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Abstract Background The treatment for atopic dermatitis (AD) has been the focus of clinical research, and behavioral intervention is considered an indispensable treatment method. To our knowledge, no relevant meta-analysis has evaluated the effects of behavioral interventions on atopic dermatitis. Objectives To evaluate the effects of behavioral interventions on atopic dermatitis. Methods The authors searched PubMed, EMBASE, and Cochrane CENTRAL to retrieve relevant RCTs (up to Feb 2022). The search strategy involved a combination of related keywords. The Cochrane Q and I2 statistics were used to assess heterogeneity. Results Six RCTs involving seven reports with 246 patients were included. The results suggested that behavioral interventions could relieve eczema severity (correlation coefficient [r = −0.39]; p < 0.001) and scratching severity significantly (r = −0.19; p = 0.017), while not affect itching intensity (r = −0.02; p = 0.840). A sensitivity analysis confirmed the robustness of the results. Study limitations An important limitation of this study was the insufficient number of RCTs and the limited sample size. In addition, the study lacked a control group receiving a type of intervention other than the experimental protocol. Another limitation was the short duration of follow-up. Conclusions This study suggests that behavioral interventions could be effective in treating atopic dermatitis by reducing eczema and scratching severity. Additionally, habit-reversal behavioral therapy may be more effective for treating atopic dermatitis.

Thermoelectric generators (TEGs) integrated with solar energy and radiative cooling offer a promising approach for generating power. Concentrated solar energy enhances generation by increasing the solar flux density. However, the relationship between thermoelectric generation and concentration ratio remains not well understood. In this study, the finite element method is employed to investigate the temperature difference across TEGs and the change in generation performance with varying concentration ratios. The results show that due to the mismatch among cooling, heating, and parasitic heat conduction powers, the optimal power generation does not occur at the maximum concentration ratio. As the concentration ratio increases, the temperature difference across the single TEG and its output power initially increase and then decrease. To further improve the power generation performance at high concentration ratios, this study introduces TEG stacking strategy to enhance waste heat recovery by increasing thermal resistance. Outdoor experiments with self-made solar absorptive coating and radiative cooling coating validate this approach, achieving a temperature difference of 64.17 °C and a power density of 135.57 W m−2 by stacking three TEGs, outperforming a single TEG by over three times. This study offers a potential method to continuously power remote off-grid communities and low-power devices.

Abstract Background Periventricular nodular heterotopia (PNH), associated with FLNA mutations, is a rare clinical condition potentially associated with multiple systemic conditions, including cardiac, pulmonary, skeletal, and cutaneous diseases. However, due to a paucity of information in the literature, accurate prognostic advice cannot be provided to patients with the disease. Case presentation We report a 2-year-old female whose PNH was associated with a nonsense mutation in the q28 region of the X chromosome, in exon 31 of FLNA (c.5159dupA). The patient is currently seizure-free and has no congenital heart disease, lung disease or skeletal or joint issues, and her development is normal. Conclusions FLNA-associated PNH is a genetically-heterogeneous disease, and the FLNA mutation, c.5159dupA (p.Tyr1720*) is a newly identified pathogenic variant. FLNA characterization will help the clinical diagnosis and treatment of PNH and provide individualized genetic counseling for patients.

Abstract Pansharpening integrates the high spectral content of multispectral (MS) images and the fine spatial information of the corresponding panchromatic (PAN) images to produce a high spectral-spatial resolution image. Traditional pansharpening methods compensate for the spatial lack of the MS image using the PAN image details, which easily causes spectral distortion. To achieve spectral fidelity, a spectral preservation model based on spectral contribution and dempendence with detail injection for pansharpening is proposed. In the proposed model, first, an efficacy coefficient (CE) based on the spatial difference between the MS and PAN images is designed to suppress the impact of the detail injection on the spectra. Second, the spectral contribution and dependence (SCD) between the MS bands and pixels are considered to strengthen the internal adaptation of the spectra. Finally, a spectrally preserved model based on CE and SCD is designed to force the fused image fidelity in spectra when the MS image is pansharpened with the details of the PAN image. Experimental results show that the proposed model is effective.

