Objective: To investigate the association of physical activity and screen time with overweight and obesity among children and adolescents with special needs in Tianjin, so as to provide scientific evidence for childhood obesity prevention and intervention measures in the population. Methods: From January 2022 to June 2024, 296 children and adolescents with intellectual disabilities and autism spectrum disorders aged 2-18 years were recruited from special education schools and institutions in Tianjin. Height and weight were measured, and a standardized questionnaire was used to assess physical activity and screen time. Binary Logistic regression analysis was carried out to investigate the association of physical activity and screen time with overweight and obesity. Results: The prevalence of overweight and obesity among children and adolescents with special needs in Tianjin were 17.2% and 21.6%, respectively, and the combined prevalence of overweight and obesity was 38.9%. The median of moderatetovigorous physical activity (MVPA) time was 0.20 h/d, and physical activity sufficiency rate was 7.8%. The median of screen time was 1.79 h/d, and the screen time compliance rate was 68.2%. The binary Logistic regression results showed that lower levels of MVPA time and increased screen time were associated with a higher risk of overweight and obesity among children and adolescents with special needs [OR(95%CI)=1.80(1.06-3.07), 2.40(1.42-4.07),P<0.05]. Conclusions Insufficient physical activity and excessive screen time are associated with an increased risk of overweight and obesity among children and adolescents with special needs. Therefore, comprehensive intervention measures should be implemented as early as possible to prevent and reduce the incidence of overweight and obesity in this population.

Objective: To understand the current situation and related factors of screening myopia among students in special education schools, so as to provide evidence for promoting the health level of this population. Methods: From November 2021 to December 2023, a total of 281 students from 6 special education schools in 5 districts of Tianjin were selected by cluster random sampling method for computer optometry visual acuity examination for non ciliary paralysis and questionnaire survey. Multiple Logistic regression was performed to analyze the influencing factors of screening myopia among special education students. Results: The screening myopia detection rate among these special education students in Tianjin was 27.0%, and the screening myopia detection rates of students with autism, developmental delays, and intellectual disabilities were 22.4%, 12.5%, and 33.0%, respectively. The degree of myopia increased with age ( χ 2 trend =22.65, P <0.01). Multivariate Logistic regression analysis showed that age(10-13 years old: OR =5.40, 14-17 years old: OR =8.40, 18-23 years old: OR =6.02), accommodation(non resident: OR =0.29), daily mobile phone usage ≥2 hours ( OR =2.37), and daily computer/tablet usage ≥2 hours ( OR =2.70) were the risk factors for screening myopia among special education students ( P <0.05). Conclusions The detection rate and degree of screening myopia increase with age in special education students. Prolonged screen time exposure is a primary risk factor for screening myopia in special education students. Effective myopia prevention and control strategies should be designed according to the characteristics of special education students.

Purpose: The aim of this study was to investigate the feasibility of an intelligent handheld ultrasound (US) device for assisting non-expert general practitioners (GPs) in detecting carotid plaques (CPs) in community populations. Methods: This prospective parallel controlled trial recruited 111 consecutive community residents. All of them underwent examinations by non-expert GPs and specialist doctors using handheld US devices (setting A, setting B, and setting C). The results of setting C with specialist doctors were considered the gold standard. Carotid intima-media thickness (CIMT) and the features of CPs were measured and recorded. The diagnostic performance of GPs in distinguishing CPs was evaluated using a receiver operating characteristic curve. Inter-observer agreement was compared using the intragroup correlation coefficient (ICC). Questionnaires were completed to evaluate clinical benefits. Results: Among the 111 community residents, 80, 96, and 112 CPs were detected in settings A, B, and C, respectively. Setting B exhibited better diagnostic performance than setting A for detecting CPs (area under the curve, 0.856 vs. 0.749; P<0.01). Setting B had better consistency with setting C than setting A in CIMT measurement and the assessment of CPs (ICC, 0.731 to 0.923). Moreover, measurements in setting B required less time than the other two settings (44.59 seconds vs. 108.87 seconds vs. 126.13 seconds, both P<0.01). Conclusion Using an intelligent handheld US device, GPs can perform CP screening and achieve a diagnostic capability comparable to that of specialist doctors.

