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.

Objective: To investigate the current status of benefit finding among young and middle-aged patients with type 2 diabetes mellitus (T2DM) and analyze its influencing factors, so as to provide a reference for improving the level of benefit finding in this population. Methods: From November 2022 to May 2023, young and middle-aged patients with T2DM aged 18-59 years hospitalized in the endocrinology departments of 2 tertiary hospitals in Hengyang City, Hunan Province were selected as survey subjects by a convenience sampling method. Basic demographic information was collected using a general questionnaire survey. Benefit finding, resourcefulness, and stigma were evaluated using the Benefit Finding Scale, the Chinese Version of the Resourcefulness Scale, and the Type 2 Diabetes Stigma Assessment Scale, respectively. A multiple linear regression model was used to analyze the influencing factors of benefit finding among young and middle-aged patients with T2DM. Results: A total of 305 young and middle-aged patients with T2DM were investigated, including 222 males (72.79%) and 83 females (27.21%). There were 231 cases aged 45-59 years, accounting for 75.74%. The scores for benefit finding, resourcefulness, and stigma were (42.86±6.06), (75.12±11.30), and (41.20±10.10), respectively. Multiple linear regression analysis showed that young and middle-aged patients with T2DM who were male (β′=0.088), aged 18-<45 years (β′=0.083), absence of diabetes complications (β′=0.124), and had higher resourcefulness scores (β′=0.679) had higher levels of benefit finding, while patients with higher stigma scores (β′=-0.097) had lower levels of benefit finding. Conclusion The level of benefit finding among young and middle-aged patients with T2DM was moderate, and was related to gender, age, diabetes complications, resourcefulness, and stigma.

Blood transfusion is an important life-support measure in modern medicine. As clinical demand for transfusion continues to rise, blood supply remains under persistent strain, making the efficient utilization of this precious medical resource a critical component of blood management. Accurate prediction of transfusion needs is of great significance for optimizing blood resource allocation and conserving blood products. Traditional transfusion decision-making relies on clinical experience and simplified scoring systems, which are insufficient to meet the demands of precision medicine. Machine learning (ML) technology can integrate multidimensional data—including demographic characteristics, laboratory indicators, surgical information, and dynamic physiological waveforms—to construct high-performance predictive models, offering a new approach to transfusion prediction. This article systematically reviews the application progress of ML in transfusion prediction across clinical scenarios such as trauma, the perioperative period, and obstetrics. In trauma, ML has been applied to early massive transfusion prediction, prehospital transfusion decision-making, and precise prediction in pediatric trauma. In the perioperative field, applications span specialties including traumatic brain injury, orthopedic surgery, cardiovascular and major vascular surgery, and hepatic surgery. In obstetrics, models can effectively predict postpartum hemorrhage and transfusion requirements associated with cesarean section. Studies have shown that algorithms such as random forest, gradient boosting machines, and deep neural networks achieve area under the receiver operating characteristic curve (AUC) values of 0.83-0.98 in massive transfusion prediction, 0.75-0.97 in perioperative scenarios, and 0.80-0.89 in obstetric transfusion prediction—significantly outperforming traditional scoring tools. However, current studies are generally limited by single-center retrospective designs, insufficient external validation, and inadequate reporting standards; limited model interpretability and challenges in clinical integration further constrain practical translation. Future research should focus on constructing multicenter data cohorts, integrating multimodal data, conducting prospective implementation studies, and deeply embedding models within electronic health record systems, thereby advancing the application of ML to optimize clinical transfusion decision-making.