Neurotrophin receptor-interacting melanoma-associated antigen homolog (NRAGE), a type II melanoma-associated antigen, plays a critical role in cell processes that are involved in the tumorigenesis of various cancers. However, the effect of NRAGE on acute myeloid leukemia (AML) is rarely reported. The expression of NRAGE in AML tissues and the survival rates between different AML groups were obtained from the GEPIA tool. Human AML cell lines were cultured and transfected with siRNA targeting NRAGE. The ability of AML cells to proliferate and cell cycle were examined. Western blotting was performed to detect the activity of the extracellular signal-regulated kinase (ERK) signaling pathway in AML cells. NRAGE expression was enhanced in AML tissues relative to control tissues, and the high NRAGE expression in AML patients is associated with a poor prognosis. The capacity of AML cells to survive and proliferate was significantly decreased and its cell cycle was arrested at the G1 phase after NRAGE was silenced. Furthermore, silencing NRAGE induced the inactivation of the ERK signaling pathway. Furthermore, supplement of tert-Butylhydroquinone, an ERK activator, improved the reduced ability of AML cell survival and proliferation as well as cell cycle arrest induced by NRAGE knockdown. In this study, NRAGE was identified as a tumor promoter in AML, which had an effect on cell proliferation, cell survival, and cell cycle through the ERK signaling pathway in AML cells.

Abstract Background Quality sleep is essential for physical and mental health. We aimed to analyze sleep disorders in children with acute leukemia and explore associated factors. Methods General data and sleep disorders in children with acute leukemia during chemotherapy were collected by general questionnaires, Children's Sleep Disorders Scale and the Parenting Stress Index-short form. Results In total, 173 valid questionnaires were collected. The total Sleep Disorder Scale score > 39 is considered a sleep disorder, while sleep disorders accounted for 45.66% (79/173). In the cohort, 167 children had acute lymphoblastic leukemia, with 40.12% (67/167) having sleep disorders, while six children had acute non-lymphoblastic leukemia, with 50.00% (3/6) having sleep disorders. Single- and multi-factor regression analyses of age, gender, number of children in the family, and time spent using electronic devices showed that factors influencing sleep disorders in these children were mainly parental scolding and adenoid hypertrophy. Children with sleep disorders had more parental stress than those without sleep disorders (P

Abstract Background Increasing biomedical studies have shown that the dysfunction of miRNAs is closely related with many human diseases. Identifying disease-associated miRNAs would contribute to the understanding of pathological mechanisms of diseases. Supervised learning-based computational methods have continuously been developed for miRNA-disease association predictions. Negative samples of experimentally-validated uncorrelated miRNA-disease pairs are required for these approaches, while they are not available due to lack of biomedical research interest. Existing methods mainly choose negative samples from the unlabelled ones randomly. Therefore, the selection of more reliable negative samples is of great importance for these methods to achieve satisfactory prediction results. Results In this study, we propose a computational method termed as KR-NSSM which integrates two semi-supervised algorithms to select more reliable negative samples for miRNA-disease association predictions. Our method uses a refined K-means algorithm for preliminary screening of likely negative and positive miRNA-disease samples. A Rocchio classification-based method is applied for further screening to receive more reliable negative and positive samples. We implement ablation tests in KR-NSSM and find that the combination of the two selection procedures would obtain more reliable negative samples for miRNA-disease association predictions. Comprehensive experiments based on fivefold cross-validations demonstrate improvements in prediction accuracy on six classic classifiers and five known miRNA-disease association prediction models when using negative samples chose by our method than by previous negative sample selection strategies. Moreover, 469 out of 1123 selected positive miRNA-disease associations by our method are confirmed by existing databases. Conclusions Our experiments show that KR-NSSM can screen out more reliable negative samples from the unlabelled ones, which greatly improves the performance of supervised machine learning methods in miRNA-disease association predictions. We expect that KR-NSSM would be a useful tool in negative sample selection in biomedical research.

