Objective: To explore the potential classification of 24 hour activity time allocation among college students in Yinchuan and its association with active health levels, so as to provide references for optimizing activity time allocation to enhance active health levels. Methods: From November 18 to December 6, 2024, a stratified cluster random sampling method was used to select 2 422 first and second year college students from full time undergraduate institutions in Yinchuan. The Chinese College Students 24 hour Movement Behaviors Questionnaire (24 h MBQ) and Active Health Behavior Scale were used to assess 24 hour activity time allocation and evaluate active health levels. Latent profile analysis (LPA) was employed to categorize activity types, and a binary Logistic regression analysis was conducted to analyze the relationship between active health levels and activity types. Results: A total of 1 087 students (44.9%) were found of meeting active health standards, and significant statistical differences were found in active health levels across different genders, grades, academic qualities, sources of origin and academic categories ( χ 2= 22.03 , 7.65, 25.50, 10.12, 43.44, all P <0.01). Moreover, significant statistical differences could also be found among college students 24 hour activity time across different genders, ages, grades, sources of origin, academic qualities, and academic categories ( t/Z/H/F=-5.70-111.39, P <0.05).The 24 hour activity time allocation was classified into four types:academic high ( 6.9 %), low activity rest (8.8%), light activity (67.8%), and high activity dynamic (16.4%). Significant statistical differences were observed in activity time allocation categories across different ages, academic qualities and academic categories ( χ 2=15.52-108.46, all P <0.05). Using the high activity dynamic type as a reference, the light activity type ( OR=0.39, 95%CI =0.31-0.50), low activity rest type ( OR=0.10, 95%CI =0.06-0.15), and academic high type ( OR=0.03, 95%CI =0.02-0.07) had lower active health levels among college students (all P <0.01). Conclusion There is a significant difference in 24 hour activity time allocation among college students in Yinchuan, and different activity types are associated with active health levels.

Objective: To investigate the relationship of metabolic score for insulin resistance (METS-IR) with bone mineral content (BMC) and bone metabolic markers levels among adolescents, so as to provide a scientific foundation for the early identification and prevention of bone related diseases. Methods: From 2017 to 2019 and 2023, a total of 1 414 adolescents aged 12-18 years from Yinchuan were selected using a method combining convenient sampling with stratified cluster random sampling. The data of basic information, body mass index, BMC, serum osteocalcin (OC), type I collagen cross linked C-terminal peptide (CTX) and calcium (Ca), METS-IR among adolescents were obtained by questionnaire survey, physical measurement and laboratory examination,and METS-IR was divided into four groups Q1-Q 4 according to P 25 , P 50 and P 75 . Logistic regression models combined with restricted cubic splines were employed to analyze the relationship between METS-IR and low BMC as well as low bone metabolic markers. The receiver operating characteristic (ROC) curve was used to evaluate METS-IR effectiveness in diagnosing low BMC. Results: The levels of BMC, OC, CTX, Ca and METS-IR in the surveyed adolescents were (2.66±0.52)kg, (20.49±13.77) ng/mL , (2 460.89±1 818.96)pg/mL, (2.47±0.67)mmol/L, 30.63±7.58. After adjusting for gender, age and physical activity level, METS-IR in Q 4 group had a reduced risk of low BMC and low CTX [ OR (95% CI )=0.03(0.01-0.07), 0.45(0.32-0.65)] and an elevated risk of low OC [ OR (95%CI )=1.85(1.28-2.67)], compared with the Q 1 group (all P <0.05). Gender stratified analyses revealed similar trends for both males and females (all P <0.05). Non linear dose response relationships were observed between METS-IR and low BMC ( P total trend <0.01, P non linearity =0.01), as well as low OC ( P total trend <0.01, P non linearity =0.01), while a linear relationship was detected with low CTX ( P total trend <0.01, P non linearity =0.72). ROC curves revealed that METS-IR had the best diagnostic performance for low BMC (AUC=0.85, 95% CI=0.82-0.88, P <0.01). Conclusions Higher METS-IR score is linked to reduced risk of low BMC and CTX but increase risk of low OC among adolescents. These findings suggest METS-IR is a reliable indicator for assessing BMC and early predicting bone health risk among adolescents.