Diabetic tendinopathy is a common and disabling musculoskeletal complication of diabetes, clinically characterized by tendon thickening, pain, impaired healing capacity and compromised biomechanical performance, collectively undermining joint function and quality of life. Its pathogenesis is multifactorial. On the one hand, chronic hyperglycaemia promotes the abnormal accumulation of advanced glycation end products (AGEs) within tendon collagen, leading to non-enzymatic crosslinking and engagement of the receptor for AGEs (RAGE), which in turn triggers inflammasome activation and sustains inflammatory responses. On the other hand, the diabetic milieu disrupts collagen metabolic homeostasis, impairs microvascular function and induces peripheral neuropathy; together, these alterations drive extracellular matrix degeneration and weaken the mechanical properties of tendon. Exercise, as a non-pharmacological intervention, can ameliorate these pathological changes through multiple integrated mechanisms. First, exercise attenuates tendon inflammation and restores microenvironmental homeostasis. Regular physical activity reduces AGE-RAGE signalling, thereby suppressing downstream expression of tumour necrosis factor (TNF) and interleukin-1β (IL-1β), while upregulating the anti-inflammatory cytokine interleukin-10 (IL-10). This shift from a pro-inflammatory to a pro-resolving milieu not only restrains chronic inflammation but may also limit excessive inflammasome activation in tenocytes and tissue-resident immune cells. Second, exercise enhances local expression of insulin-like growth factor 1 (IGF-1), thereby activating the phosphoinositide 3-kinase (PI3K)-protein kinase B (Akt) signalling pathway. This axis stimulates tenocyte proliferation, augments synthesis of type I collagen —— the principal load-bearing component of tendon —— and thereby promotes tissue repair, preserves tensile strength and supports matrix synthesis and structural integrity. In addition, exercise improves blood supply and nutrient delivery to tendon by increasing the expression of vascular endothelial growth factor (VEGF), connective tissue growth factor (CTGF) and the small leucine-rich proteoglycan decorin (DCN), thereby enhancing capillary growth, coordinating collagen fibrillogenesis and actively suppressing pathological vascular calcification. Exercise also increases the expression of angiopoietin-like 4 (ANGPTL4), fibroblast growth factor 2 (FGF-2) and CD34. Through the concerted actions of these factors, exercise promotes angiogenesis, restores the microvascular network and ensures adequate oxygen and nutrient supply to relatively ischaemic tendon tissue. Finally, exercise elevates levels of brain-derived neurotrophic factor (BDNF) and nerve growth factor (NGF), supporting neuronal survival, axonal growth and the function of sensory and sympathetic nerve endings within tendon, thereby exerting neurotrophic and neuromodulatory effects and improving tendon innervation and neuromuscular control. Moreover, exercise upregulates collapsin response mediator protein 2 (CRMP-2), a molecule involved in axonal guidance and regeneration, and moderately increases the activity of substance P (SP), thereby helping to regulate neurogenic inflammation, pain perception and trophic support for tenocytes. Drawing together current evidence, this review systematically summarizes the mechanisms underlying diabetic tendinopathy and examines the mechanistic basis of exercise intervention, with the aim of providing a theoretical framework for the development of precise exercise strategies for affected individuals. However, several key issues remain unresolved: the therapeutic efficacy and mechanistic specificity of different exercise modalities in diabetic tendinopathy have yet to be defined, and early diagnostic biomarkers remain insufficiently characterized. Future studies should apply multi-omics approaches to profile AGE subtypes, miRNA signatures and collagen metabolic products, and should also clarify the adverse effects and underlying mechanisms of excessive exercise or mechanical overloading in diabetic tendinopathy. Such efforts will further elucidate the therapeutic effects and mechanisms of exercise in diabetic tendinopathy and provide a stronger basis for precision exercise prescription and fitness guidance in patients with diabetes.

[Objective] To analyze the influencing factors of repeat blood donor lapsing using a zero-inflated poisson regression model (ZIP). [Methods] The blood donation behavior of 12 498 whole blood donors from 2020 was tracked until December 31, 2023. The factors influencing the frequency of blood donations in a given year was analyzed using ZIP, and donors with 0 blood donation in that year were considered to have lapsed. The changes in relevant influencing factors associated with each blood donation were measured and modeled for analysis. [Results] The zero-inflated part of ZIP showed that the risk of lapsing of male blood donors was 2.24 times that of female blood donors (OR 95% CI:1.864-2.696, P<0.001); the risk of lapsing of the 35-44 age group and over 45 age group was respectively 40% (OR 95% CI:0.455-0.790, P<0.001) and 61%(OR 95% CI:0.268-0.578, P<0.001) lower than that of the under 25 age group; the risk of lapsing for those who have donated blood twice and ≥3 times was respectively 50% (OR 95% CI:0.405-0.609, P<0.001) and 81% (OR 95% CI:0.154-0.225, P<0.001) lower than that of first-time donors; the risk of lapsing of those with junior high or high school education was 1.2 times that of those with a college degree or higher (OR 95% CI:1.033-1.384, P<0.05); the risk of lapsing for the divorced group was 2.02 times that of the married group (OR 95% CI:1.445-2.820, P<0.001); the risk of lapsing for those with an income (Yuan) of 10 000 to 50 000, 50 000 to 100 000 and more than 100 000 was respectively 0.67 (OR 95% CI:0.552-0.818, P<0.001), 0.72 (OR 95% CI:0.591-0.884, P=0.002) and 0.67 (OR 95% CI:0.535-0.834, P<0.001) times that of those with an income (Yuan) of less than 10 000. The results of the Poisson part are consistent with the results of the zero-inflated part in terms of age and education level. [Conclusion] Blood donor lapsing is overall related to factors such as gender, age, donation frequency, education, marital status and family income. It's essential to care for those blood donors prone to lapse to retain more regular blood donors.

