This study investigates the clinical efficacy of MRI-based adaptive brachytherapy (IGABT) using combined intracavitary and interstitial techniques in the curative treatment of patients with advanced cervical cancer (LACC). A retrospective analysis was conducted on 149 LACC patients treated at a single center. The therapeutic protocol included intensity-modulated external beam radiotherapy (IMRT) and IGABT. Dosimetric parameters were evaluated for relevance for local control (LC), progression-free survival (PFS), and overall survival (OS) using Kaplan–Meier estimation, Cox regression, and log-rank test. Patients predominantly presented with stage III/IV tumors (81%, FIGO 2018). The median high-risk clinical target volume (hrCTV) was 34 cm3, with a median D90% dose of 88.9 GyEQD2. At 24 months, OS, PFS, and LC rates were 86%, 57%, and 81%, respectively. FIGO stage, tumor volume, and histology were significant predictors of PFS. Higher total hrCTV doses were strongly correlated with improved LC and PFS, emphasizing the importance of precise dosimetric optimization in IGABT and confirming the critical role of IGABT in achieving very good LC rates for LACC. The reported LC rates are comparable to landmark studies, such as INTERLACE and KEYNOTE-A18. This study validates the effectiveness of MRI-guided IGABT in enhancing local tumor control in advanced-stage cervical cancer while providing insights into the prognostic implications of dosimetric parameters such as hrCTV and point A. Future research should address the persistent challenge of distant metastases by exploring the integration of novel systemic treatment options.

• Book : 32(3)
• Pub. Date : 2025
• Page : pp.136-136
• Keyword :

Background: Primary intrathoracic synovial sarcoma (SS) is a rare entity. The objective of this study was to evaluate survival outcomes for patients with intrathoracic SS presenting with localized disease at diagnosis. Methods: We conducted a retrospective review of 63 patients diagnosed with intrathoracic SS between 1997 and 2020. The Kaplan–Meier method and log-rank test were used to estimate the progression-free survival (PFS), overall survival (OS), local recurrence-free survival (LRFS), and metastasis-free survival (MFS). The hazard ratios were estimated by using Cox proportional hazards regression. Median follow-up time, age-at-diagnosis, and primary tumor size were 31 months (range: 4–218 months), 43 years (range: 18–77), and 7 cm (range: 1–23), respectively. Results: Sixty-two of sixty-three (98%) patients had their primary tumor resected, from whom eighteen (29%) and forty-three (69%) had received neo/adjuvant radiotherapy and chemotherapy, respectively. Median PFS, OS, and MFS were 1.2, 3.0, and 1.1 years, respectively. Based on multivariable analyses, patients with ≥5 cm tumor size had poorer OS (versus < 5 cm; HR: 2.66; 95% CI: 1.16, 6.11; LR-p = 0.014). Importantly, the receipt of neo/adjuvant chemotherapy was the only factor associated with both a more favorable PFS (HR: 0.33; 95% CI: 0.17, 0.65; LR-p = 0.0002) and a more favorable MFS (median 1.33 years versus no chemo 0.5 years; HR: 0.35; 95% CI: 0.17, 0.73; LR-p = 0.005). Conclusions: Outcomes associated with intrathoracic SS remain poor. Factors associated with poorer outcomes include larger tumors and omission of chemotherapy in the management of localized disease. We recommend providing perioperative chemotherapy to all patients with ≥5 cm tumor size to improve progression and metastasis-free survival.

