Concurrent and longitudinal associations of dissatisfaction with height and weight, body mass index with sleep disturbance among adolescents in China.

Sleep disturbance in adolescents is a significant global public health issue that can result in various physical and mental disorders. Height and weight dissatisfaction, as subjective perceptions of body shape, are common in adolescence and may affect individuals' sleep situations. However, the association of them is unclear. This study aimed to examine the cross-sectional and longitudinal associations among height dissatisfaction, weight dissatisfaction, body mass index (BMI), and sleep disturbance in adolescents. A total of 27,260 participants completed measures of sleep disturbance, height and weight satisfaction, and BMI. The cross-sectional and longitudinal effects of height satisfaction, weight satisfaction, and BMI as well as their changes in sleep disturbance over time were tested. Height satisfaction (ps < 0.001) and weight satisfaction (ps < 0.001) were negatively associated with concurrent sleep disturbance after adjusting for socio-demographic covariates and previous sleep disturbance. Height satisfaction (ps < 0.001) and weight satisfaction (ps < 0.001) at T1 as well as their increases (ps < 0.001) over 6 months predicted decreases in sleep disturbance over time. The cross-sectional and longitudinal associations between BMI and sleep disturbance were not significant (ps > 0.42). Moreover, height dissatisfaction (ORs = 1.06 to 1.34) and weight dissatisfaction (ORs = 1.21 to 1.36) were risk factors for concurrent and long-term sleep disturbance while adjusting socio-demographic covariates and previous sleep disturbance. Greater attention to subjective perception of height satisfaction and weight satisfaction, instead of BMI is needed.

Evolving ω-amine transaminase AtATA guided by substrate-enzyme binding free energy for enhancing activity and stability against non-natural substrates.

In the field of chiral amine synthesis, ω-amine transaminase (ω-ATA) is one of the most established enzymes capable of asymmetric amination under optimal conditions. However, the applicability of ω-ATA toward more non-natural complex molecules remains limited due to its low transamination activity, thermostability, and narrow substrate scope. Here, by employing a combined approach of computational virtual screening strategy and combinatorial active-site saturation test/iterative saturation mutagenesis strategy, we have constructed the best variant M14C3-V5 (M14C3-V62A-V116S-E117I-L118I-V147F) with improved ω-ATA from Aspergillus terreus (AtATA) activity and thermostability toward non-natural substrate 1-acetylnaphthalene, which is the ketone precursor for producing the intermediate (R)-(+)-1-(1-naphthyl)ethylamine [(R)-NEA] of cinacalcet hydrochloride, showing activity enhancement of up to 3.4-fold compared to parent enzyme M14C3 (AtATA-F115L-M150C-H210N-M280C-V149A-L182F-L187F). The computational tools YASARA, Discovery Studio, Amber, and FoldX were applied for predicting mutation hotspots based on substrate-enzyme binding free energies and to show the possible mechanism with features related to AtATA structure, catalytic activity, and stability in silico analyses. M14C3-V5 achieved 71.8% conversion toward 50 mM 1-acetylnaphthalene in a 50 mL preparative-scale reaction for preparing (R)-NEA. Moreover, M14C3-V5 expanded the substrate scope toward aromatic ketone compounds. The generated virtual screening strategy based on the changes in binding free energies has successfully predicted the AtATA activity toward 1-acetylnaphthalene and related substrates. Together with experimental data, these approaches can serve as a gateway to explore desirable performances, expand enzyme-substrate scope, and accelerate biocatalysis.IMPORTANCEChiral amine is a crucial compound with many valuable applications. Their asymmetric synthesis employing ω-amine transaminases (ω-ATAs) is considered an attractive method. However, most ω-ATAs exhibit low activity and stability toward various non-natural substrates, which limits their industrial application. In this work, protein engineering strategy and computer-aided design are performed to evolve the activity and stability of ω-ATA from Aspergillus terreus toward non-natural substrates. After five rounds of mutations, the best variant, M14C3-V5, is obtained, showing better catalytic efficiency toward 1-acetylnaphthalene and higher thermostability than the original enzyme, M14C3. The robust combinational variant acquired displayed significant application value for pushing the asymmetric synthesis of aromatic chiral amines to a higher level.

