Injectable temperature-sensitive hydrogel facilitating endoscopic submucosal dissection.

Purpose: Early gastrointestinal tumors can be removed by endoscopic procedures. Endoscopic mucosal dissection (ESD) requires submucosal fluid injection to provide mucosal elevation and prevent intraoperative perforation. However, the clinically applied normal saline mucosal elevation height is low for a short time, which often requires multiple intraoperative injections that increase the inconvenience and procedure time. In addition, recently researched submucosal injection materials (SIM) suffer from complex preparation, poor economy, and poor biocompatibility. Therefore, there is an urgent need for a new type of SIM that can provide long, safe and effective mucosal elevation in support of the endoscopic procedures. Methods: The FS hydrogel is based on polyethylene-polypropylene glycol (F-127) mixed with sodium alginate (SA). The different physicochemical properties of FS hydrogels were characterized through various experiments. Afterward, various biosafety assessments were carried out. Finally, the performance of FS hydrogels was evaluated by in vitro submucosal injection and in vivo swine ESD. Results: The experimental results show that the FS hydrogel is liquid at room temperature, making it easy to inject, and when injected under the mucosa, it undergoes temperature-induced cross-linking, transforming from a liquid to a solid state to provide long-lasting mucosal augmentation. At the same time, the FS hydrogel exhibits controllable gelation, stability, and biocompatibility. The results of in vitro submucosal injections and in vivo ESD procedures showed that FS achieves high mucosal augmentation and provides good submucosal cushioning in the long term. Conclusion: In summary, the F-127/SA hydrogel is simple to synthesize, cost-effective, safe, easy to store, and able to assist ESD well from the perspective of practical clinical problems, indicating that the FS hydrogel can be an ideal potent submucosal injection substitution.

Harnessing artificial intelligence for prostate cancer management.

Prostate cancer (PCa) is a common malignancy in males. The pathology review of PCa is crucial for clinical decision-making, but traditional pathology review is labor intensive and subjective to some extent. Digital pathology and whole-slide imaging enable the application of artificial intelligence (AI) in pathology. This review highlights the success of AI in detecting and grading PCa, predicting patient outcomes, and identifying molecular subtypes. We propose that AI-based methods could collaborate with pathologists to reduce workload and assist clinicians in formulating treatment recommendations. We also introduce the general process and challenges in developing AI pathology models for PCa. Importantly, we summarize publicly available datasets and open-source codes to facilitate the utilization of existing data and the comparison of the performance of different models to improve future studies.

Xiaoqing: A Q&A model for glaucoma based on LLMs.

Glaucoma is one of the leading cause of blindness worldwide. Individuals affected by glaucoma, including patients and their family members, frequently encounter a deficit in dependable support beyond the confines of clinical environments. Seeking advice via the internet can be a difficult task due to the vast amount of disorganized and unstructured material available on these sites, nevertheless. This research explores how Large Language Models (LLMs) can be leveraged to better serve medical research and benefit glaucoma patients. We introduce Xiaoqing, a Natural Language Processing (NLP) model specifically tailored for the glaucoma field, detailing its development and deployment. To evaluate its effectiveness, we conducted two forms of experiments: comparative and experiential. In the comparative analysis, we presented 22 glaucoma-related questions in simplified Chinese to three medical NLP models (Xiaoqing LLMs, HuaTuo, Ivy GPT) and two general models (ChatGPT-3.5 and ChatGPT-4), covering a range of topics from basic glaucoma knowledge to treatment, surgery, research, management standards, and patient lifestyle. Responses were assessed for informativeness and readability. The experiential experiment involved glaucoma patients and non-patients interacting with Xiaoqing, collecting and analyzing their questions and feedback on the same criteria. The findings demonstrated that Xiaoqing notably outperformed the other models in terms of informativeness and readability, suggesting that Xiaoqing is a significant advancement in the management and treatment of glaucoma in China. We also provide a Web-based version of Xiaoqing, allowing readers to directly experience its functionality. The Web-based Xiaoqing is available at https://qa.glaucoma-assistant.com//qa.

BCL-XL inhibitors enhance the apoptotic efficacy of BRAF inhibitors in BRAFV600E colorectal cancer.