Purpose: The aim of this study was to investigate the feasibility of an intelligent handheld ultrasound (US) device for assisting non-expert general practitioners (GPs) in detecting carotid plaques (CPs) in community populations. Methods: This prospective parallel controlled trial recruited 111 consecutive community residents. All of them underwent examinations by non-expert GPs and specialist doctors using handheld US devices (setting A, setting B, and setting C). The results of setting C with specialist doctors were considered the gold standard. Carotid intima-media thickness (CIMT) and the features of CPs were measured and recorded. The diagnostic performance of GPs in distinguishing CPs was evaluated using a receiver operating characteristic curve. Inter-observer agreement was compared using the intragroup correlation coefficient (ICC). Questionnaires were completed to evaluate clinical benefits. Results: Among the 111 community residents, 80, 96, and 112 CPs were detected in settings A, B, and C, respectively. Setting B exhibited better diagnostic performance than setting A for detecting CPs (area under the curve, 0.856 vs. 0.749; P<0.01). Setting B had better consistency with setting C than setting A in CIMT measurement and the assessment of CPs (ICC, 0.731 to 0.923). Moreover, measurements in setting B required less time than the other two settings (44.59 seconds vs. 108.87 seconds vs. 126.13 seconds, both P<0.01). Conclusion Using an intelligent handheld US device, GPs can perform CP screening and achieve a diagnostic capability comparable to that of specialist doctors.

Purpose: The aim of this study was to investigate the feasibility of an intelligent handheld ultrasound (US) device for assisting non-expert general practitioners (GPs) in detecting carotid plaques (CPs) in community populations. Methods: This prospective parallel controlled trial recruited 111 consecutive community residents. All of them underwent examinations by non-expert GPs and specialist doctors using handheld US devices (setting A, setting B, and setting C). The results of setting C with specialist doctors were considered the gold standard. Carotid intima-media thickness (CIMT) and the features of CPs were measured and recorded. The diagnostic performance of GPs in distinguishing CPs was evaluated using a receiver operating characteristic curve. Inter-observer agreement was compared using the intragroup correlation coefficient (ICC). Questionnaires were completed to evaluate clinical benefits. Results: Among the 111 community residents, 80, 96, and 112 CPs were detected in settings A, B, and C, respectively. Setting B exhibited better diagnostic performance than setting A for detecting CPs (area under the curve, 0.856 vs. 0.749; P<0.01). Setting B had better consistency with setting C than setting A in CIMT measurement and the assessment of CPs (ICC, 0.731 to 0.923). Moreover, measurements in setting B required less time than the other two settings (44.59 seconds vs. 108.87 seconds vs. 126.13 seconds, both P<0.01). Conclusion Using an intelligent handheld US device, GPs can perform CP screening and achieve a diagnostic capability comparable to that of specialist doctors.

Purpose: The aim of this study was to investigate the feasibility of an intelligent handheld ultrasound (US) device for assisting non-expert general practitioners (GPs) in detecting carotid plaques (CPs) in community populations. Methods: This prospective parallel controlled trial recruited 111 consecutive community residents. All of them underwent examinations by non-expert GPs and specialist doctors using handheld US devices (setting A, setting B, and setting C). The results of setting C with specialist doctors were considered the gold standard. Carotid intima-media thickness (CIMT) and the features of CPs were measured and recorded. The diagnostic performance of GPs in distinguishing CPs was evaluated using a receiver operating characteristic curve. Inter-observer agreement was compared using the intragroup correlation coefficient (ICC). Questionnaires were completed to evaluate clinical benefits. Results: Among the 111 community residents, 80, 96, and 112 CPs were detected in settings A, B, and C, respectively. Setting B exhibited better diagnostic performance than setting A for detecting CPs (area under the curve, 0.856 vs. 0.749; P<0.01). Setting B had better consistency with setting C than setting A in CIMT measurement and the assessment of CPs (ICC, 0.731 to 0.923). Moreover, measurements in setting B required less time than the other two settings (44.59 seconds vs. 108.87 seconds vs. 126.13 seconds, both P<0.01). Conclusion Using an intelligent handheld US device, GPs can perform CP screening and achieve a diagnostic capability comparable to that of specialist doctors.

