Thermally Conductive and UV-EMI Shielding Electronic Textiles for Unrestricted and Multifaceted Health Monitoring.

Skin-attachable electronics have garnered considerable research attention in health monitoring and artificial intelligence domains, whereas susceptibility to electromagnetic interference (EMI), heat accumulation issues, and ultraviolet (UV)-induced aging problems pose significant constraints on their potential applications. Here, an ultra-elastic, highly breathable, and thermal-comfortable epidermal sensor with exceptional UV-EMI shielding performance and remarkable thermal conductivity is developed for high-fidelity monitoring of multiple human electrophysiological signals. Via filling the elastomeric microfibers with thermally conductive boron nitride nanoparticles and bridging the insulating fiber interfaces by plating Ag nanoparticles (NPs), an interwoven thermal conducting fiber network (0.72 W m-1 K-1) is constructed benefiting from the seamless thermal interfaces, facilitating unimpeded heat dissipation for comfort skin wearing. More excitingly, the elastomeric fiber substrates simultaneously achieve outstanding UV protection (UPF = 143.1) and EMI shielding (SET > 65, X-band) capabilities owing to the high electrical conductivity and surface plasmon resonance of Ag NPs. Furthermore, an electronic textile prepared by printing liquid metal on the UV-EMI shielding and thermally conductive nonwoven textile is finally utilized as an advanced epidermal sensor, which succeeds in monitoring different electrophysiological signals under vigorous electromagnetic interference. This research paves the way for developing protective and environmentally adaptive epidermal electronics for next-generation health regulation.

Superelastic Radiative Cooling Metafabric for Comfortable Epidermal Electrophysiological Monitoring.

Epidermal electronics with superb passive-cooling capabilities are of great value for both daytime outdoor dressing comfort and low-carbon economy. Herein, a multifunctional and skin-attachable electronic is rationally developed on a porous all-elastomer metafabric for efficient passive daytime radiative cooling (PDRC) and human electrophysiological monitoring. The cooling characteristics are realized through the homogeneous impregnation of polytetrafluoroethylene microparticles in the styrene-ethylene-butylene-styrene fibers, and the rational regulation of microporosity in SEBS/PTFE metafabrics, thus synergistically backscatter ultraviolet-visible-near-infrared light (maximum reflectance over 98.0%) to minimize heat absorption while efficiently emit human-body midinfrared radiation to the sky. As a result, the developed PDRC metafabric achieves approximately 17 °C cooling effects in an outdoor daytime environment and completely retains its passive cooling performance even under 50% stretching. Further, high-fidelity electrophysiological monitoring capability is also implemented in the breathable and skin-conformal metafabric through liquid metal printing, enabling the accurate acquisition of human electrocardiograph, surface electromyogram, and electroencephalograph signals for comfortable and lengthy health regulation. Hence, the fabricated superelastic PDRC metafabric opens a new avenue for the development of body-comfortable electronics and low-carbon wearing technologies.

Integrated analysis for treatment scheme of sodium-glucose cotransporter 2 inhibitors in patients with diabetic kidney disease: a real-world study.

Sodium-glucose cotransporter 2 inhibitors (SGLT2i) are recommended for type 2 diabetes mellitus patients with impaired renal function, but the actual situation of SGLT2i using is unclear. Therefore, in this real-world study, we analyzed the treatment scheme and clinical characteristics of SGLT2i in patients with diabetic kidney disease (DKD). We included DKD patients hospitalized in the First Affiliated Hospital of Zhengzhou University from October 2017 to March 2020. The Apriori algorithm of association rules was used to analysis treatment scheme prescribing SGLT2i and other different combinations of hypoglycemic drugs. SGLT2i was used in 781 (12.3%) of 6336 DKD patients, both number and proportion of patients using SGLT2i increased from 2017 to 2020 (1.9% to 33%). Nighty-eight percent of all DKD patients using SGLT2i were combined with other glucose-lowering agents, and insulin, metformin and alpha-glucosidase inhibitors are most commonly used in combination with hypoglycemic drugs. Multivariate analysis showed that compared with non-SGLT2i group, patients using SGLT2i were associated with younger age, higher BMI, higher HbA1c, preserved kidney function, dyslipidemia and combined with ACEI/ARB and statins. In this real-world study, use of SGLT2i in DKD patients is still low. Most patients performed younger age and in the early stages of chronic kidney disease with poor glycemic control. Clinical inertia should be overcome to fully exert the cardiorenal protective effects of SGLT2 inhibitors, with attention to rational drug use.

Quasi-Homogeneous and Hierarchical Electronic Textiles with Porosity-Hydrophilicity Dual-Gradient for Unidirectional Sweat Transport, Electrophysiological Monitoring, and Body-Temperature Visualization.