Bidirectional associations between short sleep duration, insomnia symptoms, and psychotic-like experiences in adolescents.

This study investigates the prospective associations between short sleep duration, insomnia symptoms, and psychotic-like experiences (PLEs) in a large sample of Chinese adolescents. This study utilized a three-timepoint repeated cross-sectional survey with two nested longitudinal subsamples. A total of 17,722 adolescents were assessed at baseline (April 21 to May 12, 2021) and six months later (December 17 to 26, 2021). Out of these, 15,694 adolescents provided complete responses to the questions at baseline and one year later (May 17 - June 6, 2022). A self-administered questionnaire was used to measure sample characteristics (at baseline), sleep duration, insomnia symptoms, and PLEs (at each assessment), and negative life events (at two follow-ups). Baseline short sleep duration and insomnia symptoms predicted frequent PLEs at both follow-up assessments. Additionally, baseline frequent PLEs also predicted insomnia symptoms at six months and one year later. However, when controlling for confounders, PLEs at baseline only predicted short sleep duration at six months, and not at one year. This study reveals bidirectional prospective relationships between short sleep duration, insomnia symptoms, and PLEs, even after controlling for covariates. Therefore, it is crucial to assess both sleep patterns and PLEs in order to promote optimal sleep and mental health among adolescents.

Prevalence, correlates, and mental health outcomes of social jetlag in Chinese school-age adolescents: A large-scale population-based study.

BACKGROUND: This cross-sectional study aimed to examine the prevalence and correlates of social jetlag (SJL) in Chinese adolescents, as well as to test the relationships between SJL and mental health problems. METHODS: A total of 106979 students (Mage = 13.0 ± 1.8 years; Nmale = 58296 [54.5 %]) from Shenzhen, China completed an online survey from May 24th to June 5th, 2022. Information on sociodemographics, lifestyles, sleep characteristics, anxiety symptoms, and depressive symptoms was collected by a self-administered questionnaire. Multivariate and binary logistic regression were adopted for data analysis. RESULTS: 17.8 % of participants experienced SJL ≥ 2 h. To adjust the accumulated sleep debt, sleep-corrected SJL (SJLsc) was calculated and 8.3 % of individuals self-reported SJLsc ≥ 2 h. Both SJL and SJLsc show an increasing trend with age. Risk factors of SJL included females, poor parental marital status, being overweight, physically inactive, smoking, drinking, and having a late chronotype. Moreover, males, having siblings, boarding at school, short sleep duration, experiencing insomnia, and frequent nightmares were significantly associated with an increased risk of SJLsc. After adjusting for all covariates, adolescents with SJLsc ≥ 2 h were more likely to have anxiety symptoms (OR: 1.35, 95 % CI: 1.24-1.48) and depressive symptoms (OR: 1.35, 95 % CI: 1.25-1.46) than those with SJLsc < 1 h. CONCLUSIONS: SJL is common among Chinese school-age adolescents. This study is valuable for the development of prevention and intervention strategies for SJL in adolescents at the population level. Additionally, the strong links between SJLsc and emotional problems underscore the critical significance of addressing SJL as a key aspect of adolescent well-being.

Perceived school bullying and psychotic-like experiences in sexual minority adolescents: the mediating and moderating roles of resilience.