Metastatic BRAFV600E colorectal cancer (CRC) carries an extremely poor prognosis and is in urgent need of effective new treatments. While the BRAFV600E inhibitor encorafenib in combination with the EGFR inhibitor cetuximab (Enc+Cet) was recently approved for this indication, overall survival is only increased by 3.6 months and objective responses are observed in only 20% of patients. We have found that a limitation of Enc+Cet treatment is the failure to efficiently induce apoptosis in BRAFV600E CRCs, despite inducing expression of the pro-apoptotic protein BIM and repressing expression of the pro-survival protein MCL-1. Here, we show that BRAFV600E CRCs express high basal levels of the pro-survival proteins MCL-1 and BCL-XL, and that combining encorafenib with a BCL-XL inhibitor significantly enhances apoptosis in BRAFV600E CRC cell lines. This effect was partially dependent on the induction of BIM, as BIM deletion markedly attenuated BRAF plus BCL-XL inhibitor-induced apoptosis. As thrombocytopenia is an established on-target toxicity of BCL-XL inhibition, we also examined the effect of combining encorafenib with the BCL-XL -targeting PROTAC DT2216, and the novel BCL-2/BCL-XL inhibitor dendrimer conjugate AZD0466. Combining encorafenib with DT2216 significantly increased apoptosis induction in vitro, while combining encorafenib with AZD0466 was well tolerated in mice and further reduced growth of BRAFV600E CRC xenografts compared to either agent alone. Collectively, these findings demonstrate that combined BRAF and BCL-XL inhibition significantly enhances apoptosis in pre-clinical models of BRAFV600E CRC and is a combination regimen worthy of clinical investigation to improve outcomes for these patients.

Advanced gastrointestinal stromal tumor: reliable classification of imatinib plasma trough concentration via machine learning.

AIM: Patients with advanced gastrointestinal stromal tumors (GISTs) exhibiting an imatinib plasma trough concentration (IM Cmin) under 1100 ng/ml may show a reduced drug response rate, leading to the suggestion of monitoring for IM Cmin. Consequently, the objective of this research was to create a customized IM Cmin classification model for patients with advanced GISTs from China. METHODS: Initial data and laboratory indicators from patients with advanced GISTs were gathered, and the above information was segmented into a training set, validation set, and testing set in a 6:2:2 ratio. Key variables associated with IM Cmin were identified to construct the classification model using the least absolute shrinkage and selection operator (LASSO) regression and forward stepwise binary logistic regression. Within the training and validation sets, nine ML classification models were constructed via the resampling method and underwent comparison through the Brier scores, the areas under the receiver-operating characteristic curve (AUROC), the decision curve, and the precision-recall (AUPR) curve to determine the most suitable model for this dataset. Two methods of internal validation were used to assess the most suitable model's classification performance: tenfold cross-validation and random split-sample validation (test set), and the value of the test set AUROC was used to evaluate the model's classification performance. RESULTS: Six key variables (gender, daily IM dose, metastatic site, red blood cell count, platelet count, and percentage of neutrophils) were ultimately selected to construct the classification model. In the validation set, it is found by comparison that the Extreme Gradient Boosting (XGBoost) model has the largest AUROC, the lowest Brier score, the largest area under the decision curve, and the largest AUPR value. Furthermore, as evaluated via internal verification, it also performed well in the test set (AUROC = 0.725). CONCLUSION: For patients with advanced GISTs who receive IM, initial data and laboratory indicators could be used to accurately estimate whether the IM Cmin is below 1100 ng/ml. The XGBoost model may stand a chance to assist clinicians in directing the administration of IM.

Exploring the causal association between rheumatoid arthritis and the risk of cervical cancer: a two-sample Mendelian randomization study.

