesia Care for Respiratory Endoscopy].

Sedation and/or analgesia are required during respiratory endoscopy procedure to reduce the patient's anxiety, unpleasantness and pain. At present most of diagnostic respiratory endoscopy cases are carried out outside the operating room without the involvement of anesthesiologists in Japan. Inadequate anesthesia/sedation may cause patient's discomfort and/or serious injury. Anesthesiologists are expected to play a part in the sedation and analgesia education/training for nonanesthesiologists to ameliorate patient satisfaction and safety. Interventional endoscopy procedures are more invasive and painful than simple diagnostic ones for patients, and sometimes result in a crisis such as respiratory tract bleeding and airway obstruction. Since the number of interventional cases and high-risk patients will increase with the society aging, peri-procedure management by anesthesiologists or monitored anesthesia care will become popular in the future.

Rheb (Ras homologue enriched in brain)-dependent mammalian target of rapamycin complex 1 (mTORC1) activation becomes indispensable for cardiac hypertrophic growth after early postnatal period.

Cardiomyocytes proliferate during fetal life but lose their ability to proliferate soon after birth and further increases in cardiac mass are achieved through an increase in cell size or hypertrophy. Mammalian target of rapamycin complex 1 (mTORC1) is critical for cell growth and proliferation. Rheb (Ras homologue enriched in brain) is one of the most important upstream regulators of mTORC1. Here, we attempted to clarify the role of Rheb in the heart using cardiac-specific Rheb-deficient mice (Rheb(-/-)). Rheb(-/-) mice died from postnatal day 8 to 10. The heart-to-body weight ratio, an index of cardiomyocyte hypertrophy, in Rheb(-/-) was lower than that in the control (Rheb(+/+)) at postnatal day 8. The cell surface area of cardiomyocytes isolated from the mouse hearts increased from postnatal days 5 to 8 in Rheb(+/+) mice but not in Rheb(-/-) mice. Ultrastructural analysis indicated that sarcomere maturation was impaired in Rheb(-/-) hearts during the neonatal period. Rheb(-/-) hearts exhibited no difference in the phosphorylation level of S6 or 4E-BP1, downstream of mTORC1 at postnatal day 3 but showed attenuation at postnatal day 5 or 8 compared with the control. Polysome analysis revealed that the mRNA translation activity decreased in Rheb(-/-) hearts at postnatal day 8. Furthermore, ablation of eukaryotic initiation factor 4E-binding protein 1 in Rheb(-/-) mice improved mRNA translation, cardiac hypertrophic growth, sarcomere maturation, and survival. Thus, Rheb-dependent mTORC1 activation becomes essential for cardiomyocyte hypertrophic growth after early postnatal period.

The role of autophagy in cardiomyocytes in the basal state and in response to hemodynamic stress.

Autophagy, an evolutionarily conserved process for the bulk degradation of cytoplasmic components, serves as a cell survival mechanism in starving cells. Although altered autophagy has been observed in various heart diseases, including cardiac hypertrophy and heart failure, it remains unclear whether autophagy plays a beneficial or detrimental role in the heart. Here, we report that the cardiac-specific loss of autophagy causes cardiomyopathy in mice. In adult mice, temporally controlled cardiac-specific deficiency of Atg5 (autophagy-related 5), a protein required for autophagy, led to cardiac hypertrophy, left ventricular dilatation and contractile dysfunction, accompanied by increased levels of ubiquitination. Furthermore, Atg5-deficient hearts showed disorganized sarcomere structure and mitochondrial misalignment and aggregation. On the other hand, cardiac-specific deficiency of Atg5 early in cardiogenesis showed no such cardiac phenotypes under baseline conditions, but developed cardiac dysfunction and left ventricular dilatation one week after treatment with pressure overload. These results indicate that constitutive autophagy in the heart under baseline conditions is a homeostatic mechanism for maintaining cardiomyocyte size and global cardiac structure and function, and that upregulation of autophagy in failing hearts is an adaptive response for protecting cells from hemodynamic stress.

Evaluating health information systems-related errors using the human, organization, process, technology-fit (HOPT-fit) framework.