On-skin electronics based on impermeable elastomers and stacking structures often suffer from inferior sweat-repelling capabilities and severe mechanical mismatch between sub-layers employed, which significantly impedes their lengthy wearing comfort and functionality. Herein, inspired by the transpiration system of vascular plants and the water diode phenomenon, a hierarchical nonwoven electronic textile (E-textile) with multi-branching microfibers and robust interlayer adhesion is rationally developed. The layer-by-layer electro-airflow spinning method and selective oxygen plasma treatment are utilized to yield a porosity-hydrophilicity dual-gradient. The resulting E-textile shows unidirectional, nonreversible, and anti-gravity water transporting performance even upon large-scale stretching (250%), excellent mechanical matching between sub-layers, as well as a reversible color-switching ability to visualize body temperature. More importantly, the conducting and skin-conformal E-textile demonstrates accurate and stable detecting capability for biomechanical and bioelectrical signals when applied as an on-skin bioelectrode, including different human activities, electrocardiography, electromyogram, and electrodermal activity signals. Further, the E-textile can be efficiently implemented in human-machine interfaces to build a gesture-controlled dustbin and a smart acousto-optic alarm. Hence, this hierarchically-designed E-textile with integrated functionalities offers a practical and innovative method for designing comfortable and daily applicable on-skin electronics.

Integrated analysis of potential gene crosstalk between non-alcoholic fatty liver disease and diabetic nephropathy.

Background: Growing evidence indicates that non-alcoholic fatty liver disease (NAFLD) is related to the occurrence and development of diabetic nephropathy (DN). This bioinformatics study aimed to explore optimal crosstalk genes and related pathways between NAFLD and DN. Methods: Gene expression profiles were downloaded from Gene Expression Omnibus. CIBERSORT algorithm was employed to analyze the similarity of infiltrating immunocytes between the two diseases. Protein-protein interaction (PPI) co-expression network and functional enrichment analysis were conducted based on the identification of common differentially expressed genes (DEGs). Least absolute shrinkage and selection operator (LASSO) regression and Boruta algorithm were implemented to initially screen crosstalk genes. Machine learning models, including support vector machine, random forest model, and generalized linear model, were utilized to further identify the optimal crosstalk genes between DN and NAFLD. An integrated network containing crosstalk genes, transcription factors, and associated pathways was developed. Results: Four gene expression datasets, including GSE66676 and GSE48452 for NAFLD and GSE30122 and GSE1009 for DN, were involved in this study. There were 80 common DEGs between the two diseases in total. The PPI network built with the 80 common genes included 77 nodes and 83 edges. Ten optimal crosstalk genes were selected by LASSO regression and Boruta algorithm, including CD36, WIPI1, CBX7, FCN1, SLC35D2, CP, ZDHHC3, PTPN3, LPL, and SPP1. Among these genes, LPL and SPP1 were the most significant according to NAFLD-transcription factor network. Five hundred twenty-nine nodes and 1,113 edges comprised the PPI network of activated pathway-gene. In addition, 14 common pathways of these two diseases were recognized using Gene Ontology (GO) analysis; among them, regulation of the lipid metabolic process is closely related to both two diseases. Conclusions: This study offers hints that NAFLD and DN have a common pathogenesis, and LPL and SPP1 are the most relevant crosstalk genes. Based on the common pathways and optimal crosstalk genes, our proposal carried out further research to disclose the etiology and pathology between the two diseases.

Perspiration-Wicking and Luminescent On-Skin Electronics Based on Ultrastretchable Janus E-Textiles.

Stretchable electronics have attracted surging attention for next-generation smart wearables, yet traditional flexible devices fabricated on hermetical elastic substrates cannot satisfy lengthy wearing comfort and signal stability due to their poor moisture and air permeability. Herein, perspiration-wicking and luminescent on-skin electrodes are fabricated on superelastic nonwoven textiles with a Janus configuration. Through the electrospin-assisted face-to-face assembly of all-SEBS microfibers with differentiated diameters and composition, porosity and wettability asymmetry are constructed across the textile, endowing it with antigravity water transport capability for continuous sweat release. Also, the phosphor particles evenly encapsulated in the elastic fibers empower the Janus textile with stable light-emitting capability under extreme stretching in a dark environment. Additionally, the precise printing of highly conductive liquid metal (LM) circuits onto the matrix not only equips the electronic textile with broad detectability for various biophysical and electrophysiological signals but also enables successful implementation of human-machine interface (HMIs) to control a mechanical claw.

Felbamate add-on therapy for drug-resistant focal epilepsy.

