Polyphyllin I Mitigated IL-1ß-Induced Chondrocytes Damage through Downregulating TWIST1 Expression.

BACKGROUND: Osteoarthritis (OA) is a chronic joint disease characterized by the degradation of articular cartilage. Polyphyllin I (PPI) has anti-inflammatory effects in many diseases. However, the mechanism of PPI in OA remains unclear.

Adverse Childhood Experiences May Dampen the Protective Role of Sleep Duration on Adolescent Obesity Risk.

The purpose of this study was to explore Adverse Childhood Experiences (ACEs) as a moderator between sleep duration/irregularity and overweight/obesity in U.S. adolescents. Using the National Survey of Children's Health 2017-2018 cross-sectional dataset, we included adolescents with available sleep and Body Mass Index (BMI) data. In a sample of 24,100 adolescents (mean age = 13.56 years, 49.35% female; 51% White), parents reported adolescent's sleep duration/irregularity, and number of ACEs. Logistic regression estimated the interaction between sleep duration/irregularity and the number of ACEs on overweight/obesity risk (BMI ≥ 85th percentile-for-age) using a stepwise approach and accounting for complex survey design. In the 24,100 adolescents, 33% were overweight/obese, 50% had ≥ 1 ACE, 37% slept < 8-10 h/night, and 14% had irregular sleep. Accounting for covariates and ACEs, every hour increase in sleep duration was associated with 6% decrease in overweight/obesity odds. There was a significant interaction between sleep duration and ACEs; the association between increasing sleep duration and decreasing odds of overweight/obesity was significant only in adolescents without ACEs (OR = 0.87, 95% CI [0.80, 0.95], p < 0.001). Increasing sleep duration is a recognized intervention target to decrease obesity risk, yet in adolescents experiencing ≥ 1 ACE, this protective role may be dampened. Future work may explore mechanisms for overweight/obesity development to inform interventions for adolescents facing adversity.

Optimization of Covert Communication in Multi-Sensor Asymmetric Noise Systems.

This work investigates wireless covert communication in a multi-sensor asymmetric noise scenario. We adopt KL (Kullback-Leibler) divergence as the covertness constraint metric and mutual information as the transmission rate metric. To accurately approximate KL divergence and mutual information in covert communication, we employ the Taylor series expansion technique. Analytical expressions for KL divergence and mutual information in covert communication are derived, and we optimize the amplitude gain and phase angles based on these analytical expressions. Our findings underscore the importance of phase angle selection in covert communication within asymmetric noise systems. We propose an effective method for optimizing the transmission amplitude gain and phase angles in scenarios with asymmetric noise. Numerical results validate the effectiveness and superiority of our proposed method.

An automatic method using MFCC features for sleep stage classification.

Sleep stage classification is a necessary step for diagnosing sleep disorders. Generally, experts use traditional methods based on every 30 seconds (s) of the biological signals, such as electrooculograms (EOGs), electrocardiograms (ECGs), electromyograms (EMGs), and electroencephalograms (EEGs), to classify sleep stages. Recently, various state-of-the-art approaches based on a deep learning model have been demonstrated to have efficient and accurate outcomes in sleep stage classification. In this paper, a novel deep convolutional neural network (CNN) combined with a long short-time memory (LSTM) model is proposed for sleep scoring tasks. A key frequency domain feature named Mel-frequency Cepstral Coefficient (MFCC) is extracted from EEG and EMG signals. The proposed method can learn features from frequency domains on different bio-signal channels. It firstly extracts the MFCC features from multi-channel signals, and then inputs them to several convolutional layers and an LSTM layer. Secondly, the learned representations are fed to a fully connected layer and a softmax classifier for sleep stage classification. The experiments are conducted on two widely used sleep datasets, Sleep Heart Health Study (SHHS) and Vincent's University Hospital/University College Dublin Sleep Apnoea (UCDDB) to test the effectiveness of the method. The results of this study indicate that the model can perform well in the classification of sleep stages using the features of the 2-dimensional (2D) MFCC feature. The advantage of using the feature is that it can be used to input a two-dimensional data stream, which can be used to retain information about each sleep stage. Using 2D data streams can reduce the time it takes to retrieve the data from the one-dimensional stream. Another advantage of this method is that it eliminates the need for deep layers, which can help improve the performance of the model. For instance, by reducing the number of layers, our seven layers of the model structure takes around 400 s to train and test 100 subjects in the SHHS1 dataset. Its best accuracy and Cohen's kappa are 82.35% and 0.75 for the SHHS dataset, and 73.07% and 0.63 for the UCDDB dataset, respectively.