OBJECTIVE: Whether rheumatoid arthritis patients have an increased risk of cervical cancer remains controversial, and further research is needed on this clinical question. This study aims to investigate the association between rheumatoid arthritis and the susceptibility to cervical cancer by employing Mendelian randomization methodology, utilizing the extensive dataset from human genome-wide association data analysis. METHODS: The publicly accessible MR base database was utilized to obtain the complete genome, relevant research findings, and summarized data pertaining to rheumatoid arthritis and cervical cancer. Genetic tool variables, specifically single-nucleotide polymorphisms closely linked to rheumatoid arthritis, were chosen for analysis. Four methods, namely inverse variance weighted analysis, weighted median analysis, weighted mode, and MR-Egger regression, were employed. Statistical analysis was conducted to explore the potential association between rheumatoid arthritis and susceptibility to cervical cancer. RESULTS: The results of the inverse variance weighted analysis (OR = 1.096, 95% CI: 1.018-1.180, P = 0.015) indicate a significant causal relationship between rheumatoid arthritis and an increased risk of cervical cancer. Furthermore, the absence of horizontal pleiotropic effects (MR-Egger intercept = 0.00025, P = 0.574) and heterogeneity (QEgger = 2.239, I2Egger = 0.225, PEgger = 0.268, QIVW = 2.734, I2IVW = 0.220, PIVW = 0.999) suggests that the observed association is not influenced by confounding factors. Sensitivity analysis and other statistical methods also support the conclusion that genetic pleiotropy does not introduce bias to the findings. CONCLUSION: There is a causal relationship between rheumatoid arthritis and the occurrence of cervical cancer. People with rheumatoid arthritis is one of the high-risk groups for early screening of cervical cancer. The IL-18 may play a significant role in elevating the risk of cervical cancer among rheumatoid arthritis patients.

LYSTO: The Lymphocyte Assessment Hackathon and Benchmark Dataset.

We introduce LYSTO, the Lymphocyte Assessment Hackathon, which was held in conjunction with the MICCAI 2019 Conference in Shenzhen (China). The competition required participants to automatically assess the number of lymphocytes, in particular T-cells, in images of colon, breast, and prostate cancer stained with CD3 and CD8 immunohistochemistry. Differently from other challenges setup in medical image analysis, LYSTO participants were solely given a few hours to address this problem. In this paper, we describe the goal and the multi-phase organization of the hackathon; we describe the proposed methods and the on-site results. Additionally, we present post-competition results where we show how the presented methods perform on an independent set of lung cancer slides, which was not part of the initial competition, as well as a comparison on lymphocyte assessment between presented methods and a panel of pathologists. We show that some of the participants were capable to achieve pathologist-level performance at lymphocyte assessment. After the hackathon, LYSTO was left as a lightweight plug-and-play benchmark dataset on grand-challenge website, together with an automatic evaluation platform.

Endoscopic resection of giant esophageal subepithelial lesions: experience from a large tertiary center.

BACKGROUND AND AIMS: Increased reports on endoscopic resection (ER) of esophageal giant subepithelial lesions (g-SELs) have emerged in recent years. The aim of this study was to evaluate the efficacy, technical difficulty, and safety through our single-center experience. METHODS: Seventy-five patients with g-SELs undergoing endoscopic resection were included in the training set. Clinicopathologic features, procedure-related characteristics, postprocedural outcomes, and follow-up data were analyzed. A predictive nomogram model for procedural difficulty was proposed based on the multivariable logistic regression analysis. Internal and external validations were conducted to verify the model performance. RESULTS: The overall en bloc resection rate was 93.3%. Intraoperative and postoperative adverse events occurred in 7 (9.3%) and 13 (17.3%) patients, respectively. No recurrence or metastasis was observed. Thirty-two (42.7%) patients underwent a difficult procedure. Age (adjusted odds ratio [aOR], .915; P = .004), maximal tumor diameter ≥8 cm (aOR, 9.896; P = .009), irregular shape (aOR, 4.081; P = .053), extraluminal growth pattern (aOR, 5.419; P = .011), and submucosal tunneling endoscopic resection (aOR, .109; P = .042) were found to be statistically or clinically significant factors for predicting endoscopic resection difficulty, based on which a nomogram model was developed. Internal and external validations of the nomogram via receiver-operating characteristic curves and calibration curves achieved favorable results. CONCLUSIONS: Endoscopic resection serves as a promising therapeutic option for esophageal g-SELs. A younger patient age, large tumor size, irregular shape, and extraluminal growth may indicate increased endoscopic resection difficulty, whereas a submucosal tunneling endoscopic resection procedure tends to be of lower difficulty. Our nomogram model performs well for predicting endoscopic resection difficulty for esophageal g-SELs.

Light mixed-supervised segmentation for 3D medical image data.