BACKGROUND: This is an updated version of the Cochrane Review first published in 2011, and most recently updated in 2019. Epilepsy is a chronic and disabling neurological disorder, affecting approximately 1% of the population. Up to 30% of people with epilepsy have seizures that are resistant to currently available antiepileptic drugs and require treatment with multiple antiepileptic drugs in combination. Felbamate is a second-generation antiepileptic drug that can be used as add-on therapy to standard antiepileptic drugs. OBJECTIVES: To evaluate the efficacy and tolerability of felbamate versus placebo when used as an add-on treatment for people with drug-resistant focal-onset epilepsy. SEARCH METHODS: For the latest update, we searched the Cochrane Register of Studies (CRS Web) and MEDLINE (Ovid, 1946 to 13 July 2021) on 15 July 2021. There were no language or time restrictions. We reviewed the reference lists of retrieved studies to search for additional reports of relevant studies. We also contacted the manufacturers of felbamate and experts in the field for information about any unpublished or ongoing studies. SELECTION CRITERIA: We searched for randomised placebo-controlled add-on studies of people of any age with drug-resistant focal seizures. The studies could be double-blind, single-blind or unblinded and could be of parallel-group or cross-over design. DATA COLLECTION AND ANALYSIS: Two review authors independently selected studies for inclusion and extracted information. In the case of disagreements, a third review author arbitrated. Review authors assessed the following outcomes: 50% or greater reduction in seizure frequency; absolute or percentage reduction in seizure frequency; treatment withdrawal; adverse effects; quality of life. MAIN RESULTS: We included four randomised controlled trials, representing a total of 236 participants, in the review. Two trials had parallel-group design, the third had a two-period cross-over design, and the fourth had a three-period cross-over design. We judged all four studies to be at an unclear risk of bias overall. Bias arose from the incomplete reporting of methodological details, the incomplete and selective reporting of outcome data, and from participants having unstable drug regimens during experimental treatment in one trial. Due to significant methodological heterogeneity, clinical heterogeneity and differences in outcome measures, it was not possible to perform a meta-analysis of the extracted data. Only one study reported the outcome of 50% or greater reduction in seizure frequency, whilst three studies reported percentage reduction in seizure frequency compared to placebo. One study claimed an average seizure reduction of 35.8% with add-on felbamate whilst another study claimed a more modest reduction of 4.2%. Both studies reported that seizure frequency increased with add-on placebo and that there was a significant difference in seizure reduction between felbamate and placebo (P = 0.0005 and P = 0.018, respectively). The third study reported a 14% reduction in seizure frequency with add-on felbamate but stated that the difference between treatments was not significant. There were conflicting results regarding treatment withdrawal. One study reported a higher treatment withdrawal for placebo-randomised participants, whereas the other three studies reported higher treatment withdrawal rates for felbamate-randomised participants. Notably, the treatment withdrawal rates for felbamate treatment groups across all four studies remained reasonably low (less than 10%), suggesting that felbamate may be well tolerated. Felbamate-randomised participants most commonly withdrew from treatment due to adverse effects. The adverse effects consistently reported by all four studies were headache, dizziness and nausea. All three adverse effects were reported by 23% to 40% of felbamate-treated participants versus 3% to 15% of placebo-treated participants. We assessed the evidence for all outcomes using GRADE and rated the evidence as very low certainty, meaning that we have little confidence in the findings reported. We mainly downgraded evidence for imprecision due to the narrative synthesis conducted and the low number of events. We stress that the true effect of felbamate could likely be significantly different from that reported in this current review update. AUTHORS' CONCLUSIONS: In view of the methodological deficiencies, the limited number of included studies and the differences in outcome measures, we have found no reliable evidence to support the use of felbamate as an add-on therapy in people with drug-resistant focal-onset epilepsy. A large-scale, randomised controlled trial conducted over a longer period of time is required to inform clinical practice.

Prediction and Risk Stratification of Cardiovascular Disease in Diabetic Kidney Disease Patients.