AIMS: This two-wave, longitudinal study aimed to examine the potential moderating and mediating effects of resilience on the association between perceived school bullying and psychotic-like experiences among Chinese sexual minority adolescents. METHODS: A total of 4192 senior high students were included and 984 (23.5%) of them were identified as a sexual minority (mean age = 16.68 years, SD = 0.71). Participants completed two online surveys during April 21 to May 12, 2021 and December 17 to 26, 2021, respectively, as well as completed self-report measures of sample characteristics, perceived school bullying, resilience, and psychotic-like experiences (including two dimensions: delusional experiences and hallucinatory experiences). RESULTS: Perceived school bullying and resilience were associated with psychotic-like experiences in sexual minority adolescents. Resilience mediated the relationship between perceived school bullying and subsequent psychotic-like experiences (b = 0.03, 95% CI = 0.01 ~ 0.04)/ delusional experiences (b = 0.03, 95% CI = 0.01 ~ 0.04)/ hallucinatory experiences (b = 0.02, 95% CI = 0.01 ~ 0.03). Additionally, resilience only moderated the associations of perceived school bullying with hallucinatory experiences (b = -0.06, 95% CI = -0.12 ~ -0.01). CONCLUSIONS: These findings indicated that resilience plays a crucial role in mediating or moderating the relationship between perceived school bullying and psychotic-like experiences. Assessing and reducing school bullying, as well as promoting resilience, may have important clinical implications for reducing the risk of psychotic-like experiences in sexual minority adolescents.

Efficacy of scleral buckling for the treatment of rhegmatogenous retinal detachment using a novel foldable capsular buckle.

AIM: To evaluate the effectiveness and safety of scleral buckling for the treatment of rhegmatogenous retinal detachment (RRD) using a novel foldable capsular buckle (FCB). METHODS: This was a series of case observation studies. Eighteen patients (18 eyes) who visited our ophthalmology department between August 2020 and August 2022 and were treated for RRD with scleral buckling using FCB were included. The procedure was similar to conventional scleral buckling, while a balloon-like FCB was placed onto the retinal break with balanced salt solution filling for a broad, external indentation instead of the silicone buckle. The retinal reattachment rate, best corrected visual acuity (BCVA), intraocular pressure (IOP), refractive dioptre and astigmatism degree, and complications were evaluated and recorded. RESULTS: There were 7 males and 11 females aged 19-58y. The average time course of RRD was 12d, ranging from 7-20d. The retinal break was located in the superior quadrants in 8 eyes and in the inferior quadrants in 10 eyes, with macula-off detachments in 12 eyes. The patients were followed-up for at least 6mo. The final retinal reattachment rate was 100%. The BCVA was significantly improved compared with the baseline (P<0.05). There was no significant change in refractive dioptre or astigmatism degree at each follow-up (all P>0.05). Three patients had transiently high IOPs within one week after surgery. Mild diplopia occurred in 5 patients after surgery and then disappeared after the balloon fluid was removed. CONCLUSION: The success rate of FCB scleral buckling for RRD is satisfactory. This procedure can be expected to be applied in new, uncomplicated cases of RRD.

GNNGL-PPI: multi-category prediction of protein-protein interactions using graph neural networks based on global graphs and local subgraphs.

Most proteins exert their functions by interacting with other proteins, making the identification of protein-protein interactions (PPI) crucial for understanding biological activities, pathological mechanisms, and clinical therapies. Developing effective and reliable computational methods for predicting PPI can significantly reduce the time-consuming and labor-intensive associated traditional biological experiments. However, accurately identifying the specific categories of protein-protein interactions and improving the prediction accuracy of the computational methods remain dual challenges. To tackle these challenges, we proposed a novel graph neural network method called GNNGL-PPI for multi-category prediction of PPI based on global graphs and local subgraphs. GNNGL-PPI consisted of two main components: using Graph Isomorphism Network (GIN) to extract global graph features from PPI network graph, and employing GIN As Kernel (GIN-AK) to extract local subgraph features from the subgraphs of protein vertices. Additionally, considering the imbalanced distribution of samples in each category within the benchmark datasets, we introduced an Asymmetric Loss (ASL) function to further enhance the predictive performance of the method. Through evaluations on six benchmark test sets formed by three different dataset partitioning algorithms (Random, BFS, DFS), GNNGL-PPI outperformed the state-of-the-art multi-category prediction methods of PPI, as measured by the comprehensive performance evaluation metric F1-measure. Furthermore, interpretability analysis confirmed the effectiveness of GNNGL-PPI as a reliable multi-category prediction method for predicting protein-protein interactions.

