Objective To comprehensively analyze the current epidemic characteristics and disease burden of brucellosis in Tongliao City, and to provide a basis for the prevention and control strategy of brucellosis in Tongliao City. Methods The report data of brucellosis in Tongliao City from 2018 to 2023 were collected. Descriptive methods were used for data analysis, and the disability-adjusted life years and indirect economic losses were calculated. Results From 2018 to 2023, a total of 22 034 cases were reported in Tongliao City, with an average annual incidence of 136.17/100 000. The incidence was statistically different between men and women ( χ²=12.23, P=0.032). The majority of cases were farmers (94.25%), followed by herdsmen (1.67%). The age group was concentrated between 30-60 years old (79.30%), among which the majority of cases were in the 40-50 years group (6 883/22 034). The onset time had seasonal characteristics, and the peak period was from March to August (the seasonal index was between 115.40%-151.29%). In terms of regional distribution, cases were reported in all counties (banners). The average annual incidence was highest in Kulun Banner (233.85/100 000) and Zalut Banner (210.13/100 000), and lowest in Keerqin District (42.28/100 000) and Holingol City (31.87/100 000). The analysis of disease burden showed that a total of 677.55 person-years (YLD) were lost from 2018 to 2023, with an average annual loss of 112.92 person-years. The total indirect economic loss was 59.3576 million yuan, with an average annual loss of 9.892 9 million yuan, and the people over 60 years old had the lowest annual loss. Conclusion The overall brucellosis epidemic in Tongliao City has shown a fluctuating downward trend. The epidemic prevention and control should be strengthened in farmers, people aged 40-50 years old, and areas such as Zalut Banner and Kulun Banner to further control the epidemic of brucellosis.

In situ continuous monitoring technology based on sweat detection can reflect the changes of human metabolic status,electrolyte balance and disease markers in real time,which can provide important dynamic data support for personalized health management,but it still faces bottlenecks such as lack of reliability of sweat sampling,high cross-interference among markers,and difficulty of dynamic continuous monitoring.Wearable sweat sensors based on microfluidic chips can effectively improve the detection accuracy of sweat markers by means of precise fluidic manipulation,multi-channel parallel analysis architecture,and chip surface functionalization modification techniques,providing a powerful tool for revealing the mysteries of human physiology at molecular level,and showing great potential for application in the field of personalized health monitoring.This paper focused on microfluidic chip-based multi-channel sweat sensors,and reviewed the recent progresses of microfluidic chips in sweat collection capability,wearable sensing implementation,and artificial intelligence technique synergizing to achieve simultaneous multi-parameter detection of sweat from the perspective of multi-channel synergistic sensing.Meanwhile,for industrialization bottlenecks such as crosstalk of sensing signals and wireless energy supply,this paper explored feasible solutions and technical routes,providing a theoretical framework and development direction for construction of a next-generation intelligent sweat monitoring system.By summarizing the practical needs in this field through an overview,this paper aimed to provide theoretical references and practical guidance for the development of more efficient wearable microfluidics.

The development and validation of clinical prediction models based on artificial intelligence (AI) and machine learning methods have become increasingly widespread. However, the prediction model bias risk and applicability evaluation tool developed in 2019 (i.e., PROBAST-2019) has shown significant limitations. Therefore, an expanded and updated version of the PROBAST-2019 tool was released in 2025, known as the PROBAST+AI tool. The tool is divided into two parts including model development and model evaluation. It aims to comprehensively and systematically evaluate potential methodological quality issues in model development, bias risks in model evaluation, and the applicability of models, regardless of the modeling method used. This paper provides a systematic interpretation of the PROBAST+AI tool's items and case analyses, with the aim of guiding and assisting researchers engaged in related studies and promoting the high-quality development of clinical predictive model research.

The dried mature peel of Citrus reticulata, a plant in the Rutaceae family and its cultivated varieties, is a commonly used Chinese medicinal material known as Chenpi(Citri Reticulatae Pericarpium). It is rich in nutritional components and medicinal value, with pharmacological effects including relieving cough and eliminating phlegm, strengthening the spleen and drying dampness, protecting the liver and benefiting the stomach, tonifying Qi, and calming the mind. Huanglongbing(HLB), also known as Citrus Huanglongbing, is a destructive disease in citrus production that seriously threatens the development of the citrus industry. HLB causes symptoms such as the inability of Rutaceae plants to produce mature fruit, gradual weakening of the tree, and eventual death, posing a significant threat to the yield and quality of Chenpi. Due to the uneven distribution of the HLB pathogen in infected plants, accurate detection of the pathogen requires the collection of a large number of plant samples. Current sample pretreatment methods, such as traditional extraction methods and commercial extraction kits, are time-consuming and involve multiple steps, which significantly increase the difficulty and workload of HLB diagnosis and have become a bottleneck in HLB detection. In this study, a rapid high-throughput detection method combining alkali lysis and TaqMan qPCR was developed. This method allows the pretreatment of multiple samples within 5 min, and the entire detection process can be completed within 45 min, with a detection limit of 6.67 fg·μL~(-1). The alkali lysis method and commercial kits were used for parallel detection of field-collected citrus samples, and the results showed no significant difference. The sample pretreatment method established in this study is characterized by low cost, simplicity, and high efficiency. Combined with TaqMan qPCR, it can provide technical support for early and on-site diagnosis of HLB. This method is of great significance for disease prevention and control in the citrus industry and is expected to help improve the yield and quality of citrus medicinal materials.
Citrus/microbiology* ; Plant Diseases/microbiology* ; Rhizobiaceae/physiology* ; High-Throughput Screening Assays/methods* ; Liberibacter/physiology*

Heart rate is a crucial indicator of human health with significant physiological importance. Traditional contact methods for measuring heart rate, such as electrocardiograph or wristbands, may not always meet the need for convenient health monitoring. Remote photoplethysmography (rPPG) provides a non-contact method for measuring heart rate and other physiological indicators by analyzing blood volume pulse signals. This approach is non-invasive, does not require direct contact, and allows for long-term healthcare monitoring. Deep learning has emerged as a powerful tool for processing complex image and video data, and has been increasingly employed to extract heart rate signals remotely. This article reviewed the latest research advancements in rPPG-based heart rate measurement using deep learning, summarized available public datasets, and explored future research directions and potential advancements in non-contact heart rate measurement.
Humans ; Deep Learning ; Heart Rate/physiology* ; Photoplethysmography/methods* ; Video Recording ; Face ; Monitoring, Physiologic/methods* ; Signal Processing, Computer-Assisted

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.