Background: Diabetic kidney disease (DKD) patients are facing an extremely high risk of cardiovascular disease (CVD), which is a major cause of death for DKD patients. We aimed to build a deep learning model to predict CVD risk among DKD patients and perform risk stratifying, which could help them perform early intervention and improve personal health management. Methods: A retrospective cohort study was conducted to assess the risk of the occurrence of composite cardiovascular disease, which includes coronary heart disease, cerebrovascular diseases, congestive heart failure, and peripheral artery disease, in DKD patients. A least absolute shrinkage and selection operator (LASSO) regression was used to perform the variable selection. A deep learning-based survival model called DeepSurv, based on a feed-forward neural network was developed to predict CVD risk among DKD patients. We compared the model performance with the conventional Cox proportional hazards (CPH) model and the Random survival forest (RSF) model using the concordance index (C-index), the area under the curve (AUC), and integrated Brier scores (IBS). Results: We recruited 890 patients diagnosed with DKD in this retrospective study. During a median follow-up of 10.4 months, there are 289 patients who sustained a subsequent CVD. Seven variables, including age, high density lipoprotein (HDL), hemoglobin (Hb), systolic blood pressure (SBP), smoking status, 24 h urinary protein excretion, and total cholesterol (TC), chosen by LASSO regression were used to develop the predictive model. The DeepSurv model showed the best performance, achieved a C-index of 0.767(95% confidence intervals [CI]: 0.717-0.817), AUC of 0.780(95%CI: 0.721-0.839), and IBS of 0.067 in the validation set. Then we used the cut-off value determined by ROC (receiver operating characteristic) curve to divide the patients into different risk groups. Moreover, the DeepSurv model was also applied to develop an online calculation tool for patients to conduct risk monitoring. Conclusion: A deep-learning-based predictive model using seven clinical variables can effectively predict CVD risk among DKD patients and perform risk stratification. An online calculator allows its easy implementation.

Xylem-Inspired Polyimide/MXene Aerogels with Radial Lamellar Architectures for Highly Sensitive Strain Detection and Efficient Solar Steam Generation.

Polyimide aerogels with mechanical robustness, great compressibility, excellent antifatigue properties, and intriguing functionality have captured enormous attention in diverse applications. Here, enlightened by the xylem parenchyma of dicotyledonous stems, a radially architectured polyimide/MXene composite aerogel (RPIMX) with reversible compressibility is developed by combining the interfacial enhancing strategy and radial ice-templating method. The strong interaction between MXene flakes and polymer can glue the MXene to form continuous lamellae, the ice crystals grow preferentially along the radial temperature gradient can effectively constrain the lamellae to create a biomimetic radial lamellar architecture. As a result, the nature-inspired RPIMX composite aerogel with centrosymmetric lamellar structure and oriented channels manifests excellent mechanical strength, electrical conductivity, and water transporting capability along the longitudinal direction, endowing itself with intriguing applications for accurate human motion monitoring and efficient photothermal evaporation. These exciting properties make the biomimetic RPIMX aerogels promising candidates for flexible piezoresistive sensors and photothermal evaporators.

The 30-days hospital readmission risk in diabetic patients: predictive modeling with machine learning classifiers.

BACKGROUND AND OBJECTIVES: Diabetes mellitus is a major chronic disease that results in readmissions due to poor disease control. Here we established and compared machine learning (ML)-based readmission prediction methods to predict readmission risks of diabetic patients. METHODS: The dataset analyzed in this study was acquired from the Health Facts Database, which includes over 100,000 records of diabetic patients from 1999 to 2008. The basic data distribution characteristics of this dataset were summarized and then analyzed. In this study, 30-days readmission was defined as a readmission period of less than 30 days. After data preprocessing and normalization, multiple risk factors in the dataset were examined for classifier training to predict the probability of readmission using ML models. Different ML classifiers such as random forest, Naive Bayes, and decision tree ensemble were adopted to improve the clinical efficiency of the classification. In this study, the Konstanz Information Miner platform was used to preprocess and model the data, and the performances of the different classifiers were compared. RESULTS: A total of 100,244 records were included in the model construction after the data preprocessing and normalization. A total of 23 attributes, including race, sex, age, admission type, admission location, length of stay, and drug use, were finally identified as modeling risk factors. Comparison of the performance indexes of the three algorithms revealed that the RF model had the best performance with a higher area under receiver operating characteristic curve (AUC) than the other two algorithms, suggesting that its use is more suitable for making readmission predictions. CONCLUSION: The factors influencing 30-days readmission predictions in diabetic patients, including number of inpatient admissions, age, diagnosis, number of emergencies, and sex, would help healthcare providers to identify patients who are at high risk of short-term readmission and reduce the probability of 30-days readmission. The RF algorithm with the highest AUC is more suitable for making 30-days readmission predictions and  deserves further validation in clinical trials.

Application of Big Data and Artificial Intelligence in COVID-19 Prevention, Diagnosis, Treatment and Management Decisions in China.

COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), spread rapidly and affected most of the world since its outbreak in Wuhan, China, which presents a major challenge to the emergency response mechanism for sudden public health events and epidemic prevention and control in all countries. In the face of the severe situation of epidemic prevention and control and the arduous task of social management, the tremendous power of science and technology in prevention and control has emerged. The new generation of information technology, represented by big data and artificial intelligence (AI) technology, has been widely used in the prevention, diagnosis, treatment and management of COVID-19 as an important basic support. Although the technology has developed, there are still challenges with respect to epidemic surveillance, accurate prevention and control, effective diagnosis and treatment, and timely judgement. The prevention and control of sudden infectious diseases usually depend on the control of infection sources, interruption of transmission channels and vaccine development. Big data and AI are effective technologies to identify the source of infection and have an irreplaceable role in distinguishing close contacts and suspicious populations. Advanced computational analysis is beneficial to accelerate the speed of vaccine research and development and to improve the quality of vaccines. AI provides support in automatically processing relevant data from medical images and clinical features, tests and examination findings; predicting disease progression and prognosis; and even recommending treatment plans and strategies. This paper reviews the application of big data and AI in the COVID-19 prevention, diagnosis, treatment and management decisions in China to explain how to apply big data and AI technology to address the common problems in the COVID-19 pandemic. Although the findings regarding the application of big data and AI technologies in sudden public health events lack validation of repeatability and universality, current studies in China have shown that the application of big data and AI is feasible in response to the COVID-19 pandemic. These studies concluded that the application of big data and AI technology can contribute to prevention, diagnosis, treatment and management decision making regarding sudden public health events in the future.

Comparative Effectiveness and Safety of High-Intensity Focused Ultrasound for Uterine Fibroids: A Systematic Review and Meta-Analysis.

Background: Uterine fibroids are common benign tumors among premenopausal women. High- intensity focused ultrasound (HIFU) is an emerging non-invasive intervention which uses the high-intensity ultrasound waves from ultrasound probes to focus on the targeted fibroids. However, the efficacy of HIFU in comparison with that of other common treatment types in clinical procedure remains unclear. Objective: To investigate the comparative effectiveness and safety of HIFU with other techniques which have been widely used in clinical settings. Methods: We searched the Cochrane Central Register of Controlled Trials, PubMed, EMBASE, Cumulative Index to Nursing & Allied Health Literature, Web of Science, ProQuest Nursing & Allied Health Database, and three Chinese academic databases, including randomized controlled trials (RCTs), non-RCTs, and cohort studies. The primary outcome was the rate of re-intervention, and the GRADE approach was used to interpret the findings. Results: About 18 studies met the inclusion criteria. HIFU was associated with an increased risk of re-intervention rate in comparison with myomectomy (MYO) [pooled odds ratio (OR): 4.05, 95% confidence interval (CI): 1.82-8.9]. The results favored HIFU in comparison with hysterectomy (HYS) on the change of follicle-stimulating hormone [pooled mean difference (MD): -7.95, 95% CI: -8.92-6.98), luteinizing hormone (MD: -4.38, 95% CI: -5.17-3.59), and estradiol (pooled MD: 43.82, 95% CI: 36.92-50.72)]. HIFU had a shorter duration of hospital stay in comparison with MYO (pooled MD: -4.70, 95% CI: -7.46-1.94, p < 0.01). It had a lower incidence of fever (pooled OR: 0.15, 95% CI: 0.06-0.39, p < 0.01) and a lower incidence of major adverse events (pooled OR: 0.04, 95% CI: 0.00-0.30, p < 0.01) in comparison with HYS. Conclusions: High-intensity focused ultrasound may help maintain feminity and shorten the duration of hospital stay. High-quality clinical studies with a large sample size, a long-term follow-up, and the newest HIFU treatment protocol for evaluating the re-intervention rate are suggested to be carried out. Clinical decision should be based on the specific situation of the patients and individual values.

Construction of a knowledge graph for diabetes complications from expert-reviewed clinical evidences.

A knowledge graph is a structured representation of data that can express entity and relational knowledge. More attention has been paid to the study of a clinical knowledge graph, especially in the field of chronic diseases. However, knowledge graph construction is based mainly on electronic medical records and other data sources, and the authority of the constructed knowledge graph presents some problems. Therefore, regarding the quality of evidence, this study, in combination with experimental research on system evaluation and meta-analysis presents some new information, On the basis of evidence-based medicine (EBM), the secondary results of systematic evaluation and meta-analyses of social, psychological, and behavioral aspects were extracted as data for the core nodes and edges of a knowledge graph to construct a graph of type 2 diabetes (T2D) and its complications. In this study, relevant life-style evidence that are factors for the risk of diabetic retinopathy (DR), diabetic nephropathy (DN), diabetic foot (DF), and diabetic depression (DD), and the results of several of the relevant clinical test, including bariatric surgery, myopia, lipid-lowering drugs, lipid-lowering drug duration, blood glucose control, disease course, glycosylated hemoglobin, fasting blood glucose, hypertension, sex, smoking and other common lifestyle characteristics were finally extracted. The evidence-based knowledge graph of the DM complications was constructed by extracting relevant disease, risk factors, risk outcomes, and other diabetes entities and the strength of the data for the odds ratio (OR) or relative risk (RR) correlations from clinical evidence. Moreover, the risk prediction models constructed using a logistic model were incorporated into the knowledge graph to visualize the risk score of DM complications for each user. In short, the EBM-powered construction of the knowledge graph could provide high-quality information to support decisions for the prevention and control of diabetes and its complications.

