Osteoporosis guidelines on TCM drug therapies: a systematic quality evaluation and content analysis

ObjectiveThe aims of this study were to evaluate the quality of osteoporosis guidelines on traditional Chinese medicine (TCM) drug therapies and to analyze the specific recommendations of these guidelines.MethodsWe systematically collected guidelines, evaluated the quality of the guidelines using the Appraisal of Guidelines Research and Evaluation (AGREE) II tool, and summarized the recommendations of TCM drug therapies using the Patient-Intervention-Comparator-Outcome (PICO) model as the analysis framework.Results and conclusionsA total of 20 guidelines were included. Overall quality evaluation results revealed that four guidelines were at level A, four at level B, and 12 at level C, whose quality needed to be improved in the domains of “stakeholder involvement”, “rigor of development”, “applicability” and “editorial independence”. Stratified analysis suggested that the post-2020 guidelines were significantly better than those published before 2020 in the domains of “scope and purpose”, “stakeholder involvement” and “editorial independence”. Guidelines with evidence systems were significantly better than those without evidence systems in terms of “stakeholder involvement”, “rigor of development”, “clarity of presentation” and “applicability”. The guidelines recommended TCM drug therapies for patients with osteopenia, osteoporosis and osteoporotic fracture. Recommended TCM drugs were mainly Chinese patent medicine alone or combined with Western medicine, with the outcome mainly focused on improving bone mineral density (BMD)

Predicting 1-, 3-, 5-, and 8-year all-cause mortality in a community-dwelling older adult cohort: relevance for predictive, preventive, and personalized medicine

Background: Population aging is a global public health issue involving increased prevalence of age-related diseases, and concomitant burden on medical resources and the economy. Ninety-two diseases have been identified as age-related, accounting for 51.3% of the global adult disease burden. The economic cost per capita for older people over 60 years is 10 times that of the younger population. From the aspects of predictive, preventive, and personalized medicine (PPPM), developing a risk-prediction model can help identify individuals at high risk for all-cause mortality and provide an opportunity for targeted prevention through personalized intervention at an early stage. However, there is still a lack of predictive models to help community-dwelling older adults do well in healthcare. Objectives: This study aims to develop an accurate 1-, 3-, 5-, and 8-year all-cause mortality risk-prediction model by using clinical multidimensional variables, and investigate risk factors for 1-, 3-, 5-, and 8-year all-cause mortality in community-dwelling older adults to guide primary prevention. Methods: This is a two-center cohort study. Inclusion criteria: (1) community-dwelling adult, (2) resided in the districts of Chaonan or Haojiang for more than 6 months in the past 12 months, and (3) completed a health examination. Exclusion criteria: (1) age less than 60 years, (2) more than 30 incomplete variables, (3) no signed informed consent. The primary outcome of the study was all-cause mortality obtained from face-to-face interviews, telephone interviews, and the medical death database from 2012 to 2021. Finally, we enrolled 5085 community-dwelling adults, 60 years and older, who underwent routine health screening in the Chaonan and Haojiang districts, southern China, from 2012 to 2021. Of them, 3091 participants from Chaonan were recruited as the primary training and internal validation study cohort, while 1994 participants from Haojiang were recruited as the external validation cohort. A total of 95 clinical multidimensional variables, including demographics, lifestyle behaviors, symptoms, medical history, family history, physical examination, laboratory tests, and electrocardiogram (ECG) data were collected to identify candidate risk factors and characteristics. Risk factors were identified using least absolute shrinkage and selection operator (LASSO) models and multivariable Cox proportional hazards regression analysis. A nomogram predictive model for 1-, 3-, 5- and 8-year all-cause mortality was constructed. The accuracy and calibration of the nomogram prediction model were assessed using the concordance index (C-index), integrated Brier score (IBS), receiver operating characteristic (ROC), and calibration curves. The clinical validity of the model was assessed using decision curve analysis (DCA). Results: Nine independent risk factors for 1-, 3-, 5-, and 8-year all-cause mortality were identified, including increased age, male, alcohol status, higher daily liquor consumption, history of cancer, elevated fasting glucose, lower hemoglobin, higher heart rate, and the occurrence of heart block. The acquisition of risk factor criteria is low cost, easily obtained, convenient for clinical application, and provides new insights and targets for the development of personalized prevention and interventions for high-risk individuals. The areas under the curve (AUC) of the nomogram model were 0.767, 0.776, and 0.806, and the C-indexes were 0.765, 0.775, and 0.797, in the training, internal validation, and external validation sets, respectively. The IBS was less than 0.25, which indicates good calibration. Calibration and decision curves showed that the predicted probabilities were in good agreement with the actual probabilities and had good clinical predictive value for PPPM. Conclusion: The personalized risk prediction model can identify individuals at high risk of all-cause mortality, help offer primary care to prevent all-cause mortality, and provide personalized medical treatment for these high-risk individuals from the PPPM perspective. Strict control of daily liquor consumption, lowering fasting glucose, raising hemoglobin, controlling heart rate, and treatment of heart block could be beneficial for improving survival in elderly populations

