Simplifying Multimodal With Single EOG Modality for Automatic Sleep Staging.

Polysomnography (PSG) recordings have been widely used for sleep staging in clinics, containing multiple modality signals (i.e., EEG and EOG). Recently, many studies have combined EEG and EOG modalities for sleep staging, since they are the most and the second most powerful modality for sleep staging among PSG recordings, respectively. However, EEG is complex to collect and sensitive to environment noise or other body activities, imbedding its use in clinical practice. Comparatively, EOG is much more easily to be obtained. In order to make full use of the powerful ability of EEG and the easy collection of EOG, we propose a novel framework to simplify multimodal sleep staging with a single EOG modality. It still performs well with only EOG modality in the absence of the EEG. Specifically, we first model the correlation between EEG and EOG, and then based on the correlation we generate multimodal features with time and frequency guided generators by adopting the idea of generative adversarial learning. We collected a real-world sleep dataset containing 67 recordings and used other four public datasets for evaluation. Compared with other existing sleep staging methods, our framework performs the best when solely using the EOG modality. Moreover, under our framework, EOG provides a comparable performance to EEG.

Intake of compound probiotics accelerates the construction of immune function and gut microbiome in Holstein calves.

Acquired immunity is an important way to construct the intestinal immune barrier in mammals, which is almost dependent on suckling. To develop a new strategy for accelerating the construction of gut microbiome, newborn Holstein calves were continuously fed with 40 mL of compound probiotics (containing Lactobacillus plantarum T-14, Enterococcus faecium T-11, Saccharomyces cerevisiae T-209, and Bacillus licheniformis T-231) per day for 60 days. Through diarrhea rate monitoring, immune index testing, antioxidant capacity detection, and metagenome sequencing, the changes in diarrhea incidence, average daily gain, immune index, and gut microbiome of newborn calves within 60 days were investigated. Results indicated that feeding the compound probiotics reduced the average diarrhea rate of calves by 42.90%, increased the average daily gain by 43.40%, raised the antioxidant indexes of catalase, superoxide dismutase, total antioxidant capacity, and Glutathione peroxidase by 22.81%, 6.49%, 8.33%, and 13.67%, respectively, and increased the immune indexes of IgA, IgG, and IgM by 10.44%, 4.85%, and 6.12%, respectively. Moreover, metagenome sequencing data showed that feeding the compound probiotics increased the abundance of beneficial strains (e.g., Lactococcus lactis and Bacillus massionigeriensis) and decreased the abundance of some harmful strains (e.g., Escherichia sp. MOD1-EC5189 and Mycobacterium brisbane) in the gut microbiome of calves, thus contributing to accelerating the construction of healthy gut microbiome in newborn Holstein calves. IMPORTANCE: The unstable gut microbiome and incomplete intestinal function of newborn calves are important factors for the high incidence of early diarrhea. This study presents an effective strategy to improve the overall immunity and gut microbiome in calves and provides new insights into the application of compound probiotics in mammals.

Unsupervised Domain Adaptation With Class-Aware Memory Alignment.

Unsupervised domain adaptation (UDA) is to make predictions on unlabeled target domain by learning the knowledge from a label-rich source domain. In practice, existing UDA approaches mainly focus on minimizing the discrepancy between different domains by mini-batch training, where only a few instances are accessible at each iteration. Due to the randomness of sampling, such a batch-level alignment pattern is unstable and may lead to misalignment. To alleviate this risk, we propose class-aware memory alignment (CMA) that models the distributions of the two domains by two auxiliary class-aware memories and performs domain adaptation on these predefined memories. CMA is designed with two distinct characteristics: class-aware memories that create two symmetrical class-aware distributions for different domains and two reliability-based filtering strategies that enhance the reliability of the constructed memory. We further design a unified memory-based loss to jointly improve the transferability and discriminability of features in the memories. State-of-the-art (SOTA) comparisons and careful ablation studies show the effectiveness of our proposed CMA.

