Search

Background: Both diabetes and osteoporosis have developed into major global public health problems due to the increasing aging population. It is crucial to screen populations at higher risk of developing osteoporosis for disease prevention and management in postmenopausal women with type 2 diabetes (T2D). This study aims to quantitatively investigate the association between risk factors and bone mineral density (BMD) and develop a self-assessment tool for early osteoporosis screening in postmenopausal women with T2D. Methods: We retrospectively enrolled 1,309 postmenopausal women with T2D. Linear regression methods were used to assess the association between risk factors and BMD. Additionally, a multivariate logistic regression analysis was performed to identify independent risk factors associated with osteoporosis. Utilizing the logistic regression machine learning algorithm, we developed an osteoporosis screening tool that categorizes the population into three risk regions based on age and body mass index (BMI), indicating low, moderate, and high prevalence of osteoporosis in the age-BMI plane. Results: Older age and lower BMI were independently associated with decreased BMD. The BMD at the total hip, femur neck, and lumbar spine differed by 12.9, 10.9, and 15.5 mg/cm2 for each 1 unit increase in BMI, respectively. Both age and BMI were identified as independent predictors of osteoporosis. The osteoporosis screening tool was developed by using two straight lines with equations of BMI = 0.56 * age−4.12 and BMI = 0.56 * age−10.88; there were no significant differences in the prevalence of osteoporosis among the training, internal test, and external test datasets in the low-, moderate-, and high-risk regions. Conclusion: We have successfully developed and validated a self-assessment tool for early osteoporosis screening in postmenopausal women with T2D for the first time. BMI was identified as a significant modifiable risk factor. Our study may improve awareness of osteoporosis and is valuable for disease prevention and management for postmenopausal women with T2D. [ABSTRACT FROM AUTHOR]

Background: Vessels encapsulating tumor cluster (VETC) is a critical prognostic factor and therapeutic predictor of hepatocellular carcinoma (HCC). However, noninvasive evaluation of VETC remains challenging. Purpose: To develop and validate a deep learning radiomic (DLR) model of dynamic contrast‐enhanced MRI (DCE‐MRI) for the preoperative discrimination of VETC and prognosis of HCC. Study type: Retrospective. Population: A total of 221 patients with histologically confirmed HCC and stratified this cohort into training set (n = 154) and time‐independent validation set (n = 67). Field Strength/Sequence: A 1.5 T and 3.0 T; DCE imaging with T1‐weighted three‐dimensional fast spoiled gradient echo. Assessment: Histological specimens were used to evaluate VETC status. VETC+ cases had a visible pattern (≥5% tumor area), while cases without any pattern were VETC−. The regions of intratumor and peritumor were segmented manually in the arterial, portal‐venous and delayed phase (AP, PP, and DP, respectively) of DCE‐MRI and reproducibility of segmentation was evaluated. Deep neural network and machine learning (ML) classifiers (logistic regression, decision tree, random forest, SVM, KNN, and Bayes) were used to develop nine DLR, 54 ML and clinical–radiological (CR) models based on AP, PP, and DP of DCE‐MRI for evaluating VETC status and association with recurrence. Statistical Tests: The Fleiss kappa, intraclass correlation coefficient, receiver operating characteristic curve, area under the curve (AUC), Delong test and Kaplan–Meier survival analysis. P value <0.05 was considered as statistical significance. Results: Pathological VETC+ were confirmed in 68 patients (training set: 46, validation set: 22). In the validation set, DLR model based on peritumor PP (peri‐PP) phase had the best performance (AUC: 0.844) in comparison to CR (AUC: 0.591) and ML (AUC: 0.672) models. Significant differences in recurrence rates between peri‐PP DLR model‐predicted VETC+ and VETC− status were found. Data Conclusions: The DLR model provides a noninvasive method to discriminate VETC status and prognosis of HCC patients preoperatively. Evidence Level: 4. Technical Efficacy: Stage 2. [ABSTRACT FROM AUTHOR]

