Mechanism of ERBB2 gene overexpression by the formation of super-enhancer with genomic structural abnormalities in lung adenocarcinoma without clinically actionable genetic alterations.

BACKGROUND: In an extensive genomic analysis of lung adenocarcinomas (LUADs), driver mutations have been recognized as potential targets for molecular therapy. However, there remain cases where target genes are not identified. Super-enhancers and structural variants are frequently identified in several hundred loci per case. Despite this, most cancer research has approached the analysis of these data sets separately, without merging and comparing the data, and there are no examples of integrated analysis in LUAD. METHODS: We performed an integrated analysis of super-enhancers and structural variants in a cohort of 174 LUAD cases that lacked clinically actionable genetic alterations. To achieve this, we conducted both WGS and H3K27Ac ChIP-seq analyses using samples with driver gene mutations and those without, allowing for a comprehensive investigation of the potential roles of super-enhancer in LUAD cases. RESULTS: We demonstrate that most genes situated in these overlapped regions were associated with known and previously unknown driver genes and aberrant expression resulting from the formation of super-enhancers accompanied by genomic structural abnormalities. Hi-C and long-read sequencing data further corroborated this insight. When we employed CRISPR-Cas9 to induce structural abnormalities that mimicked cases with outlier ERBB2 gene expression, we observed an elevation in ERBB2 expression. These abnormalities are associated with a higher risk of recurrence after surgery, irrespective of the presence or absence of driver mutations. CONCLUSIONS: Our findings suggest that aberrant gene expression linked to structural polymorphisms can significantly impact personalized cancer treatment by facilitating the identification of driver mutations and prognostic factors, contributing to a more comprehensive understanding of LUAD pathogenesis.

Comparative analysis of liver resection in Non-B Non-C and hepatitis virus-associated hepatocellular carcinoma.

BACKGROUND: The incidence of non-hepatitis B and non-hepatitis C hepatocellular carcinoma (NBNC-HCC) is increasing in our country. This study assesses the feasibility of employing an identical surgical treatment strategy for resectable NBNC-HCC as that for hepatitis virus-associated HCC (HV-HCC). METHODS: A retrospective analysis (1993-2023) of 1321 curative liver resections for HCC at a single institution was performed. Propensity score matching ensured a balanced comparison of preoperative clinical factors, including tumor status and background liver condition. RESULTS: The proportion of NBNC-HCC cases has gradually increased, reaching up to 70 %. After matching, 294 of 473 NBNC-HCC patients and 294 of 848 HV-HCC patients were compared. Operative outcomes, including operation time, blood loss, type of surgical procedure, and morbidity, were comparable. Long-term outcome analysis showed similar recurrence-free survival (HR: 0.86, 95 % CI: 0.70-1.06, P = 0.167) and overall survival (HR: 0.98, 95 % CI: 0.79-1.23, P = 0.865) for NBNC-HCC. Multivariable analysis identified ICGR15 ≥ 15 %, ALBI grade 2 or 3, aspartate aminotransferase ≥40, tumor size > 5 cm, multiple tumors, macrovascular invasion, and microvascular invasion as independent prognostic factors for overall survival, while hepatitis B or C virus status lost significance. CONCLUSIONS: Despite the increasing incidence of NBNC-HCC, comparable outcomes were achieved between the two groups of matched cohort.

Advances in cancer DNA methylation analysis with methPLIER: use of non-negative matrix factorization and knowledge-based constraints to enhance biological interpretability.

DNA methylation is an epigenetic modification that results in dynamic changes during ontogenesis and cell differentiation. DNA methylation patterns regulate gene expression and have been widely researched. While tools for DNA methylation analysis have been developed, most of them have focused on intergroup comparative analysis within a dataset; therefore, it is difficult to conduct cross-dataset studies, such as rare disease studies or cross-institutional studies. This study describes a novel method for DNA methylation analysis, namely, methPLIER, which enables interdataset comparative analyses. methPLIER combines Pathway Level Information Extractor (PLIER), which is a non-negative matrix factorization (NMF) method, with regularization by a knowledge matrix and transfer learning. methPLIER can be used to perform intersample and interdataset comparative analysis based on latent feature matrices, which are obtained via matrix factorization of large-scale data, and factor-loading matrices, which are obtained through matrix factorization of the data to be analyzed. We used methPLIER to analyze a lung cancer dataset and confirmed that the data decomposition reflected sample characteristics for recurrence-free survival. Moreover, methPLIER can analyze data obtained via different preprocessing methods, thereby reducing distributional bias among datasets due to preprocessing. Furthermore, methPLIER can be employed for comparative analyses of methylation data obtained from different platforms, thereby reducing bias in data distribution due to platform differences. methPLIER is expected to facilitate cross-sectional DNA methylation data analysis and enhance DNA methylation data resources.