Purpose: The aim of this study was to investigate the feasibility of an intelligent handheld ultrasound (US) device for assisting non-expert general practitioners (GPs) in detecting carotid plaques (CPs) in community populations. Methods: This prospective parallel controlled trial recruited 111 consecutive community residents. All of them underwent examinations by non-expert GPs and specialist doctors using handheld US devices (setting A, setting B, and setting C). The results of setting C with specialist doctors were considered the gold standard. Carotid intima-media thickness (CIMT) and the features of CPs were measured and recorded. The diagnostic performance of GPs in distinguishing CPs was evaluated using a receiver operating characteristic curve. Inter-observer agreement was compared using the intragroup correlation coefficient (ICC). Questionnaires were completed to evaluate clinical benefits. Results: Among the 111 community residents, 80, 96, and 112 CPs were detected in settings A, B, and C, respectively. Setting B exhibited better diagnostic performance than setting A for detecting CPs (area under the curve, 0.856 vs. 0.749; P<0.01). Setting B had better consistency with setting C than setting A in CIMT measurement and the assessment of CPs (ICC, 0.731 to 0.923). Moreover, measurements in setting B required less time than the other two settings (44.59 seconds vs. 108.87 seconds vs. 126.13 seconds, both P<0.01). Conclusion Using an intelligent handheld US device, GPs can perform CP screening and achieve a diagnostic capability comparable to that of specialist doctors.

ObjectiveTransfer RNA-derived fragments (tRFs) are a recently characterized and rapidly expanding class of small non-coding RNAs, typically ranging from 13 to 50 nucleotides in length. They are derived from mature or precursor tRNA molecules through specific cleavage events and have been implicated in a wide range of cellular processes. Increasing evidence indicates that tRFs play important regulatory roles in gene expression, primarily by interacting with target messenger RNAs (mRNAs) to induce transcript degradation, in a manner partially analogous to microRNAs (miRNAs). However, despite their emerging biological relevance and potential roles in disease mechanisms, there remains a significant lack of computational tools capable of systematically predicting the interaction landscape between tRFs and their target mRNAs. Existing databases often rely on limited interaction features and lack the flexibility to accommodate novel or user-defined tRF sequences. The primary goal of this study was to develop a machine learning based prediction algorithm that enables high-throughput, accurate identification of tRF:mRNA binding events, thereby facilitating the functional analysis of tRF regulatory networks. MethodsWe began by assembling a manually curated dataset of 38 687 experimentally verified tRF:mRNA interaction pairs and extracting seven biologically informed features for each pair: (1) AU content of the binding site, (2) site pairing status, (3) binding region location, (4) number of binding sites per mRNA, (5) length of the longest consecutive complementary stretch, (6) total binding region length, and (7) seed sequence complementarity. Using this dataset and feature set, we trained 4 distinct machine learning classifiers—logistic regression, random forest, decision tree, and a multilayer perceptron (MLP)—to compare their ability to discriminate true interactions from non-interactions. Each model’s performance was evaluated using overall accuracy, receiver operating characteristic (ROC) curves, and the corresponding area under the ROC curve (AUC). The MLP consistently achieved the highest AUC among the four, and was therefore selected as the backbone of our prediction framework, which we named tRF Prospect. For biological validation, we retrieved 3 high-throughput RNA-seq datasets from the gene expression omnibus (GEO) in which individual tRFs were overexpressed: AS-tDR-007333 (GSE184690), tRF-3004b (GSE197091), and tRF-20-S998LO9D (GSE208381). Differential expression analysis of each dataset identified genes downregulated upon tRF overexpression, which we designated as putative targets. We then compared the predictions generated by tRF Prospect against those from three established tools—tRFTar, tRForest, and tRFTarget—by quantifying the number of predicted targets for each tRF and assessing concordance with the experimentally derived gene sets. ResultsThe proposed algorithm achieved high predictive accuracy, with an AUC of 0.934. Functional validation was conducted using transcriptome-wide RNA-seq datasets from cells overexpressing specific tRFs, confirming the model’s ability to accurately predict biologically relevant downregulation of mRNA targets. When benchmarked against established tools such as tRFTar, tRForest, and tRFTarget, tRF Prospect consistently demonstrated superior performance, both in terms of predictive precision and sensitivity, as well as in identifying a higher number of true-positive interactions. Moreover, unlike static databases that are limited to precomputed results, tRF Prospect supports real-time prediction for any user-defined tRF sequence, enhancing its applicability in exploratory and hypothesis-driven research. ConclusionThis study introduces tRF Prospect as a powerful and flexible computational tool for investigating tRF:mRNA interactions. By leveraging the predictive strength of deep learning and incorporating a broad spectrum of interaction-relevant features, it addresses key limitations of existing platforms. Specifically, tRF Prospect: (1) expands the range of detectable tRF and target types; (2) improves prediction accuracy through multilayer perceptron model; and (3) allows for dynamic, user-driven analysis beyond database constraints. Although the current version emphasizes miRNA-like repression mechanisms and faces challenges in accurately capturing 5'UTR-associated binding events, it nonetheless provides a critical foundation for future studies aiming to unravel the complex roles of tRFs in gene regulation, cellular function, and disease pathogenesis.