Abstract Background This study aimed to investigate the effects of STOML2 and the relationship between STOML2 and PAI-1 in the development of multiple myeloma (MM). Methods Cell proliferation was tested using CCK-8 assay and cell colony formation assay. Glucose consumption, lactate production and ATP level were measured using commercial kits. The mRNA and protein expression were assessed using quantitative real-time polymerase chain reaction (qRT-PCR) and western blotting, respectively. Results Both mRNA and protein expression of STOML2 were upregulated in MM patients compared to healthy volunteers. CCK-8 and colony formation assays demonstrated that STOML2 silencing inhibited cell proliferation in MM cells. Knockdown of STOML2 reduced glucose consumption, lactate production and ATP/ADP ratios. STOML2 silencing by shSTOML2 led to reduced PAI-1 expression. Overexpression of PAI-1 reversed the inhibitory effects of shSTOML2 on MM cell growth. Conclusion Results from this study demonstrated that STOML2 silencing inhibits cell proliferation and glycolysis through downregulation of PAI-1 expression, suggesting a new therapeutic target for MM.

The accuracy of temperature and relative humidity (RH) profiles retrieved by the ground-based microwave radiometer (MWR) is crucial for meteorological research. In this study, the four-year measurements of brightness temperature measured by the microwave radiometer from Huangpu meteorological station in Guangzhou, China, and the radiosonde data from the Qingyuan meteorological station (70 km northwest of Huangpu station) during the years from 2018 to 2021 are compared with the sonde data. To make a detailed comparison on the performance of machine learning models in retrieving the temperature and RH profiles, four machine learning algorithms, namely Deep Learning (DL), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost) and Random Forest (RF), are employed and verified. The results show that the DL model performs the best in temperature retrieval (with the root-mean-square error and the correlation coefficient of 2.36 and 0.98, respectively), while the RH of the four machine learning methods shows different excellence at different altitude levels. The integrated machine learning (ML) RH method is proposed here, in which a certain method with the minimum RMSE is selected from the four methods of DL, GBM, XGBoost and RF for a certain altitude level. Two cases on 29 January 2021 and on 10 February 2021 are used for illustration. The case on 29 January 2021 illustrates that the DL model is suitable for temperature retrieval and the ML model is suitable for RH retrieval in Guangzhou. The case on 10 February 2021 shows that the ML RH method reaches over 85% before precipitation, implying the application of the ML RH method in pre-precipitation warnings.

ObjectiveThe purpose of this study is to analyze the clinical data of a child with acute empyema caused by Haemophilus influenzae, and to investigate the diagnosis and treatment of this disease through literature review to improve the clinical understanding of this kind of disease.MethodsA 6-year-old female with acute H. influenzae empyema was treated at the Department of Pediatrics of The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China. The pleural puncture fluid turned out to be yellow turbid pus, and the pleural effusion was diagnosed as empyema according to the classification of pleural effusions. High-throughput sequencing revealed the presence of H. influenzae. After comprehensive treatment, including antibiotics, closed pleural drainage, and intrapleural injection of urokinase, the pleural effusion was absorbed and discharged. A systematic literature search in Pubmed, Embase, Scopus, CNKI, Wanfang, and VIP Chinese databases revealed no cases of acute empyema in children caused by H. influenza and treated with urokinase.ResultsThere was no bronchopleural fistula and tension pneumothorax during the treatment. One month after discharge, chest computed tomography (CT) revealed no pleural thickening and normal pulmonary function.ConclusionPneumonia in the child worsened after an initial improvement of symptoms, which is an issue that requires further medical attention. High-throughput sequencing of pathogens in pleural effusion can improve the detection rate. This study indicated that closed pleural drainage combined with intrapleural injection of urokinase is an effective treatment for H. influenzae empyema in children.

