Objective To systematically analyze the characteristics of the disease burden of hypertensive heart disease (HHD) in the elderly (≥60 years) globally and in China from 1990 to 2021, and to predict its future trends from 2022 to 2040, with the aim of providing data support for optimizing comprehensive prevention and control strategies for HHD. Methods Based on the Global Burden of Disease (GBD) 2021 database, the number of prevalent cases and disability-adjusted life years (DALYs) of HHD in the elderly were extracted for the world, China, and five regions categorized by sociodemographic index (SDI). Joinpoint regression was used to analyze the temporal trends of age-standardized prevalence rate and age-standardized DALYs rate of HHD in the elderly. A three-factor decomposition method was applied to evaluate the relative contributions of aging, population growth, and epidemiological changes to the variations in the elderly HHD burden. Additionally, a Bayesian age-period-cohort model was used to predict the elderly HHD burden from 2022 to 2040. Results In 2021, the number of prevalent elderly HHD cases reached 10 283 000 globally and 3 412 400 in China, representing increases of 179.20% and 159.20% respectively, compared with 1990. The DALYs of elderly HHD were 18 812 700 person-years globally and 4 731 400 person-years in China, rising by 76.08% and 29.45% respectively from 1990. Meanwhile, the growth rates of the number of prevalent cases and DALYs of elderly HHD varied across different SDI regions. From 1990 to 2021, the age-standardized prevalence rate of elderly HHD in China, as well as the age-standardized DALYs rate of elderly HHD both globally and in China, showed significant downward trends (all average annual percentage changes<0, all P<0.001). In 2021, the 70-74 years age group accounted for the highest proportion of prevalent cases and DALYs of elderly HHD, both globally and in China. Decomposition analysis revealed that population growth was the dominant factor driving the increase in the elderly HHD burden across all regions. The prediction model results indicated that the number of prevalent cases and DALYs of elderly HHD would continue to rise globally and in China from 2022 to 2040, with the growth rate of the elderly HHD burden in China between 2021 and 2040 expected to exceed the global average. Conclusion Over the past 32 years, although the age-standardized disease rates of elderly HHD have mainly shown a downward trend globally and in China, the absolute number of the disease burden has increased substantially. The projection model indicates a continued upward trajectory, with the growth rate in China higher than the global average. Therefore, there is an urgent need to implement precise prevention and control strategies to effectively mitigate the disease burden of elderly HHD.

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

To address the growing challenge of an increasing number of elderly lung cancer patients amidst China's aging population and to fill the gap in quality control standards for surgical treatment in this special population, this study aimed to develop a standardized surgical evaluation system for elderly lung cancer patients tailored to China's national conditions. The system was established through a literature review, integrated the pathophysiological characteristics of elderly patients, and was constructed following review, feedback, and revision by experts from multiple thoracic surgery centers. Employing a 100-point scoring system, it comprises three primary domains: physical infrastructure and geriatric adaptability foundational conditions (10 points); management level and perioperative care models (20 points); and technical proficiency and clinical outcomes (70 points). The system places a strong emphasis on geriatric adaptability, proposing specific, quantifiable indicators for age-friendly facility modifications, control of elderly-specific complications, multidisciplinary collaboration, and standardized perioperative management. It provides a convenient and measurable assessment tool for quality control in the surgical treatment of elderly lung cancer in China, which is expected to promote the standardization and homogenization of diagnosis and treatment.

Objective To explore the predictive value of artificial intelligence (AI)-based lung nodule CT quantitative analysis for the invasion degree of lung adenocarcinoma spectrum lesions. Methods According to the invasion degree of lung adenocarcinoma spectrum lesions, patients with surgically and pathologically confirmed lung adenocarcinoma spectrum lesions from January to June 2023 in Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University were retrospectively collected and divided into a non-invasive group and an invasive group, including atypical adenomatous hyperplasia, adenocarcinoma in situ, and minimally invasive adenocarcinoma patients in the non-invasive group, and invasive adenocarcinoma patients in the invasive group. All enrolled patients underwent chest CT before surgery, and then the lung nodules were quantitatively analyzed using an AI-based computer-aided diagnosis system to compare the related quantitative parameters of lung nodules that have been surgically removed and pathologically confirmed as lung adenocarcinoma spectrum lesions between the two groups. The relationship between various CT quantitative features and the invasion degree of lung adenocarcinoma spectrum lesions was analyzed. Results A total of 149 patients (149 lesions) were included, including 42 males and 107 females, aged 29-81 (56.35±10.75) years. There were 72 patients in the non-invasive group and 77 patients in the invasive group. Statistical differences were observed between the two groups in long diameter, short diameter, volume, surface area, mass, maximum cross-sectional area, 3D long diameter, maximum CT value, minimum CT value, average CT value, entropy, kurtosis, skewness, malignancy probability and other indicators (P<0.05). Multivariate binary logistic regression analysis showed that long diameter [OR=1.687, 95%CI (1.364, 2.085), P<0.001], average CT value [OR=1.006, 95%CI (1.002, 1.009), P=0.002], and malignancy probability [OR=1.034, 95%CI (1.005, 1.063), P=0.020] were independent risk factors for the invasion degree of lung adenocarcinoma. The predictive model combining the above parameters demonstrated optimal performance, with an area under the receiver operating characteristic curve of 0.951, sensitivity of 0.818, and specificity of 0.972. Using a Nomogram to quantify the three independent risk factors, the cross-validation was performed to evaluate the stability of the model, and the average C-index of cross-validation was 0.950, with each fold C-index >0.75, indicating that the prediction performance of the model was stable, and the calibration curve and decision curve indicated good predictive performance. Conclusion The visualization prediction model constructed by AI-based quantitative analysis of lung nodules in CT demonstrates significant discriminative effectiveness in the assessment of invasiveness in lung adenocarcinoma spectrum lesions. This visualization prediction model can provide a quantitative decision-making basis for the preoperative identification of the degree of invasiveness in lung adenocarcinoma spectrum lesions.