DiffMDD: A Diffusion-Based Deep Learning Framework for MDD Diagnosis Using EEG.

Major Depression Disorder (MDD) is a common yet destructive mental disorder that affects millions of people worldwide. Making early and accurate diagnosis of it is very meaningful. Recently, EEG, a non-invasive technique of recording spontaneous electrical activity of brains, has been widely used for MDD diagnosis. However, there are still some challenges in data quality and data size of EEG: (1) A large amount of noise is inevitable during EEG collection, making it difficult to extract discriminative features from raw EEG; (2) It is difficult to recruit a large number of subjects to collect sufficient and diverse data for model training. Both of the challenges cause the overfitting problem, especially for deep learning methods. In this paper, we propose DiffMDD, a diffusion-based deep learning framework for MDD diagnosis using EEG. Specifically, we extract more noise-irrelevant features to improve the model's robustness by designing the Forward Diffusion Noisy Training Module. Then we increase the size and diversity of data to help the model learn more generalized features by designing the Reverse Diffusion Data Augmentation Module. Finally, we re-train the classifier on the augmented dataset for MDD diagnosis. We conducted comprehensive experiments to test the overall performance and each module's effectiveness. The framework was validated on two public MDD diagnosis datasets, achieving the state-of-the-art performance.

Narcolepsy Diagnosis With Sleep Stage Features Using PSG Recordings.

Narcolepsy is a sleep disorder affecting millions of people worldwide and causes serious public health problems. It is hard for doctors to correctly and objectively diagnose narcolepsy. Polysomnography (PSG) recordings, a gold standard for sleep monitoring and quality measurement, can provide abundant and objective cues for the narcolepsy diagnosis. There have been some studies on automatic narcolepsy diagnosis using PSG recordings. However, the sleep stage information, an important cue for narcolepsy diagnosis, has not been fully utilized. For example, some studies have not considered the sleep stage information to diagnose narcolepsy. Although some studies consider the sleep stage information, the stages are manually scored by experts, which is time-consuming and subjective. And the framework using sleep stages scored automatically for narcolepsy diagnosis is designed in a two-phase learning manner, where sleep staging in the first phase and diagnosis in the second phase, causing cumulative error and degrading the performance. To address these challenges, we propose a novel end-to-end framework for automatic narcolepsy diagnosis using PSG recordings. In particular, adopting the idea of multi-task learning, we take the sleep staging as our auxiliary task, and then combine the sleep stage related features with narcolepsy related features for our primary task of narcolepsy diagnosis. We collected a dataset of PSG recordings from 77 participants and evaluated our framework on it. Both of the sleep stage features and the end-to-end fashion contribute to diagnosis performance. Moreover, we do a comprehensive analysis on the relationship between sleep stages and narcolepsy, correlation of different channels, predictive ability of different sensing data, and diagnosis results in subject level.

Analysis of carotid vulnerable plaque MRI high-risk features and clinical risk factors associated with concomitant acute cerebral infarction.

