Deep learning method can be used to automatically analyze electrocardiogram (ECG) data and rapidly implement arrhythmia classification, which provides significant clinical value for the early screening of arrhythmias. How to select arrhythmia features effectively under limited abnormal sample supervision is an urgent issue to address. This paper proposed an arrhythmia classification algorithm based on an adaptive multi-feature fusion network. The algorithm extracted RR interval features from ECG signals, employed one-dimensional convolutional neural network (1D-CNN) to extract time-domain deep features, employed Mel frequency cepstral coefficients (MFCC) and two-dimensional convolutional neural network (2D-CNN) to extract frequency-domain deep features. The features were fused using adaptive weighting strategy for arrhythmia classification. The paper used the arrhythmia database jointly developed by the Massachusetts Institute of Technology and Beth Israel Hospital (MIT-BIH) and evaluated the algorithm under the inter-patient paradigm. Experimental results demonstrated that the proposed algorithm achieved an average precision of 75.2%, an average recall of 70.1% and an average F ₁-score of 71.3%, demonstrating high classification accuracy and being able to provide algorithmic support for arrhythmia classification in wearable devices.
Humans ; Arrhythmias, Cardiac/diagnosis* ; Algorithms ; Electrocardiography/methods* ; Neural Networks, Computer ; Signal Processing, Computer-Assisted ; Deep Learning ; Classification Algorithms

Protein liquid-liquid phase separation (LLPS), a pivotal phenomenon intricately linked to cellular processes, is regulated by various other proteins. However, there is still a lack of high-throughput methods for screening protein regulators of LLPS in target proteins. Here, we developed a CRISPR/Cas9-based screening method to identify protein phase separation regulators by integrating bimolecular fluorescence complementation (BiFC) and fluorescence-activated cell sorting (FACS). Using this newly developed method, we screened the RNA-binding proteins that regulate PABPN1 phase separation and identified the tumor suppressor QKI as a promoter of PABPN1 phase separation. Furthermore, QKI exhibits decreased expression levels and diminished nuclear localization in colorectal cancer cells, resulting in reduced PABPN1 phase separation, which, in turn, promotes alternative polyadenylation (APA), cell proliferation, and migration in colorectal cancer.
Humans ; Colorectal Neoplasms/genetics* ; RNA-Binding Proteins/genetics* ; Poly(A)-Binding Protein I/genetics* ; CRISPR-Cas Systems ; Flow Cytometry ; Cell Proliferation ; Cell Line, Tumor ; Cell Movement

Hepatocellular carcinoma (HCC) expresses abundant glycolytic enzymes and displays comprehensive glucose metabolism reprogramming. Aldolase A (ALDOA) plays a prominent role in glycolysis; however, little is known about its role in HCC development. In the present study, we aim to explore how ALDOA is involved in HCC proliferation. HCC proliferation was markedly suppressed both in vitro and in vivo following ALDOA knockout, which is consistent with ALDOA overexpression encouraging HCC proliferation. Mechanistically, ALDOA knockout partially limits the glycolytic flux in HCC cells. Meanwhile, ALDOA translocated to nuclei and directly interacted with c-Jun to facilitate its Thr93 phosphorylation by P21-activated protein kinase; ALDOA knockout markedly diminished c-Jun Thr93 phosphorylation and then dampened c-Jun transcription function. A crucial site Y364 mutation in ALDOA disrupted its interaction with c-Jun, and Y364S ALDOA expression failed to rescue cell proliferation in ALDOA deletion cells. In HCC patients, the expression level of ALDOA was correlated with the phosphorylation level of c-Jun (Thr93) and poor prognosis. Remarkably, hepatic ALDOA was significantly upregulated in the promotion and progression stages of diethylnitrosamine-induced HCC models, and the knockdown of A ldoa strikingly decreased HCC development in vivo. Our study demonstrated that ALDOA is a vital driver for HCC development by activating c-Jun-mediated oncogene transcription, opening additional avenues for anti-cancer therapies.

A 20-year-old male patient presented to the Department of Dermatology of Peking Union Medical College Hospital with complaints of an 8-year history of facial scarring, swelling of the lower limbs, and a 4-year history of scalp thickening. Physical examination showed thickening furrowing wrinkling of the skin on the face and behind the ears, ciliary body hirsutism, blepharoptosis, and cutis verticis gyrate. Both lower limbs were swollen, especially the knees and ankles. The skin of the palms and soles of the feet was keratinized and thickened. Laboratory examination using bone and joint X-ray showed periostosis of the proximal middle phalanges and metacarpals of both hands, distal ulna and radius, tibia and fibula, distal femurs, and metatarsals.Genetic testing revealed two variants in SLCO2A1, c.1658T > A and c.96+4A > C.Through multidisciplinary consultation, we confirmed the diagnosis of pachydermoperiostosis.