ObjectiveTo explore the correlation between sleep disorders and attention-deficit–hyperactivity disorder (ADHD) in children.MethodsWe studied 100 Chinese children (70 boys and 30 girls; mean age, 8.77 ± 2.39 years). Parents completed the Children's Sleep Disturbance Scale (SDSC) and the Swanson Nolan and Pelham Version IV Scale (SNAP-IV) questionnaires. SNAP-IV and SDSC scores were compared in children with and without sleep disorders and ADHD.ResultsThere were significant differences in SDSC scores, Arousal Disorder (AD) scores, and Sleep Breathing Disorder (SBD) scores between children with and without ADHD (P < 0.05). The sleep disorder group had higher SNAP-IV scores than the non-sleep disorder group (P < 0.05). Children with sleep disorders showed higher ADHD symptom values (inattention, hyperactivity/impulsivity, and oppositional defiance) than children without sleep disorders (P < 0.01). There was a moderate correlation between SDSC scores and SNAP-IV scores (r = 0.486, P < 0.05). Using SNAP-IV scores as the dependent variable, multiple linear regression analysis was applied, and a statistically significant effect of AD and Sleep–Wake Transition Disorder (SWTD) scores on SNAP-IV scores was found (P < 0.05). The area under the curve (95% CI) of the SDSC score for predicting sleep disorders with ADHD was 0.714 (0.606, 0.821; P = 0.0005).ConclusionChildren with ADHD are prone to sleep disorders. The higher the ADHD symptom score, the more sleeping problems. Sleep disorders can also cause or exacerbate ADHD symptoms, and the ADHD symptom score correlates with sleep disorder severity. We can reduce the severity of attention-deficit–hyperactivity in children with ADHD by improving their sleep with behavioral sleep interventions.

ObjectiveTo study the diagnosis efficacy of controlled attenuation parameters (CAP) and liver stiffness measurement (LSM) in the transient elastography of non-alcoholic fatty liver disease (NAFLD) and its subtypes in children with obesity.MethodsRetrospectively analyze children with obesity in the Childhood Obesity Clinic of the Affiliated Hospital of Hangzhou Normal University from July 2020 to March 2021. The correlation between clinical data and NAFLD subtypes was analyzed, and included the relevant clinical data into the receiver operating characteristic curve for diagnosis and prediction.Results120 children aged between 6.1 and 17.8 years, with 70 males (58.33%), 50 females (41.67%), and a ratio of 1.4:1, were enrolled in the study. CAP and LSM correlated in all subtypes of NAFLD. The correlation was significant for diagnosing NAFLD in children with obesity when CAP > 258.00 dB/m and LSM > 4.65 kPa. It was also significant for NASH diagnosis when CAP > 276.00 dB/m and LSM > 5.15 kPa, while it was less significant for diagnosing NAFLD in children with obesity.ConclusionsCAP and LSM have diagnostic efficacy for NAFLD and its subtypes in children with obesity, with optimal predictive values of CAP > 258.00 dB/m and LSM > 4.65 kPa for NAFLD in children with obesity, and CAP > 276.00 dB/m and LSM > 5.15 kPa for NASH in children with obesity.

In recent decades, survival was significantly improved in B cell acute lymphoblastic leukemia (B-ALL) patients. But refractory and relapsed B-ALL still has aggressive clinical behavior and poor prognosis. Especially, the patients with central nervous system infiltration is very difficult to achieve complete remissions with routine treatment. Chimeric antigen receptor-modified T-cell therapy targeting CD-19 has shown to be a beneficial treatment approach in refractory and relapsed B cell acute lymphoblastic leukemia (r/r ALL). However, there are very few studies reporting to treatment of refractory and relapsed B cell ALL with central nervous system infiltration. Here, we reported one single case of a patient diagnosed with relapsed B cell ALL with CNS infiltration who was successfully treated by second generation CAR containing a co-stimulator CD28 or 4-1BB therapy. Long-term proliferation of CAR-T cells in peripheral blood and bone marrow was observed more than 18 months. After CAR-T treatment, the patient got toxicity of grade 1 cytokine release syndrome and achieved significantly 36 months event free survival of follow-up. It is suggested that CD-19 CAR containing CD28 or 4-1BB costimulatory may be an effective therapy in refractory and relapsed B cell ALL with central nervous system infiltration. Its toxicity is mild, and its safety is high.Clinical Trial Registration:ClinicalTrials.gov Identifier: NCT02349698.