Secure User Pairing and Power Allocation for Downlink Non-Orthogonal Multiple Access against External Eavesdropping.

We propose a secure user pairing (UP) and power allocation (PA) strategy for a downlink Non-Orthogonal Multiple Access (NOMA) system when there exists an external eavesdropper. The secure transmission of data through the downlink is constructed to optimize both UP and PA. This optimization aims to maximize the achievable sum secrecy rate (ASSR) while adhering to a limit on the rate for each user. However, this poses a challenge as it involves a mixed integer nonlinear programming (MINLP) problem, which cannot be efficiently solved through direct search methods due to its complexity. To handle this gracefully, we first divide the original problem into two smaller issues, i.e., an optimal PA problem for two paired users and an optimal UP problem. Next, we obtain the closed-form optimal solution for PA between two users and UP in a simplified NOMA system involving four users. Finally, the result is extended to a general 2K-user NOMA system. The proposed UP and PA method satisfies the minimum rate constraints with an optimal ASSR as shown theoretically and as validated by numerical simulations. According to the results, the proposed method outperforms random UP and that in a standard OMA system in terms of the ASSR and the average ASSR. It is also interesting to find that increasing the number of user pairs will bring more performance gain in terms of the average ASSR.

MixSleepNet: A Multi-Type Convolution Combined Sleep Stage Classification Model.

BACKGROUND AND OBJECTIVE: Sleep staging is an essential step for sleep disorder diagnosis, which is time-intensive and laborious for experts to perform this work manually. Automatic sleep stage classification methods not only alleviate experts from these demanding tasks but also enhance the accuracy and efficiency of the classification process. METHODS: A novel multi-channel biosignal-based model constructed by the combination of a 3D convolutional operation and a graph convolutional operation is proposed for the automated sleep stages using various physiological signals. Both the 3D convolution and graph convolution can aggregate information from neighboring brain areas, which helps to learn intrinsic connections from the biosignals. Electroencephalogram (EEG), electromyogram (EMG), electrooculogram (EOG) and electrocardiogram (ECG) signals are employed to extract time domain and frequency domain features. Subsequently, these signals are input to the 3D convolutional and graph convolutional branches, respectively. The 3D convolution branch can explore the correlations between multi-channel signals and multi-band waves in each channel in the time series, while the graph convolution branch can explore the connections between each channel and each frequency band. In this work, we have developed the proposed multi-channel convolution combined sleep stage classification model (MixSleepNet) using ISRUC datasets (Subgroup 3 and 50 random samples from Subgroup 1). RESULTS: Based on the first expert's label, our generated MixSleepNet yielded an accuracy, F1-score and Cohen kappa scores of 0.830, 0.821 and 0.782, respectively for ISRUC-S3. It obtained accuracy, F1-score and Cohen kappa scores of 0.812, 0.786, and 0.756, respectively for the ISRUC-S1 dataset. In accordance with the evaluations conducted by the second expert, the comprehensive accuracies, F1-scores, and Cohen kappa coefficients for the ISRUC-S3 and ISRUC-S1 datasets are determined to be 0.837, 0.820, 0.789, and 0.829, 0.791, 0.775, respectively. CONCLUSION: The results of the performance metrics by the proposed method are much better than those from all the compared models. Additional experiments were carried out on the ISRUC-S3 sub-dataset to evaluate the contributions of each module towards the classification performance.

Exploration of Sex and Age as Moderators Between Social Cumulative Risk and Sleep in a Representative Sample of Children and Adolescents Living in the United States.