• Book : 17(5)
• Pub. Date : 2025
• Page : pp.745-745
• Keyword :

본 연구에서는 소나무 새싹 추출물(PBE)이 대식세포 활성화에 미치는 영향과 관련 메커니즘을 조사하였다. PBE는 RAW 264.7 대식세포로부터 NO를 비롯하여 다양한 cytokines (TNF-α 및 IL-6)과 chemokines (MIP-2 및 MCP-1)을 유도하였다. 대식세포로부터 cytokines와 chemokines를 유도하는 PBE의 활성은 mRNA 발현에서도 확인되었다. 또한 PBE는 대식세포의 활성화 조절하는 주요 신호전달 체계인 MAPK (ERK, JNK 및 p38)와 IκB의 인산화를 유도하는 것으로 나타났다. 이러한 결과는 PBE가 MAPK 및 NF-κB 경로를 통해 대식세포를 활성화하여 면역조절과 관련된 cytokines 및 chemokines의 분비를 유도한다는 것을 의미한다. TLR2 또는 TLR4 녹아웃 마우스의 골수 유래 대식세포(BMDM)에 PBE를 처리하는 실험에서, PBE의 cytokines 활성은 주로 TLR4 경로를 통해 매개되고, 또한 TLR2 경로도 부분적으로 매개하는 것으로 나타났다. 대식세포의 기능과 관련하여 PBE는 세포 표면에서 MHC-I 및 MHC-II 분자의 발현을 크게 상향 조절하였으며, 세포 내 활성 산소종(ROS)의 생성을 유도하였다. 또한 PBE는 대식세포의 식세포(phagocytosis) 기능도 높이는 것으로 확인되었다. 암전이 억제실험에 있어서, PBE를 경구 투여할 경우, B16-BL6 흑색종에 의해 생성되는 폐 전이를 유의하게 억제하고, 또한 종양의 증식과 종양에 의해 형성되는 혈관신생을 유의하게 억제하는 것으로 나타났다. 이상의 결과는 PBE가 대식세포를 활성화하는 면역증강 작용을 가지며, 또한 암의 전이와 증식을 억제하는 항암활성도 보유한다는 것을 시사한다.

• Book : 35(2)
• Pub. Date : 2025
• Page : pp.144-154
• Keyword :

Nuclear fusion is a promising energy source. The International Thermonuclear Experimental Reactor aims to study the feasibility of tokamak-type reactors and test technologies and materials for commercial use. One major challenge is developing materials for the reactor’s divertor, which supports high thermal flux. Tungsten was chosen as the plasma-facing material, while a CuCrZr alloy will be used in the cooling pipes. However, the gradient between the working temperatures of these materials requires the use of a thermal barrier interlayer between them. To this end, refractory high-entropy (CrFeTiTa)70W30 and VFeTiTaW alloys were prepared by mechanical alloying and sintering, and their thermal and irradiation resistance was evaluated. Both alloys showed phase growth after annealing at 1100 °C for 8 days, being more pronounced for higher temperatures (1300 °C and 1500 °C). The VFeTiTaW alloy presented greater phase growth, suggesting lower microstructural stability, however, no new phases were formed. Both (as-sintered) alloys were irradiated with Ar+ (150 keV) with a fluence of 2.4 × 1020 at/m2, as well as He+ (10 keV) and D+ (5 keV) both with a fluence of 5 × 1021 at/m2. The morphology of the surface of both samples was analyzed before and after irradiation showing no severe morphologic changes, indicating high irradiation resistance. Additionally, the VFeTiTaW alloy presented a lower deuterium retention (8.58%) when compared to (CrFeTiTa)70W30 alloy (14.41%).

• Book : 18(5)
• Pub. Date : 2025
• Page : pp.1030-1030
• Keyword :

• Book : 1075()
• Pub. Date : 2025
• Page : pp.170357-170357
• Keyword :