Climate warming increases carbon assimilation by plant growth and also accelerates permafrost CO2 emissions; however, the overall ecosystem CO2 balance in permafrost regions and its economic impacts remain largely unknown. Here we synthesize in situ measurements of net ecosystem CO2 exchange to assess current and future carbon budgets across the northern permafrost regions using the random forest model and calculate their economic implications under the Shared Socio‐economic Pathways (SSPs) based on the PAGE‐ICE model. We estimate a contemporary CO2 emission of 1,539 Tg C during the nongrowing season and CO2 uptake of 2,330 Tg C during the growing season, respectively. Air temperature and precipitation exert the most control over the net ecosystem exchange in the nongrowing season, while leaf area index plays a more important role in the growing season. This region will probably shift to a carbon source after 2,057 under SSP5‐8.5, with a net emission of 17 Pg C during 2057–2100. The net economic benefits of CO2 budget will be $4.5, $5.0, and $2.9 trillion under SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5, respectively. Our results imply that a high‐emission pathway will greatly reduce the economic benefit of carbon assimilation in northern permafrost regions. Plain Language Summary: The permafrost regions account for approximately 22% of the land area in the northern hemisphere. The soil organic carbon stored in permafrost is about twice as much as currently contained in the atmosphere. Once permafrost thaws, the soil organic carbon will be utilized by microbes, and large amounts of CO2 will be released, further accelerating climate warming. On the other hand, warming significantly promotes vegetation growth and makes more carbon to be absorbed. The current and future carbon balance in northern permafrost regions remains largely unknown. Here, we calculated the carbon budget based on in situ observations of CO2 flux. Our results provide a deep insight into understanding how much carbon has been assimilated and released in northern permafrost ecosystems. Our findings have important implications for the future role of northern permafrost in regulating the ecosystem carbon cycle and economic benefit. Key Points: Northern permafrost regions are currently carbon sinks of approximately 791 Tg C per yearThe carbon sink will decrease with climate warming and will likely shift to a source in 2057 under a high emission pathwayEconomic benefits of carbon sinks will be greatly reduced under a high emission pathway [ABSTRACT FROM AUTHOR]

Objective: The objective of this study was to employ ensemble clustering and tree-based risk model approaches to identify interactions between clinicogenomic features for colorectal cancer using the 100,000 Genomes Project. Results: Among the 2211 patients with colorectal cancer (mean age of diagnosis: 67.7; 59.7% male), 16.3%, 36.3%, 39.0% and 8.4% had stage 1, 2, 3 and 4 cancers, respectively. Almost every patient had surgery (99.7%), 47.4% had chemotherapy, 7.6% had radiotherapy and 1.4% had immunotherapy. On average, tumour mutational burden (TMB) was 18 mutations/Mb and 34.4%, 31.3% and 25.7% of patients had structural or copy number mutations in KRAS, BRAF and NRAS, respectively. In the fully adjusted Cox model, patients with advanced cancer [stage 3 hazard ratio (HR) = 3.2; p < 0.001; stage 4 HR = 10.2; p < 0.001] and those who had immunotherapy (HR = 1.8; p < 0.04) or radiotherapy (HR = 1.5; p < 0.02) treatment had a higher risk of dying. The ensemble clustering approach generated four distinct clusters where patients in cluster 2 had the best survival outcomes (1-year: 98.7%; 2-year: 96.7%; 3-year: 93.0%) while patients in cluster 3 (1-year: 87.9; 2-year: 70.0%; 3-year: 53.1%) had the worst outcomes. Kaplan–Meier analysis and log rank test revealed that the clusters were separated into distinct prognostic groups (p < 0.0001). Survival tree or recursive partitioning analyses were performed to further explore risk groups within each cluster. Among patients in cluster 2, for example, interactions between cancer stage, grade, radiotherapy, TMB, BRAF mutation status were identified. Patients with stage 4 cancer and TMB ≥ 1.6 mutations/Mb had 4 times higher risk of dying relative to the baseline hazard in that cluster. [ABSTRACT FROM AUTHOR]