BACKGROUND: Youth who face adversity are at a disproportionate risk for poor sleep health across the life course. Identifying whether the association between adversity and poor sleep varies based upon age and sex is needed. This study aims to explore sex and age as moderators between social risk and sleep in a sample of U.S. youth. METHODS: This study analyzed data of 32,212 U.S. youth (6-17 years) whose primary caregiver participated in the 2017-2018 National Survey of Children's Health. A social cumulative risk index (SCRI) score was calculated from 10 parental, family, and community risk indicators. Nighttime sleep duration was the number of hours the child slept during the past week. Weeknight sleep irregularity was operationalized as whether the child sometimes/rarely/never went to bed at the same time. Generalized logistic regression models estimated associations between SCRI and sleep duration/irregularity, with age and sex as moderators. RESULTS: Age moderated the association between SCRI and short sleep (OR = 1.12, p < 0.001), such that the magnitude of the SCRI-sleep relationship was 12% greater in school-age children. Sex was not a significant moderator. In stratified models by age group, age was positively associated with short sleep in both groups, with a greater magnitude in school-age children. Female school-age children were less likely to have short sleep than males. CONCLUSIONS: Younger children with greater social cumulative risk factors may be more vulnerable to short sleep duration. Further research into the mechanisms underlying the relationships between social risk and sleep health in school-age children is needed.

Energy Consumption Minimization in Unmanned Aerial Vehicle-Enabled Secure Wireless Sensor Networks.

In wireless sensor networks (WSNs), unmanned aerial vehicles (UAVs) are considered an effective data collection tool. In this paper, we investigate the energy-efficient data collection problem in a UAV-enabled secure WSN without knowing the instantaneous channel state information of the eavesdropper (Eve). Specifically, the UAV collected the information from all the wireless sensors at the scheduled time and forward it to the fusion center while Eve tries to eavesdrop on this confidential information from the UAV. To surmount this intractable and convoluted mixed-integer non-convex problem, we propose an efficient iterative optimization algorithm using the block coordinate descent (BCD) method to minimize the maximum energy consumption of the ground sensor nodes (GSNs) under the constraints of secrecy outage probability (SOP), connection outage probability (COP), minimum secure data, information causality, and UAV trajectory. Numerical results demonstrate the superiority of the algorithm we proposed in energy consumption and secrecy rate compared with other schemes.

Efficacy and safety of tart cherry supplementary citrate mixture on gout patients: a prospective, randomized, controlled study.

BACKGROUND: Low urine pH, which may be mediated by metabolic syndrome (MetS), is common in gout. Tart cherries are shown to improve MetS symptoms and possess anti-inflammatory properties. However, the efficacy of tart cherry supplements on urine pH has yet to be studied. OBJECTIVES: This study aimed to investigate the efficacy and safety of tart cherry supplementary citrate (TaCCi) mixture on urine pH, serum urate (sUA), C-reactive protein (CRP), and gout flares in gout patients initiating urate-lowering therapy (ULT), in comparison to citrate mixture and sodium bicarbonate. METHODS: A prospective, randomized (1:1:1), open-label, parallel-controlled trial was conducted among 282 men with gout and fasting urine pH ≤ 6, who were initiating ULT with febuxostat (initially 20 mg daily, escalating to 40 mg daily if serum urate ≥ 360 µmol/L). Participants were randomized to groups taking either sodium bicarbonate, citrate mixture, or TaCCi mixture. All participants were followed every 4 weeks until week 12. Urine pH and sUA were co-primary outcomes, with various biochemical and clinical secondary endpoints. RESULTS: Urine pH increased to a similar extent in all three groups. SUA levels declined in all three groups as well, with no significant differences observed between the groups. At week 12, the TaCCi mixture group exhibited a greater reduction in the urine albumin/creatinine ratio (UACR) compared to the other two groups (p < 0.05). Participants taking TaCCi mixture or citrate mixture experienced fewer gout flares than those in the sodium bicarbonate group over the study period (p < 0.05). Additionally, the TaCCi mixture group had a lower CRP level at week 12 relative to the other two groups (p < 0.01). Adverse events were similar across all three groups. CONCLUSION: The TaCCi mixture had similar efficacy and safety on urine alkalization and sUA-lowering as the citrate mixture and sodium bicarbonate in patients with gout. However, the TaCCi mixture resulted in greater improvements in UACR and CRP, which suggests that tart cherry supplements may provide additional benefits for renal protection and reduce inflammation in gout, particularly when starting ULT. TRIAL REGISTRATION: This project was registered in ChiCTR ( www.chictr.org.cn ), with the registration number: ChiCTR2100050749.

3DSleepNet: A Multi-Channel Bio-Signal Based Sleep Stages Classification Method Using Deep Learning.

