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Microbiome in women with endometriosis and the in vitro effects of Lactobacillus reuteri on human endometrium

2026-06-11T06:44:52Z

Title: Microbiome in women with endometriosis and the in vitro effects of Lactobacillus reuteri on human endometrium Authors: Lee, Jae Hoon; Jung, Gee Soo; Kim, Kyungmin; Park, Hyemin; Park, Yunjeong; Lee, Inha; Lee, Min Jung; Lee, Ji-Ho; Choi, Young Sik; Cho, SiHyun Abstract: Endometriosis (EMS) is a chronic inflammatory disorder affecting similar to 10% of reproductive-age women, with increasing evidence implicating the microbiome in its pathogenesis through immunomodulation and estrogen metabolism. This study investigated microbiome composition in the vagina, endometrium, and peritoneal fluid (PF) of women with and without EMS and further assessed the effects of Lactobacillus reuteri (L. reuteri) on endometrial (EM) cells in vitro. Samples from 41 patients were analyzed using 16S rRNA gene sequencing, targeting the V3-V4 regions. Western blotting, ELISA, and LC-MS/MS were employed to evaluate protein expression and estrogen metabolism during EM-L. reuteri co-culture with or without estradiol-17-glucuronide (E2G). Microbiome analysis revealed no significant differences in alpha or beta diversity between EMS and controls across all compartments. However, LEfSe analysis identified several taxa with differential abundance, with L. reuteri consistently altered in both vagina and EM. Across the menstrual cycle, EM and vaginal microbiomes were stable, whereas PF microbiota showed phase-dependent variation involving 60 genera and 76 species. In vitro, L. reuteri alone did not alter endometriosis-related proteins, but in the presence of E2G, it reduced BAX/Bcl-2 ratios and increased p-NF-kappa B, suggesting anti-apoptotic and pro-inflammatory shifts. Progesterone receptor alpha/beta expression decreased, while estrogen receptor levels remained unchanged. L. reuteri increased beta-glucuronidase activity but did not enhance E2G-to-estradiol conversion. These findings highlight L. reuteri as a potentially important species in EMS, with in vitro evidence suggesting survival-promoting effects under estrogenic conditions. Further research should explore multi-species interactions and hormonal contexts to clarify microbial contributions to EMS pathogenesis. IMPORTANCE Although Lactobacillus reuteri appeared more abundant in the vagina and endometrium of controls, suggesting a protective role, in vitro findings paradoxically indicated anti-apoptotic and pro-inflammatory effects under estrogenic conditions, underscoring the need for further investigation of multi-species microbial interactions and hormonal contexts in endometriosis pathogenesis.

Dynamic machine learning prediction of persistent AKI after cardiac surgery with modifiable perioperative risk factors

2026-06-10T05:55:43Z

Title: Dynamic machine learning prediction of persistent AKI after cardiac surgery with modifiable perioperative risk factors Authors: Han, Changho; Kim, Hyun Il; Song, Jong Wook; Shin, Heesoo; Kwak, Young-Lan; Soh, Sarah; Yoon, Dukyong Abstract: Early identification and prevention of persistent acute kidney injury (pAKI) remain challenging due to delayed biochemical markers and limited tools to differentiate between transient and persistent forms. We retrospectively analyzed data of 2,285 patients who underwent cardiac surgery with cardiopulmonary bypass (CPB) and developed 3 machine learning (ML) models for predicting pAKI: model 1 (preoperative data); model 2 (intraoperative and immediate postoperative variables); and model 3 (data up to 48 h post-ICU admission). pAKI occurred in 168 patients. Predictive performance improved across models, reflecting the value of time-updated data. SHapley Additive exPlanations highlighted baseline factors (estimated glomerular filtration rate and hemoglobin) in model 1 and perioperative factors associated with pAKI risk (post-CPB perfusion pressure, transfusion volume, and hemoglobin trends) in models 2 and 3 as dominant contributors. Our dynamic ML model enables early risk stratification and identification of perioperative factors associated with pAKI risk, providing a foundation for hypothesis generation and future investigation.

