Ehime Medical Center

Abstract Commercially available disposable products such as cell culture utensils and catheters do not necessarily possess sufficient quality for in vitro fertilization (IVF), from the perspective of pyrogen contamination. We aimed to comprehensively analyze the pyrogen contamination status, including bacterial endotoxins, of the products used for IVF in assisted reproductive technology (ART) and improve their cleanliness using a new sterilization technology. Pyrogen contamination levels were evaluated using a direct human cell-based pyrogen test that is not affected by the recovery ratio, unlike bacterial endotoxin tests. Pyrogen inactivation tests were performed using low-temperature ozone/hydrogen peroxide gas treatment. The residual hydrogen peroxide was colorimetrically quantified, and the effectiveness of its removal by drying treatment was evaluated using germ cell viability as an indicator. Significant amounts of pyrogen, from 0.014 to 1.110 EU/product, were detected in seven of the twenty products. Pyrogen contamination levels were reduced below the detection limit by ozone/hydrogen peroxide gas sterilization. Hydrogen peroxide remained on the surface of the GPS dish but was reduced to a level that did not affect human sperm viability and embryo development after drying at 80°C for 24 h following sterilization. These products may carry a potential risk of reducing ART success rates, and pyrogen contamination levels may exceed the previously reported allowable level of 0.01–0.02 EU/mL in human IVF during actual use. Our study suggests that the manufacturing of products free from pyrogens and without adverse effects on germ cells is possible using ozone/hydrogen peroxide gas sterilization and subsequent drying technologies.

We report a case of a 92-year-old elderly male with Stage IVA oligometastatic prostate adenocarcinoma to the pelvic nodes managed successfully with stereotactic body radiotherapy (SBRT) alone. The patient declined androgen deprivation therapy (ADT) with concerns about quality of life and reported side effects. He was treated with weekly SBRT targeting the prostate and pelvic lymph nodes without a rectal spacer, experiencing only Grade 1 urinary toxicity. The prostate-specific antigen (PSA) significantly declined from 12.05 to 0.16 ng/ml at the six-month follow-up. This case shows the feasibility and tolerability of SBRT as monotherapy in select elderly patients with regional risk node-positive prostate cancer.

Background and AimsEarly identification of metabolic dysfunction–associated steatotic liver disease (MASLD) is critical for risk stratification and timely intervention. Conventional noninvasive indices (fatty liver index, hepatic steatosis index, Zhejiang University index, and MASLD index) are limited by linear assumptions and moderate predictive accuracy. We aimed to develop and externally validate a neural network model for noninvasive detection of hepatic steatosis.MethodsWe used a retrospective cohort of 17,465 Japanese health checkup participants (2010–2020). All clinical data were obtained at the Ehime General Health Care Association. The cohort was split into a development cohort (n = 8426) and an internal validation cohort (n = 9039). A feedforward neural network was trained using clinical and biochemical variables, including body mass index, abdominal circumference, and existing indices. External validation used the Third National Health and Nutrition Examination Survey cohort (n = 9759), with hepatic steatosis defined by ultrasonography (Gallbladder and Upper Abdominal Ultrasound Hepatic Steatosis Profile Rating≥2). Model performance was assessed via area under the receiver operating characteristic curve, calibration, decision curve analysis, and subgroup analyses.ResultsThe neural network achieved an area under the receiver operating characteristic curve of 0.922 (95% confidence interval: 0.913–0.931) in internal validation and 0.924 (95% confidence interval: 0.917–0.931) in external validation, outperforming fatty liver index, hepatic steatosis index, Zhejiang University index, and MASLD indices (all P < .001). At the optimal Youden cutoff (0.357), sensitivity and specificity were 89% and 86%, respectively. Calibration analysis and decision curve analysis confirmed strong agreement between predicted and observed risk. Abdominal circumference and body mass index were the most influential predictors.ConclusionOur neural network model outperforms conventional indices in detecting moderate-to-severe hepatic steatosis and may facilitate early, scalable MASLD screening in primary care and low-resource settings.UMIN No. 11953.