Complex socio-technical health information systems (HIS) issues can create new error risks. Therefore, we evaluated the management of HIS-related errors using the proposed human, organization, process, and technology-fit framework to identify the lessons learned. Qualitative case study methodology through observation, interview, and document analysis was conducted at a 1000-bed Japanese specialist teaching hospital. Effective management of HIS-related errors was attributable to many socio-technical factors including continuous improvement, safety culture, strong management and leadership, effective communication, preventive and corrective mechanisms, an incident reporting system, and closed feedback loops. Enablers of medication errors include system sophistication and process factors like workarounds, variance, clinical workload, slips and mistakes, and miscommunication. The case management effectiveness in handling the HIS-related errors can guide other clinical settings. The potential of HIS to minimize errors can be achieved through continual, systematic, and structured evaluation. The case study validated the applicability of the proposed evaluation framework that can be applied flexibly according to study contexts to inform HIS stakeholders in decision-making. The comprehensive and specific measures of the proposed framework and approach can be a useful guide for evaluating complex HIS-related errors. Leaner and fitter socio-technical components of HIS can yield safer system use.

Antimicrobial resistance patterns of WHO priority pathogens isolated in hospitalized patients in Japan: A tertiary center observational study.

BACKGROUND: After issuing the "Global action plan on antimicrobial resistance" in 2015, the World Health Organization (WHO) established a priority pathogens list for supporting research and development of novel antimicrobials. We conducted a comprehensive analysis of the WHO priority organisms in a Japanese tertiary hospital to apprehend the local AMR epidemiology. METHODS: Data were obtained from electrical medical records in Osaka University Hospital between January 2010 and March 2021. The critical, high, and medium "priority pathogens list" categories of the WHO were used to compare results between the early (2010-2015) and late (2016-2021) phases. RESULTS: Out of 52,130 culture-positive specimens, a total of 9,872 (18.9%) contained WHO priority isolates. In comparison to early phases, late phases were likely to have higher rates of carbapenem resistance in Pseudomonas aeruginosa (15.7% vs 25.0%, P<0.001), 3rd generation cephalosporin resistance in Escherichia coli (11.5% vs 17.8%, P<0.001) as well as Klebsiella pneumoniae (1.6% vs 4.4%, P<0.001), and ampicillin resistance in Haemophilus influenzae (2.4% vs 3.9%, P<0.001). After 2015, however, the proportion of methicillin-resistant and vancomycin-intermediate Staphylococcus aureus was low. In this study, in-hospital mortality was comparable among patients with resistance to the three WHO priority pathogen types: critical (5.9%), high (3.9%), and medium (3.8%), and no significant change was observed between two phases in each category. However, significant interactions for in-hospital mortality were observed in subgroup analyses between "critical priority" AMR and the presence of comorbid conditions, such as chronic kidney disease or diabetes mellitus. CONCLUSIONS: To implement better antimicrobial stewardship policies and practices, local priority pathogens and "high-risk" patients for in-hospital death need to be acknowledged and evaluated periodically.

Oversampling effect in pretraining for bidirectional encoder representations from transformers (BERT) to localize medical BERT and enhance biomedical BERT.

BACKGROUND: Pretraining large-scale neural language models on raw texts has made a significant contribution to improving transfer learning in natural language processing. With the introduction of transformer-based language models, such as bidirectional encoder representations from transformers (BERT), the performance of information extraction from free text has improved significantly in both the general and medical domains. However, it is difficult to train specific BERT models to perform well in domains for which few databases of a high quality and large size are publicly available. OBJECTIVE: We hypothesized that this problem could be addressed by oversampling a domain-specific corpus and using it for pretraining with a larger corpus in a balanced manner. In the present study, we verified our hypothesis by developing pretraining models using our method and evaluating their performance. METHODS: Our proposed method was based on the simultaneous pretraining of models with knowledge from distinct domains after oversampling. We conducted three experiments in which we generated (1) English biomedical BERT from a small biomedical corpus, (2) Japanese medical BERT from a small medical corpus, and (3) enhanced biomedical BERT pretrained with complete PubMed abstracts in a balanced manner. We then compared their performance with those of conventional models. RESULTS: Our English BERT pretrained using both general and small medical domain corpora performed sufficiently well for practical use on the biomedical language understanding evaluation (BLUE) benchmark. Moreover, our proposed method was more effective than the conventional methods for each biomedical corpus of the same corpus size in the general domain. Our Japanese medical BERT outperformed the other BERT models built using a conventional method for almost all the medical tasks. The model demonstrated the same trend as that of the first experiment in English. Further, our enhanced biomedical BERT model, which was not pretrained on clinical notes, achieved superior clinical and biomedical scores on the BLUE benchmark with an increase of 0.3 points in the clinical score and 0.5 points in the biomedical score. These scores were above those of the models trained without our proposed method. CONCLUSIONS: Well-balanced pretraining using oversampling instances derived from a corpus appropriate for the target task allowed us to construct a high-performance BERT model.

