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Solute transport family 7A member 7 (SLC7A7) mutations contribute to lysinuric protein intolerance (LPI), which is the mechanism of action that has been extensively studied. In colorectal cancer (CRC), SLC7A7 appears to play a role, but the features and mechanisms are not yet well understood. Survival was analyzed using the Kaplan–Meier analysis. Enrichment analysis was performed to characterize, immune infiltration, methylation, genetic instability, and crucial pathways of SLC7A7. Afterward, functional experiments were conducted in vitro to investigate how SLC7A7 affects tumor metastasis. Mechanistically, quantitative real-time PCR (qRT-PCR), western blot (WB), and methylated RNA immunoprecipitation (me-RIP) were carried out to confirm the methylation modification of SLC7A7 and related functions. High levels of expression of SLC7A7 are predictive of a worse prognosis for CRC patients. Enrichment analysis showed that SLC7A7 was significantly enriched during EMT and could be enriched in the Wnt/β-catenin signaling pathway, immune infiltration analysis of pan-cancer showed that SLC7A7 was significantly enriched in macrophages, and methylation analysis showed that SLC7A7 methylation modification affected the prognosis of specific cancers. SLC7A7 was indicated to promote the migration and invasion of CRC cells in in vitro functional experiments. Mechanistically, SLC7A7 was observed to potentially interact with the Wnt/β-catenin signaling pathway, possibly by influencing adenomatous polyposis coli (APC) expression. Furthermore, we identified that SLC7A7 undergoes N7-methylguanosine (m7G) modification, which may regulate SLC7A7 mRNA stability, with Quaking (QKI) potentially playing a role in this process by recognizing the m7G modification. Our results indicate that SLC7A7 may promote CRC metastasis through the SLC7A7/APC/Wnt/β-catenin signaling pathway. Moreover, m7G modification might be involved in regulating SLC7A7 mRNA stability, highlighting a novel layer of regulation.

Renal dysfunction (RD) often characterizes the worse course of patients with advanced heart failure (AHF). Many prognosis assessments are hindered by researcher biases, redundant predictors, and lack of clinical applicability. In this study, we enroll 1736 AHF/RD patients, including data from Henan Province Clinical Research Center for Cardiovascular Diseases (which encompasses 11 hospital subcenters), and Beth Israel Deaconess Medical Center. We developed an AI hybrid modeling framework, assembling 12 learners with different feature selection paradigms to expand modeling schemes. The optimized strategy is identified from 132 potential schemes to establish an explainable survival assessment system: AIHFLevel. The conditional inference survival tree determines a probability threshold for prognostic stratification. The evaluation confirmed the system’s robustness in discrimination, calibration, generalization, and clinical implications. AIHFLevel outperforms existing models, clinical features, and biomarkers. We also launch an open and user-friendly website www.hf-ai-survival.com , empowering healthcare professionals with enhanced tools for continuous risk monitoring and precise risk profiling. Here the authors show an AI-powered assessment system (AIHFLevel, www.hf-ai-survival.com ) empowering healthcare professionals for continuous risk monitoring and prognosis assessment of patients who have advanced heart failure with renal dysfunction.

Background To establish a prognostic risk profile for ovarian cancer (OC) patients based on cancer-associated fibroblasts (CAFs) and gain a comprehensive understanding of their role in OC progression, prognosis, and therapeutic efficacy. Methods Data on OC single-cell RNA sequencing (scRNA-seq) and total RNA-seq were collected from the GEO and TCGA databases. Seurat R program was used to analyze scRNA-seq data and identify CAFs clusters corresponding to CAFs markers. Differential expression analysis was performed on the TCGA dataset to identify prognostic genes. A CAF-associated risk signature was designed using Lasso regression and combined with clinicopathological variables to develop a nomogram. Functional enrichment and the immune landscape were also analyzed. Results Five CAFs clusters were identified in OC using scRNA-seq data, and 2 were significantly associated with OC prognosis. Seven genes were selected to develop a CAF-based risk signature, primarily associated with 28 pathways. The signature was a key independent predictor of OC prognosis and relevant in predicting the results of immunotherapy interventions. A novel nomogram combining CAF-based risk and disease stage was developed to predict OC prognosis. Conclusion The study highlights the importance of CAFs in OC progression and suggests potential for innovative treatment strategies. A CAF-based risk signature provides a highly accurate prediction of the prognosis of OC patients, and the developed nomogram shows promising results in predicting the OC prognosis.