Association between Retinal Vascular Geometric Changes and Cognitive Impairment: A Systematic Review and Meta-Analysis.

BACKGROUND AND PURPOSE: Previous studies have explored the association between retinal vascular changes and cognitive impairment. The retinal vasculature shares some characteristics with the cerebral vasculature, and quantitative changes in it could indicate cognitive impairment. Hence, a comprehensive meta-analysis was performed to clarify the potential relationship between retinal vascular geometric changes and cognitive impairment. METHODS: Relevant databases were scrupulously and systematically searched for retinal vascular geometric changes including caliber, tortuosity, and fractal dimension (FD), and for cognitive impairment. The Newcastle-Ottawa Scale was used to evaluate the methodological quality of included studies. RevMan was used to perform the meta-analysis and detect publication bias. Sensitivity analyses were also performed. RESULTS: Five studies that involved 2,343 subjects were finally included in the meta-analysis. The results showed that there was no significant association between central retinal artery equivalents (Z=1.17) or central retinal venular equivalents (Z=1.74) and cognitive impairment (both p>0.05). Similarly, no significant difference was detected in retinal arteriolar tortuosity (Z=0.91) and venular tortuosity (Z=1.31) (both p>0.05). However, the retinal arteriolar FD (mean difference: -0.03, 95% CI: -0.05, -0.01) and venular FD (mean difference: -0.03, 95% CI: -0.05, -0.02) were associated with cognitive impairment. CONCLUSIONS: A smaller retinal microvascular FD might be associated with cognitive impairment. Further large-sample and well-controlled original studies are required to confirm the present findings.

Intravenous immunoglobulins for epilepsy.

BACKGROUND: Epilepsy is a common neurological condition, with an estimated incidence of 50 per 100,000 persons. People with epilepsy may present with various types of immunological abnormalities, such as low serum immunoglobulin A (IgA) levels, lack of the immunoglobulin G (IgG) subclass and identification of certain types of antibodies. Intravenous immunoglobulin (IVIg) treatment may represent a valuable approach and its efficacy has important implications for epilepsy management. This is an update of a Cochrane review first published in 2011 and last updated in 2017. OBJECTIVES: To examine the effects of IVIg on the frequency and duration of seizures, quality of life and adverse effects when used as monotherapy or as add-on treatment for people with epilepsy. SEARCH METHODS: For the latest update, we searched the Cochrane Register of Studies (CRS Web) (20 December 2018), MEDLINE (Ovid, 1946 to 20 December 2018), Web of Science (1898 to 20 December 2018), ISRCTN registry (20 December 2018), WHO International Clinical Trials Registry Platform (ICTRP, 20 December 2018), the US National Institutes of Health ClinicalTrials.gov (20 December 2018), and reference lists of articles. SELECTION CRITERIA: Randomised or quasi-randomised controlled trials of IVIg as monotherapy or add-on treatment in people with epilepsy. DATA COLLECTION AND ANALYSIS: Two review authors independently assessed the trials for inclusion and extracted data. We contacted study authors for additional information. Outcomes included percentage of people rendered seizure-free, 50% or greater reduction in seizure frequency, adverse effects, treatment withdrawal and quality of life. MAIN RESULTS: We included one study (61 participants). The included study was a randomised, double-blind, placebo-controlled, multicentre trial which compared the treatment efficacy of IVIg as an add-on with a placebo add-on in patients with drug-resistant epilepsy. Seizure freedom was not reported in the study. There was no significant difference between IVIg and placebo in 50% or greater reduction in seizure frequency (RR 1.89, 95% CI 0.85 to 4.21; one study, 58 participants; low-certainty evidence). The study reported a statistically significant effect for global assessment in favour of IVIg (RR 3.29, 95% CI 1.13 to 9.57; one study, 60 participants; low-certainty evidence). No adverse effects were demonstrated. We found no randomised controlled trials that investigated the effects of IVIg monotherapy for epilepsy. Overall, the included study was rated at low to unclear risk of bias. Using GRADE methodology, the certainty of the evidence was rated as low. AUTHORS' CONCLUSIONS: We cannot draw any reliable conclusions regarding the efficacy of IVIg as a treatment for epilepsy. Further randomised controlled trials are needed.

Computer aided diabetic retinopathy detection based on ophthalmic photography: a systematic review and Meta-analysis.