Development of the Clinical Specimen Information Management System for Multicenter Clinical Studies.

Some multicenter clinical studies require the acquisition of clinical specimens from patients, and the centralized management and analysis of clinical specimens at a research institution. In such cases, it is necessary to manage clinical specimens with anonymized patient information. In addition, clinical specimens need to be managed in connection with clinical information in clinical studies. In this study, we have developed a clinical specimen information management system that works with electronic data capture system for efficient specimen information management and the system workflow has verified at Osaka University Hospital. In addition, by combining this system with medical image collection system that we have developed previously, the integrated management of clinical information, medical image, and clinical specimen information will become possible. This specimen information management system may be expected to provide the platform for integrated analysis utilizing clinical information, medical image, and data from clinical specimens in multicenter clinical studies.

Classification of Diagnostic Certainty in Radiology Reports with Deep Learning.

A radiology report is prepared for communicating clinical information about observed abnormal structures and clinically important findings with referring clinicians. However, such observations and findings are often accompanied by ambiguous expressions, which can prevent clinicians from accurately interpreting the content of reports. To systematically assess the degree of diagnostic certainty for each observation and finding in a report, we defined an ordinal scale comprising five classes: definite, likely, may represent, unlikely, and denial. Furthermore, we applied a deep learning classification model to determine its applicability to in-house radiology reports. We trained and evaluated the model using 540 in-house chest computed tomography reports. The deep learning model achieved a micro F1-score of 97.61%, which indicated that our ordinal scale was suitable for measuring the diagnostic certainty of observations and findings in a report.

Implementation of a Multilingual Electronic Medical Questionnaire: For Use Inside or Outside the Hospital.

We implemented a multilingual medical questionnaire system, which allows patients to answer questionnaires both in and out of the hospital. The response data are sent to and stored as structured data on the server in hospital information system, and could be converted to Japanese and quoted as part of progress notes in the electronic medical record.

Explainable Machine Learning Classification to Identify Vulnerable Groups Among Parenting Mothers: Web-Based Cross-Sectional Questionnaire Study.

BACKGROUND: One life event that requires extensive resilience and adaptation is parenting. However, resilience and perceived support in child-rearing vary, making the real-world situation unclear, even with postpartum checkups. OBJECTIVE: This study aimed to explore the psychosocial status of mothers during the child-rearing period from newborn to toddler, with a classifier based on data on the resilience and adaptation characteristics of mothers with newborns. METHODS: A web-based cross-sectional survey was conducted. Mothers with newborns aged approximately 1 month (newborn cohort) were analyzed to construct an explainable machine learning classifier to stratify parenting-related resilience and adaptation characteristics and identify vulnerable populations. Explainable k-means clustering was used because of its high explanatory power and applicability. The classifier was applied to mothers with infants aged 2 months to 1 year (infant cohort) and mothers with toddlers aged >1 year to 2 years (toddler cohort). Psychosocial status, including depressed mood assessed by the Edinburgh Postnatal Depression Scale (EPDS), bonding assessed by the Postpartum Bonding Questionnaire (PBQ), and sleep quality assessed by the Pittsburgh Sleep Quality Index (PSQI) between the classified groups, was compared. RESULTS: A total of 1559 participants completed the survey. They were split into 3 cohorts, comprising populations of various characteristics, including parenting difficulties and psychosocial measures. The classifier, which stratified participants into 5 groups, was generated from the self-reported scores of resilience and adaptation in the newborn cohort (n=310). The classifier identified that the group with the greatest difficulties in resilience and adaptation to a child's temperament and perceived support had higher incidences of problems with depressed mood (relative prevalence [RP] 5.87, 95% CI 2.77-12.45), bonding (RP 5.38, 95% CI 2.53-11.45), and sleep quality (RP 1.70, 95% CI 1.20-2.40) compared to the group with no difficulties in perceived support. In the infant cohort (n=619) and toddler cohort (n=461), the stratified group with the greatest difficulties had higher incidences of problems with depressed mood (RP 9.05, 95% CI 4.36-18.80 and RP 4.63, 95% CI 2.38-9.02, respectively), bonding (RP 1.63, 95% CI 1.29-2.06 and RP 3.19, 95% CI 2.03-5.01, respectively), and sleep quality (RP 8.09, 95% CI 4.62-16.37 and RP 1.72, 95% CI 1.23-2.42, respectively) compared to the group with no difficulties. CONCLUSIONS: The classifier, based on a combination of resilience and adaptation to the child's temperament and perceived support, was able identify psychosocial vulnerable groups in the newborn cohort, the start-up stage of childcare. Psychosocially vulnerable groups were also identified in qualitatively different infant and toddler cohorts, depending on their classifier. The vulnerable group identified in the infant cohort showed particularly high RP for depressed mood and poor sleep quality.