BACKGROUND: Double-stranded RNA-specific adenosine deaminase 1 (ADAR1) binds to double-stranded RNA and catalyzes the deamination of adenosine (A) to inosine (I). The functional mechanism of ADAR1 in non-small cell lung cancer (NSCLC) remains incompletely understood. This study aimed to investigate the prognostic significance of ADAR1 in NSCLC and to elucidate its potential role in regulating tumor cell proliferation and migration. METHODS: Data from The Cancer Genome Atlas (TCGA) and cBioPortal were analyzed to assess the correlation between high ADAR1 expression and clinicopathological features as well as prognosis in lung cancer. We performed Western blot (WB), cell proliferation assays, Transwell invasion/migration assays, and nude mouse xenograft modeling to examine the phenotypic changes and molecular mechanisms induced by ADAR1 knockdown. Furthermore, the ADAR1 p150 overexpression model was utilized to validate the proposed mechanism. RESULTS: ADAR1 expression was significantly elevated in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) tissues compared with adjacent non-tumor tissues (LUAD: P=3.70×10-15, LUSC: P=0.016). High ADAR1 expression was associated with poor prognosis (LUAD: P=2.03×10-2, LUSC: P=2.81×10-2) and distant metastasis (P=0.003). Gene Set Enrichment Analysis (GSEA) indicated that elevated ADAR1 was associated with mitogen-activated protein kinase/extracellular signal-regulated kinase (MAPK/ERK) pathway activation, matrix metalloproteinase-9 (MMP-9) expression, and cell adhesion. ADAR1 and MMP-9 levels showed a strongly positive correlation (P=6.45×10-34) in 10 lung cancer cell lines, highest in H1581. Knockdown of ADAR1 in H1581 cells induced a rounded cellular morphology with reduced pseudopodia. Concomitantly, it suppressed cell proliferation, invasion, migration, and in vivo tumorigenesis. It also suppressed ERK phosphorylation and downregulated cellular Finkel-Biskis-Jinkins murine osteosarcoma viral oncogene homolog (c-FOS), MMP-9, N-cadherin, and Vimentin. Conversely, ADAR1 p150 overexpression in PC9 cells enhanced ERK phosphorylation and increased c-FOS and MMP-9 expression. CONCLUSIONS High ADAR1 expression is closely associated with poor prognosis and distant metastasis in NSCLC patients. Mechanistically, ADAR1 may promote proliferation, invasion, migration, and tumorigenesis in lung cancer cells via the ERK/c-FOS/MMP-9 axis.
Humans ; Lung Neoplasms/physiopathology* ; Adenosine Deaminase/genetics* ; Matrix Metalloproteinase 9/genetics* ; Cell Proliferation ; Carcinoma, Non-Small-Cell Lung/physiopathology* ; Cell Movement ; Animals ; Mice ; RNA-Binding Proteins/genetics* ; Female ; Male ; Cell Line, Tumor ; Proto-Oncogene Proteins c-fos/genetics* ; Middle Aged ; MAP Kinase Signaling System ; Gene Expression Regulation, Neoplastic ; Mice, Nude ; Extracellular Signal-Regulated MAP Kinases/genetics*

Poly(ADP-ribose) polymerase 1 (PARP1) is a multifunctional protein involved in diverse cellular functions, notably DNA damage repair. Pharmacological inhibition of PARP1 has therapeutic benefits for various pathologies. Despite the increased use of PARP inhibitors, challenges persist in achieving PARP1 selectivity and effective blood-brain barrier (BBB) penetration. The development of a PARP1-specific positron emission tomography (PET) radioligand is crucial for understanding disease biology and performing target occupancy studies, which may aid in the development of PARP1-specific inhibitors. In this study, we leverage the recently identified PARP1 inhibitor, AZD9574, to introduce the design and development of its ¹⁸F-isotopologue ([¹⁸F]AZD9574). Our comprehensive approach, encompassing pharmacological, cellular, autoradiographic, and in vivo PET imaging evaluations in non-human primates, demonstrates the capacity of [¹⁸F]AZD9574 to specifically bind to PARP1 and to successfully penetrate the BBB. These findings position [¹⁸F]AZD9574 as a viable molecular imaging tool, poised to facilitate the exploration of pathophysiological changes in PARP1 tissue abundance across various diseases.