BACKGROUND: Accurate 3D semantic segmentation models are essential for many clinical applications. To train a model for 3D segmentation, voxel-level annotation is necessary, which is expensive to obtain due to laborious work and privacy protection. To accurately annotate 3D medical data, such as MRI, a common practice is to annotate the volumetric data in a slice-by-slice contouring way along principal axes. PURPOSE: In order to reduce the annotation effort in slices, weakly supervised learning with a bounding box (Bbox) was proposed to leverage the discriminating information via a tightness prior assumption. Nevertheless, this method requests accurate and tight Bboxes, which will significantly drop the performance when tightness is not held, that is when a relaxed Bbox is applied. Therefore, there is a need to train a stable model based on relaxed Bbox annotation. METHODS: This paper presents a mixed-supervised training strategy to reduce the annotation effort for 3D segmentation tasks. In the proposed approach, a fully annotated contour is only required for a single slice of the volume. In contrast, the rest of the slices with targets are annotated with relaxed Bboxes. This mixed-supervised method adopts fully supervised learning, relaxed Bbox prior, and contrastive learning during the training, which ensures the network exploits the discriminative information of the training volumes properly. The proposed method was evaluated on two public 3D medical imaging datasets (MRI prostate dataset and Vestibular Schwannoma [VS] dataset). RESULTS: The proposed method obtained a high segmentation Dice score of 85.3% on an MRI prostate dataset and 83.3% on a VS dataset with relaxed Bbox annotation, which are close to a fully supervised model. Moreover, with the same relaxed Bbox annotations, the proposed method outperforms the state-of-the-art methods. More importantly, the model performance is stable when the accuracy of Bbox annotation varies. CONCLUSIONS: The presented study proposes a method based on a mixed-supervised learning method in 3D medical imaging. The benefit will be stable segmentation of the target in 3D images with low accurate annotation requirement, which leads to easier model training on large-scale datasets.

Synthesis-based imaging-differentiation representation learning for multi-sequence 3D/4D MRI.

Multi-sequence MRIs can be necessary for reliable diagnosis in clinical practice due to the complimentary information within sequences. However, redundant information exists across sequences, which interferes with mining efficient representations by learning-based models. To handle various clinical scenarios, we propose a sequence-to-sequence generation framework (Seq2Seq) for imaging-differentiation representation learning. In this study, not only do we propose arbitrary 3D/4D sequence generation within one model to generate any specified target sequence, but also we are able to rank the importance of each sequence based on a new metric estimating the difficulty of a sequence being generated. Furthermore, we also exploit the generation inability of the model to extract regions that contain unique information for each sequence. We conduct extensive experiments using three datasets including a toy dataset of 20,000 simulated subjects, a brain MRI dataset of 1251 subjects, and a breast MRI dataset of 2101 subjects, to demonstrate that (1) top-ranking sequences can be used to replace complete sequences with non-inferior performance; (2) combining MRI with our imaging-differentiation map leads to better performance in clinical tasks such as glioblastoma MGMT promoter methylation status prediction and breast cancer pathological complete response status prediction. Our code is available at https://github.com/fiy2W/mri_seq2seq.

The immune response of hepatocellular carcinoma after locoregional and systemic therapies: The available combination option for immunotherapy.

Hepatocellular carcinoma (HCC) is associated with a highly heterogeneous immune environment that produces an immune response to various locoregional treatments (LRTs), which in turn affects the effectiveness of immunotherapy. Although LRTs still dominate HCC therapies, 50-60% of patients will ultimately be treated with systemic therapies and might receive those treatments for the rest of their life. TACE, SIRT, and thermal ablation can dramatically increase the immunosuppressive state of HCC, a condition that can be addressed by combination with immunotherapy to restore the activity of lymphocytes and the secretion of cellular immune factors. Immune treatment with locoregional and systemic treatments has dramatically changed the management of HCC. In this review, we examine the research on the changes in the immune microenvironment after locoregional or systemic treatment. We also summarize the regulation of various immune cells and immune factors in the tumor microenvironment and discuss the different infiltration degrees of immune cells and factors on the prognosis of HCC to better compare the efficacy between different treatment methods from the perspective of the tumor microenvironment. This information can be used to help develop treatment options for the upcoming new era of HCC treatment in the future.