The popularity and convenience of mobile medical image analysis and diagnosis in mobile edge computing (MEC) environments have greatly improved the efficiency and quality of healthcare services, necessitating the use of deep neural networks (DNNs) for image analysis. However, DNNs face performance and energy constraints when operating on the mobile side, and are limited by communication costs and privacy issues when operating on the edge side, and previous edge-end collaborative approaches have shown unstable performance and low search efficiency when exploring classification strategies. To address these issues, we propose a DNN edge-optimized collaborative inference strategy (MOCI) for medical image diagnosis, which optimizes data transfer and computation allocation by combining compression techniques and multi-agent reinforcement learning (MARL) methods. The MOCI strategy first uses coding and quantization-based compression methods to reduce the redundancy of image data during transmission at the edge, and then dynamically segments the DNN model through MARL and executes it collaboratively between the edge and the mobile device. To improve policy stability and adaptability, MOCI introduces the optimal transmission distance (Wasserstein) to optimize the policy update process, and uses the long short-term memory (LSTM) network to improve the model’s adaptability to dynamic task complexity. The experimental results show that the MOCI strategy can effectively solve the collaborative inference task of medical image diagnosis and significantly reduce the latency and energy consumption with less than a 2% loss in classification accuracy, with a maximum reduction of 38.5% in processing latency and 71% in energy consumption compared to other inference strategies. In real-world MEC scenarios, MOCI has a wide range of potential applications that can effectively promote the development and application of intelligent healthcare.

Objective This study aims to evaluate the impact of gynecologic oncologists' laparoscopic proficiency on survival outcomes in cervical cancer patients. Methods A cohort of 1,965 cervical cancer cases from four clinical centers in China was analyzed, including abdominal radical hysterectomy (ARH), laparoscopic radical hysterectomy (LRH), and robotic radical hysterectomy (RRH). The median operative time (MOT) for LRH was used as a measure of surgical proficiency. Survival outcomes of ARH vs. LRH were compared in early-stage cervical cancer patients without adjuvant therapy to identify a critical MOT threshold. Below this threshold, no significant differences in prognosis were observed between ARH and LRH. Propensity score matching and mixed-effects Cox regression were used to adjust for baseline risk factors and random effects, validating the finding across all LRH cases. Results The Kaplan–Meier analysis showed that improved prognosis was associated with reduced MOT. When gynecologic oncologists had an MOT within 210 min, LRH vs ARH was no longer a significant risk factor (HR 1.1998; 95% CI: 0.9785–1.4713; p  = 0.07998). Propensity score matching and mixed-effects Cox regression were used to further clarify the significant prognostic differences in LRH performed by different level surgeons. Conclusion MOT reflects surgical efficiency and serves as a key indicator of the operative proficiency of gynecologic oncologists, which is pivotal in determining the survival prognosis of cervical cancer patients undergoing LRH. For surgeons with rigorous laparoscopic training, the survival outcomes of LRH are expected to be comparable to those of ARH.

Deregulated microRNAs and their roles in tumorigenesis have attracted much attention in recent years. Although miR-503 was shown to be important in tumorigenesis, its role in osteosarcoma remains unknown. In this study, we focused on the expression and mechanisms of miR-503 in osteosarcoma development. We found that miR-503 was down-regulated in osteosarcoma cell lines and primary tumor samples, and the restoration of miR-503 reduced cell proliferation, migration and invasion. Low level of miR-503 in patients with osteosarcoma was associated with considerably shortened disease-free survival. Furthermore, bioinformatic prediction and experimental validation revealed that the anti-tumor effect of miR-503 was probably exerted through targeting and repressing of L1CAM expression. L1CAM was up-regulated in osteosarcoma cell lines and primary tumor samples and the expression level of L1CAM were negatively correlated with miR-503 levels in osteosarcoma tissues. Collectively, our data identify the important roles of miR-503 in osteosarcoma pathogenesis, indicating its potential application in cancer therapy.

Cervical cancers are the most common forms of cancer that occur in women globally and are difficult to be cured in their terminal stages. Tetrandrine (TET), a monomeric compound isolated from a traditional Chinese medicine, Radix , exhibits anticancer effects on different tumor types. However, the mechanisms by which TET regulates the proliferation, apoptosis, migration, and invasion in cervical cancer remain unclear. Thus, this study aimed to investigate the therapeutic effects of TET on cervical cancer in vitro and in vivo. Cell Counting Kit-8, immunofluorescence, flow cytometry, wound healing, and transwell migration assays were used to detect cell proliferation, apoptosis, and migration and invasion, respectively, in vitro. In addition, immunohistochemical assays were performed to evaluate tumor growth and apoptosis in vivo. Moreover, Western blotting was used to examine active caspase 3, matrix metalloproteinase (MMP)2, and MMP9 protein levels in vitro and in vivo. The results revealed that TET significantly inhibited SiHa cell proliferation in vitro and suppressed tumor growth in vivo. Meanwhile, TET was revealed to induce cervical cancer cell apoptosis by upregulating active caspase 3 in vitro and in vivo. Furthermore, the migration and invasion of SiHa cells were inhibited by TET accompanied with MMP2 and MMP9 downregulation. We have shown that TET inhibited cervical tumor growth and migration in vitro and in vivo for the first time. The accumulating evidence suggests that TET could be a potential therapeutic agent for the treatment of cervical cancer.