Rationale: Patients with noncavitary nodular bronchiectatic (NB) Mycobacterium avium complex pulmonary disease (MAC-PD) are treated intermittently three times per week, although no randomized controlled trials have been conducted comparing three times weekly with daily therapy. Objectives: To assess the tolerability, safety, and efficacy of intermittent versus daily treatment in patients with previously untreated noncavitary NB MAC-PD. Methods: In an open-label study, patients were randomly assigned to the intermittent therapy group receiving clarithromycin 1,000 mg, rifampicin 600 mg, and ethambutol 25 mg/kg (maximum 1,000 mg) three days per week or the daily therapy group receiving clarithromycin 800 mg, rifampicin 450 mg, and ethambutol 15 mg/kg (maximum 750 mg) daily for 1 year. The primary endpoint was the proportion of patients requiring modification of the initial treatment regimen. Results: Twenty-one Japanese hospitals participated in the study, enrolling 141 patients between May 2019 and December 2021. The full analysis set included 138 participants (intermittent therapy = 70; daily therapy = 68). There were no significant differences between the intermittent and daily therapy groups in terms of the regimen modification rate (20.0% [14 of 70] vs. 33.8% [23 of 68]; adjusted odds ratio, 0.48; 95% confidence interval, 0.22 to 1.05; P = 0.06) or culture conversion (70.3% vs. 80.0%; P = 0.53), time to culture conversion (28.0 vs. 28.5 d; P = 0.89), improvement in chest CT findings (60.9% vs. 71.0%; P = 0.30), or clarithromycin resistance development (1.4% vs. 0%; P = 1.00). Elevated aspartate aminotransferase (16.9% vs. 41.2%; P = 0.003) and alanine aminotransferase (18.3% vs. 44.1%; P = 0.002) were more common in the daily treatment group, whereas elevated bilirubin (11.3% vs. 1.5%; P = 0.04) and dysgeusia (14.1% vs. 1.5%; P = 0.01) were more common in the intermittent treatment group. The daily treatment group exhibited a greater absolute change in the 36-Item Short Form Health Survey physical aspect score (-2.5 points) than the intermittent treatment group (2.1 points) (P = 0.01). Conclusions: Intermittent treatment was not significantly better tolerated than daily treatment for noncavitary NB MAC-PD. However, further studies with larger numbers of patients are needed. Clinical trial registered with https://jrct.mhlw.go.jp/en-top (jRCTs031190008).

Prostate cancer causes a high frequency of bone metastases. The purpose of this study was to identify molecules that regulate prostate cancer bone metastasis as potential therapeutic and/or prognostic targets in prostate cancer, using integrative genome-wide data. Based on the biological characterization of prostate cancer cell lines and integrated big data analysis of clinical samples, we identified CTHRC1 as a candidate gene that regulates prostate cancer bone metastasis. The knockdown of CTHRC1 suppressed cell migration and cell adhesion, but not cell proliferation, in vitro and in vivo. In a mouse tibia xenograft model, CTHRC1 knockdown markedly suppressed metastatic growth of prostate cancer cells in the bone microenvironment. CTHRC1 also promoted in vitro osteoclast differentiation. The analysis of 343 human prostate cancer biopsy samples showed that CTHRC1 expression was positively correlated with bone metastasis, indicating that CTHRC1 could be a useful predictive marker for bone metastasis. Our findings illustrate the clinical relevance of CTHRC1 and demonstrate that CTHRC1 plays an important role in prostate cancer bone metastasis. CTHRC1 may be useful for predicting prostate cancer bone metastasis and may lead to the development of new methods for the prevention and treatment of metastasis.

Observational Health Data Sciences and Informatics (OHDSI) is an international research community dedicated to large-scale observational studies using real-world healthcare data. Participation in OHDSI requires mapping local terminology systems to the OHDSI Standard Vocabulary (OSV) and transforming healthcare data into the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM), a standardized database schema. Adherence to the OSV and CDM enables the integration of datasets from different countries and regions, facilitating international cross-sectional analyses and supporting the discovery of large-scale evidence and new medical knowledge. Despite the globally recognized healthcare technology and systems excellence in Japan, Japanese real-world data (RWD) remain underutilized in international research. This is primarily due to reliance on domestically managed controlled terminologies in Japan that are not aligned with international controlled terminologies such as SNOMED CT, making mapping Japanese RWD to the CDM challenging. In addition, the wide variety of pharmaceutical products in Japan has hindered the establishment of mappings to RxNorm, the standardized drug terminology used in OHDSI. Previously, we used a Large Language Model (LLM) to map Japanese pharmaceutical data to RxNorm. A sampling-based evaluation confirmed that the LLM could accurately identify mapping candidates. Pharmacists and a medical informatics researcher validated these mappings, resulting in an ingredient-based mapping of Japanese pharmaceutical terms to RxNorm. Researchers interested in pharmacoepidemiology, pharmacoeconomics, and drug-related clinical decision support systems integrated with Japanese RWD can benefit significantly from this dataset. It also contains information about the target drugs, their translated names, LLM-generated suggestions, and reference data, making it suitable for developing and validating natural language processing and machine learning techniques for terminology mapping.