Loss-of-Function but Not Gain-of-Function Properties of Mutant TP53 Are Critical for the Proliferation, Survival, and Metastasis of a Broad Range of Cancer Cells.

Mutations in the tumor suppressor TP53 cause cancer and impart poor chemotherapeutic responses, reportedly through loss-of-function, dominant-negative effects and gain-of-function (GOF) activities. The relative contributions of these attributes is unknown. We found that removal of 12 different TP53 mutants with reported GOFs by CRISPR/Cas9 did not impact proliferation and response to chemotherapeutics of 15 human cancer cell lines and colon cancer-derived organoids in culture. Moreover, removal of mutant TP53/TRP53 did not impair growth or metastasis of human cancers in immune-deficient mice or growth of murine cancers in immune-competent mice. DepMap mining revealed that removal of 158 different TP53 mutants had no impact on the growth of 391 human cancer cell lines. In contrast, CRISPR-mediated restoration of wild-type TP53 extinguished the growth of human cancer cells in vitro. These findings demonstrate that LOF but not GOF effects of mutant TP53/TRP53 are critical to sustain expansion of many tumor types. SIGNIFICANCE: This study provides evidence that removal of mutant TP53, thereby deleting its reported GOF activities, does not impact the survival, proliferation, metastasis, or chemotherapy responses of cancer cells. Thus, approaches that abrogate expression of mutant TP53 or target its reported GOF activities are unlikely to exert therapeutic impact in cancer. See related commentary by Lane, p. 211 . This article is featured in Selected Articles from This Issue, p. 201.

A real-time deep learning-based system for colorectal polyp size estimation by white-light endoscopy: development and multicenter prospective validation.

BACKGROUND: The choice of polypectomy device and surveillance intervals for colorectal polyps are primarily decided by polyp size. We developed a deep learning-based system (ENDOANGEL-CPS) to estimate colorectal polyp size in real time. METHODS: ENDOANGEL-CPS calculates polyp size by estimating the distance from the endoscope lens to the polyp using the parameters of the lens. The depth estimator network was developed on 7297 images from five virtually produced colon videos and tested on 730 images from seven virtual colon videos. The performance of the system was first evaluated in nine videos of a simulated colon with polyps attached, then tested in 157 real-world prospective videos from three hospitals, with the outcomes compared with that of nine endoscopists over 69 videos. Inappropriate surveillance recommendations caused by incorrect estimation of polyp size were also analyzed. RESULTS: The relative error of depth estimation was 11.3% (SD 6.0%) in successive virtual colon images. The concordance correlation coefficients (CCCs) between system estimation and ground truth were 0.89 and 0.93 in images of a simulated colon and multicenter videos of 157 polyps. The mean CCC of ENDOANGEL-CPS surpassed all endoscopists (0.89 vs. 0.41 [SD 0.29]; P<0.001). The relative accuracy of ENDOANGEL-CPS was significantly higher than that of endoscopists (89.9% vs. 54.7%; P<0.001). Regarding inappropriate surveillance recommendations, the system's error rate is also lower than that of endoscopists (1.5% vs. 16.6%; P<0.001). CONCLUSIONS: ENDOANGEL-CPS could potentially improve the accuracy of colorectal polyp size measurements and size-based surveillance intervals.

Effect of artificial intelligence on novice-performed colonoscopy: a multicenter randomized controlled tandem study.

BACKGROUND AND AIMS: The efficacy and safety of colonoscopy performed by artificial intelligence (AI)-assisted novices remain unknown. The aim of this study was to compare the lesion detection capability of novices, AI-assisted novices, and experts. METHODS: This multicenter, randomized, noninferiority tandem study was conducted across 3 hospitals in China from May 1, 2022, to November 11, 2022. Eligible patients were randomized into 1 of 3 groups: the CN group (control novice group, withdrawal performed by a novice independently), the AN group (AI-assisted novice group, withdrawal performed by a novice with AI assistance), or the CE group (control expert group, withdrawal performed by an expert independently). Participants underwent a repeat colonoscopy conducted by an AI-assisted expert to evaluate the lesion miss rate and ensure lesion detection. The primary outcome was the adenoma miss rate (AMR). RESULTS: A total of 685 eligible patients were analyzed: 229 in the CN group, 227 in the AN group, and 229 in the CE group. Both AMR and polyp miss rate were lower in the AN group than in the CN group (18.82% vs 43.69% [P < .001] and 21.23% vs 35.38% [P < .001], respectively). The noninferiority margin was met between the AN and CE groups of both AMR and polyp miss rate (18.82% vs 26.97% [P = .202] and 21.23% vs 24.10% [P < .249]). CONCLUSIONS: AI-assisted colonoscopy lowered the AMR of novices, making them noninferior to experts. The withdrawal technique of new endoscopists can be enhanced by AI-assisted colonoscopy. (Clinical trial registration number: NCT05323279.).