ObjectiveIn nature, objects cast shadows due to illumination, forming the basis for stereoscopic perception. Birds need to adapt to changes in lighting (meaning they can recognize stereoscopic shapes even when shadows look different) to accurately perceive different three-dimensional forms. However, how neurons in the key visual brain area in birds handle these lighting changes remains largely unreported. In this study, pigeons (Columba livia) were used as subjects to investigate how neurons in pigeon’s ventrolateral mesopallium (MVL) represent stereoscopic shapes consistently, regardless of changes in lighting. MethodsVisual cognitive training combined with neuronal recording was employed. Pigeons were first trained to discriminate different stereoscopic shapes (concave/convex). We then tested whether and how light luminance angle and surface appearance of the stereoscopic shapes affect their recognition accuracy, and further verify whether the results rely on specify luminance color. Simultaneously, neuronal firing activity of neurons was recorded with multiple electrode array implanted from the MVL during the presentation of difference shapes. The response was finally analyzed how selectively they responded to different stereoscopic shapes and whether their selectivity was affected by the changes of luminance condition (like lighting angle) or surface look. Support vector machine (SVM) models were trained on neuronal population responses recorded under one condition (light luminance angle of 45°) and used to decode responses under other conditions (light luminance angle of 135°, 225°, 315°) to verify the invariance of responses to different luminance conditions. ResultsBehavioral results from 6 pigeons consistently showed that the pigeons could reliably identify the core 3D shape (over 80% accuracy), and this ability wasn’t affected by changes in light angle or surface appearance. Statistical analysis of 88 recorded neurons from 6 pigeons revealed that 83% (73/88) showed strong selectivity for specific 3D shapes (selectivity index>0.3), and responses to convex shapes were consistently stronger than to concave shapes. These shape-selective responses remained stable across changes in light angle and surface appearance. Neural patterns were consistent under both blue and orange lighting. The decoding accuracy achieves above 70%, suggesting stable responses under different conditions (e.g., different lighting angles or surface appearance). ConclusionNeurons in the pigeon MVL maintain a consistent neural encoding pattern for different stereoscopic shapes, unaffected by illumination or surface appearance. This ensures stable object recognition by pigeons in changing visual environments. Our findings provide new physiological evidence for understanding how birds achieve stable perception (“invariant neural representations”) while coping with variations in the visual field.

Asthma is a diverse disease that can be categorized into various phenotypes and endotypes, including obesity-re-lated asthma and allergic asthma. "Treatable traits (TTs)" represent a new approach to managing asthma. Asthma accompanied by dyslipidemia would be a distinct asthma phenotype that is becoming increasingly common. Therefore, dyslipidemia can potentially serve as a target for the management of asthma. Nevertheless, it remains highly under-researched compared to other observable traits. Gaining knowledge about the clinical and inflammatory characteristics, underlying mechanisms, and potential therapeutic medications for asthma with dyslipidemia is crucial for its effective management. This review aimed to provide a comprehensive overview of asthma with dyslipidemia, consolidating existing knowledge and ongoing research.
Humans ; Asthma/complications* ; Dyslipidemias/epidemiology*