OBJECTIVE To investigate the application value of narrow-band CE-Chirp ASSR(NB CE-Chirp ASSR)and modulated acoustic ASSR in the assessment of hearing threshold in children with hearing impairment.METHODS Forty-eight children with sensorineural hearing loss were tested by pure tone audiometry(PTA),NB CE-Chirp ASSR and modulated acoustic ASSR.According to the results of pure tone audiometry,they were divided into mild to moderate group(48 ears)and severe to profound group(48 ears).The difference and correlation between pure tone hearing threshold and ASSR response threshold of different stimuli were compared between 500-4 000 Hz.RESULTS The absolute differences between the NB CE-Chirp ASSR response threshold and the PTA threshold in 48 children were all smaller than the differences between the modulated acoustic ASSR response threshold and the PTA threshold,and the differences were statistically significant(P<0.001).Under the same stimulus sound,the absolute difference between ASSR response threshold and pure tone hearing threshold in the mild to moderate group was higher than that in the severe to very severe group.The correlation coefficients between NB CE-Chirp ASSR threshold and pure tone hearing threshold are higher than those between modulated sound ASSR threshold and pure tone hearing threshold at 500-4 000 Hz.The test time of NB CE-Chirp ASSR[(20.92±9.33)min]was significantly shorter than that of modulated acoustic ASSR[(33.68±10.97)min](P=0.004).CONCLUSION NB CE-Chirp ASSR can more accurately assess the hearing threshold of children with different degrees of hearing loss than modulated acoustic ASSR.

Objective Depression,a most common psychiatric disease,is defined in Traditional Chinese Medicine(TCM)as Yu Syndrome,i.e.,depression disorder,or Baihe Disease,i.e.,lily bulb disease,a category of emotional disorders treated with lily-based TCM preparations.In TCM,depression is managed through syndrome differentiation and treatment,which is characterized by high efficacy and safety.However,there is no unified standard for the classification of depression syndromes,which leads to a disconnection between the analysis of patients'medication patterns and their actual syndromes and hinders the study of medication patterns specific to particular syndromes.Therefore,this study is focused on investigating the medication patterns of different sub-types of depression patients based on an objective classification system of depression.Methods We searched for and retrieved clinical literature on TCM formulas for depression from relevant databases,including China National Knowledge Infrastructure(CNKI),Wanfang Data,VIP Database,Sinomed,Web of Science,and PubMed.Information on patient symptoms and medication was standardized.Then,the symptoms and the medication frequency of depression patients were statistically analyzed.We used the K-means clustering method combined with implicit structural analysis to objectively categorize depression patients into sub-types.In addition,the main symptoms and core TCM formulas of each sub-type of depression patients were identified.On the basis of objective classification system,we also statistically analyzed the characteristics of herbs used on depression patients,including the 4 basic properties,the 5 flavors,the attributes,the therapeutic efficacy,and the co-occurrence patterns,which may help reveal the medication patterns.Results A total of 3 537 publications and 4 434 prescriptions were included in the analysis.By using the K-means algorithm and latent structure analysis methods,patients with depression were categorized into 9 sub-types,with Cluster 6 accounting for the largest proportion.The most common symptoms among depression patients were insomnia and a depressed mood.Medication frequency analysis showed that Radix Bupleuri(Chai Hu),Radix Paeoniae Alba(Bai Shao),Poria(Fu Ling),Rhizoma Chuanxiong(Chuan Xiong),and Radix Curcumae(Yu Jin)were the most commonly used TCM herbs.For the depression sub-types of Clusters 1,2,and 6,blood-activating and stasis-dissolving herbs were used most often.The depression sub-types of Clusters 3,4,5,8,and 9 were mainly treated with qi-regulating herbs,while the depression sub-type of Cluster 7 was treated with qi-supplementing herbs.Depression patients were mostly treated with herbs that were cold or warm in nature and had sweet,bitter,and pungent flavors.Moreover,treatments for Cluster 1 and Cluster 6 mainly targeted the spleen meridian,while those for Cluster 2,Cluster 3,Cluster 4 and Cluster 5 mainly targeted the heart meridian.The treatments for the other sub-types mainly targeted the liver meridian.The core TCM formulas for the 9 depression sub-types included Zishui Qinggan Decoction,Danzhi Xiaoyao Powder,Huanglian Wendan Tang,Chaihu Guizhi Tang,Modified Xiaoyao Powder,Qinggan Jieyu Tang,Xiaoyao Powder,Xuefu Zhuyu Decoction,and Bazhen Decoction.The most commonly used Chinese herbal medicinal formulas were Gan Cao-Chai Hu,Bai Shao-Chai Hu,and Chen Pi-Chai Hu.Conclusion Based on machine learning,this study reveals the scientific aspects of TCM typing and syndrome-based treatment.It clarifies the rationale for targeting different symptoms in depression treatment and provides theoretical support for clinicians to make medication prescriptions.It also presents a new perspective for investigating TCM medication patterns.