Sleep disturbance and internalizing symptoms in adolescents: a moderated mediation model of self-control and mindfulness.

OBJECTIVES: Despite accumulating evidence regarding the impact of sleep disturbance on internalizing symptoms among adolescents, the underlying psychological mechanisms remain inadequately explored. This study aimed to investigate a conceptual framework elucidating how sleep disturbance influences internalizing symptoms in adolescents through the mediating role of self-control, with mindfulness as a moderator. METHODS: In this cross-sectional study, 1876 Chinese adolescents (Mage = 14.88 years, SD = 1.47 years, range = 12-19 years, 44.7% boys) completed the Youth Self-Rating Insomnia Scale (YSIS), Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7), Brief Self-control Scale (BSCS), and Mindful Attention Awareness Scale-Children (MAAS-C) to provide data on sleep-related variables, internalizing symptoms (anxiety and depression), self-control, and mindfulness, respectively. The PROCESS macro for SPSS was applied to perform moderated mediation analysis. RESULTS: Sleep disturbance demonstrated a significant positive correlation with internalizing symptoms in adolescents, including anxiety (ß = 0.481, p < 0.001) and depression (ß = 0.543, p < 0.001). Self-control served as a mediator between sleep disturbance and two forms of internalizing symptoms. Moreover, mindfulness moderated the pathways from self-control to internalizing symptoms (for anxiety symptoms: ß = 0.007, p < 0.001; for depression symptoms: ß = 0.006, p < 0.001), and the mediating relationships were weaker for adolescents exhibiting higher levels of mindfulness. CONCLUSIONS: Our findings enhance understanding of the impact, pathways, and influencing factors of sleep disturbance on adolescent internalizing symptoms, suggesting the importance of enhancing mindfulness levels in addressing self-control deficits and subsequently reducing internalizing symptoms among adolescents.

Access to Functionalized Cyclohex-2-enones from a Multicomponent Cascade Reaction of Readily Available Alkynes, Ketones, and Ethyl Acetoacetate.

The synthesis of cyclohex-2-enone derivatives is a topic of current interest in organic chemistry. A novel three-component cascade reaction of alkynes with ketones and ethyl acetoacetate has been uncovered. This process provides di- and trisubstituted cyclohex-2-enones in good yields with excellent functional group tolerance. A variety of terminal alkynes and a wide range of aryl, alkyl, and cyclic ketones are viable in this transformation. Successful scale-up preparation and synthetic transformations have demonstrated the potential of this simple operating protocol.

Negative life events and sleep disturbance among adolescents: Intolerance of uncertainty as mediator and moderator.

BACKGROUND: While negative life events (NLEs) have been linked to an increased risk of sleep disturbance among adolescents, the mechanisms of this impact still lack further examination. The current study aimed to explore whether intolerance of uncertainty (IU), a dispositional transdiagnostic vulnerability factor for psychopathology, could act as a mediator and/or moderator in the link from NLEs to sleep disturbance. METHODS: A longitudinal nested subsample of 54,240 Chinese adolescents (aged 9-19) were surveyed at baseline (Timepoint 1) and six months later (Timepoint 2). They completed questionnaires to assess their IU, NLEs, sleep disturbance and sociodemographic characteristics. Mediation and moderation analyses were conducted to test our hypotheses. RESULTS: Upon adjusting for covariates, IU was found to mediate the relationship between NLEs and residual changes in sleep disturbance over a six-month period, with the mediation effect accounting for 31.8%. Additionally, the moderating role of IU in this relationship was also identified, suggesting that a high level of IU exacerbated the effect of NLEs on sleep disturbance. CONCLUSIONS: In conclusion, our findings shed light on the dual roles of IU in the link from NLEs to sleep disturbance, holding significant practical implications for preventing and intervening in sleep disturbance among adolescents. To mitigate the risk of sleep disturbance among adolescents experiencing NLEs, timely assessments of IU and tailored interventions to enhance uncertainty tolerance are necessary.