BACKGROUND: This study aimed to investigate the correlation between the high-risk characteristics of high-resolution MRI carotid vulnerable plaques and the clinical risk factors and concomitant acute cerebral infarction (ACI). METHODS: Forty-five patients diagnosed with a single vulnerable carotid plaque by MRI were divided into two groups based on whether they had ipsilateral ACI. The clinical risk factors and the observation values or frequency of occurrence of high-risk MRI phenotypes of plaque volume, LRNC, IPH and ulcer were statistically compared between the two groups. RESULTS: A total of 45 vulnerable carotid artery plaques were found in 45 patients, 23 patients with ACI and 22 patients without ACI. There were no significant differences in age, sex, smoking, serum TC, TG and LDL between the two groups (all P > 0.05), but the ACI group had significantly more patients with hypertension (P < 0.05) and the without ACI group coronary heart disease (P < 0.05). The volume of vulnerable carotid plaque in the group with ACI (1004.19 ± 663.57 mm3) was significantly larger than that in the group without ACI (487.21 ± 238.64 mm3) (P < 0.05). The phenotype of vulnerable carotid artery plaque was 13 cases of LRNC, 8 cases of LRNC + IPH, 5 cases of LRNC + Ulcer, and 19 cases of LRNC + IPH + Ulcer. There was no significant difference in this distribution between the two groups (all P > 0.05) with the exception of LRNC + IPH + Ulcer. The 14 cases of LRNC + IPH + LRNC + IPH + Ulcer (60.87%) in the group with ACI and was significantly greater than the 5 (22.73%) in patients without ACI (P < 0.05). CONCLUSION: It is preliminarily thought that hypertension is the main clinical risk factor for vulnerable carotid plaques with ACI and the combination of plaque volume with vulnerable carotid plaque and LRNC + IPH + Ulcer is a high-risk factor for complicated ACI. It has high clinical therapeutic value due to the accurate diagnosis of responsible vessels and plaques with high-resolution MRI.

Single-pixel imaging of a translational object.

Image-free tracking methods based on single-pixel detectors (SPDs) can track a moving object at a very high frame rate, but they rarely can achieve simultaneous imaging of such an object. In this study, we propose a method for simultaneously obtaining the relative displacements and images of a translational object. Four binary Fourier patterns and two differential Hadamard patterns are used to modulate one frame of the object and then modulated light signals are obtained by SPD. The relative displacements and image of the moving object can be gradually obtained along with the detection. The proposed method does not require any prior knowledge of the object and its motion. The method has been verified by simulations and experiments, achieving a frame rate of 3332 Hz to acquire relative displacements of a translational object at a spatial resolution of 128 × 128 pixels using a 20000-Hz digital micro-mirror device. This proposed method can broaden the application of image-free tracking methods and obtain spatial information about moving objects.

A cluster of two psittacosis cases among women farmers exposed to Chlamydia psittaci-infected domestic poultry in Zhejiang Province, China.

A cluster of Chlamydia psittaci (C. psittaci) cases was reported in Zhejiang Province, China, 2019. This study evaluates the extent of the outbreak and determines the source of infection. Real-time PCR and sequencing of the ompA gene of C. psittaci were performed to identify the cases, the domesticated poultry and close contacts. The index patient was a 76-year-old woman with chronic vertigo, and Case 2 was a 64-year-old female farmer with herpes zoster. Both women bought psittaci-infected chickens or ducks from the same mobile street vendor and raised them for 10 days and 23 days before fever onset. There were no direct contact between the two women. C. psittaci test was positive for the two patients, one sick chicken, three healthy ducks and the vendor's chicken cage. Phylogenetic analysis showed that all seven C. psittaci positive samples carried identical ompA genotype A of C. psittaci. Of all of the patients' 148 close contacts, none tested positive for C. psittaci, or developed acute respiratory symptoms. Both patients were discharged after a 4-week hospital stay. In conclusion, the source of this cluster was the poultry infected with C. psittaci, which occasionally cause infections in farmers, but inter-human transmission seems unlikely.

Comprehensive narrative review of real-world COVID-19 vaccines: viewpoints and opportunities.