Lower doses of carvedilol in Japanese heart failure patients with reduced ejection fraction could show the potential to be non-inferior to higher doses in US patients: An international collaborative observational study.

The Japanese national guidelines recommend significantly lower doses of carvedilol for heart failure with reduced ejection fraction (HFrEF) management than the US guidelines. Using real-world data, we determined whether initial and target doses of carvedilol in Japanese patients (JPNs) differ from those in US patients (USPs), especially in Asian Americans (ASA) and Caucasians (CA), and investigated differences in outcomes. We collected data from the electronic medical records, including demographics, carvedilol dosing, tolerability, cardiac functional indicators like EF, cardiovascular events including all-cause deaths, and laboratory values from the University of California, San Diego Health and Osaka University. JPNs had significantly lower doses (mg/day) of carvedilol initiation (66 USPs composed of 38 CAs and 28 ASAs, 17.1±16.2; 93 JPNs, 4.3±4.2, p<0.001) and one year after initiation (33.0±21.8; 11.2±6.5, p<0.001), and a significantly lower relative rate (RR) of dose discontinuation and reduction than USPs (RR: 0.406, 95% confidence interval (CI): 0.181-0.911, p<0.05). CAs showed the highest reduction rate (0.184), and ASAs had the highest discontinuation rate (0.107). A slight mean difference with narrow 95% CI ranges straddling zero was observed between the two regions in the change from the baseline of each cardiac functional indicator (LVEF, -0.68 [-5.49-4.12]; LVDd, -0.55 [-3.24-2.15]; LVDd index, -0.25 [-1.92-1.43]; LVDs, -0.03 [-3.84-3.90]; LVDs index, -0.04 [-2.38-2.30]; heart rate, 1.62 [-3.07-6.32]). The event-free survival showed no difference (p = 0.172) among the races. Conclusively, despite JPNs exhibiting markedly lower carvedilol doses, their dose effectiveness has the potential to be non-inferior to that in USPs. Dose de-escalation, not discontinuation, could be an option in some Asian and ASA HFrEF patients intolerable to high doses of carvedilol.

Autophagy-mediated degradation is necessary for regression of cardiac hypertrophy during ventricular unloading.

Cardiac hypertrophy occurs in response to a variety of stresses as a compensatory mechanism to maintain cardiac output and normalize wall stress. Prevention or regression of cardiac hypertrophy can be a major therapeutic target. Although regression of cardiac hypertrophy occurs after control of etiological factors, the molecular mechanisms remain to be clarified. In the present study, we investigated the role of autophagy in regression of cardiac hypertrophy. Wild-type mice showed cardiac hypertrophy after continuous infusion of angiotensin II for 14 days using osmotic minipumps, and regression of cardiac hypertrophy was observed 7 days after removal of the minipumps. Autophagy was induced during regression of cardiac hypertrophy, as evidenced by an increase in microtubule-associated protein 1 light chain 3 (LC3)-II protein level. Then, we subjected cardiac-specific Atg5-deficient (CKO) and control mice (CTL) to angiotensin II infusion for 14 days. CKO and CTL developed cardiac hypertrophy to a similar degree without contractile dysfunction. Seven days after removal of the minipumps, CKO showed significantly less regression of cardiac hypertrophy compared with CTL. Regression of pressure overload-induced cardiac hypertrophy after unloading was also attenuated in CKO. These results suggest that autophagy is necessary for regression of cardiac hypertrophy during unloading of neurohumoral and hemodynamic stress.

Extracting Clinical Information From Japanese Radiology Reports Using a 2-Stage Deep Learning Approach: Algorithm Development and Validation.