Global supply chains amplify economic costs of future extreme heat risk

Evidence shows a continuing increase in the frequency and severity of global heatwaves, raising concerns about the future impacts of climate change and the associated socioeconomic costs. Here we develop a disaster footprint analytical framework by integrating climate, epidemiological and hybrid input–output and computable general equilibrium global trade models to estimate the midcentury socioeconomic impacts of heat stress. We consider health costs related to heat exposure, the value of heat-induced labour productivity loss and indirect losses due to economic disruptions cascading through supply chains. Here we show that the global annual incremental gross domestic product loss increases exponentially from 0.03 ± 0.01 (SSP 245)–0.05 ± 0.03 (SSP 585) percentage points during 2030–2040 to 0.05 ± 0.01–0.15 ± 0.04 percentage points during 2050–2060. By 2060, the expected global economic losses reach a total of 0.6–4.6% with losses attributed to health loss (37–45%), labour productivity loss (18–37%) and indirect loss (12–43%) under different shared socioeconomic pathways. Small- and medium-sized developing countries suffer disproportionately from higher health loss in South-Central Africa (2.1 to 4.0 times above global average) and labour productivity loss in West Africa and Southeast Asia (2.0–3.3 times above global average). The supply-chain disruption effects are much more widespread with strong hit to those manufacturing-heavy countries such as China and the USA, leading to soaring economic losses of 2.7 ± 0.7% and 1.8 ± 0.5%, respectively.

Correlation Between C-MYC, BCL-2, and BCL-6 Protein Expression and Gene Translocation as Biomarkers in Diagnosis and Prognosis of Diffuse Large B-cell Lymphoma

This study investigates the protein expression of C-MYC, BCL-2, and BCL-6 in diffuse large B-cell lymphoma (DLBCL) and their relationship with genetic abnormalities. A retrospective study of 42 cases on paraffin-embedded tissue specimens diagnosed with DLBCL was performed using immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH). The expression of C-MYC, BCL-2, BCL-6 protein, and gene abnormalities in these tissue samples was analyzed. The relationship in genetic abnormalities and Ki-67, Hans classification, gender, and age was also evaluated. It was found that the positive rate of C-MYC expression was 47.6% (20/42), the rate of C-MYC gene abnormality was 26.2% (11/42), in which gene translocation accounted for 23.8% (10/42) and gene amplification 2.4% (1/42); C-MYC protein expression was positively correlated with C-MYC gene translocation (χ2 = 11.813; P = 0.001); C-MYC gene translocation was mainly found in germinal center B cell type (χ2 = 4.029; P = 0.045). The positive rate of BCL-2 protein expression was 85.71% (36/42), the positive rate of translocation was 42.86% (18/42) and the amplification rate was 26.19% (11/42); the overexpression of BCL-2 protein was correlated with the BCL-2 translocation (χ2 = 3.407; P = 0.029). The positive rate of BCL-6 protein expression was 45.24% (19/42), the positive rate of BCL-6 translocation was 14.29% (6/42) and the positive rate of BCL-6 amplification was 7.14% (3/42); the overexpression of BCL-6 protein was significantly correlated with BCL-6 translocation (χ2 = 6.091; P = 0.014). The Ki-67 index was significantly higher in C-MYC translocation cases than in non-C-MYC translocation cases (χ2 = 4.492; P = 0.034). Taken together, our results suggest that the protein expression of C-MYC, BCL-2, and BCL-6 are positively correlated with their gene translocation. Overexpression of C-MYC, BCL-2, BCL-6 protein suggests the possibility of translocation. Therefore, immunohistochemical detection of C-MYC, BCL-2, and BCL-6 are useful in diagnosis and prognosis of DLBCL