Analysis of Factors Hindering the Dissemination of Medical Information Standards.

Many medical information standards are not widely used in Japan, and this hinders the promotion of the use of real-world data. However, the complex intertwining of many factors hindering the dissemination of medical information standards makes it difficult to solve this problem. This study analyzed and visualized relationships among factors that inhibit the dissemination of medical information standards. Five medical informatics experts affiliated with universities and hospitals were interviewed about the factors that hinder the dissemination of medical information standards in Japan. The presented factors were analyzed using the interpretive structural modeling (ISM) method and the decision-making trial and evaluation laboratory (DEMATEL) method. We found that "legislation" and "reliability" were important inhibiting factors for the dissemination of medical information standards in Japan. We also found a six-layered structure in which "reliability" was satisfied when "legislation" was in place and "expectations" and "personal information" were resolved. The DEMATEL analysis indicated the relationships and classifications of factors hindering the dissemination of medical information standards. Since the adoption of medical information standards does not directly lead to revenue for medical institutions, it is possible to meet the "expectation" of improving the quality of medical care by ensuring "legislation" and "reliability", that is, ensuring the dependability of medical treatment. The results of this study visually show the structure of the factors and will help solve the problems that hinder the effective and efficient spread of standards. Solving these problems may support the efficient use of real-world data.

Data-Sharing Gateway System Design for Large-Scale Medical Information Collection with Distributed EMR Storage.

The collection and use of large-scale medical information for developing artificial intelligence engines are actively ongoing. In Japan, collection systems have been built to collect data for medical image analysis and disease repositories. In the experimental project for the next generation medical infrastructure law, a centrally integrated basic system was developed, and standardized electronic medical record (EMR) storage data distributed to each hospital were transferred into one data center and imported to a database for secondary use. The law requires a mechanism for maintaining a list of notified or opted-out patients. To operate these systems, safe and efficient secondary use of collected information is essential not just for the law but also for large-scale data collection projects, such as multifacility clinical research. This paper considers whole-somely, requirements for providing medical care information to data collection projects and proposes additional requirements for a gateway system under development.

Acceptance of the Use of Artificial Intelligence in Medicine Among Japan's Doctors and the Public: A Questionnaire Survey.

BACKGROUND: The use of artificial intelligence (AI) in the medical industry promises many benefits, so AI has been introduced to medical practice primarily in developed countries. In Japan, the government is preparing for the rollout of AI in the medical industry. This rollout depends on doctors and the public accepting the technology. Therefore it is necessary to consider acceptance among doctors and among the public. However, little is known about the acceptance of AI in medicine in Japan. OBJECTIVE: This study aimed to obtain detailed data on the acceptance of AI in medicine by comparing the acceptance among Japanese doctors with that among the Japanese public. METHODS: We conducted an online survey, and the responses of doctors and members of the public were compared. AI in medicine was defined as the use of AI to determine diagnosis and treatment without requiring a doctor. A questionnaire was prepared referred to as the unified theory of acceptance and use of technology, a model of behavior toward new technologies. It comprises 20 items, and each item was rated on a five-point scale. Using this questionnaire, we conducted an online survey in 2018 among 399 doctors and 600 members of the public. The sample-wide responses were analyzed, and then the responses of the doctors were compared with those of the public using t tests. RESULTS: Regarding the sample-wide responses (N=999), 653 (65.4%) of the respondents believed, in the future, AI in medicine would be necessary, whereas only 447 (44.7%) expressed an intention to use AI-driven medicine. Additionally, 730 (73.1%) believed that regulatory legislation was necessary, and 734 (73.5%) were concerned about where accountability lies. Regarding the comparison between doctors and the public, doctors (mean 3.43, SD 1.00) were more likely than members of the public (mean 3.23, SD 0.92) to express intention to use AI-driven medicine (P<.001), suggesting that optimism about AI in medicine is greater among doctors compared to the public. CONCLUSIONS: Many of the respondents were optimistic about the role of AI in medicine. However, when asked whether they would like to use AI-driven medicine, they tended to give a negative response. This trend suggests that concerns about the lack of regulation and about accountability hindered acceptance. Additionally, the results revealed that doctors were more enthusiastic than members of the public regarding AI-driven medicine. For the successful implementation of AI in medicine, it would be necessary to inform the public and doctors about the relevant laws and to take measures to remove their concerns about them.