AIM: To ensure the diagnostic value of computer aided techniques in diabetic retinopathy (DR) detection based on ophthalmic photography (OP). METHODS: PubMed, EMBASE, Ei village, IEEE Xplore and Cochrane Library database were searched systematically for literatures about computer aided detection (CAD) in DR detection. The methodological quality of included studies was appraised by the Quality Assessment Tool for Diagnostic Accuracy Studies (QUADAS-2). Meta-DiSc was utilized and a random effects model was plotted to summarize data from those included studies. Summary receiver operating characteristic curves were selected to estimate the overall test performance. Subgroup analysis was used to identify the efficiency of CAD in detecting DR, exudates (EXs), microaneurysms (MAs) as well as hemorrhages (HMs), and neovascularizations (NVs). Publication bias was analyzed using STATA. RESULTS: Fourteen articles were finally included in this Meta-analysis after literature review. Pooled sensitivity and specificity were 90% (95%CI, 85%-94%) and 90% (95%CI, 80%-96%) respectively for CAD in DR detection. With regard to CAD in EXs detecting, pooled sensitivity, specificity were 89% (95%CI, 88%-90%) and 99% (95%CI, 99%-99%) respectively. In aspect of MAs and HMs detection, pooled sensitivity and specificity of CAD were 42% (95%CI, 41%-44%) and 93% (95%CI, 93%-93%) respectively. Besides, pooled sensitivity and specificity were 94% (95%CI, 89%-97%) and 87% (95%CI, 83%-90%) respectively for CAD in NVs detection. No potential publication bias was observed. CONCLUSION: CAD demonstrates overall high diagnostic accuracy for detecting DR and pathological lesions based on OP. Further prospective clinical trials are needed to prove such effect.

Felbamate add-on therapy for drug-resistant focal epilepsy.

BACKGROUND: This is an updated version of the Cochrane Review previously published in 2017.Epilepsy is a chronic and disabling neurological disorder, affecting approximately 1% of the population. Up to 30% of people with epilepsy have seizures that are resistant to currently available antiepileptic drugs and require treatment with multiple antiepileptic drugs in combination. Felbamate is a second-generation antiepileptic drug that can be used as add-on therapy to standard antiepileptic drugs. OBJECTIVES: To evaluate the efficacy and tolerability of felbamate versus placebo when used as an add-on treatment for people with drug-resistant focal-onset epilepsy. SEARCH METHODS: For the latest update we searched the Cochrane Register of Studies (CRS Web), MEDLINE, ClinicalTrials.gov and the WHO International Clinical Trials Registry Platform (ICTRP), on 18 December 2018. There were no language or time restrictions. We reviewed the reference lists of retrieved studies to search for additional reports of relevant studies. We also contacted the manufacturers of felbamate and experts in the field for information about any unpublished or ongoing studies. SELECTION CRITERIA: We searched for randomised placebo-controlled add-on studies of people of any age with drug-resistant focal seizures. The studies could be double-blind, single-blind or unblinded and could be of parallel-group or cross-over design. DATA COLLECTION AND ANALYSIS: Two review authors independently selected studies for inclusion and extracted information. In the case of disagreements, the third review author arbitrated. Review authors assessed the following outcomes: 50% or greater reduction in seizure frequency; absolute or percentage reduction in seizure frequency; treatment withdrawal; adverse effects; quality of life. MAIN RESULTS: We included four randomised controlled trials, representing a total of 236 participants, in the review. Two trials had parallel-group design, the third had a two-period cross-over design, and the fourth had a three-period cross-over design. We judged all four studies to be at an unclear risk of bias overall. Bias arose from the incomplete reporting of methodological details, the incomplete and selective reporting of outcome data, and from participants having unstable drug regimens during experimental treatment in one trial. Due to significant methodological heterogeneity, clinical heterogeneity and differences in outcome measures, it was not possible to perform a meta-analysis of the extracted data.Only one study reported the outcome, 50% or greater reduction in seizure frequency, whilst three studies reported percentage reduction in seizure frequency compared to placebo. One study claimed an average seizure reduction of 35.8% with add-on felbamate while another study claimed a more modest reduction of 4.2%. Both studies reported that seizure frequency increased with add-on placebo and that there was a significant difference in seizure reduction between felbamate and placebo (P = 0.0005 and P = 0.018, respectively). The third study reported a 14% reduction in seizure frequency with add-on felbamate but stated that the difference between treatments was not significant. There were conflicting results regarding treatment withdrawal. One study reported a higher treatment withdrawal for placebo-randomised participants, whereas the other three studies reported higher treatment withdrawal rates for felbamate-randomised participants. Notably, the treatment withdrawal rates for felbamate treatment groups across all four studies remained reasonably low (less than 10%), suggesting that felbamate may be well tolerated. Felbamate-randomised participants most commonly withdrew from treatment due to adverse effects. The adverse effects consistently reported by all four studies were: headache, dizziness and nausea. All three adverse effects were reported by 23% to 40% of felbamate-treated participants versus 3% to 15% of placebo-treated participants.We assessed the evidence for all outcomes using GRADE and found it as being very-low certainty, meaning that we have little confidence in the findings reported. We mainly downgraded evidence for imprecision due to the narrative synthesis conducted and the low number of events. We stress that the true effect of felbamate could likely be significantly different from that reported in this current review update. AUTHORS' CONCLUSIONS: In view of the methodological deficiencies, the limited number of included studies and the differences in outcome measures, we have found no reliable evidence to support the use of felbamate as an add-on therapy in people with drug-resistant focal-onset epilepsy. A large-scale, randomised controlled trial conducted over a longer period of time is required to inform clinical practice.