A novel multi-channel-based 3D convolutional neural network (3D-CNN) is proposed in this paper to classify sleep stages. Time domain features, frequency domain features, and time-frequency domain features are extracted from electroencephalography (EEG), electromyogram (EMG), and electrooculogram (EOG) channels and fed into the 3D-CNN model to classify sleep stages. Intrinsic connections among different bio-signals and different frequency bands in time series and time-frequency are learned by 3D convolutional layers, while the frequency relations are learned by 2D convolutional layers. Partial dot-product attention layers help this model find the most important channels and frequency bands in different sleep stages. A long short-term memory unit is added to learn the transition rules among neighboring epochs. Classification experiments were conducted using both ISRUC-S3 datasets and ISRUC-S1, sleep-disorder datasets. The experimental results showed that the overall accuracy achieved 0.832 and the F1-score and Cohen's kappa reached 0.814 and 0.783, respectively, on ISRUC-S3, which are a competitive classification performance with the state-of-the-art baselines. The overall accuracy, F1-score, and Cohen's kappa on ISRUC-S1 achieved 0.820, 0.797, and 0.768, respectively, which also demonstrate its generality on unhealthy subjects. Further experiments were conducted on ISRUC-S3 subset to evaluate its training time. The training time on 10 subjects from ISRUC-S3 with 8549 epochs is 4493s, which indicates its highest calculation speed compared with the existing high-performance graph convolutional networks and [Formula: see text]Net architecture algorithms.

Prevalence of antimicrobial use and healthcare-associated infections in China: Results from the first point prevalence survey in 18 hospitals in Shanxi Province.

OBJECTIVES: Inappropriate antimicrobial use (AMU) and healthcare-associated infections (HAIs) are important drivers of antimicrobial resistance, but data from the developing world are scarce. We conducted the first point prevalence survey (PPS) to determine the prevalence of AMU and HAIs and the suggested targeted interventions for appropriate AMU and HAI prevention in Shanxi Province, China. METHODS: A multicentre PPS was performed in 18 hospitals in Shanxi. Detailed data on AMU and HAI were collected using the Global-PPS method developed by the University of Antwerp and the methodology developed by the European Centre for Disease Prevention and Control, respectively. RESULTS: There were 2171 (28.2%) of the 7707 inpatients receiving at least one antimicrobial. The most commonly prescribed antimicrobials were levofloxacin (11.9%), ceftazidime (11.2%), and cefoperazone and beta-lactamase inhibitor (10.3%). Out of the total indications, 89.2% of antibiotics were prescribed for therapeutic, 8.0% for prophylaxis, and 2.8% for either unknown or other. Of the total surgical prophylaxis, 96.0% of antibiotics were given for more than one day. In general, antimicrobials were given mainly parenterally (95.4%) and empirically (83.3%). A total of 264 active HAIs were identified in 239 patients (3.1%), of which 139 (52.3%) were culture positive. The most common HAI was pneumonia (41.3%). CONCLUSIONS: This survey indicated the relatively low prevalence of AMU and HAIs in Shanxi Province. However, this study has also highlighted several priority areas and targets for quality improvement, and repeated PPSs in the future will be useful to gauge progress at controlling AMU and HAIs.

Elevated serum CA72-4 predicts gout flares during urate lowering therapy initiation: a prospective cohort study.

OBJECTIVE: Gout flares during urate-lowering therapy (ULT) initiation are common, but predictors of these flares are poorly understood. The aim of this study was to determine whether serum CA72-4 is an independent predictor for gout flares during ULT initiation. METHODS: A prospective cohort study was conducted between March 2021 and January 2022. Men with gout, at least one gout flare in the past year, and at least three serum CA72-4 measurements in the previous six months were enrolled. Participants were grouped according to their highest recorded serum CA72-4 levels (above or within the normal range). All participants took oral febuxostat 20 mg daily without flare prophylaxis therapy, and attended face-to-face visits every four weeks until 24 weeks. The incidence of gout flare was compared between the two groups. Backward stepwise logistic regression analyses were used to identify risk factors associated with flares. Receiver operating characteristic curve analysis was used to evaluate prediction efficacy. RESULTS: A total of 193 completed the study (79 with high CA72-4; 114 with normal CA72-4). The cumulative incidence of at least one gout flare was 48.1% (62.1% in the high CA72-4 group, 38.4% in the normal CA72-4 group, P = 0.001), and recurrent (≥2) flares was 33.0% (47.1% in the high CA72-4 group, 23.2% in the normal CA72-4, P < 0.001). High CA72-4, disease duration, intra-articular tophus size, glucose, high-density lipoprotein-cholesterol and ESR were independent risk factors for gout flares. Serum CA72-4 alone predicted recurrent flares with an area under the curve of 0.63 (95% CI = 0.54, 0.71), and 0.78 (95% CI = 0.71, 0.85) when combined with other independent variables. CONCLUSION: High serum CA72-4 predicts the risk of gout flares during ULT initiation. TRIAL REGISTRATION: ChiCTR; https://www.chictr.org.cn/; ChiCTR2100043573.

