Objective: Whether elevation in plasma levels of amyloid and tau protein biomarkers are better indicators of cognitive decline than higher baseline levels in patients with amnestic mild cognitive impairment (aMCI) and Alzheimer’s disease (AD) remains understudied. Methods: We included 67 participants with twice testing for AD-related plasma biomarkers via immunomagnetic reduction (IMR) assays (amyloid beta [Aβ]1-40, Aβ1-42, total tau [t-Tau], phosphorylated tau [p-Tau] 181, and alpha-synuclein [α-Syn]) and the Mini-Mental State Examination (MMSE) over a 1-year interval. We examined the correlation between biomarker levels (baseline vs. longitudinal change) and annual changes in the MMSE scores. Receiver operating characteristic curve analysis was conducted to compare the biomarkers. Results: After adjustment, faster cognitive decline was correlated with lower baseline levels of t-Tau (β=0.332, p=0.030) and p-Tau 181 (β=0.369, p=0.015) and rapid elevation of t-Tau (β=-0.330, p=0.030) and p-Tau 181 levels (β=-0.431, p=0.004). However, the levels (baseline and longitudinal changes) of Aβ1-40, Aβ1-42, and α-Syn were not correlated with cognitive decline. aMCI converters had lower baseline levels of p-Tau 181 (p=0.002) but larger annual changes (p=0.001) than aMCI non-converters. The change in p-Tau 181 levels showed better discriminatory capacity than the change in t-Tau levels in terms of identifying AD conversion in patients with aMCI, with an area under curve of 86.7% versus 72.2%. Conclusion We found changes in p-Tau 181 levels may be a suitable biomarker for identifying AD conversion.

Objective: Whether elevation in plasma levels of amyloid and tau protein biomarkers are better indicators of cognitive decline than higher baseline levels in patients with amnestic mild cognitive impairment (aMCI) and Alzheimer’s disease (AD) remains understudied. Methods: We included 67 participants with twice testing for AD-related plasma biomarkers via immunomagnetic reduction (IMR) assays (amyloid beta [Aβ]1-40, Aβ1-42, total tau [t-Tau], phosphorylated tau [p-Tau] 181, and alpha-synuclein [α-Syn]) and the Mini-Mental State Examination (MMSE) over a 1-year interval. We examined the correlation between biomarker levels (baseline vs. longitudinal change) and annual changes in the MMSE scores. Receiver operating characteristic curve analysis was conducted to compare the biomarkers. Results: After adjustment, faster cognitive decline was correlated with lower baseline levels of t-Tau (β=0.332, p=0.030) and p-Tau 181 (β=0.369, p=0.015) and rapid elevation of t-Tau (β=-0.330, p=0.030) and p-Tau 181 levels (β=-0.431, p=0.004). However, the levels (baseline and longitudinal changes) of Aβ1-40, Aβ1-42, and α-Syn were not correlated with cognitive decline. aMCI converters had lower baseline levels of p-Tau 181 (p=0.002) but larger annual changes (p=0.001) than aMCI non-converters. The change in p-Tau 181 levels showed better discriminatory capacity than the change in t-Tau levels in terms of identifying AD conversion in patients with aMCI, with an area under curve of 86.7% versus 72.2%. Conclusion We found changes in p-Tau 181 levels may be a suitable biomarker for identifying AD conversion.

Objective: Whether elevation in plasma levels of amyloid and tau protein biomarkers are better indicators of cognitive decline than higher baseline levels in patients with amnestic mild cognitive impairment (aMCI) and Alzheimer’s disease (AD) remains understudied. Methods: We included 67 participants with twice testing for AD-related plasma biomarkers via immunomagnetic reduction (IMR) assays (amyloid beta [Aβ]1-40, Aβ1-42, total tau [t-Tau], phosphorylated tau [p-Tau] 181, and alpha-synuclein [α-Syn]) and the Mini-Mental State Examination (MMSE) over a 1-year interval. We examined the correlation between biomarker levels (baseline vs. longitudinal change) and annual changes in the MMSE scores. Receiver operating characteristic curve analysis was conducted to compare the biomarkers. Results: After adjustment, faster cognitive decline was correlated with lower baseline levels of t-Tau (β=0.332, p=0.030) and p-Tau 181 (β=0.369, p=0.015) and rapid elevation of t-Tau (β=-0.330, p=0.030) and p-Tau 181 levels (β=-0.431, p=0.004). However, the levels (baseline and longitudinal changes) of Aβ1-40, Aβ1-42, and α-Syn were not correlated with cognitive decline. aMCI converters had lower baseline levels of p-Tau 181 (p=0.002) but larger annual changes (p=0.001) than aMCI non-converters. The change in p-Tau 181 levels showed better discriminatory capacity than the change in t-Tau levels in terms of identifying AD conversion in patients with aMCI, with an area under curve of 86.7% versus 72.2%. Conclusion We found changes in p-Tau 181 levels may be a suitable biomarker for identifying AD conversion.