Objective: To explore the causal link of cumulative ecological risk and future orientation with health risk behaviors among higher vocational college students, so as to provide reference for reducing and preventing health risk behaviors among higher vocational college students. Methods: A longitudinal follow up study was conducted on 612 students using convenience sampling from 2 vocational colleges in Hunan Province. The Cumulative Ecological Risk Scale, Future Orientation Scale, and Health Risk Behavior Scale were used during three follow up visits (T1: September 2022, T2: June 2023, T3: March 2024), and a cross lagged panel model was constructed to examine the longitudinal causal relationship of cumulative ecological risk, future orientation and health risk behaviors. Analysis of longitudinal intermediary effect between variables by Bootstrap. Results: The cumulative ecological risk scores of T1, T2 and T3 among higher vocational college students were (2.94±1.44,2.99±1.63,3.02±1.54), future orientation scores (40.49±4.71,41.51±5.72,41.06±4.35) and health risk behavior scores (3.73±2.01,3.49±2.00,3.23±2.00). The results of repeated measures ANOVA showed that the future orientation score of T2 was higher than that of T1, and the main effect of measurement time was statistically significant ( F=5.09，P＜0.01,η 2=0.02). The health risk behavior score of T1 was higher than that of T2, and the health risk behavior score of T2 was higher than that of T3, and the main effect of measurement time was statistically significant ( F=10.12，P＜0.01,η 2=0.03).The cross lagged model showed good adaptability, with χ 2/df =7.20 ( P <0.01), relative fitting indicators GFI=0.98, CFI=0.99, TLI=0.96, IFI=0.99, NFI =0.99, and absolute fitting indicator RMSEA =0.06. Among them, the T1, T2 cumulative ecological risk showed negatively predictive effects on T2, T3 future orientation ( β =-0.24, -0.47 ), and T1, T2 cumulative ecological risk positively predicted T2, T3 health risk behavior ( β =0.20, 0.24), while T1, T2 future orientation negatively predicted T2, T3 health risk behavior ( β =-0.25, -0.18) ( P ＜0.01). Bootstrap test analysis found that T2 future orientation had a longitudinal mediating effect ( β=0.04, P ＜0.01) on the T1 cumulative ecological risk and T3 health risk behavior. Conclusions The accumulation of ecological risk among higher vocational college students can positively predict health risk behaviors, while future orientation can negatively predict healthrisk behaviors. Moreover, future orientation plays a longitudinal mediating role between accumulated ecological risks and health risk behaviors.

ObjectiveTo investigate the triglyceride-glucose (TyG) index and the level of serum homocysteine (Hcy) in association with the incidence of stroke in type 2 diabetes mellitus (T2DM) patients. MethodsBased on the chronic disease risk factor surveillance cohort in Pudong New Area, Shanghai, excluding those with stroke in baseline survey, T2DM patients who joined the cohort from January 2016 to October 2020 were selected as the research subjects. During the follow-up period, a total of 318 new-onset ischemic stroke patients were selected as the case group, and a total of 318 individuals matched by gender without stroke were selected as the control group. The Cox proportional hazards regression model was used to adjust for confounding factors and explore the serum TyG index and the Hcy biochemical indicator in association with the risk of stroke. ResultsThe Cox proportional hazards regression results showed that after adjusting for confounding factors, the risk of stroke in T2DM patients with 10 μmol·L⁻¹-¹ and Hcy>15 μmol·L⁻¹ was 1.532 times (95%CI: 1.017‒2.309 times, P=0.041) and 1.738 times (95%CI: 1.119‒2.699 times, P=0.014) higher respectively, compared to T2DM patients with Hcy≤10 μmol·L⁻¹. T2DM patients with TyG>9.074 had 1.396 times higher risk of stroke (95%CI: 1.091‒1.787, P=0.008) compared to those with TyG≤9.074. The study found that urea and α1-antitrypsin (α1-AT) were independent risk factors for the incidence of stroke in T2DM patients. For every unit increase in urea andα1-AT, the risk of stroke in T2DM patients increased by 233.6% (HR=3.336, 95%CI: 1.588‒7.009, P=0.001) and 11.3% (HR=1.113, 95%CI: 1.028‒1.205, P=0.008), respectively. Cumulative risk curves showed that Hcy>10 μmol·L⁻¹ and TyG>9.074 accelerated the time to stroke occurrence in T2DM patients. ConclusionElevated levels of Hcy>10 μmol·L⁻¹ and a higher TyG index are risk factors for stroke in T2DM patients. Effective interventions for Hcy and TyG should be conducted for diabetic patients to reduce the incidence of stroke.