Abstract Background In the field of drug repositioning, it is assumed that similar drugs may treat similar diseases, therefore many existing computational methods need to compute the similarities of drugs and diseases. However, the calculation of similarity depends on the adopted measure and the available features, which may lead that the similarity scores vary dramatically from one to another, and it will not work when facing the incomplete data. Besides, supervised learning based methods usually need both positive and negative samples to train the prediction models, whereas in drug-disease pairs data there are only some verified interactions (positive samples) and a lot of unlabeled pairs. To train the models, many methods simply treat the unlabeled samples as negative ones, which may introduce artificial noises. Herein, we propose a method to predict drug-disease associations without the need of similarity information, and select more likely negative samples. Results In the proposed EMP-SVD (Ensemble Meta Paths and Singular Value Decomposition), we introduce five meta paths corresponding to different kinds of interaction data, and for each meta path we generate a commuting matrix. Every matrix is factorized into two low rank matrices by SVD which are used for the latent features of drugs and diseases respectively. The features are combined to represent drug-disease pairs. We build a base classifier via Random Forest for each meta path and five base classifiers are combined as the final ensemble classifier. In order to train out a more reliable prediction model, we select more likely negative ones from unlabeled samples under the assumption that non-associated drug and disease pair have no common interacted proteins. The experiments have shown that the proposed EMP-SVD method outperforms several state-of-the-art approaches. Case studies by literature investigation have found that the proposed EMP-SVD can mine out many drug-disease associations, which implies the practicality of EMP-SVD. Conclusions The proposed EMP-SVD can integrate the interaction data among drugs, proteins and diseases, and predict the drug-disease associations without the need of similarity information. At the same time, the strategy of selecting more reliable negative samples will benefit the prediction.

The effects of hot water bath and steam pretreatments were investigated, considering color, anthocyanin profile and antioxidant activity of blueberry (Vaccinium ashei) juice. The juice maximum total phenolic content, anthocyanin, and antioxidant activity were observed using the steam pretreatment processing method. Eleven anthocyanins were isolated from the blueberry juice of steam treatment processing, while only 10 from the hot water bath and 8 from the control sample. After storage at 40°C for 10 days, the anthocyanin retention rate of the juice from steam treatment processing was higher than in that from hot water bath and control sample (30.71%, 23.77%, and 19.91%, respectively). The antioxidant capacity was also significantly higher and the hue angle (H°) was lower in the juice from steam pretreatment processing. Steam pretreatment process could prevent color deterioration, increase variety and content of anthocyanins, and improve antioxidant activity in the blueberry juice.

Abstract Background Prediction of drug-disease interactions is promising for either drug repositioning or disease treatment fields. The discovery of novel drug-disease interactions, on one hand can help to find novel indictions for the approved drugs; on the other hand can provide new therapeutic approaches for the diseases. Recently, computational methods for finding drug-disease interactions have attracted lots of attention because of their far more higher efficiency and lower cost than the traditional wet experiment methods. However, they still face several challenges, such as the organization of the heterogeneous data, the performance of the model, and so on. Methods In this work, we present to hierarchically integrate the heterogeneous data into three layers. The drug-drug and disease-disease similarities are first calculated separately in each layer, and then the similarities from three layers are linearly fused into comprehensive drug similarities and disease similarities, which can then be used to measure the similarities between two drug-disease pairs. We construct a novel weighted drug-disease pair network, where a node is a drug-disease pair with known or unknown treatment relation, an edge represents the node-node relation which is weighted with the similarity score between two pairs. Now that similar drug-disease pairs are supposed to show similar treatment patterns, we can find the optimal graph cut of the network. The drug-disease pair with unknown relation can then be considered to have similar treatment relation with that within the same cut. Therefore, we develop a semi-supervised graph cut algorithm, SSGC, to find the optimal graph cut, based on which we can identify the potential drug-disease treatment interactions. Results By comparing with three representative network-based methods, SSGC achieves the highest performances, in terms of both AUC score and the identification rates of true drug-disease pairs. The experiments with different integration strategies also demonstrate that considering several sources of data can improve the performances of the predictors. Further case studies on four diseases, the top-ranked drug-disease associations have been confirmed by KEGG, CTD database and the literature, illustrating the usefulness of SSGC. Conclusions The proposed comprehensive similarity scores from multi-views and multiple layers and the graph-cut based algorithm can greatly improve the prediction performances of drug-disease associations.