The IMPACT framework for evaluating generative AI in critical care: development and multinational consensus validation

2026-06-17T01:52:16Z

Title: The IMPACT framework for evaluating generative AI in critical care: development and multinational consensus validation Authors: Yeh, Yu-Chang; Shih, Ming-Chieh; De Backer, Daniel; Celi, Leo Anthony; See, Kay Choong; Fujii, Tomoko; Ling, Lowell; Mongkolpun, Wasineenart; Hu, Hsiang-Wei; Chen, Hsuan-Yu; Chen, Wei-Cheng; Cholley, Bernard; Fong, Kean Khang; Ryu, Ho-Geol; Na, Sungwon; Egi, Moritoki; Chan, Wing-Sum; Chen, Kuan-Fu; Kamaleswaran, Rishikesan; Chuang, Yu-Chen; Yang, Chi-Ju; Hsiao, Wei-Ling; Lai, Sheng-Ru; Ku, David; Jahan, Ahsina; Martin, Greg S. Abstract: Background: Generative artificial intelligence (GenAI) is increasingly used for clinical decision support in critical care, yet standardized methods for evaluating GenAI content in intensive care settings are lacking. Existing metrics assess textual similarity but fail to capture clinical accuracy, reasoning quality, or urgency. Methods: We developed and validated the IMPACT framework through a five-phase multinational panel consensus process. Reporting adhered to the ACCORD guideline. A steering committee of eight persons provided clinical and methodological oversight. Panelists were recruited through purposive sampling to ensure geographic and multidisciplinary representation. Content validity was assessed using the Content Validity Ratio (CVR) and Item-level Content Validity Index (I-CVI), with retention thresholds set at 70% agreement and I-CVI >= 0.80. Results: A total of 58 panelists from 12 countries and regions participated, with 42 completing formal consensus voting. Participants included intensivists, physicians with AI research expertise, information technology specialists, and other critical care professionals. All six IMPACT domains exceeded validity thresholds (mean agreement 89.3%, CVR = 0.79, I-CVI = 0.92). Of 24 candidate subitems, 21 met retention criteria (mean agreement 85.7%, CVR = 0.71, I-CVI = 0.90). Three subitems were removed due to insufficient consensus and conceptual overlap. The validated framework comprises six domains with 21 subitems. Conclusions: The IMPACT framework provides a consensus-validated approach for evaluating GenAI clinical decision support in intensive care, addressing gaps in current evaluation methods.

Respiratory Depression Following Concomitant Infusion of Remimazolam and Remifentanil Using Targeted Effect-Site Concentrations: A Randomized Controlled Trial

2026-06-18T01:50:06Z

Title: Respiratory Depression Following Concomitant Infusion of Remimazolam and Remifentanil Using Targeted Effect-Site Concentrations: A Randomized Controlled Trial Authors: Kim, Ha Yeon; Min, Sang Kee; Moon, Jee Hwan; Kwak, Hyeongjin; Park, Soo Jung Abstract: Background and Objectives: Remimazolam and remifentanil are ultra-short-acting agents that are used for sedation and analgesia, respectively. Their combined effect on respiratory function is unclear. We evaluated whether co-administration produced dose-dependent respiratory depression and loss of consciousness (LOC) preceded oxygen desaturation. Materials and Methods: A randomized, double-blind trial was conducted from May to July 2024. Female patients (20-65 years; n = 108; American Society of Anesthesiologists physical status I-II) undergoing elective gynecological surgery were selected. Patients received remifentanil via target-controlled infusion (TCI) at effect-site concentrations (C-e) of 1.0, 1.5, or 2.0 ng/mL (Groups 1.0, 1.5, and 2.0) combined with a fixed C-e of 500 ng/mL remimazolam. Respiratory variables, timing of LOC, bispectral index, and adverse events were recorded. Results: Respiratory depression increased in a dose-dependent manner. Jaw thrust was required in 52.8% of Group 1.0 and 91.7% of Group 2.0 (p < 0.001). The need for 100% oxygen increased from 30.6% to 69.4% (p = 0.001). Minute ventilation decreased only in Group 2.0 (p = 0.008). Involuntary movements were frequent in Group 1.0 (p = 0.005). Conclusions: Remimazolam-remifentanil co-administration via TCI induced dose-dependent respiratory depression and pre-LOC desaturation. Therefore, continuous monitoring and careful titration are essential.