The identification of gene signature and critical pathway associated with childhood-onset type 2 diabetes.

In general, type 2 diabetes (T2D) usually occurs in middle-aged and elderly people. However, the incidence of childhood-onset T2D has increased all across the globe. Therefore, it is very important to determine the molecular and genetic mechanisms of childhood-onset T2D. In this study, the dataset GSE9006 was downloaded from the GEO (Gene Expression Omnibus database); it includes 24 healthy children, 43 children with newly diagnosed Type 1 diabetes (T1D), and 12 children with newly diagnosed T2D. These data were used for differentially expressed genes (DGEs) analysis and weighted co-expression network analysis (WGCNA). We identified 192 up-regulated genes and 329 down-regulated genes by performing DEGs analysis. By performing WGGNA, we found that blue module (539 genes) was highly correlated to cyan module (97 genes). Gene ontology (GO) and pathway enrichment analyses were performed to figure out the functions and related pathways of genes, which were identified in the results of DEGs and WGCNA. Genes with conspicuous logFC and in the high correlated modules were input into GeneMANIA, which is a plugin of Cytoscape application. Thus, we constructed the protein-protein interaction (PPI) network (92 nodes and 254 pairs). Eventually, we analyzed the transcription factors and references related to genes with conspicuous logFC or high-degree genes, which were present in both the modules of WGCNA and PPI network. Current research shows that EGR1 and NAMPT can be used as marker genes for childhood-onset T2D. Gestational diabetes and chronic inflammation are risk factors that lead to the development of childhood-onset T2D.

A comprehensive competitive endogenous RNA network pinpoints key molecules in diabetic retinopathy.

Diabetic retinopathy (DR) is a severe microvascular complication of diabetes and the primary cause of vision loss in diabetic patients. Previous research has revealed that long non­coding RNAs (lncRNAs) and microRNAs (miRNAs) play pivotal roles in the pathogenesis of DR. However, the roles of lncRNA­miRNA­mRNA interactions in DR are poorly understood. In the present study, we aimed to compute a global triple network of competitive endogenous RNAs (ceRNAs) in order to pinpoint essential molecules. We found that there were 802 nodes (121 lncRNA nodes, 17 miRNA nodes, and 664 mRNA nodes) and 949 edges in the ceRNA network. Further functional analysis suggested that some molecules were specifically related to DR. Surprisingly, these molecules were involved in visual perception, eye development, and lens development in camera­type eye. In summary, our study highlighted specific lncRNAs and miRNAs related to the pathogenesis of DR, which might be used as potential diagnostic biomarkers and therapeutic targets for DR.

iT2DMS: a Standard-Based Diabetic Disease Data Repository and its Pilot Experiment on Diabetic Retinopathy Phenotyping and Examination Results Integration.

Type 2 diabetes mellitus (T2DM) is a common chronic disease, and the fragment data collected through separated vendors makes continuous management of DM patients difficult. The lack of standard of fragment data from those diabetic patients also makes the further potential phenotyping based on the diabetic data difficult. Traditional T2DM data repository only supports data collection from T2DM patients, lack of phenotyping ability and relied on standalone database design, limiting the secondary usage of these valuable data. To solve these issues, we proposed a novel T2DM data repository framework, which was based on standards. This repository can integrate data from various sources. It would be used as a standardized record for further data transfer as well as integration. Phenotyping was conducted based on clinical guidelines with KNIME workflow. To evaluate the phenotyping performance of the proposed system, data was collected from local community by healthcare providers and was then tested using algorithms. The results indicated that the proposed system could detect DR cases with an average accuracy of about 82.8%. Furthermore, these results had the promising potential of addressing fragmented data. The proposed system has integrating and phenotyping abilities, which could be used for diabetes research in future studies.