Objective: Whether elevation in plasma levels of amyloid and tau protein biomarkers are better indicators of cognitive decline than higher baseline levels in patients with amnestic mild cognitive impairment (aMCI) and Alzheimer’s disease (AD) remains understudied. Methods: We included 67 participants with twice testing for AD-related plasma biomarkers via immunomagnetic reduction (IMR) assays (amyloid beta [Aβ]1-40, Aβ1-42, total tau [t-Tau], phosphorylated tau [p-Tau] 181, and alpha-synuclein [α-Syn]) and the Mini-Mental State Examination (MMSE) over a 1-year interval. We examined the correlation between biomarker levels (baseline vs. longitudinal change) and annual changes in the MMSE scores. Receiver operating characteristic curve analysis was conducted to compare the biomarkers. Results: After adjustment, faster cognitive decline was correlated with lower baseline levels of t-Tau (β=0.332, p=0.030) and p-Tau 181 (β=0.369, p=0.015) and rapid elevation of t-Tau (β=-0.330, p=0.030) and p-Tau 181 levels (β=-0.431, p=0.004). However, the levels (baseline and longitudinal changes) of Aβ1-40, Aβ1-42, and α-Syn were not correlated with cognitive decline. aMCI converters had lower baseline levels of p-Tau 181 (p=0.002) but larger annual changes (p=0.001) than aMCI non-converters. The change in p-Tau 181 levels showed better discriminatory capacity than the change in t-Tau levels in terms of identifying AD conversion in patients with aMCI, with an area under curve of 86.7% versus 72.2%. Conclusion We found changes in p-Tau 181 levels may be a suitable biomarker for identifying AD conversion.

Objective: Whether elevation in plasma levels of amyloid and tau protein biomarkers are better indicators of cognitive decline than higher baseline levels in patients with amnestic mild cognitive impairment (aMCI) and Alzheimer’s disease (AD) remains understudied. Methods: We included 67 participants with twice testing for AD-related plasma biomarkers via immunomagnetic reduction (IMR) assays (amyloid beta [Aβ]1-40, Aβ1-42, total tau [t-Tau], phosphorylated tau [p-Tau] 181, and alpha-synuclein [α-Syn]) and the Mini-Mental State Examination (MMSE) over a 1-year interval. We examined the correlation between biomarker levels (baseline vs. longitudinal change) and annual changes in the MMSE scores. Receiver operating characteristic curve analysis was conducted to compare the biomarkers. Results: After adjustment, faster cognitive decline was correlated with lower baseline levels of t-Tau (β=0.332, p=0.030) and p-Tau 181 (β=0.369, p=0.015) and rapid elevation of t-Tau (β=-0.330, p=0.030) and p-Tau 181 levels (β=-0.431, p=0.004). However, the levels (baseline and longitudinal changes) of Aβ1-40, Aβ1-42, and α-Syn were not correlated with cognitive decline. aMCI converters had lower baseline levels of p-Tau 181 (p=0.002) but larger annual changes (p=0.001) than aMCI non-converters. The change in p-Tau 181 levels showed better discriminatory capacity than the change in t-Tau levels in terms of identifying AD conversion in patients with aMCI, with an area under curve of 86.7% versus 72.2%. Conclusion We found changes in p-Tau 181 levels may be a suitable biomarker for identifying AD conversion.