Abstract Background Galectin-3 is a multifunctional carbohydrate-binding lectin that is involved in inflammation, tissue remodeling, and fibrosis. High circulating levels of galectin-3 are associated with all-cause mortality, cardiovascular (CV) mortality, and/or major adverse CV events in patients with CV diseases such as heart failure and coronary artery disease (CAD). However, the associations of galectin-3 with non-CV mortality and cancer mortality in patients with suspected or known CAD are unknown. Methods Serum galectin-3 levels were measured in 2,418 patients with suspected or known CAD undergoing elective coronary angiography. The outcomes were all-cause death, CV death, non-CV death, cancer death, and other non-CV death. Patients were followed up over a 6-year period. Results During the follow-up, 536 (22.2%) deaths occurred, including 166 (6.9%) CV deaths, 326 (13.5%) non-CV deaths, 128 (5.3%) cancer deaths, 198 (8.2%) other non-CV deaths, and 44 (1.8%) undetermined deaths. After adjustment for potential clinical confounders (i.e., age, sex, body mass index, hypertension, dyslipidemia, diabetes, current smoker, estimated glomerular filtration rate, the Gensini score, previous myocardial infarction, previous stroke, previous heart failure hospitalization, atrial fibrillation, valvular heart disease, malignancies, anemia, antihypertensive drug use, statin use, and aspirin use) and established CV biomarkers (i.e., N-terminal pro-brain natriuretic peptide, high-sensitivity cardiac troponin I, and high-sensitivity C-reactive protein), galectin-3 levels were significantly associated with all-cause death (hazard ratio [HR] for 1-SD increase, 1.22; 95% confidence interval [CI], 1.13–1.31), CV death (HR, 1.16; 95% CI, 1.02–1.33), non-CV death (HR, 1.27; 95% CI, 1.15–1.40), cancer death (HR, 1.31; 95% CI, 1.07–1.61), and other non-CV death (HR, 1.30; 95% CI, 1.15–1.46). The addition of galectin-3 levels to the model with potential clinical confounders and established CV biomarkers significantly improved the prediction of all-cause death (P&amp;lt;0.001 for continuous net reclassification improvement [NRI], P&amp;lt;0.001 for integrated discrimination improvement [IDI]) and cancer death (P=0.001 for NRI, P=0.005 for IDI), but not that of CV death (P=0.601 for NRI, P= 0.708 for IDI), non-CV death (P=0.060 for NRI, P=0159 for IDI), or other non-CV death (P=0.266 for NRI, P=0.699 for IDI). Conclusions The galectin-3 level independently predicted all-cause mortality and cancer mortality in patients with suspected or known CAD.

ObjectivesThis study evaluated the efficacy of integrating a retrieval-augmented generation (RAG) model and a large language model (LLM) to improve the accuracy of drug name mapping across international vocabularies.MethodsDrug ingredient names were translated into English using the Japanese Accepted Names for Pharmaceuticals. Drug concepts were extracted from the standard vocabulary of OHDSI, and the accuracy of mappings between translated terms and RxNorm was assessed by vector similarity, using the BioBERT-generated embedded vectors as the baseline. Subsequently, we developed LLMs with RAG that distinguished the final candidates from the baseline. We assessed the efficacy of the LLM with RAG in candidate selection by comparing it with conventional methods based on vector similarity.ResultsThe evaluation metrics demonstrated the superior performance of the combined LLM + RAG over traditional vector similarity methods. Notably, the hit rates of the Mixtral 8x7b and GPT-3.5 models exceeded 90%, significantly outperforming the baseline rate of 64% across stratified groups of PO drugs, injections, and all interventions. Furthermore, the r-precision metric, which measures the alignment between model judgment and human evaluation, revealed a notable improvement in LLM performance, ranging from 41% to 50% compared to the baseline of 23%.ConclusionsIntegrating an RAG and an LLM outperformed conventional string comparison and embedding vector similarity techniques, offering a more refined approach to global drug information mapping.