Background: Radiology reports are usually written in a free-text format, which makes it challenging to reuse the reports. Objective: For secondary use, we developed a 2-stage deep learning system for extracting clinical information and converting it into a structured format. Methods: Our system mainly consists of 2 deep learning modules: entity extraction and relation extraction. For each module, state-of-the-art deep learning models were applied. We trained and evaluated the models using 1040 in-house Japanese computed tomography (CT) reports annotated by medical experts. We also evaluated the performance of the entire pipeline of our system. In addition, the ratio of annotated entities in the reports was measured to validate the coverage of the clinical information with our information model. Results: The microaveraged F1-scores of our best-performing model for entity extraction and relation extraction were 96.1% and 97.4%, respectively. The microaveraged F1-score of the 2-stage system, which is a measure of the performance of the entire pipeline of our system, was 91.9%. Our system showed encouraging results for the conversion of free-text radiology reports into a structured format. The coverage of clinical information in the reports was 96.2% (6595/6853). Conclusions: Our 2-stage deep system can extract clinical information from chest and abdomen CT reports accurately and comprehensively.

Impact of an Electronic Medical Record-Connected Questionnaire on Efficient Nursing Documentation: Usability and Efficacy Study.

BACKGROUND: Documentation tasks comprise a large percentage of nurses' workloads. Nursing records were partially based on a report from the patient. However, it is not a verbatim transcription of the patient's complaints but a type of medical record. Therefore, to reduce the time spent on nursing documentation, it is necessary to assist in the appropriate conversion or citation of patient reports to professional records. However, few studies have been conducted on systems for capturing patient reports in electronic medical records. In addition, there have been no reports on whether such a system reduces the time spent on nursing documentation. OBJECTIVE: This study aims to develop a patient self-reporting system that appropriately converts data to nursing records and evaluate its effect on reducing the documenting burden for nurses. METHODS: An electronic medical record-connected questionnaire and a preadmission nursing questionnaire were administered. The questionnaire responses entered by the patients were quoted in the patient profile for inpatient assessment in the nursing system. To clarify its efficacy, this study examined whether the use of the electronic questionnaire system saved the nurses' time entering the patient profile admitted between August and December 2022. It also surveyed the usability of the electronic questionnaire between April and December 2022. RESULTS: A total of 3111 (78%) patients reported that they answered the electronic medical questionnaire by themselves. Of them, 2715 (88%) felt it was easy to use and 2604 (85%) were willing to use it again. The electronic questionnaire was used in 1326 of 2425 admission cases (use group). The input time for the patient profile was significantly shorter in the use group than in the no-use group (P<.001). Stratified analyses showed that in the internal medicine wards and in patients with dependent activities of daily living, nurses took 13%-18% (1.3 to 2 minutes) less time to enter patient profiles within the use group (both P<.001), even though there was no difference in the amount of information. By contrast, in the surgical wards and in the patients with independent activities of daily living, there was no difference in the time to entry (P=.50 and P=.20, respectively), but there was a greater amount of information in the use group. CONCLUSIONS: The study developed and implemented a system in which self-reported patient data were captured in the hospital information network and quoted in the nursing system. This system contributes to improving the efficiency of nurses' task recordings.

Enhancing evidence-informed policymaking in medicine and healthcare: stakeholder involvement in the Commons Project for rare diseases in Japan.

BACKGROUND: Although stakeholder involvement in policymaking is attracting attention in the fields of medicine and healthcare, a practical methodology has not yet been established. Rare-disease policy, specifically research priority setting for the allocation of limited research resources, is an area where evidence generation through stakeholder involvement is expected to be effective. We generated evidence for rare-disease policymaking through stakeholder involvement and explored effective collaboration among stakeholders. METHODS: We constructed a space called 'Evidence-generating Commons', where patients, family members, researchers, and former policymakers can share their knowledge and experiences and engage in continual deliberations on evidence generation. Ten rare diseases were consequently represented. In the 'Commons', 25 consecutive workshops were held predominantly online, from 2019 to 2021. These workshops focused on (1) clarification of difficulties faced by rare-disease patients, (2) development and selection of criteria for priority setting, and (3) priority setting through the application of the criteria. For the first step, an on-site workshop using sticky notes was held. The data were analysed based on KJ method. For the second and third steps, workshops on specific themes were held to build consensus. The workshop agendas and methods were modified based on participants' feedback. RESULTS: The 'Commons' was established with 43 participants, resulting in positive effects such as capacity building, opportunities for interactions, mutual understanding, and empathy among the participants. The difficulties faced by patients with rare diseases were classified into 10 categories. Seven research topics were identified as priority issues to be addressed including 'impediments to daily life', 'financial burden', 'anxiety', and 'burden of hospital visits'. This was performed by synthesising the results of the application of the two criteria that were particularly important to strengthen future research on rare diseases. We also clarified high-priority research topics by using criteria valued more by patients and family members than by researchers and former policymakers, and criteria with specific perspectives. CONCLUSION: We generated evidence for policymaking in the field of rare diseases. This study's insights into stakeholder involvement can enhance evidence-informed policymaking. We engaged in comprehensive discussions with policymakers regarding policy implementation and planned analysis of the participants' experiences in this project.