SummaryType 1 diabetes (T1D) is characterized by the autoimmune destruction of most insulin-producing β-cells, along with dysregulated glucagon secretion from pancreatic α-cells. We conducted an integrated analysis that combines electrophysiological and transcriptomic profiling, along with machine learning, of islet cells from T1D donors to investigate the mechanisms underlying their dysfunction. Surviving β-cells exhibit altered electrophysiological properties and transcriptomic signatures indicative of increased antigen presentation, metabolic reprogramming, and impaired protein translation. In α-cells, we observed hyper-responsiveness and increased exocytosis, which are associated with upregulated immune signaling, disrupted transcription factor localization and lysosome homeostasis, as well as dysregulation of mTORC1 complex signaling. Notably, key genetic risk signals for T1D were enriched in transcripts related to α-cell dysfunction, including MHC class I which were closely linked with α-cell dysfunction. Our data provide novel insights into the molecular underpinnings of islet cell dysfunction in T1D, highlighting pathways that may be leveraged to preserve residual β-cell function and modulate α-cell activity. These findings underscore the complex interplay between immune signaling, metabolic stress, and cellular identity in shaping islet cell phenotypes in T1D.HighlightsSurviving β-cells in T1D show disrupted electrical function linked to metabolic reprogramming and immune stress.Transcripts associated with α-cell dysfunction are enriched in genetic risk alleles for T1D.Upregulated MHC class I and impaired nuclear localization of key transcription factors associate with α-cell dysfunction in T1D.T1D α-cells exhibit increased hyper-activity, lysosomal imbalance and impaired mTORC1 signaling, which promotes dysregulated glucagon secretion.

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• Pub. Date : 2025
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• Book : ()
• Pub. Date : 2025
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• Pub. Date : 2025
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ABSTRACTAccurately predicting individual antidepressant treatment response could expedite the lengthy trial‐and‐error process of finding an effective treatment for major depressive disorder (MDD). We tested and compared machine learning‐based methods that predict individual‐level pharmacotherapeutic treatment response using cortical morphometry from multisite longitudinal cohorts. We conducted an international analysis of pooled data from six sites of the ENIGMA‐MDD consortium (n = 262 MDD patients; age = 36.5 ± 15.3 years; 154 (59%) female; mean response rate = 57%). Treatment response was defined as a ≥ 50% reduction in symptom severity score after 4–12 weeks post‐initiation of antidepressant treatment. Structural MRI was acquired before, or < 14 days after, treatment initiation. The cortex was parcellated using FreeSurfer, from which cortical thickness and surface area were measured. We tested several machine learning pipeline configurations, which varied in (i) the way we presented the cortical data (i.e., average values per region of interest, as a vector containing voxel‐wise cortical thickness and surface area measures, and as cortical thickness and surface area projections), (ii) whether we included clinical data, and the (iii) machine learning model (i.e., gradient boosting, support vector machine, and neural network classifiers) and (iv) cross‐validation methods (i.e., k‐fold and leave‐one‐site‐out) we used. First, we tested if the overall predictive performance of the pipelines was better than chance, with a corrected 10‐fold cross‐validation permutation test. Second, we compared if some machine learning pipeline configurations outperformed others. In an exploratory analysis, we repeated our first analysis in three subpopulations, namely patients (i) from a single site, (ii) with comparable response rates, and (iii) showing the least (first quartile) and the most (fourth quartile) treatment response, which we call the extreme (non‐)responders subpopulation. Finally, we explored the effect of including subcortical volumetric data on model performance. Overall, performance predicting antidepressant treatment response was not significantly better than chance (balanced accuracy = 50.5%; p = 0.66) and did not vary with alternative pipeline configurations. Exploratory analyses revealed that performance across models was only significantly better than chance in the extreme (non‐)responders subpopulation (balanced accuracy = 63.9%, p = 0.001). Including subcortical data did not alter the observed model performance. Cortical structural MRI alone could not reliably predict individual pharmacotherapeutic treatment response in MDD. None of the used machine learning pipeline configurations outperformed the others. In exploratory analyses, we found that predicting response in the extreme (non‐)responders subpopulation was feasible on both cortical data alone and combined with subcortical data, which suggests that specific MDD subpopulations may exhibit response‐related patterns in structural data. Future work may use multimodal data to predict treatment response in MDD.

• Book : 46(1)
• Pub. Date : 2025
• Page :
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• Book : ()
• Pub. Date : 2025
• Page :
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