Due to advances in telemedicine, mobile medical care, wearable health monitoring, and electronic skin, great efforts have been directed to non-invasive monitoring and treatment of disease. These processes generally involve disease detection from interstitial fluid (ISF) instead of blood, and transdermal drug delivery. However, the quantitative extraction of ISF and the level of drug absorption are greatly affected by the individual’s skin permeability, which is closely related to the properties of the stratum corneum (SC). Therefore, measurement of SC impedance has been proposed as an appropriate way for assessing individual skin differences. In order to figure out the current status and research direction of human SC impedance detection, investigations regarding skin impedance measurement have been reviewed in this paper. Future directions are concluded after a review of impedance models, electrodes, measurement methods and systems, and their applications in treatment. It is believed that a well-matched skin impedance model and measurement method will be established for clinical and point-of care applications in the near future.

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Skin penetration is related to efficiencies of drug delivery or ISF extraction. Normally, the macro-electrode is employed in skin permeability promotion and evaluation, which has the disadvantages of easily causing skin damage when using electroporation or reverse iontophoresis by alone; furthermore, it has large measurement error, low sensitivity, and difficulty in integration. To resolve these issues, this paper presents a flexible interdigital microelectrode for evaluating skin penetration by sensing impedance and a method of synergistical combination of electroporation and reverse iontophoresis to promote skin penetration. First, a flexible interdigital microelectrode was designed with a minimal configuration circuit of electroporation and reverse iontophoresis for future wearable application. Due to the variation of the skin impedance correlated with many factors, relative changes of it were recorded at the end of supply, different voltage, or constant current, times, and duration. It is found that the better results can be obtained by using electroporation for 5 min then reverse iontophoresis for 12 min. By synergistically using electroporation and reverse iontophoresis, the penetration of skin is promoted. The results tested in vivo suggest that the developed microelectrode can be applied to evaluate and promote the skin penetration and the designed method promises to leave the skin without damage. The electrode and the method may be beneficial for designing noninvasive glucose sensors.

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BackgroundPredicting type 2 chronic rhinosinusitis with nasal polyps (CRSwNP) may help for selection of appropriate surgical procedures or pharmacotherapies in advance. However, an accurate non-invasive method for diagnosis of type 2 CRSwNP is presently unavailable.MethodsTo optimize the technique for collecting nasal secretion (NasSec), 89 CRSwNP patients were tested using nasal packs made with four types of materials. Further, Th2low and Th2highCRSwNP defined by clustering analysis in another 142 CRSwNP patients using tissue biomarkers, in the meanwhile, inflammatory biomarkers were detected in NasSec of the same patients collected by the selected nasal pack. A diagnostic model was established by machine learning algorithms to predict Th2highCRSwNP using NasSecs biomarkers.ResultsConsidering the area under receiver operating characteristic curve (AUC) for IL-5 in NasSec, nasal pack in polyvinyl alcohol (PVA) was superior to other materials for NasSec collection. When Th2low and Th2highCRSwNP clusters were defined, logistic regression and decision tree model for prediction of Th2highCRSwNP demonstrated high AUCs values of 0.92 and 0.90 respectively using biomarkers of NasSecs. Consequently, the pre-pruned decision tree model; based on the levels of IL-5 in NasSec (≤ 15.04 pg/mL), blood eosinophil count (≤ 0.475*109/L) and absence of comorbid asthma; was chosen to define Th2lowCRSwNP from Th2highCRSwNP for routine clinical use.ConclusionsTaken together, a decision tree model based on a combination of NasSec biomarkers and clinical features can accurately define type 2 CRSwNP patients and therefore may be of benefit to patients in receiving appropriate therapies in daily clinical practice.

Early prediction of sepsis can help to identify potential risks in time and help take necessary measures to prevent more dangerous situations from occurring. In PhysioNet/Computing in Cardiology Challenge 2019, we integrate Long Short Term Memory (LSTM) recurrent neural network and ensemble learning to achieve early sepsis prediction. Specifically, we tackle the problem of class imbalance and data missing firstly, and then we manually extract features according to the prior knowledge from the medical field. In addition, we regard the prediction of sepsis as a time series prediction problem and pre-train LSTM-based models as feature extractors to obtain the "deep" features on time series that might be related to the onset of sepsis. Manual features and "deep" features are then used to train prediction models under the framework of ensemble learning, including Extreme Gradient Boosting (XGBoost) and Gradient Boosting Decision Tree (GBDT) regressor. The final normalized utility score our team (UCAS_DataMiner) have obtained was 0.313 on full hidden test set.

Objective: Spirometry, as the gold standard approach in the diagnosis of chronic obstructive pulmonary disease (COPD), has strict end of test (EOT) criteria (e.g. complete exhalation), which cannot be met by patients with compromised health states. Thus, significant parameters measured by spirometry, such as forced vital capacity (FVC), have limited accuracies. To address this issue, the present study aimed to develop models based on support vector regression (SVR) to predict values of FVC under the condition that the EOT criteria were not fully met. Approach: The prediction models for the quantification of FVC were developed based on SVR. A total of 354 subjects underwent conventional spirometry (CS), and the resulting data of forced expiratory volumes in 1 s (FEV1), peak expiratory flow (PEF), age and gender were used as input features, while the resulting values of the FVC were used as the target feature in the prediction models. Next, three prediction models (mixed model, normal model and abnormal model) were established according to the criterion in the diagnosis of COPD that a postbronchodilator shows an FEV1/FVC ratio lower than 0.70. Then, 35 subjects were recruited to be tested using both CS and a low-degree-of-EOT criteria spirometry (LDCS), which did not fully meet the EOT criteria of CS. In LDCS, subjects were allowed to terminate the procedure at their own will at any time after the technicians had assumed that both acceptable values of FEV1 and PEF had been obtained. Quantified values of FVC derived from both CS and LDCS were compared to validate the performances of the developed prediction models. Main results: The FVC prediction performances of the normal model and abnormal model were better than that of the mixed model. The root mean squared error are lower than 0.35 l and the accuracies are higher up to 95%. One-tailed t test results demonstrate that the absolute differences in the measured and predicted values are not significantly different from 0.15 l for both the abnormal model and the normal model. Significance: Our study shows the possibility of predicting FVC with acceptable precision in cases where the EOT criteria of spirometry were not fully met, which can be beneficial for patients who cannot or did not achieve full exhalation in spirometry.

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