Associations between sleep and overweight/obesity in adolescents vary by race/ethnicity and socioeconomic status.

AIM: To examine the interaction between sleep and social determinants of health (SDOH) [race/ethnicity and socioeconomic status (SES)] on overweight/obesity in adolescents. DESIGN: Cross-sectional. METHODS: We conducted a secondary analysis using the 2017-2018 National Survey of Children's Health data. We included adolescents (10-17 years old) who had sleep and body mass index (BMI) data available (n = 24,337) in analyses (samples with BMI <5th percentile excluded). Parents reported children's sleep duration and regularity. High BMI (≥85th percentile) for age defines overweight/obesity. We selected SDOH (race/ethnicity, family income, primary caregiver education and neighbourhood condition) and covariates (age, sex, smoking, exercise and depression) using a hierarchical model-building approach. Accounting for complex survey design, logistic regression estimated the interaction between sleep and SDOH. RESULTS: There were significant interactions between sleep duration and SDOH. The association between increasing sleep and decreasing odds of overweight/obesity only showed in the following subgroups: White, family income ≥400% federal poverty level (FPL) or primary caregiver' education ≥ high school. Compared with these subgroups, Hispanic adolescents and adolescents whose family income was below 100% FPL and whose caregiver education was below high school had weakened and reversed associations. Sleep regularity was not associated with overweight/obesity. CONCLUSIONS: Increasing sleep duration was associated with a decreased risk of overweight/obesity, but the association was not present in adolescents from racial/ethnic minority groups (i.e. Hispanic) and those with low SES. IMPACT: The study findings suggest that associations between sleep and overweight/obesity vary by race and SES. Identification of additional mechanisms for obesity is needed for racial/ethnic minority groups and those from families with low SES. Also, the complexity of these relationships underscores the importance of community-based needs assessment in the design of targeted and meaningful interventions to address complex health conditions such as poor sleep and obesity.

Profiling of serum oxylipins identifies distinct spectrums and potential biomarkers in young people with very early onset gout.

OBJECTIVE: Oxylipins modulate inflammation via complex pathways. The oxylipin profile in gout remains unexplored. In this study, we systemically profiled oxylipins in young men and identified new oxylipin biomarkers for clinical use in differentiating gout from hyperuricaemia. MATERIAL AND METHODS: Oxylipin profiling was performed in 90 men (30 very early onset gout, 30 asymptomatic hyperuricaemia [HU] and 30 normouricaemia [NU], all aged <20 years) divided into discovery and validation sample sets. The dataset was analysed based on orthogonal projection to latent structure-discriminant analysis. Correlation network and pathway enrichment were conducted to reveal potential oxylipin-involved pathways of gout. Candidate oxylipins were further evaluated and optimized in the validation cohort, and differential oxylipin biomarkers combined with or without serum urate were applied to construct diagnostic models. RESULTS: In discovery stage, 21 differential oxylipins in the gout vs HU comparisons and 14 differential oxylipins in the gout vs NU comparisons were discovered. Correlation network analysis was performed and 14(S)-HDHA (14S-hydroxy-4Z,7Z,10Z,12E,16Z,19Z-docosahexaenoic acid) was identified as a hub metabolite in both comparisons. Seven down-regulated oxylipins in the gout vs HU group and five down-regulated oxylipins in the gout vs NU group were validated. Diagnostic models were constructed with the above oxylipins, with 14(S)-HDHA alone having an area under the curve of 1 (95% CI, 1, 1) in both comparisons. CONCLUSIONS: Young men with very early onset gout have distinct oxylipin spectrums, especially those derived from arachidonic acid, eicosapentaenoic acid and docosahexaenoic acid. Differential oxylipins could serve as candidate serum biomarkers in differentiating gout from hyperuricaemia.