Stakeholder involvement is significant for effective policymaking in the field of rare diseases. However, practical methods for this involvement have not yet been established. Therefore, we developed the 'Commons project' to generate valuable policymaking information and explore effective ways for stakeholders' collaboration. This article explains the process and results of 25 continuous workshops, held from 2019 to 2021 with 43 participants, including patients, family members, researchers, and former policymakers. The main achievements of the discussion that took place in the 'Commons' included a presentation of the overview of the difficulties faced by patients with rare diseases and formulation of high priority research topics.First, the difficulties faced by patients with rare diseases were grouped into 10 categories. Second, seven research topics were identified as priority issues including 'impediments to daily life', 'financial burden', 'anxiety', and 'burden of hospital visits'. During the project process, positive effects such as capacity building, opportunities for interactions, mutual understanding, and empathy among the participants, were identified. Beyond the context of the field of rare diseases and science of policy, these findings are useful for the future of society, including co-creation among stakeholders and patient and public involvement. Based on this study's results, we have initiated communications with policy stakeholders in the field of rare diseases, with the aim of policy implementation.

Calpain protects the heart from hemodynamic stress.

Calpains make up a family of Ca(2+)-dependent intracellular cysteine proteases that include ubiquitously expressed µ- and m-calpains. Both are heterodimers consisting of a distinct large catalytic subunit (calpain 1 for µ-calpain and calpain 2 for m-calpain) and a common regulatory subunit (calpain 4). The physiological roles of calpain remain unclear in the organs, including the heart, but it has been suggested that calpain is activated by Ca(2+) overload in diseased hearts, resulting in cardiac dysfunction. In this study, cardiac-specific calpain 4-deficient mice were generated to elucidate the role of calpain in the heart in response to hemodynamic stress. Cardiac-specific deletion of calpain 4 resulted in decreased protein levels of calpains 1 and 2 and showed no cardiac phenotypes under base-line conditions but caused left ventricle dilatation, contractile dysfunction, and heart failure with interstitial fibrosis 1 week after pressure overload. Pressure-overloaded calpain 4-deficient hearts took up a membrane-impermeant dye, Evans blue, indicating plasma membrane disruption. Membrane repair assays using a two-photon laser-scanning microscope revealed that calpain 4-deficient cardiomyocytes failed to reseal a plasma membrane that had been disrupted by laser irradiation. Thus, the data indicate that calpain protects the heart from hemodynamic stresses, such as pressure overload.

Proposal of diagnostic process model for computer based diagnosis.

We aim at making a diagnosis support system that can be put to practical use. We proposed a diagnostic process model based on simple knowledge which can be gleaned from textbooks. We defined clinical finding (CF) as a general concept for patient's symptom or findings etc., whose value is expressed by Boolean. We call the combination of several CFs a "CF pattern", and a set of CF patterns with concomitant diseases "case base". We consider diagnosis as a process of searching an instance from the case base whose CF pattern is concomitant with that of a patient. The diseases which have the same CF pattern are candidates for diagnosis. Then we select a CF which is present in part of the candidates and check whether it is present or absent in the patient in order to narrow down the candidates. Because the case base does not exist in reality, the probability of CF pattern is calculated by the product of CF occurrence rate assuming that occurrence of CF is independent. Therefore the knowledge required for diagnosis is frequency of disease under sex and age group and CF-disease relation (CF and its occurrence rate in the disease). By processing these two types of knowledge, diagnosis can be made.