Cardiovascular disease (CVD), chronic kidney disease (CKD), and metabolic disorders are the 3 major chronic diseases threatening human health, which are closely related and often coexist, significantly increasing the difficulty of disease management. In response, the American Heart Association (AHA) proposed a novel disease concept of “cardiovascular-kidney-metabolic (CKM) syndrome” in October 2023, which has triggered widespread concern about the co-treatment of heart and kidney diseases and the prevention and treatment of metabolic disorders around the world. This review posits that effectively managing CKM syndrome requires a new and multidimensional paradigm for diagnosis and risk prediction that integrates biological insights, advanced technology and social determinants of health (SDoH). We argue that the core pathological driver is a “metabolic toxic environment”, fueled by adipose tissue dysfunction and characterized by a vicious cycle of systemic inflammation and oxidative stress, which forms a common pathway to multi-organ injury. The at-risk population is defined not only by biological characteristics but also significantly impacted by adverse SDoH, which can elevate the risk of advanced CKM by a factor of 1.18 to 3.50, underscoring the critical need for equity in screening and care strategies. This review systematically charts the progression of diagnostic technologies. In diagnostics, we highlight a crucial shift from single-marker assessments to comprehensive multi-marker panels. The synergistic application of traditional biomarkers like NT-proBNP (reflecting cardiac stress) and UACR (indicating kidney damage) with emerging indicators such as systemic immune-inflammation index (SII) and Klotho protein facilitates a holistic evaluation of multi-organ health. Furthermore, this paper explores the pivotal role of non-invasive monitoring technologies in detecting subclinical disease. Techniques like multi-wavelength photoplethysmography (PPG) and impedance cardiography (ICG) provide a real-time window into microcirculatory and hemodynamic status, enabling the identification of early, often asymptomatic, functional abnormalities that precede overt organ failure. In imaging, progress is marked by a move towards precise, quantitative evaluation, exemplified by artificial intelligence-powered quantitative computed tomography (AI-QCT). By integrating AI-QCT with clinical risk factors, the predictive accuracy for cardiovascular events within 6 months significantly improves, with the area under the curve (AUC) increasing from 0.637 to 0.688, demonstrating its potential for reclassifying risk in CKM stage 3. In the domain of risk prediction, we trace the evolution from traditional statistical tools to next-generation models. The new PREVENT equation represents a major advancement by incorporating key kidney function markers (eGFR, UACR), which can enhance the detection rate of CKD in primary care by 20%-30%. However, we contend that the future lies in dynamic, machine learning-based models. Algorithms such as XGBoost have achieved an AUC of 0.82 for predicting 365-day cardiovascular events, while deep learning models like KFDeep have demonstrated exceptional performance in predicting kidney failure risk with an AUC of 0.946. Unlike static calculators, these AI-driven tools can process complex, multimodal data and continuously update risk profiles, paving the way for truly personalized and proactive medicine. In conclusion, this review advocates for a paradigm shift toward a holistic and technologically advanced framework for CKM management. Future efforts must focus on the deep integration of multimodal data, the development of novel AI-driven biomarkers, the implementation of refined SDoH-informed interventions, and the promotion of interdisciplinary collaboration to construct an efficient, equitable, and effective system for CKM screening and intervention.

Temporomandibular joint (TMJ) diseases are common clinical conditions. The number of patients with TMJ diseases is large, and the etiology, epidemiology, disease spectrum, and treatment of the disease remain controversial and unknown. To understand and master the current situation of the occurrence, development and prevention of TMJ diseases, as well as to identify the patterns in etiology, incidence, drug sensitivity, and prognosis is crucial for alleviating patients′suffering.This will facilitate in-depth medical research, effective disease prevention measures, and the formulation of corresponding health policies. Cohort construction and research has an irreplaceable role in precise disease prevention and significant improvement in diagnosis and treatment levels. Large-scale cohort studies are needed to explore the relationship between potential risk factors and outcomes of TMJ diseases, and to observe disease prognoses through long-term follw-ups. The consensus aims to establish a standard conceptual frame work for a cohort study on patients with TMJ disease while providing ideas for cohort data standards to this condition. TMJ disease cohort data consists of both common data standards applicable to all specific disease cohorts as well as disease-specific data standards. Common data were available for each specific disease cohort. By integrating different cohort research resources, standard problems or study variables can be unified. Long-term follow-up can be performed using consistent definitions and criteria across different projects for better core data collection. It is hoped that this consensus will be facilitate the development cohort studies of TMJ diseases.

italic>Platycodon grandiflorum (Jacq.) A. DC is one of the most commonly used bulk medicinal herbs. It has important value in the fields of medicine, food and cosmetics, and its market demand is increasing year by year, and it has a good development prospect. In this study, based on 403 distribution records and 8 environmental variables, we used Maxent model to predict the potential distribution of P. grandiflorum under climate change. The results showed that the model simulation effect was the best when the Maxent parameter was FC (feature combination) = LQPH (L: linear features; Q: quadratic features; P: product features; H: hinge features) and RM (regularization multiplier) = 2.1, AUC (area under curve) = 0.901, and the model prediction showed that P. grandiflorum was widely distributed in 28 provinces of China under the current climate conditions, with a suitable area of 2 337 419.98 km². Under the influence of climate change in the future, the area of suitable habitat of P. grandiflorum showed a decreasing trend, and the distribution center as a whole showed a trend of northward and eastward shift. The distribution area of P. grandiflorum in the three main producing areas is affected by climate change, and the overall trend is that the future suitable distribution area of Chifeng in Inner Mongolia increases, while the future suitable distribution area of Zibo in Shandong and Taihe, Bozhou in Anhui decreases or even disappears under the SSP5-8.5 scenario (shared socioeconomic pathways). It is suggested to maintain and protect the ecological environment and germplasm resources of the reserved area suitable for the growth of P. grandiflorum, and increase the cultivation research in the expansion area of P. grandiflorum, so as to lay a foundation for the subsequent expansion of P. grandiflorum planting and to promote the protection and sustainable development of P. grandiflorum resources.