Unified framework for patient-derived, tumor-organoid-based predictive testing of standard-of-care therapies in metastatic colorectal cancer.

Predictive drug testing of patient-derived tumor organoids (PDTOs) holds promise for personalizing treatment of metastatic colorectal cancer (mCRC), but prospective data are limited to chemotherapy regimens with conflicting results. We describe a unified framework for PDTO-based predictive testing across standard-of-care chemotherapy and biologic and targeted therapy options. In an Australian community cohort, PDTO predictions based on treatment-naive patients (n = 56) and response rates from first-line mCRC clinical trials achieve 83% accuracy for forecasting responses in patients receiving palliative treatments (18 patients, 29 treatments). Similar assay accuracy is achieved in a prospective study of third-line or later mCRC treatment, AGITG FORECAST-1 (n = 30 patients). "Resistant" predictions are associated with inferior progression-free survival; misclassification rates are similar by regimen. Liver metastases are the optimal site for sampling, with testing achievable within 7 weeks for 68.8% cases. Our findings indicate that PDTO drug panel testing can provide predictive information for multifarious standard-of-care therapies for mCRC.

The Mediation and Moderation Effect Association among Physical Activity, Body-Fat Percentage, Blood Pressure, and Serum Lipids among Chinese Adults: Findings from the China Health and Nutrition Surveys in 2015.

Physical activity (PA) is of benefit and particularly important for cardiovascular disease risk factors as being sedentary becomes a lifestyle habit. Research into Chinese complex association among physical activity, body-fat percentage (BF%), blood pressure, and serum lipids is limited. The present study is based on an observational study among adults (>18 years old) residing in fifteen provinces in China. Data of 10,148 adult participants in the 2015 China Health and Nutrition Survey (CHNS) were analyzed. The simple mediation effect models with covariates were utilized to assess the association among PA and blood pressure or serum lipids, and BF% was played as a mediator. The serial multiple-mediator models with covariates were constructed to the further analysis of the relationship between PA and blood pressure, and BF% was the mediator 1 and blood lipids were the mediator 2. Based on the above hypothesis, the moderated mediation models with covariates were used to analyze the association among PA, BF%, and blood pressure; in addition, BF% was used as the mediator and blood lipids played as the moderator. In the simple mediation models, the model with a dependent variable was high-density lipoprotein cholesterol (HDL-C) or low-density lipoprotein cholesterol (LDL-C); BF% was played as the partly mediation effect and the proportion of contribution was 0.23 and 0.25, respectively. In the serial multiple-mediator models, blood lipids, as the second mediator, played the mediation effect; however, the effect was smaller than the BF%. In the moderated mediation model, blood lipids had the moderation effect as the moderator variable. HDL-C played a moderating role in the latter pathway of the "PA→BF%→SBP/DBP" mediation model, and LDL-C/TC played a moderating role in the direct effect of the "PA→BF%→DBP". In conclusion, BF% played a mediating role in the relationship between PA and blood pressure. HDL-C, LDL-C, and TC were more likely to act as moderating variables in the mediation model "PA→BF%→SBP/DBP". PA could directly and indirectly benefit to control the CVD risk factors simultaneously.

Risk assessment for postoperative venous thromboembolism using the modified Caprini risk assessment model in lung cancer.