Rap2B promotes proliferation, migration and invasion of human breast cancer through calcium-related ERK1/2 signaling pathway

Rap2B, a member of GTP-binding proteins, is widely upregulated in many types of tumors and promotes migration and invasion of human suprarenal epithelioma. However, the function of Rap2B in breast cancer is unknown. Expression of Rap2B was examined in breast cancer cell lines and human normal breast cell line using Western blot analysis. Using the CCK-8 cell proliferation assay, cell cycle analysis, and transwell migration assay, we also elucidated the role of Rap2B in breast cancer cell proliferation, migration, and invasion. Results showed that the expression of Rap2B is higher in tumor cells than in normal cells. Flow cytometry and Western blot analysis revealed that Rap2B elevates the intracellular calcium level and further promotes extracellular signal-related kinase (ERK) 1/2 phosphorylation. By contrast, calcium chelator BAPTM/AM and MEK inhibitor (U0126) can reverse Rap2B-induced ERK1/2 phosphorylation. Furthermore, Rap2B knockdown inhibits cell proliferation, migration, and invasion abilities via calcium related-ERK1/2 signaling. In addition, overexpression of Rap2B promotes cell proliferation, migration and invasion abilities, which could be neutralized by BAPTM/AM and U0126. Taken together, these findings shed light on Rap2B as a therapeutic target for breast cancer

Precision measurement of cardiac structure and function in cardiovascular magnetic resonance using machine learning

BACKGROUND: Measurement of cardiac structure and function from images (e.g. volumes, mass and derived parameters such as left ventricular (LV) ejection fraction [LVEF]) guides care for millions. This is best assessed using cardiovascular magnetic resonance (CMR), but image analysis is currently performed by individual clinicians, which introduces error. We sought to develop a machine learning algorithm for volumetric analysis of CMR images with demonstrably better precision than human analysis. METHODS: A fully automated machine learning algorithm was trained on 1923 scans (10 scanner models, 13 institutions, 9 clinical conditions, 60,000 contours) and used to segment the LV blood volume and myocardium. Performance was quantified by measuring precision on an independent multi-site validation dataset with multiple pathologies with n = 109 patients, scanned twice. This dataset was augmented with a further 1277 patients scanned as part of routine clinical care to allow qualitative assessment of generalization ability by identifying mis-segmentations. Machine learning algorithm ('machine') performance was compared to three clinicians ('human') and a commercial tool (cvi42, Circle Cardiovascular Imaging). FINDINGS: Machine analysis was quicker (20 s per patient) than human (13 min). Overall machine mis-segmentation rate was 1 in 479 images for the combined dataset, occurring mostly in rare pathologies not encountered in training. Without correcting these mis-segmentations, machine analysis had superior precision to three clinicians (e.g. scan-rescan coefficients of variation of human vs machine: LVEF 6.0% vs 4.2%, LV mass 4.8% vs. 3.6%; both P < 0.05), translating to a 46% reduction in required trial sample size using an LVEF endpoint. CONCLUSION: We present a fully automated algorithm for measuring LV structure and global systolic function that betters human performance for speed and precision