Background::Acute pulmonary embolism (APE) is a fatal cardiovascular disease, yet missed diagnosis and misdiagnosis often occur due to non-specific symptoms and signs. A simple, objective technique will help clinicians make a quick and precise diagnosis. In population studies, machine learning (ML) plays a critical role in characterizing cardiovascular risks, predicting outcomes, and identifying biomarkers. This work sought to develop an ML model for helping APE diagnosis and compare it against current clinical probability assessment models.Methods::This is a single-center retrospective study. Patients with suspected APE were continuously enrolled and randomly divided into two groups including training and testing sets. A total of 8 ML models, including random forest (RF), Na?ve Bayes, decision tree, K-nearest neighbors, logistic regression, multi-layer perceptron, support vector machine, and gradient boosting decision tree were developed based on the training set to diagnose APE. Thereafter, the model with the best diagnostic performance was selected and evaluated against the current clinical assessment strategies, including the Wells score, revised Geneva score, and Years algorithm. Eventually, the ML model was internally validated to assess the diagnostic performance using receiver operating characteristic (ROC) analysis.Results::The ML models were constructed using eight clinical features, including D-dimer, cardiac troponin T (cTNT), arterial oxygen saturation, heart rate, chest pain, lower limb pain, hemoptysis, and chronic heart failure. Among eight ML models, the RF model achieved the best performance with the highest area under the curve (AUC) (AUC = 0.774). Compared to the current clinical assessment strategies, the RF model outperformed the Wells score ( P = 0.030) and was not inferior to any other clinical probability assessment strategy. The AUC of the RF model for diagnosing APE onset in internal validation set was 0.726. Conclusions::Based on RF algorithm, a novel prediction model was finally constructed for APE diagnosis. When compared to the current clinical assessment strategies, the RF model achieved better diagnostic efficacy and accuracy. Therefore, the ML algorithm can be a useful tool in assisting with the diagnosis of APE.

Colorectal cancer is one of the common malignant tumors of the digestive tract.With the popularization of screening methods and advancement of endoscopic technology,an increasing number of T1 stage colorectal cancers can be discovered.Accurately predicting lymph node metastasis risk is significantly important for guiding clinical treatment decisions,reducing complications and mortality.Current research on risk factors for lymph node metastasis in T1 stage colorectal cancer covers multiple aspects including clinical pathological features,molecular phenotypes and genetic characteristics.Some studies have built prediction models by integrating these factors,which show higher sensitivity,specificity and accuracy compared to current clinical guidelines.These models provide valuable experience for clinical practice.

With the continuous improvement of medical information and intelligence, multi-modal data fusion technology is increasingly widely used in the medical field. Multi-modal data not only has a large quantity, but also has rich information content, which can provide strong support for clinical nursing decision-making. However, due to the uneven level of informatization and intelligence development among medical institutions, the development and application of nursing decision support system is still in a fragmented state. Based on this, this study explores in depth the current application status and challenges faced by multi-modal data fusion in nursing decision support systems, with the aim of providing reference for the design and improvement of nursing decision support system at all levels of medical institutions in the future.

With the continuous advancement of information technology, electronic medical records (EMR) have accumulated vast amounts of clinical data, which can assist nursing staff in making clinical decisions, thereby improving the quality of care, enhancing work efficiency, and promoting patient safety. However, due to varying levels of information technology development across medical institutions, nursing decision support systems (NDSS) remain in a fragmented development stage. This article explores the current application status and existing shortcomings of NDSS based on EMR systems, aiming to provide reference for the design and improvement of NDSS in various levels of medical institutions.