Childhood sleep: assessments, risk factors, and potential mechanisms.

BACKGROUND: Sleep problem is a highly prevalent health issue among pediatric populations across the world. In this review, we aimed to identify risk factors contributing to sleep deficiency and poor sleep hygiene in children. Potential biological, psychosocial, and environmental mechanisms as well as research gaps in the literature are also discussed. DATA SOURCES: A comprehensive search for relevant English language full-text, peer-reviewed publications was performed focusing on pediatric sleep studies from prenatal to childhood and adolescence in a variety of indexes in PubMed, SCOPUS, and Psych Info. Both relevant data based and systematic reviews are included. RESULTS: This paper summarizes many risk factors for childhood sleep problems, including biological (e.g., genetics, gender, age and puberty, prenatal factors, postnatal factors); nutritional (e.g., macronutrients, micronutrients, omega-3 fatty acids, obesity); environmental (e.g., heavy metals, noise, light, air pollution); interpersonal (e.g., family, exposure to violence, screen media use, physical injury); and community/socioeconomic variables (e.g., racial/ethnicity and cultural factors, neighborhood conditions and socioeconomic status, school factors, public health disasters/emergencies), to better understand the development of sleep problems in children. CONCLUSIONS: Poor childhood sleep is a multifactorial issue affected by a wide range of prenatal and early-life biological, environmental, and psychosocial risk factors and contributors. A better understanding of these risk factors and their mechanisms is an important first step to develop future research and prevention programs focusing on pediatric sleep problems.

Childhood sleep: physical, cognitive, and behavioral consequences and implications.

BACKGROUND: Sleep problems in children have been increasingly recognized as a major public health issue. Previous research has extensively studied and presented many risk factors and potential mechanisms for children's sleep problems. In this paper, we aimed to identify and summarize the consequences and implications of child sleep problems. DATA SOURCES: A comprehensive search for relevant English language full-text, peer-reviewed publications was performed focusing on pediatric sleep studies from prenatal to childhood and adolescence in a variety of indexes in PubMed, SCOPUS, and Psych Info published in the past two decades. Both relevant data-based articles and systematic reviews are included. RESULTS: Many adverse consequences are associated with child sleep deficiency and other sleep problems, including physical outcomes (e.g., obesity), neurocognitive outcomes (e.g., memory and attention, intelligence, academic performance), and emotional and behavioral outcomes (e.g., internalizing/externalizing behaviors, behavioral disorders). Current prevention and intervention approaches to address childhood sleep problems include nutrition, exercise, cognitive-behavioral therapy for insomnia, aromatherapy, acupressure, and mindfulness. These interventions may be particularly important in the context of coronavirus disease 2019. Specific research and policy strategies can target the risk factors of child sleep as well as the efficacy and accessibility of treatments. CONCLUSIONS: Given the increasing prevalence of child sleep problems, which have been shown to affect children's physical and neurobehavioral wellbeing, understanding the multi-aspect consequences and intervention programs for childhood sleep is important to inform future research direction as well as a public health practice for sleep screening and intervention, thus improving sleep-related child development and health.

Adaptive Auricular Point Acupressure for Sleep Disturbance in Women with Breast Cancer: A Randomized Controlled Trial.

Objectives: The study aimed to evaluate the preliminary effect and efficacy of auricular point acupressure (APA) on the quality of sleep in women with breast cancer who were undergoing chemotherapy. Sample & Setting. We conducted a randomized controlled trial on 68 patients with breast cancer who reported poor sleep quality based on the Pittsburgh Sleep Quality Index (PSQI) scores (>7). Methods & Variables. Participants were randomly assigned to an APA treatment group or a control group. Patients in the APA group had magnetic pellets attached to selected auricular points and were instructed to apply pressure to these points 4×/day for three consecutive weeks. We objectively measured sleep quality using the Actiwatch Spectrum and the PSQI at the baseline and postintervention. Statistical analyses of changes in sleep data were performed using the t-test, a rank-sum test, and analyses of covariance. Results: In patients treated with APA, the PSQI total score and sleep onset latency had significantly decreased, while the total sleep time and sleep efficiency had significantly increased. Although the total PSQI score differed between groups at the baseline, ANCOVA results showed that the APA group had a significantly lower total PSQI score. Conclusion: APA could be an inexpensive and effective approach to improving sleep quality and reducing sleep disturbance in patients with breast cancer. Further research needs a larger sample size to verify our findings.