Currently, people all over the world have been affected by coronavirus disease 2019 (COVID-19). Fighting against COVID-19 is the top priority for all the countries and nations. The development of a safe and effective COVID-19 vaccine is considered the optimal way of ending the pandemic. Three hundred and 44 vaccines were in development, with 149 undergoing clinical research and 35 authorized for emergency use as to March 15 of 2022. Many studies have shown the effective role of COVID-19 vaccines in preventing SARS-CoV-2 infections as well as serious and fatal COVID-19 cases. However, tough challenges have arisen regarding COVID-19 vaccines, including long-term immunity, emerging COVID-19 variants, and vaccine inequalities. A systematic review was performed of recent COVID-19 vaccine studies, with a focus on vaccine type, efficacy and effectiveness, and protection against SARS-CoV-2 variants, breakthrough infections, safety, deployment and vaccine strategies used in the real-world. Ultimately, there is a need to establish a unified evaluation standard of vaccine effectiveness, monitor vaccine safety and effectiveness, along with the virological characteristics of SARS-CoV-2 variants; and determine the most useful booster schedule. These aspects must be coordinated to ensure timely responses to beneficial or detrimental situations. In the future, global efforts should be directed toward effective and immediate vaccine allocations, improving vaccine coverage, SARS-CoV-2 new variants tracking, and vaccine booster development.

Answering medical questions in Chinese using automatically mined knowledge and deep neural networks: an end-to-end solution.

BACKGROUND: Medical information has rapidly increased on the internet and has become one of the main targets of search engine use. However, medical information on the internet is subject to the problems of quality and accessibility, so ordinary users are unable to obtain answers to their medical questions conveniently. As a solution, researchers build medical question answering (QA) systems. However, research on medical QA in the Chinese language lags behind work on English-based systems. This lag is mainly due to the difficulty of constructing a high-quality knowledge base and the underutilization of medical corpora in the Chinese language. RESULTS: This study developed an end-to-end solution to implement a medical QA system for the Chinese language with low cost and time. First, we created a high-quality medical knowledge graph from hospital data (electronic health/medical records) in a nearly automatic manner that trained a supervised model based on data labeled using bootstrapping techniques. Then, we designed a QA system based on a memory-based neural network and attention mechanism. Finally, we trained the system to generate answers from the knowledge base and a QA corpus on the internet. CONCLUSIONS: Bootstrapping and deep neural network techniques can construct a knowledge graph from electronic health/medical records with satisfactory precision and coverage. Our proposed context bridge mechanisms perform training with a variety of language features. Our QA system can achieve state-of-the-art quality in answering medical questions with constrained topics. As we evaluated, complex Chinese language processing techniques, such as segmentation and parsing, were not necessary for practice and complex architectures were not necessary to build the QA system. Lastly, we created an application using our method for internet QA usage.

Understanding Smartphone Users From Installed App Lists Using Boolean Matrix Factorization.

Smartphones are changing humans' lifestyles. Mobile applications (apps) on smartphones serve as entries for users to access a wide range of services in our daily lives. The apps installed on one's smartphone convey lots of personal information, such as demographics, interests, and needs. This provides a new lens to understand smartphone users. However, it is difficult to compactly characterize a user with his/her installed app list. In this article, a user representation framework is proposed, where we model the underlying relations between apps and users with Boolean matrix factorization (BMF). It builds a compact user subspace by discovering basic components from installed app lists. Each basic component encapsulates a semantic interpretation of a series of special-purpose apps, which is a reflection of user needs and interests. Each user is represented by a linear combination of the semantic basic components. With this user representation framework, we use supervised and unsupervised learning methods to understand users, including mining user attributes, discovering user groups, and labeling semantic tags to users. Extensive experiments were conducted on three data subsets from a large-scale real-world dataset for evaluation, each consisting of installed app lists from over 10 000 users. The results demonstrated the effectiveness of our user representation framework.

Two-dimensional time- and space-resolved diagnostic method for an integrated implosion process.

To precisely measure and evaluate X-ray generation and evolution in a hohlraum during an implosion process, we present a two-dimensional (2D) time- and space-resolved diagnostic method by combining a compressed ultrafast photography (CUP) system and a simplified version of space-resolving flux detector (SSRFD). Numerical experiment results showed that the reconstruction quality of the conventional CUP significantly improved owing to the addition of the external SSRFD, especially when a coded mask with a large pixel size was used in the CUP. Further, the performance of the CUP cooperation with the SSRFD was better than that of adding an external charge-coupled device or streak camera. Compared with existing ultrafast imaging techniques in laser fusion, the proposed method has a prominent advantage of measuring the 2D evolution of implosion by combining high temporal resolution of streak camera and high spatial resolution of SSRFD; moreover, it can provide guidance for designing diagnostic experiments in laser fusion research.