Objective: To investigate the epidemiological characteristics and spatial-temporal clustering characteristics of scarlet fever in Jinhua City, Zhejiang Province from 2005 to 2023, so as to provide a reference for improving the prevention and control strategy of scarlet fever. Methods: The data of scarlet fever cases in Jinhua City from 2005 to 2023 were collected from the China Information System for Disease Control and Prevention, and descriptive epidemiological method was used to analyze the epidemiological characteristics of scarlet fever. The average annual percent change (AAPC) was calculated to analyze the trend of scarlet fever incidence from 2005 to 2023. The spatial-temporal clustering of scarlet fever was identified using spatial autocorrelation analysis and space-time scanning analysis. Results: A total of 1 494 scarlet fever cases were reported in Jinhua City from 2005 to 2023, and the average annual reported incidence rate was 1.41/100 000, with no significant change trend (AAPC=1.706%, P>0.05). There were two incidence peaks, from April to June and from November to January of the next year. There were 937 males and 557 females, with a male to female ratio of 1.68∶1. The age was mainly <10 years (1 391 cases, 93.11%), of which 3-<7 years was the high incidence age group (936 cases, 62.65%). There were 1 466 cases of preschool children, students, and scattered children, accounting for 98.13%. The average annual reported incidence of scarlet fever in Dongyang City, Pujiang County, and Yongkang City was 4.58/100 000, 3.04/100 000, and 1.99/100 000, respectively. The spatial autocorrelation analysis showed that there was a positive spatial correlation between the incidence of scarlet fever in Jinhua City from 2005 to 2023 (Moran's I=0.579, P<0.05), and the high-high clustering areas were mainly distributed in Dongyang City and Pujiang County. The spatial-temporal scanning analysis showed that there were 8 spatial-temporal clustering areas of scarlet fever in Jinhua City from 2005 to 2023. The class Ⅰ clustering area was 9 towns in Dongyang City, and the clustering period was from August 2013 to December 2022. There were 7 class Ⅱ clusters, covering some streets in Pujiang County, Dongyang City, Yongkang City, Yiwu City, and Pan'an County. Conclusions From 2005 to 2023, the incidence of scarlet fever in Jinhua City was relatively low, and children aged 3-<7 years had a high incidence, and there was a spatiotemporal clustering. The peak incidence was from April to June and from November to January of the next year. Dongyang City, Pujiang County, and Yongkang City had high incidence areas.

Objective: To analyze the epidemiological characteristics of leptospirosis in Jinhua City, Zhejiang Province, from 2007 to 2024, so as to provide a basis for improving the prevention and control strategies of leptospirosis. Methods: Data pertaining to leptospirosis cases in Jinhua City from 2007 to 2024 were collected through the Monitoring and Reporting Management System of the Chinese Disease Prevention and Control Information System. Descriptive epidemiological methods were used to analyze the distribution characteristics of leptospirosis in terms of time, region, population, interval from the onset of the disease to diagnosis and the outbreak of the epidemic. Results: A total of 81 cases of leptospirosis were reported in Jinhua City from 2007 to 2024, with an average annual reported incidence of 0.08/100 000. The peak incidence occurred from August to September, with 57 cases accounting for 70.37%. Leptospirosis cases were reported in 9 counties (cities, districts) in Jinhua City. Pan'an County reported the most cases, with 52 cases accounting for 64.20%. There were 54 male cases and 27 female cases, with a male-to-female ratio of 2∶1. The majority of cases were aged over 40 years, with 73 cases accounting for 90.12%. The average reported incidence of leptospirosis showed an upward trend with the increase of age (P<0.05), and the highest incidence of leptospirosis was at the 60-<80 age group (0.21/100 000). The majority of patients were farmers, with 77 cases accounting for 95.06%. The median interval from onset to diagnosis was 4.00 (interquartile range, 6.00) days. There were significant differences in the interval from onset to diagnosis among cases in Dongyang City compared with Pan'an County, Wuyi County, and Wucheng District, between Pan'an County and Jindong District, Wucheng District, and between Wuyi County and Wucheng District (all P<0.05). In 2007, one outbreak of leptospirosis was reported, which occurred in Jiuhe Township, Pan'an County, with 36 reported cases. Conclusions The reported incidence of leptospirosis in Jinhua City from 2007 to 2024 is generally low. The high-incidence period is from August to September, and Pan'an County is the high-incidence area. Males over 40 years and farmers are the key populations for prevention and control. It is recommended to strengthen epidemic surveillance and health education for high-risk populations.