Hair serves important physiological functions, including temperature regulation and scalp protection. However, excessive shedding not only impacts these functions but can also significantly affect mental health and quality of life. Tianma Gouteng decoction (TGD) is a traditional Chinese medicine used for the treatment of various conditions, including hair loss. However, the associated mechanism underlying its anti-alopecia effect remains unknown. Therefore, this study aims to elucidate these mechanisms by employing systematic biology approaches, as well as in vitro and in vivo experimental validation. The chemical constituents of Tianma Gouteng decoction were identified using UHPLC-MS/MS, from which 39 potential bioactive components were screened, while an additional 131 putative Tianma Gouteng decoction beneficial components were extracted from the Traditional Chinese Medicine Database and Analysis Platform (TCMSP) database. We then applied a dual-dimensional network pharmacology approach to analyze the data, followed by validation studies combining molecular docking techniques with in vivo and in vitro experiments. From the 39 bioactive components, including quercetin, luteolin, fisetin, wogonin, oroxylin A, boldine, tetrahydroalstonine, and galangin A, 782 corresponding targets were identified. In particular, GSK3β and β-catenin exhibited strong binding activity with the bioactive compounds. Hence, construction of a bioactive component-target network revealed that the mechanism underlying the anti-alopecia mechanism of Tianma Gouteng decoction primarily involved the Wnt/β-catenin signaling pathway. Moreover, C57BL/6J mice exhibited measurable improvements in hair follicle regeneration following treatment with Tianma Gouteng decoction. Additionally, β-catenin and p-GSK3β levels were upregulated, while GSK3β was downregulated in Tianma Gouteng decoction-treated animals and dermal papilla cells compared to control group. These in vivo and in vitro outcomes validated the targets and pathways predicted in the network pharmacology analysis of Tianma Gouteng decoction. This study provides a systematic analysis approach to identify the underlying anti-alopecia mechanisms of Tianma Gouteng decoction, further providing theoretical support for clinical assessment of Tianma Gouteng decoction.

GDP-L-galactose phosphorylase (GGP) is a key rate-limiting enzyme in the ascorbic acid synthesis pathway, and it plays a crucial role in regulating ascorbic acid metabolism and redox balance. The upstream open reading frame (uORF) in the 5' untranslated region of the GGP gene typically suppresses translation efficiency, reducing vitamin C content. In this study, GGP uORF in rice was identified, and seven GGP uORF allele variants AL1-AL7 were generated by CRISPR/Cas9 technology; then, the effects of these variations on AsA content and osmotic stress resistance were evaluated. The AsA content of the seven mutant plants was 1.80-3.08 times greater than that of the control, and the increase of AL4 mutant was the greatest. Protoplast assays confirmed that the OsGGP uORF inhibits downstream ORF translation. Osmotic stress experiments revealed that in the mutant lines, both the activities of enzymes involved in reactive oxygen species (ROS) scavenging and the proline (Pro) content were significantly increased, while the levels of peroxides were significantly decreased. These results demonstrate that mutation of the GGP uORF significantly increases translation efficiency, AsA content, and the ability of rice to reduce ROS levels, restore osmotic balance, and improve ROS scavenging capacity under osmotic stress. This study reports the regulated osmotic stress of the GGP uORF in monocotyledonous plants for the first time and has created a variety of allelic variation germplasm resources. It provides a novel approach for improving AsA content and tolerance to salt stress in rice and other monocots via genetic approaches.

Artificial intelligence (AI) has advanced predictive modeling in medicine, yet many models remain disease‐specific and difficult to generalize across clinical settings. Key challenges include the trade‐off between interpretability and accuracy, reliance on single algorithms, limited external validation, and biased feature importance estimation. In this Perspective, we discuss how methodological advances in computational sciences, including automated machine learning (AutoML) and neural architecture search (NAS), reveal a gap between automated hybrid systems and current clinical modeling practices. To address these challenges, we outline a principled artificial intelligence hybrid ensemble framework based on three design principles: integration of diverse learners, consensus‐driven validation across independent cohorts, and transparent feature attribution using Shapley Additive exPlanations (SHAP). This framework emphasizes methodological robustness, interpretability, and cross‐disease applicability to support the translation of artificial intelligence models into clinical practice. An artificial intelligence (AI) hybrid ensemble framework integrates multiple learners with recursive feature elimination and consensus evaluation, achieving robust and interpretable predictions. By balancing accuracy, interpretability, and complexity, this approach addresses high‐dimensional challenges and enhances clinical utility, offering a generalizable paradigm for reliable prognostic modeling across diverse medical domains.