[Relationship of ADC histogram parameters with pathological grade and lymph node metastasis of prostate cancer].

OBJECTIVE: To investigate the relationship between the apparent diffusion coefficient (ADC) histogram parameters based on the whole tumor and the pathological grade and lymph node metastasis (LNM) of PCa. METHODS: This retrospective study included 82 cases of PCa confirmed pathologically and subjected to MRI preoperatively. We obtained a series of ADC histogram parameters, such as ADCmean, ADCmedian, ADC25%, ADC75%, entropy, and histogram width, by processing the ADC images via the Firevoxel Post-Processing and the SPSS24 software. We compared the parameters between the high-risk and low- or moderate-risk groups as well as between the LNM-positive and LNM-negative groups of the patients, and analyzed the diagnostic performance of the parameters with statistically significant differences. RESULTS: The high-risk group, compared with the low- or moderate-risk one, showed a significantly lower ADCmean (ï¼»590 ± 120ï¼½ vs ï¼»837 ± 142ï¼½ ×10－6 mm2/s, P < 0.01), ADCmedian (ï¼»560 ± 117ï¼½ vs ï¼»804 ± 139ï¼½ ×10－6 mm2/s, P < 0.01), ADC25% (ï¼»446.5 ± 98ï¼½ vs ï¼»717 ± 118ï¼½ ×10－6 mm2/, P < 0.01) and ADC75% (ï¼»667 ± 132ï¼½ vs ï¼»931 ± 167ï¼½ ×10－6 mm2/s, P < 0.01). The ADCmean manifested the highest diagnostic performance, with an AUC of 0.907, a sensitivity of 0.933 and a specificity of 0.796. No statistically significant difference was found between the high-risk and the low- or moderate-risk one in entropy (3.58 ± 0.39 vs 3.63 ± 0.42, P = 0.238) or the histogram width (ï¼»540 ± 73ï¼½ vs ï¼»520 ± 65ï¼½ ×10－6 mm2/s, P = 0.086). Both entropy and the histogram width were remarkably higher in the LNM-positive than in the LNM-negative group (3.95 ± 0.41 vs 3.12 ± 0.45, P < 0.01; ï¼»578 ± 59ï¼½ vs ï¼»455 ± 68ï¼½ ×10－6 mm2/s, P < 0.01), and the former had an even higher diagnostic performance, with an AUC of 0.836, a sensitivity of 0.887 and a specificity of 0.781. There were no statistically significant differences between the LNM-positive and LNM-negative groups in the ADCmean (ï¼»768 ± 135ï¼½ vs ï¼»790±128ï¼½ ×10－6 mm2/s, P = 0.402), ADCmedian (ï¼»759 ± 110ï¼½ vs ï¼»775 ± 121ï¼½ ×10－6 mm2/s, P = 0.225), ADC25% (ï¼»643 ± 91ï¼½ vs ï¼»657 ± 89ï¼½ ×10－6 mm2/s, P = 0.654) or ADC75% (ï¼»895 ± 127ï¼½ vs ï¼»872 ± 129ï¼½ ×10－6 mm2/s, P = 0.926). CONCLUSIONS: ADC histogram parameters are related to pathological grade and LNM of PCa, and the analysis of the ADC histogram based on the whole tumor has an important value for preoperative evaluation and prognostic estimation of the malignancy.

Three-dimensional reconstruction of integrated implosion targets from simulated small-angle pinhole images.