Efficacy of microwave ablation in the treatment of large benign thyroid nodules: a multi-center study.

OBJECTIVE: To evaluate the efficacy, safety, and improvement of symptoms by ultrasound-guided microwave ablation (MWA) for patients with large benign thyroid nodules (BTNs). METHODS: Eighty-seven patients with 87 BTNs (≥ 4 cm) treated with MWA between April 2015 and March 2021 were enrolled in this retrospective multicenter study, with clinical and ultrasound examinations performed at the 1st, 3rd, 6th, and 12th months. A multivariable linear mixed effects model was employed to explore the alterations in volume and volume reduction ratio (VRR), as well as the potential factors associated with VRR. RESULTS: The mean age of the 87 patients was 45.69 ± 14.21 years (range 18-76 years), and the ratio of men to women was 1:4.8. The mean volumes were much decreased at the 12th month after ablation compared to the initial volumes (p < .001). The mean VRR was 76.09% at the 12th month. The technique efficacy (VRR > 50%) was 90.80% at the 12th month. A multivariate analysis revealed that VRR was related to the initial volume (p = .015), annular flow (p = .010), and nodule composition (p = .024). The mean symptomatic score decreased from 4.40 ± 0.28 to 0.26 ± 0.06 at the 12th month (p < .001). At the same time, the mean cosmetic score decreased from 3.22 ± 0.10 to 1.31 ± 0.08 (p < .001). CONCLUSION: MWA could serve as a safe and effective therapy for large BTNs, significantly reducing the volume of BTNs and significantly improving compressive symptoms and appearance problems. CLINICAL RELEVANCE STATEMENT: Microwave ablation could serve as a safe and effective therapy for large benign thyroid nodules, leading to significant volume reduction and satisfied symptom and cosmetic alleviation period. KEY POINTS: • This multicenter study investigated the feasibility and safety of microwave ablation for large benign thyroid nodules. • After ablation, the nodule volume was significantly reduced, and patients' symptoms and appearance problems were significantly improved. • Microwave ablation is feasible for large benign thyroid nodules and has been a supplement treatment.

Anomalously bright single-molecule upconversion electroluminescence.

Efficient upconversion electroluminescence is highly desirable for a broad range of optoelectronic applications, yet to date, it has been reported only for ensemble systems, while the upconversion electroluminescence efficiency remains very low for single-molecule emitters. Here we report on the observation of anomalously bright single-molecule upconversion electroluminescence, with emission efficiencies improved by more than one order of magnitude over previous studies, and even stronger than normal-bias electroluminescence. Intuitively, the improvement is achieved via engineering the energy-level alignments at the molecule-substrate interface so as to activate an efficient spin-triplet mediated upconversion electroluminescence mechanism that only involves pure carrier injection steps. We further validate the intuitive picture with the construction of delicate electroluminescence diagrams for the excitation of single-molecule electroluminescence, allowing to readily identify the prerequisite conditions for producing efficient upconversion electroluminescence. These findings provide deep insights into the microscopic mechanism of single-molecule upconversion electroluminescence and organic electroluminescence in general.

Applying machine learning to assess the morphology of sculpted teeth.

Background/purpose: Producing tooth crowns through dental technology is a basic function of dentistry. The morphology of tooth crowns is the most important parameter for evaluating its acceptability. The procedures were divided into four steps: tooth collection, scanning skills, use of mathematical methods and software, and machine learning calculation. Materials and methods: Dental plaster rods were prepared. The effective data collected were to classify 121 teeth (15th tooth position), 342 teeth (16th tooth position), 69 teeth (21st tooth position), and 89 teeth (43rd tooth position), for a total of 621 teeth. The procedures are divided into four steps: tooth collection, scanning skills, use of mathematical methods and software, and machine learning calculation. Results: The area under the curve (AUC) value was 0, 0.5, and 0.72 in this study. The precision rate and recall rate of micro-averaging/macro-averaging were 0.75/0.73 and 0.75/0.72. If we took a newly carved tooth picture into the program, the current effectiveness of machine learning was about 70%-75% to evaluate the quality of tooth morphology. Through the calculation and analysis of the two different concepts of micro-average/macro-average and AUC, similar values could be obtained. Conclusion: This study established a set of procedures that can judge the quality of hand-carved plaster sticks and teeth, and the accuracy rate is about 70%-75%. It is expected that this process can be used to assist dental technicians in judging the pros and cons of hand-carved plaster sticks and teeth, so as to help dental technicians to learn the tooth morphology more effectively.