Objective: To explore the possibility of performing allogeneic platelet-rich plasma (PRP) treatment for patients who were not suitable for autologous PRP collection through case reports of two patients with refractory wounds treated with allogeneic PRP. Methods: The ABO-compatible allogeneic whole blood was centrifuged 3 times to obtain allogeneic PRP within 6 hours of blood collection. Then the qualified allogeneic PRP was applied to 2 cases of refractory wound on the same day. Results: The platelet concentration in allogeneic PRP was higher than 1 000×10 /L, and the test results of infectious diseases, as well as the mixing of red blood cells and white blood cells, met the standard of quality control. Both patients achieved satisfactory wound healing outcomes (3 d). Conclusions: For patients who were not suitable for autologous PRP treatment, allogeneic PRP might be a new option.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

[This corrects the article DOI: 10.1016/j.apsb.2019.01.013.].

Taxifolin (TAX) is a natural compound known for its liver protection effect, but the mechanism remains unknown. Phosphorylated proteomics analyses discovered that the phosphorylation level of NDRG1 at T328 was a key event of TAX-improved liver fibrosis. We established models with NDRG1 knockout (KO) in vivo and in vitro, demonstrating that NDRG1 KO attenuated the development of hepatocyte injury, and combining NDRG1 KO and TAX administration did not result in a reduction in protection against liver injury. Cellular thermal shift assay and surface plasma resonance analysis showed that TAX directly binds to NDRG1 rather than its upstream kinase, subsequently demonstrating that TAX regulated phosphorylation of NDRG1 at T328 through binding to its C289 site. NDRG1 T328A (phosphorylated mutation) and T328E (mimic phosphorylation) in vivo and in vitro confirmed that pNDRG1_T328 exacerbates hepatocyte injury along with DNA damage, inflammatory response, and apoptosis, thereby contributing to hepatic stellate cells (HSCs) activation. In contrast, TAX can inhibit the above pathological abnormalities and block hepatocyte injury-triggered HSCs activation and fibrosis. Overall, TAX is a potent liver protection drug primarily targeting NDRG1 and inhibiting pNDRG1_T328 in hepatocytes.

Diabetic kidney disease (DKD) with increasing global prevalence lacks effective therapeutic targets to halt or reverse its progression. Therapeutic targets supported by causal genetic evidence are more likely to succeed in randomized clinical trials. In this study, we integrated large-scale plasma proteomics, genetic-driven causal inference, and experimental validation to identify prioritized targets for DKD using the UK Biobank (UKB) and FinnGen cohorts. Among 2844 diabetic patients (528 with DKD), we identified 37 targets significantly associated with incident DKD, supported by both observational and causal evidence. Of these, 22% (8/37) of the potential targets are currently under investigation for DKD or other diseases. Our prospective study confirmed that higher levels of three prioritized targets-insulin-like growth factor binding protein 4 (IGFBP4), family with sequence similarity 3 member C (FAM3C), and prostaglandin D2 synthase (PTGDS)-were associated with a 4.35, 3.51, and 3.57-fold increased likelihood of developing DKD, respectively. In addition, population-level protein-altering variants (PAVs) analysis and in vitro experiments cross-validated FAM3C and IGFBP4 as potential new target candidates for DKD, through the classic NLR family pyrin domain containing 3 (NLRP3)-caspase-1-gasdermin D (GSDMD) apoptotic axis. Our results demonstrate that integrating omics data mining with causal inference may be a promising strategy for prioritizing therapeutic targets.