To indirectly evaluate the asymmetry of the radiation drive under limited measurement conditions in inertial confinement fusion research, we have proposed an integral method to approximate the three-dimensional self-radiation distribution of the compressed plasma core using only four pinhole images from a single laser entrance hole at a maximum projection angle of 10°. The simultaneous algebraic reconstruction technique (SART) that uses spatial constraints provided by the prior structural information and the central pinhole image is utilized in the simulation. The simulation results showed that the normalized mean square deviation between the original distribution and reconstruction results of the central radiation area of the simulated cavity was 0.4401, and the structural similarity of the cavity radiation distribution was 0.5566. Meanwhile, using more diagnostic holes could achieve better structural similarity and lower reconstruction error. In addition, the results indicated that our new proposed method could reconstruct the distribution of a compressed plasma core in a vacuum hohlraum with high accuracy.

High-purity foam-like micron-sized gold cage material with tunable plasmon properties.

Herein, by growing mono dispersed gold nanoparticles (MNPAu) on the surface of polystyrene (PS)/nanogold (Au) core-shell composites (PS@Au), we successfully synthesized a micron-sized gold cage (2.6-10.7 µm), referred to as PS@Au@MNPAu for the first time. The new micron-gold cage materials exhibit broadband absorption range from near-ultraviolet to near-infrared, which is unlike the conventional nanogold core-shell structure. The uniform growth of MNPAu on the surface forms a new photonic crystal spectrum. The strong coupling of the spectra causes anomalous absorption in the ultraviolet-near infrared band (400-900 nm). Furthermore, by removing the PS core, a nanogold cavity structure referred to as Au@MNPAu was prepared. This structure demonstrated a high purity (> 97 wt%), low density (9-223 mg/cm3), and high specific surface area (854 m2/g). As the purification process progressed, the MNPAu coupling on the surface of the micro-gold cage strengthened, resulting in the formation of peaks around 370 nm, plasma resonant peaks around 495 nm, and structural bands of photonic crystal peaks around 850 nm. The micron-sized gold cage provides hybridized and tunable plasmonic systems. The theoretical simulations indicate that this plasmon anomalous absorption phenomena can be understood as the novel form of the topological structural transitions near the percolation threshold, which is consistent experimental measurements.

The prevalence and risk factors of PTSD symptoms among medical assistance workers during the COVID-19 pandemic.

BACKGROUND: The outbreak of COVID 19 in December 2019 spread quickly and overwhelmed the local healthcare system of the epicenter. A total of 346 medical assistance teams with 42,600 reserve medics were mobilized from around the country for emergency assistance. This study aims to examine the incidence of mental health symptoms and predictors of Post-Traumatic Stress Disorders (PTSD) symptoms among the reserve medics working in Wuhan, the capital city of Hubei Province. METHODS: An anonymous survey was conducted from April 4-6, 2020 among the medics upon their returning home. A total of 225 surveys were returned with a response rate of 83%. The survey consisted of questionnaires about the medics' demographic information, social relations, mental health status (e.g. IES-R and DASS-21) and other work-related information. Mann-Whitney U Test, Kruskal-Wallis Test and hierarchical logistic regression models were used to examine the predictors of PTSD symptoms. RESULTS: During the stay in Wuhan, the medics experienced high levels of depression (46.7%), anxiety (35.6%) and stress symptoms (16.0%). Upon returning home, the overall prevalence of clinically concerned PTSD symptoms was as high as 31.6%. Further analyses revealed that PTSD symptoms and its subscales were significantly associated with age, collegial relationship and mental health status during their service. CONCLUSION: The reserve medics reported a high prevalence of depression, anxiety and stress as well as clinically concerned PTSD symptoms. Comprehensive screening and intervention programs should be in place to help reserve medics cope with mental health challenges and build resilience during the COVID 19 pandemic.

Taizhou's COVID-19 prevention and control experience with telemedicine features.