Sleep disturbance and suicidal ideation mediated by psychotic-like experiences in adolescents: a two-wave longitudinal study.

STUDY OBJECTIVES: Sleep disturbance may cause suicidal ideation (SI). This study aimed to examine their co-occurrence rate among adolescents and investigate whether psychotic-like experiences (PLEs) mediate this association. METHODS: A total of 17 722 Chinese adolescents were included in this two-wave longitudinal study. The baseline survey (time 1, T1) was conducted between April 21 and May 12, 2021, and the follow-up survey (time 2, T2) was implemented between December 17 and 26, 2021. At T1, participants completed questionnaires to assess their sleep disturbance, sleep duration, PLEs, SI, depressive symptoms, and sociodemographic characteristics. At T2, participants reported their PLEs, SI, and negative life events. RESULTS: The SI prevalence decreased from 20.2% (T1) to 18.4% (T2). Participants with SI showed significantly higher prevalence of sleep disturbance and short sleep duration compared to those without SI. Baseline sleep disturbance and short sleep duration were both associated with the increased risk of SI 6 months later. These longitudinal links were mediated by PLEs, independent of baseline covariates and negative life events. CONCLUSIONS: These findings provide a novel insight into the mechanism linking sleep problems and suicidality, and stress the significance of assessing and addressing sleep problems and PLEs for adolescent suicide prevention and intervention.

MMDTA: A Multimodal Deep Model for Drug-Target Affinity with a Hybrid Fusion Strategy.

The prediction of the drug-target affinity (DTA) plays an important role in evaluating molecular druggability. Although deep learning-based models for DTA prediction have been extensively attempted, there are rare reports on multimodal models that leverage various fusion strategies to exploit heterogeneous information from multiple different modalities of drugs and targets. In this study, we proposed a multimodal deep model named MMDTA, which integrated the heterogeneous information from various modalities of drugs and targets using a hybrid fusion strategy to enhance DTA prediction. To achieve this, MMDTA first employed convolutional neural networks (CNNs) and graph convolutional networks (GCNs) to extract diverse heterogeneous information from the sequences and structures of drugs and targets. It then utilized a hybrid fusion strategy to combine and complement the extracted heterogeneous information, resulting in the fused modal information for predicting drug-target affinity through the fully connected (FC) layers. Experimental results demonstrated that MMDTA outperformed the competitive state-of-the-art deep learning models on the widely used benchmark data sets, particularly with a significantly improved key evaluation metric, Root Mean Square Error (RMSE). Furthermore, MMDTA exhibited excellent generalization and practical application performance on multiple different data sets. These findings highlighted MMDTA's accuracy and reliability in predicting the drug-target binding affinity. For researchers interested in the source data and code, they are accessible at http://github.com/dldxzx/MMDTA.

Contrast-enhanced ultrasound evaluation of renal perfusion before angioplasty and its predictive value for hypertension.