The purpose of this study was to clarify the effect of Huotan Jiedu Tongluo Decoction on atherosclerosis(AS) injury in ApoE~(-/-) mice by regulating the ferroptosis pathway. Seventy-five ApoE~(-/-) mice were randomly divided into model group, low-, medium-, and high-dose of Huotan Jiedu Tongluo Decoction groups, and evolocumab group(n=15), and 15 C57BL/6J mice were selected as the blank group. Mice in the blank group were fed with a normal diet, and those in the other groups were fed with a high-fat diet to induce AS. From the 9th week, mice in Huotan Jiedu Tongluo Decoction groups were administrated with Huotan Jiedu Tongluo Decoction at corresponding doses by gavage, and those in the blank group and the model group were given an equal volume of distilled water. Mice in the evolocumab group were treated with evolocumab 18.2 mg·kg~(-1 )by subcutaneous injection every 2 weeks. After 8 weeks of continuous intervention, oil red O staining and hematoxylin-eosin(HE) staining were employed to observe the lipid deposition and plaque formation in the aortic root. Masson staining was used to evaluate the collagen content in the aortic root. The serum levels of total cholesterol(TC), triglycerides(TG), high-density lipoprotein cholesterol(HDL-C), and low-density lipoprotein cholesterol(LDL-C) were determined by biochemical kits. The levels of Fe~(2+), superoxide dismutase(SOD), malondialdehyde(MDA), and glutathione(GSH) in the aorta were measured by colorimetry. The protein and mRNA levels of nuclear factor erythroid 2-related factor 2(Nrf2), glutathione peroxidase 4(GPX4), solute carrier family 7 member 11(SLC7A11), and acyl-CoA synthetase long chain family member 4(ACSL4) in the aorta were detected by Western blot and RT-qPCR, respectively. The expression of Nrf2, GPX4, and SLC7A11 was localized by immunofluorescence. The results showed that low-, medium-, and high-dose Huotan Jiedu Tongluo Decoction reduced the plaque formation of aortic root and increased the collagen content in AS mice. At the same time, Huotan Jiedu Tongluo Decoction improved the lipid metabolism by lowering the levels of TC, LDL-C, and TG and elevating the level of HDL-C in the serum. Huotan Jiedu Tongluo Decoction enhanced the antioxidant capacity by elevating the levels of GSH and SOD and lowering the level of MDA in the aorta and inhibiting the accumulation of Fe~(2+) in the aorta. In addition, Huotan Jiedu Tongluo Decoction up-regulated the protein and mRNA levels of Nrf2, GPX4, and SLC7A11, while down-regulating the protein and mRNA levels of ACSL4. In summary, Huotan Jiedu Tongluo Decoction can effectively alleviate AS lesions in ApoE~(-/-) mice by activating the Nrf2/GPX4 pathway, reducing lipid peroxidation, and inhibiting ferroptosis.
Animals ; Ferroptosis/drug effects* ; Atherosclerosis/metabolism* ; Drugs, Chinese Herbal/administration & dosage* ; NF-E2-Related Factor 2/genetics* ; Mice ; Mice, Inbred C57BL ; Apolipoproteins E/metabolism* ; Male ; Phospholipid Hydroperoxide Glutathione Peroxidase/genetics* ; Signal Transduction/drug effects* ; Humans ; Mice, Knockout

ObjectiveIn the realm of forensic science, dust is a valuable type of trace evidence with immense potential for intricate investigations. With the development of DNA sequencing technologies, there is a heightened interest among researchers in unraveling the complex tapestry of microbial communities found within dust samples. Furthermore, striking disparities in the microbial community composition have been noted among dust samples from diverse geographical regions, heralding new possibilities for geographical inference based on microbial DNA analysis. The pivotal role of microbial community data from dust in geographical inference is significant, underscoring its critical importance within the field of forensic science. This study aims to delve deeply into the nuances of fungal community composition across the urban landscapes of Beijing, Fuzhou, Kunming, and Urumqi in China. It evaluates the accuracy of biogeographic inference facilitated by the internal transcribed spacer 2 (ITS2) fungal sequencing while concurrently laying a robust foundation for the operational integration of environmental DNA into geographical inference mechanisms. MethodsITS2 region of the fungal genomes was amplified using universal primers known as 5.8S-Fun/ITS4-Fun, and the resulting DNA fragments were sequenced on the Illumina MiSeq FGx platform. Non-metric multidimensional scaling analysis (NMDS) was employed to visually represent the differences between samples, while analysis of similarities (ANOSIM) and permutational multivariate analysis of variance (PERMANOVA) were utilized to statistically evaluate the dissimilarities in community composition across samples. Furthermore, using Linear Discriminant Analysis Effect Size (LEfSe) analysis to identify and filter out species that exhibit significant differences between various cities. In addition, we leveraged SourceTracker to predict the geographic origins of the dust samples. ResultsAmong the four cities of Beijing, Fuzhou, Kunming and Urumqi, Beijing has the highest species richness. The results of species annotation showed that there were significant differences in the species composition and relative abundance of fungal communities in the four cities. NMDS analysis revealed distinct clustering patterns of samples based on their biogeographic origins in multidimensional space. Samples from the same city exhibited clear clustering, while samples from different cities showed separation along the first axis. The results from ANOSIM and PERMANOVA confirmed the significant differences in fungal community composition between the four cities, with the most pronounced distinctions observed between Fuzhou and Urumqi. Notably, the biogeographic origins of all known dust samples were successfully predicted. ConclusionSignificant differences are observed in the fungal species composition and relative abundance among the cities of Beijing, Fuzhou, Kunming, and Urumqi. Employing fungal ITS2 sequencing on dust samples from these urban areas enables accurate inference of biogeographical locations. The high feasibility of utilizing fungal community data in dust for biogeographical inferences holds particular promise in the field of forensic science.