The outbreak of coronavirus disease 2019 (COVID-19) has spread rapidly around the world. As of May 30, 2020, a total of 84 568 confirmed COVID-19 cases have been recorded in China, with a mortality rate of approximately 5.5%. Taizhou is a prefecture-level city in Zhejiang Province. A total of 146 cases were diagnosed in this epidemic, with a fatality rate of 0%. This condition is due to the establishment of an "Internet +" diagnosis and treatment model based on online medical application (APP), telemedicine, WeChat service, and consultation hotline in Taizhou. Taizhou led in opening the "COVID-19 Prevention and Treatment Special Line" in China, which is conducive to pre-hospital screening, suppressing social panic, and clinical support. Hospitals also carried out related online lectures and popularization of science. We summarize Taizhou's COVID-19 prevention and control experience with telemedicine features, with a view to providing reference for the control of the epidemic at home and abroad.

Comparison of different BMI cut-offs to screen for child and adolescent obesity in urban China.

OBJECTIVES: To determine which set of BMI cut-offs is the most appropriate to define child and adolescent obesity in urban China. DESIGN: A cross-sectional study was carried out between 1 November and 31 December in 2017. SETTING: Community Healthcare Center in Minhang District, Shanghai, China. PARTICIPANTS: A total of 12 426 children and adolescents aged 7-17 years were selected by cluster random sampling. Bioelectrical impedance analysis was the gold standard to measure body composition. RESULTS: Comparisons of three sets of BMI cut-offs by sensitivity and κ value revealed that the Working Group on Obesity in China (WGOC) (sensitivity 39·9-84·0 %; κ 0·51-0·79) and WHO (sensitivity 25·5-74·5 %; κ 0·35-0·78) cut-offs were not superior to the International Obesity Task Force (IOTF) (sensitivity 47·9-92·4 %; κ 0·58-0·85) cut-offs across all subgroups. The WGOC and WHO cut-offs yielded higher misclassification rates, in the worst case, categorising 11·2 % of girls with high adiposity as normal and 44·4 % of them as overweight, while the IOTF cut-offs categorised 2·3 % as normal and 30·7 % as overweight. Individuals who were classified by the IOTF cut-offs as overweight had the lowest ratios of high adiposity (4·2-41·6 %) than by the BMI cut-offs for each subgroup. Among pubertal girls, none of the BMI-based cut-offs indicated excellent agreement with body fat percentage, and κ value of the WHO cut-offs (0·35 (95 % CI 0·29, 0·41)) was lower than the other two sets of BMI cut-offs (all P < 0·001). CONCLUSIONS: The IOTF cut-offs for Asian should be recommended for child obesity screening in urban China. Pubertal individuals need a more accurate indicator of obesity screening.

Flexible wound healing system for pro-regeneration, temperature monitoring and infection early warning.

To break the "Black-Box" status of the wound healing process under traditional dressing, which cannot achieve satisfactory repair outcome of skin wounds, a wound healing system with the abilities of pro-regeneration and real-time monitoring of wound status has become a considerable necessity. Here, by integrating the emerging bioelectronics and software, we created a flexible wound healing system. The hardware system was designed as Band-Aid shaped with a double-layer structure; the upper is the flexible temperature-sensing layer comprising the temperature sensor STH21, power manager circuit and data processing circuit, and the lower is a collagen-chitosan dermal equivalent for skin regeneration. A customized software application (app) installed on a smartphone to receive data from the sensing layer by BLE4.0 can display and analyze real-time wound temperature. Our system had high monitoring sensitivity and stability, good stretchability, excellent reliability and biocompatibility. It was applied to a pig skin wound model to reveal temperature fluctuation during the entire wound regeneration process. As a credible reference and foundation for further early warning of an adverse event, three main phases of temperature fluctuation were found: the rising phase (below 39 °C), plateau phase (39-39.5 °C), and falling phase (below 39 °C), which were accompanied by significant wound biological events, including inflammatory cell infiltration, angiogenesis and wound healing. Furthermore, verified by wound infection models of different healing phases and wound Gram's staining, early warning ahead of serious infection was realized with the use of a customized app's alarm.