Childhood lead exposure and sex-based neurobehavioral functioning in adolescence.

It is well documented that childhood lead exposure is associated with long-term decreases in intelligence quotients (IQ). Lesser known is the relationship with neurobehavioral domains, especially in adolescence. This study sought to identify cross-sectional and longitudinal associations between lead exposure and adolescent executive and visual-motor functioning and examine sex-based differences. Participants were 681 children from Jintan, China who had their blood lead levels (BLLs) assessed at age 3-5 years and 12 years old and neurobehavioral functioning assessed through the University of Pennsylvania Computerized Neurocognitive Battery (PennCNB) platform http://www.med.upenn.edu/bbl at 12 years old. Mean BLLs were 6.41 mcg/dl at age 3-5 years and 3.10 mcg/dl at 12. BLLs at 3-5 years and 12 years were used as predictors for the individual neurobehavioral domains in general linear models while controlling for father and mother occupation and education, residence location, age, and adolescent IQ. Models were run separately for males and females. In adjusted models, males BLLs at 3-5 years were associated with increased time to correctly complete tasks in multiple domains including abstraction/flexibility (ß = 19.90, 95% CI( 4.26, 35.54) and spatial processing (ß = 96.00, 95% CI 6.18, 185.82) at 12 years. For females in adjusted models, BLLs at 3-5 years were associated with increasing time to correctly complete tasks on the episodic memory domain task (ß = 34.59, 95% CI 5.33, 63.84) at 12 years. Two adolescent cross-sectional relationships remained in the adjusted models for males only, suggesting a positive association between BLLs and increasing time for correct responses on the attentional domain task (ß = 15.08, 95% CI 0.65, 29.51) and decreasing time for correct responses on the episodic memory task (ß = -73.49, 95% CI -138.91, -8.06) in males at 12 years. These associations remained with and without controlling for IQ. These results suggest that lead exposure is associated with overall deficits in male and female neurobehavioral functioning, though in different domains and different timing of exposure.

Palpable tophi and more comorbidities associated with adherence to urate-lowering medical therapy in a Chinese gout cohort.

OBJECTIVE: Urate-lowering therapy (ULT) nonadherence is common and problematic in gout. Since, sociocultural factors affect adherence, we analyzed a Chinese cohort. METHODS: We studied 903 Chinese gout patients aged 46.4±14.7 years (mean±SD), uniquely extending to assay of 2-year medication possession ratio (MPR) ≥80% defined as high adherence. Multivariable logistic regression analyses evaluated factors linked with adherence and ULT target attainment. RESULTS: Characterization of ULT outcomes in this cohort revealed that after 2 years ULT, MPR ≥80% patients had better target serum urate (SU) achievement (from 23.3% to 71.0%, P <0.001), lower flare frequency and palpable tophi compared to MPR <80%. However, only 44.7% of cohort subjects had MPR ≥80%. Male sex (OR 3.68), gout onset age >60 years (OR 3.51), disease duration >5 years (OR 1.70), more comorbidities (OR 1.74), baseline palpable tophi (OR 1.53), SU <6mg/dL (360µmol/L) (OR 1.92) and more frequent follow-up visits (OR 1.98) were significantly associated with high adherence. Nevertheless, significant independent risk factors for failed SU target achievement included male sex (OR 0.36) and more comorbidities (OR 0.85). CONCLUSION: Despite adherence to ULT linked to better outcomes for flares and tophi, the more adherent Chinese male patients and those with more comorbidities had decreased target SU attainment. Differences in adherence of Chinese gout patients compared to several primarily Western studies emphasize the importance of not stereotyping gout patients for projected nonadherence. Results underline the dual importance of identifying gout patients more likely to be ULT-adherent and leveraging adherence to drive treatment to SU target.