This study aims to establish the S_(180) tumor-bearing mice model, and to investigate the influence of Shenqi Erpi Granules(SQEPG) on immune function, as well as the drug's tumor-suppressive effect and mechanism. SPF grade KM mice(half male and half female) were randomly divided into 6 groups: a control group, a model group, a cyclophosphamide group(50 mg·kg~(-1)), as well as SQEPG groups in low-, medium-, and high-dose(5.25, 10.5, 21 g·kg~(-1)). The control group and the model group were given distilled water, and the other 4 groups were given the corresponding drugs by gavage. The administration continued for 10 days before the mice were sacrificed. The antitumor and immune regulation effects of SQEPG were evaluated. The effect of SQEPG on delayed type hypersensitivity reaction(DTH), carbon clearance index, and serum hemolysin antibody level was observed to reflect the effect on the immune function of tumor-bearing mice. Tumor weight was recorded to calculate the tumor suppression rate and the immune organ index. Hematoxylin-eosin(HE) staining was used to detect morphological changes in tumor tissues. Flow cytometry was employed to detect the percentage of CD4~+ and CD8~+ T-cells in the spleen tissues and the tumor tissue apoptosis levels. Immunohistochemistry was conducted to detect the KI67 protein expression level of tumor tissues. ELISA resorted to the detection of the following expression levels in tumor tissues: tumor necrosis factor-α(TNF-α), interleukin-2(IL-2), interferon-γ(IFN-γ). Western blot was performed to detect the expression levels of caspase-3, B-cell lymphoma-2(Bcl-2), Bcl-2-associated X protein(Bax), cyclin-dependent kinases 4(CDK4), G_1/S-specific cyclin D1(cyclin D1), and vascular endothelial growth factor A(VEGFA). The results showed that, compared with the model group, the SQEPG could increase the swelling of the auricle of the tumor-bearing mice; significantly increase the phagocytic index of carbon granule contour(P<0.05 or P<0.01), and the middle dose of SQEPG could significantly increase the antibody level of hemolysin(P<0.05); different doses of SQEPG significantly inhibit the growth of the tumor, and decrease the mass of the tumor tissues(P<0.05 or P<0.01); the low dose of SQEPG significantly decreased spleen index(P<0.05), low and high doses of SQEPG increased thymus index, while medium doses of SQEPG decreased thymus index. High doses of SQEPG significantly elevated the levels of CD4~+ and CD8~+ T-cells in the spleens of the homozygous mice(P<0.01 or P<0.001), and increased the apoptosis rate of the cells of the tumor tissues(P<0.05); Meanwhile, high-dose SQEPG elevated the levels of immunity factors such as IL-2, IFN-γ and TNF-α in the serum of tumor-bearing mice(P<0.01); medium-and high-dose SQEPG significantly lowered the rate of positive expression of KI67 protein in tumor tissues(P<0.01). Compared with the model group, high-dose SQEPG significantly up-regulated the expression of caspase-3 and Bax proteins in tumor tissues(P<0.05), and significantly down-regulated the expression of CDK4, cyclin D1, and VEGFA proteins(P<0.05 or P<0.01). In conclusion, SQEPG has the effect of improving immune function and inhibiting tumor growth in tumor-bearing mice. Its mechanism of tumor-suppressive effects may be related to apoptosis promotion, cell cycle progression block, and tumor cell proliferation inhibition.
Animals ; Mice ; Drugs, Chinese Herbal/pharmacology* ; Male ; Female ; Apoptosis/drug effects* ; Sarcoma 180/genetics* ; Humans