AUTOMATIC ACQUISITION OF CT RADIATION DOSE DATA: USING THE DIAGNOSTIC REFERENCE LEVEL FOR RADIATION DOSE OPTIMIZATION.

The present work describes that we try to construct a system that collects dose information that performed CT examination from multiple facilities and unified management. The results of analysis are compared with other National diagnostic reference level (DRL), and the results are fed back to each facility and the cause of the abnormal value is investigated for dose optimization. Medical information collected 139 144 tests from 33 CT devices in 13 facilities. Although the DRL of this study is lower than that of Japan DRL, it was higher than the DRL of each country. When collecting all the examination, it is thought that the variation of the dose due to the error other than the intended imaging site is large. In future, we should continue to collect information in order to DRL renewal and we also think that it is desirable to collect information on physique and detailed scan region as well.

Development and practice of ISMS at a Radiotherapy Hospital by using IHE Integration profiles.

Our hospital is specialized for radiation therapy and has many information devices. Various job categories are working. When we implemented an EMR, we aimed to enforce ISMS by using IHE profiles. To solve the already existed system-related problems, we selected and use some profiles (EUA, PSA, ATNA and PAM). After implementation, we audited and then some findings were pointed out. These findings are being settled by the PDCA cycle. We also found that appropriate IHE profiles were effective in the building of ISMS.

Development of Clinical Database System Specialized for Heavy Particle Therapy.

We have developed a data archiving system for study of charged particle therapy. We required a data-relation mechanism between electronic medical record system (EMR) and database system, because it needs to ensure the information consistency. This paper presents the investigation results of these techniques. The standards in the medical informatics field that we focus on are Integrating the Healthcare Enterprise (IHE) and 2) Health Level-7 (HL7) to archive the data. As a main cooperation function, we adapt 2 integration profiles of IHE as follows, 1) Patient Administration Management (PAM) Profile of IHE-ITI domain for patient demographic information reconciliation, 2) Enterprise Schedule Integration(ESI) profile of IHE-Radiation Oncology domain for order management between EMR and treatment management system(TMS). We also use HL7 Ver2.5 messages for exchanging the follow-up data and result of laboratory test. In the future, by implementation of this system cooperation, we will be able to ensure interoperability in the event of the EMR update.

Proposal of the patient location tracking and query (PLQ) of IHE integration profile for the better patient tracking.

The Patient Location Tracking Query (PLQ) is the IHE integration profile in order to find the location of patient only in a hospital, not for cross-referencing between multiple hospitals. In Japan, it is common for elderly patients to consult multiple departments in one hospital visit. To find the location of patients quickly is very important for productivity and use of resources in a hospital. We analyzed the workflow of patients and defined three actors and two transactions for PLQ integration profile. In order to collect the location of patients, multiple systems must provide the location into central management system. We proposed PLQ for the better patient care in hospitals. We believe that by using this PLQ profile hospital staffs are able to utilize the resource more efficiently.

Impact of interocclusal contacts on infrared laser fluorescence in pits of sound first permanent molars in children.

BACKGROUND: A device based on infrared laser fluorescence (IRLF) has become available as an adjunct for the diagnosis of dental caries. AIMS: The objective of this study was to clarify the differences of IRLF readings in the mesial, central and distal occlusal pits of first permanent molars. DESIGN: Sixty-four children (average age 8.0 years) were examined using IRLF. The mesial, central and distal pits of clinically healthy first permanent molars were measured. The instrument provides measurements in arbitrary units on an open-ended interval scale. RESULTS: Mean (± SE) IRLF values in the mesial pits were 4.9 ± 0.4 (upper) and 6.5 ± 0.4 (lower) and were significantly lower than those in the central (8.8 ± 0.6 and 11.5 ± 0.9) and distal (9.6 ± 0.7 and 10.4 ± 0.8) pits in the maxilla and mandible. There was no significant difference between the right (7.3 ± 0.5, 9.4 ± 0.6) and left (8.2 ± 0.5, 9.5 ± 0.6) dental arches. IRLF measurements in the mesial pits of human first permanent sound molars were lower than the central and distal pits in children whose second molars had not erupted. CONCLUSIONS: The inherently higher IRLF values of some sites should not be misinterpreted and trigger early invasive treatment.