Background: Postoperative venous thromboembolism (VTE) is a well-documented cause of morbidity and mortality in lung cancer patients. However, risk identification remains limited. In this study, we sought to analyze the risk factors for VTE and verify the predictive value of the modified Caprini risk assessment model (RAM). Methods: This prospective single-center study included patients with resectable lung cancer who underwent resection between October 2019 and March 2021. The incidence of VTE was estimated. Logistic regression was used to analyze the risk factors for VTE. Receiver operating characteristic (ROC) curve analysis was performed to test the ability of the modified Caprini RAM to predict VTE. Results: The VTE incidence was 10.5%. Several variables, including age, D-dimer, hemoglobin (Hb), bleeding, and patient confinement to bed were significantly associated with VTE after surgery. The difference between the VTE and non-VTE groups in the high-risk levels was statistically significant (P<0.001), while the low and moderate risk levels showed no significant difference. The combined use of the modified Caprini score and the Hb and D-dimer levels showed an area under the curve (AUC) was 0.822 [95% confidence interval (CI): 0.760-0.855. P<0.001]. Conclusions: The risk-stratification approach of the modified Caprini RAM is not particularly valid after lung resection in our population. The use of the modified Caprini RAM combined with Hb and D-dimer levels shows a good diagnostic performance for VTE prediction in patients with lung cancer undergoing resection.

RadioLOGIC, a healthcare model for processing electronic health records and decision-making in breast disease.

Digital health data used in diagnostics, patient care, and oncology research continue to accumulate exponentially. Most medical information, and particularly radiology results, are stored in free-text format, and the potential of these data remains untapped. In this study, a radiological repomics-driven model incorporating medical token cognition (RadioLOGIC) is proposed to extract repomics (report omics) features from unstructured electronic health records and to assess human health and predict pathological outcome via transfer learning. The average accuracy and F1-weighted score for the extraction of repomics features using RadioLOGIC are 0.934 and 0.934, respectively, and 0.906 and 0.903 for the prediction of breast imaging-reporting and data system scores. The areas under the receiver operating characteristic curve for the prediction of pathological outcome without and with transfer learning are 0.912 and 0.945, respectively. RadioLOGIC outperforms cohort models in the capability to extract features and also reveals promise for checking clinical diagnoses directly from electronic health records.

CTNNB1 in neurodevelopmental disorders.

CTNNB1 is the gene that encodes ß-catenin which acts as a key player in the Wnt signaling pathway and regulates cellular homeostasis. Most CTNNB1-related studies have been mainly focused on its role in cancer. Recently, CTNNB1 has also been found involved in neurodevelopmental disorders (NDDs), such as intellectual disability, autism, and schizophrenia. Mutations of CTNNB1 lead to the dysfunction of the Wnt signaling pathway that regulates gene transcription and further disturbs synaptic plasticity, neuronal apoptosis, and neurogenesis. In this review, we discuss a wide range of aspects of CTNNB1 and its physiological and pathological functions in the brain. We also provide an overview of the most recent research regarding CTNNB1 expression and its function in NDDs. We propose that CTNNB1 would be one of the top high-risk genes for NDDs. It could also be a potential therapeutic target for the treatment of NDDs.

Improving Outcomes in the Advanced Gastrointestinal Stromal Tumors: The Role of the Multidisciplinary Team Discussion Intervention.

OBJECTIVES: There is disagreement over the prognostic value of multidisciplinary team (MDT) discussion for advanced gastrointestinal stromal tumors (GISTs). This study examined how an MDT affected patients with advanced GISTs in terms of their overall survival (OS) and whether it may enhance their performance status (PS). METHODS: A retrospective data analysis was conducted on patients with advanced GISTs between 2000 and 2022. Depending on whether they had received the MDT discussion intervention, the patients were split into two groups. The OS between the two groups was compared using the Kaplan-Meier method. A multivariate Cox regression analysis was used to analyze the prognostic variables for advanced GIST. Fisher's test was used to investigate the relationship between an MDT and PS. RESULTS: There were 122 patients with an MDT and 117 patients without an MDT in this study. In comparison to the non-MDT group, the MDT group showed a higher survival rate (5-year OS, 42.62% vs. 28.21%, p < 0.05). MDT was an independent prognostic factor for OS in univariate and multivariate Cox regression analyses (p < 0.05). Fisher's test revealed that there were variations in PS between the two groups (p < 0.05). CONCLUSIONS: The effectiveness of an MDT in the treatment of advanced GIST was examined for the first time in this study. MDT discussion intervention is an effective measure for improving the outcomes of patients with advanced GISTs.