BACKGROUND: Atherosclerotic renal artery stenosis (ARAS) is a common disease in the elderly population. OBJECTIVE: The aim was to develop a contrast-enhanced ultrasound (CEUS)-based model for predicting post-angioplasty improvement in hypertension in patients with severe ARAS. METHODS: Thirty-five patients with severe ARAS (⩾ 70%) were included in this study, and 42 renal arteries received percutaneous transluminal renal arterial stenting. An optimal integral formula was developed from pre-interventional color-coded duplex sonography (CCDS) and CEUS parameters using least absolute shrinkage and selection operator (LASSO) regression and receiver operating characteristic (ROC) curve analysis. A model for predicting short-term hypertension improvement was established using the integral formula and clinical risk factors. Bootstrapping was used for internal validation. RESULTS: Two integral formulas, LASSO.CCDS and LASSO.CEUS, were established. ROC curves of the two integral formulas showed that LASSO.CEUS was the better formula for predicting hypertension improvement (AUC 0.816, specificity 78.6%). Univariate and multivariate regression analyses showed that duration of hypertension (OR 0.841, P= 0.027), diabetes (OR = 0.019, P= 0.010), and LASSO.CEUS (OR 7.641, P= 0.052) were predictors of short-term hypertension improvement after interventional therapy. Using LASSO.CEUS combined with clinical risk factors, the following prediction model was established: logit (short-term improvement in hypertension) = 1.879-0.173 × hypertension duration - 3.961 × diabetes + 2.034 × LASSO.CEUS (AUC 0.939). CONCLUSIONS: The model established using CEUS parameters and clinical risk factors could predict hypertension improvement after interventional therapy, but further research and verification are needed.

Tobacco smoking, second-hand smoking exposure in relation to psychotic-like experiences in adolescents.

BACKGROUND: Previous literature supports that tobacco smoking and second-hand smoking (SHS) exposure were strongly associated with poor mental health in the general population. However, there is a lack of empirical data on the relationship between tobacco smoking, SHS exposure and psychotic-like experiences (PLEs). This study conducted a cross-sectional survey to explore PLEs and the associations of PLEs with tobacco smoking and SHS exposure among adolescents in China. METHODS: A total sample of 67 182 Chinese adolescents were recruited from Guangdong province in China (53.7% boys, mean age = 12.79 years) from December 17 to 26, 2021. All adolescents have completed self-reported questionnaires on demographic characteristics, smoking status, SHS exposure and PLEs. RESULTS: Within the sample, only 1.2% of participants had an experience of tobacco smoking while approximately three-fifths reported being exposed to SHS. 10.7% of adolescents reported frequent PLEs over the past month. Adolescents who smoked showed a higher prevalence of PLEs than in non-smoking samples. After controlling for confounders, SHS exposure was a robust risk factor for PLEs with or without the effect of tobacco smoking. DISCUSSION: These findings support the importance of smoke-free legislation, and anti-smoking measures in educational settings directed at both adolescents and their caregiver, which may decrease occurring rates of PLEs among adolescents.

GHGPR-PPIS: A graph convolutional network for identifying protein-protein interaction site using heat kernel with Generalized PageRank techniques and edge self-attention feature processing block.

Accurately pinpointing protein-protein interaction site (PPIS) on the molecular level is of utmost significance for annotating protein function and comprehending the mechanisms underpinning various diseases. While numerous computational methods for predicting PPIS have emerged, they have indeed mitigated the labor and time constraints associated with traditional experimental methods. However, the predictive accuracy of these methods has yet to reach the desired threshold. In this context, we proposed a groundbreaking graph-based computational model called GHGPR-PPIS. This innovative model leveraged a graph convolutional network using heat kernel (GraphHeat) in conjunction with Generalized PageRank techniques (GHGPR) to predict PPIS. Additionally, building upon the GHGPR framework, we devised an edge self-attention feature processing block, further augmenting the performance of the model. Experimental findings conclusively demonstrated that GHGPR-PPIS surpassed all competing state-of-the-art models when evaluated on the benchmark test set. Impressively, on two distinct independent test sets and a specific protein chain, GHGPR-PPIS consistently demonstrated superior generalization performance and practical applicability compared to the comparative model, AGAT-PPIS. Lastly, leveraging the t-SNE dimensionality reduction algorithm and clustering visualization technique, we delved into an interpretability analysis of the effectiveness of GHGPR-PPIS by meticulously comparing the outputs from different stages of the model.