ObjectiveTo investigate the genetic polymorphism and structure of 47 autosomal microhaplotypes in the Han population in Changshu City, Jiangsu Province, and to evaluate the forensic efficiencies and forensic parameters. MethodsThe DNA library of unrelated individual samples was prepared according to MHSeqTyper47 kit manual and sequenced on the MiSeq FGx platform. Microhaplotype genotyping and sequencing depth statistics were processed using MHTyper. The genetic information of samples was then evaluated. The fixation index and genetic distance between the Jiangsu Changshu population and the reference populations in the 1000 Genomes Project phase 3 (1KG) were calculated, and forensic parameters were evaluated. ResultsThe fixation index and genetic distance between the Han population in Changshu, Jiangsu, and the CHB (Han Chinese in Beijing, China) reference population in 1KG were the lowest. The effective allele number (A_e) of each locus is also the closest between the two populations. The combined matching probability (CMP) of the Changshu Han population is close to the 5 populations of the East Asian reference super-population in 1KG, which is 1.25×10^-36, and the combined probability of exclusion reached 0.999 999 999 964 1. ConclusionThis study reported the genetic polymorphism and allele frequency of 47 microhaplotypes in a Han population in Changshu City, Jiangsu Province. This information provides a data basis for 47 microhaplotypes in forensic applications. In addition, the polymorphism differences between the 1KG reference population and the Han population in Changshu, Jiangsu were compared, and the genetic structure of 47 microhaplotypes in the Han population in Changshu, Jiangsu was revealed. In general, the reference data of the East Asian super-population in 1KG is more in line with the genetic characteristics of Han population in Changshu, Jiangsu.

Objective:To evaluate the efficacy and safety of Xiao′er Huangjin Zhike Granules in the treatment of cough caused by acute bronchitis in children, which is defined in TCM terms as a syndrome of phlegm-heat obstructing the lung.Methods:This was a block-randomized, double-blind, placebo-controlled, multicenter clinical trial.From January 2022 to September 2023, 359 children aged 3 to 7 years old diagnosed as acute bronchitis (lung-obstructing phlegm-heat syndrome) were enrolled from 21 participating hospitals and randomly assigned to the experimental group and placebo group in a 3︰1 ratio, and respectively treated with Xiao′er Huangjin Zhike Granules and its matching placebo.Cough resolution/general resolution rate after 7 days of treatment was used as the primary efficacy outcome for both groups.Results:(1)On the seventh day of treatment, the rate of cough disappearance/basically disappearance in the experimental group and placebo group were 73.95% and 57.61% retrospectively, which had statistically significance ( P=0.001).(2)After 7 days of treatment, the median duration of cough disappearance/basic disappearance were 5 days and 6 days in the two groups , with a statistically significant difference ( P=0.006).The area under the curve of cough symptom severity time was 7.20 ± 3.79 in the experimental group and 8.20±4.42 in the placebo group.The difference between the two groups was statistically significant ( P=0.039).(3) After 7 days of treatment, the difference between TCM syndrome score and baseline was -16.0 (-20.0, -15.0) points in the experimental group and -15.0 (-18.0, -12.0) points in the placebo group, with significant difference between the two groups ( P=0.004).In the experimental group, the clinical control rate, the markedly effective rate, the effective rate and the ineffective rate were 49.04%, 28.35%, 16.48% and 6.13% severally; and in the placebo group, the clinical control rate, the markedly effective rate, the effective rate and the ineffective rate were 38.04%, 26.09%, 29.35%, and 6.52% separately, which had statistically significant ( P=0.014).(4) There was no significant difference in the incidence of adverse events or adverse reactions during the trial between both groups.Moreover, while adverse reactions in the form of vomiting and diarrhea were occasionally reported, no serious drug-related adverse event or adverse reaction was reported.(5)The tested drug provided good treatment compliance, showing no statistically significant difference from the placebo in terms of compliance rate. Conclusions:Based on the above findings, it can be concluded that Xiao′er Huangjin Zhike Granules provides good safety, efficacy, and treatment compliance in the treatment of cough caused by acute bronchitis, and lung-obstructing phlegm-heat syndrome, in children.