Gut Microbiota: A Key Regulator in the Effects of Environmental Hazards on Modulates Insulin Resistance

Insulin resistance is a hallmark of Alzheimer’s disease (AD), type II diabetes (T2D), and Parkinson’s disease (PD). Emerging evidence indicates that these disorders are typically characterized by alterations in the gut microbiota composition, diversity, and their metabolites. Currently, it is understood that environmental hazards including ionizing radiation, toxic heavy metals, pesticides, particle matter, and polycyclic aromatic hydrocarbons are capable of interacting with gut microbiota and have a non-beneficial health effect. Based on the current study, we propose the hypothesis of “gut microenvironment baseline drift”. According to this “baseline drift” theory, gut microbiota is a temporarily combined cluster of species sharing the same environmental stresses for a short period, which would change quickly under the influence of different environmental factors. This indicates that the microbial species in the gut do not have a long-term relationship; any split, division, or recombination may occur in different environments. Nonetheless, the “baseline drift” theory considers the critical role of the response of the whole gut microbiome. Undoubtedly, this hypothesis implies that the gut microbiota response is not merely a “cross junction” switch; in contrast, the human health or disease is a result of a rich palette of gut-microbiota-driven multiple-pathway responses. In summary, environmental factors, including hazardous and normal factors, are critical to the biological impact of the gut microbiota responses and the dual effect of the gut microbiota on the regulation of biological functions. Novel appreciation of the role of gut microbiota and environmental hazards in the insulin resistance would shed new light on insulin resistance and also promote the development of new research direction and new overcoming strategies for patients.

A two-stage method for real-time baseline drift compensation in gas sensors

Baseline drift caused by slowly changing environment and other instability factors affects significantly the performance of gas sensors, resulting in reduced accuracy of gas classification and quantification of the electronic nose. In this work, a two-stage method is proposed for real-time sensor baseline drift compensation based on estimation theory and piecewise linear approximation. In the first stage, the linear information from the baseline before exposure is extracted for prediction. The second stage continuously predicts changing linear parameters during exposure by combining temperature change information and time series information, and then the baseline drift is compensated by subtracting the predicted baseline from the real sensor response. The proposed method is compared to three efficient algorithms and the experiments are conducted towards two simulated datasets and two surface acoustic wave sensor datasets. The experimental results prove the effectiveness of the proposed algorithm. Moreover, the proposed method can recover the true response signal under different ambient temperatures in real-time, which can guide the future design of low-power and low-cost rapid detection systems.

Study on Motion Management of Lung Cancer Treated by CyberKnife: Based on Tumor Position

Purpose: We investigated the movement characteristics of lung cancers and the clinical accuracy of tracking lung tumors with Synchrony Respiratory Tracking System (SRTs) during the CyberKnife treatment. We also explored the influencing factors of accuracy. These data provided the appropriate expansion margins of patients with different respiratory characteristics, which was helpful to realize the personalized design of treatment plans of CyberKnife. Methods and Materials: 73 patients with lung cancer treated with CyberKnife SRTs were selected retrospectively for this study. The patient's age, gender, respiratory characteristics and tumor datas (tumor size, anatomical position and geometric position) were recorded. During treatment, the deviation was checked every 45 s and compensated by the synchronous respiratory tracking system.Results: The total mean motion amplitudes and standard deviations of lung tumors in superior-inferior (SI), left-right (LR), and anterior-posterior (AP) directions were 4.15 ± 3.47 mm, 3.98 ± 3.21 mm and 3.79 ± 2.73 mm, respectively. The overall mean correlation errors and standard deviations were 0.86 ± 0.45 mm, 1.04 ± 0.76 mm and 0.70 ± 0.47 mm, respectively. The overall mean prediction errors and standard deviations were 0.18 ± 0.17 mm, 0.35 ± 0.39 mm and 0.35 ± 0.42 mm, respectively. The correlation errors of LR direction were less correlated with the geometric position of the tumor (r = 0.38), and not correlated with the anatomical position of the tumor (r < 0.3). The prediction errors were moderately correlated with the respiratory amplitude (r = 0.588), and less correlated with the baseline drift and the motion amplitude of the tumor (r = 0.407 and 0.365, respectively).Conclusions: The patient’s respiratory amplitude, the tumor motion amplitude, the tumor baseline drift and geometric position were the main factors affecting the tracking accuracy. Tumors at different geometric positions should be treated differently to ensure sufficient dose coverage of the lung tumor target.

Prediction of H-Type Hypertension Based on Pulse Wave MFCC Features Using Mixed Attention Mechanism

H-type hypertension increases the risks of stroke and cardiovascular disease, posing a great threat to human health. Pulse diagnosis in traditional Chinese medicine ( TCM ) combined with deep learning can independently predict suspected H-type hypertension patients by analyzing their pulse physiological activities. However, the traditional time-domain feature extraction has a higher noise and baseline drift, affecting the classification accuracy. In this literature, we propose an effective prediction on frequency-domain pulse wave features. First, we filter time-domain pulse waves via removal of high-frequency noises and baseline shift. Second, Hilbert-Huang Transform is explored to transform time-domain pulse wave into frequency-domain waveform characterized by Mel-frequency cepstral coefficients (MFCC). Finally, an improved BiLSTM model, combined with mixed attention mechanism is built to applied for prediction of H-type hypertension. With 337 clinical cases from Longhua Hospital affiliated to Shanghai University of TCM and Hospital of Integrated Traditional Chinese and Western Medicine, the 3-fold cross-validation results show that sensitivity, specificity, accuracy, F1-score and AUC reaches 93.48%, 95.27%, 97.48%, 90.77% and 0.9676, respectively. The proposed model achieves better generalization performance than the classical traditional models. In addition, we calculate the feature importance both in time-domain and frequency-domain according to purity of nodes in Random Forest and study the correlations between features and classification that has a good reference value for TCM clinical auxiliary diagnosis.

On-Site Biolayer Interferometry-Based Biosensing of Carbamazepine in Whole Blood of Epileptic Patients

On-site monitoring of carbamazepine (CBZ) that allows rapid, sensitive, automatic, and high-throughput detection directly from whole blood is of urgent demand in current clinical practice for precision medicine. Herein, we developed two types (being indirect vs. direct) of fiber-optic biolayer interferometry (FO-BLI) biosensors for on-site CBZ monitoring. The indirect FO-BLI biosensor preincubated samples with monoclonal antibodies towards CBZ (MA-CBZ), and the mixture competes with immobilized CBZ to bind towards MA-CBZ. The direct FO-BLI biosensor used sample CBZ and CBZ-horseradish peroxidase (CBZ-HRP) conjugate to directly compete for binding with immobilized MA-CBZ, followed by a metal precipitate 3,3′-diaminobenzidine to amplify the signals. Indirect FO-BLI detected CBZ within its therapeutic range and was regenerated up to 12 times with negligible baseline drift, but reported results in 25 min. However, Direct FO-BLI achieved CBZ detection in approximately 7.5 min, down to as low as 10 ng/mL, with good accuracy, specificity and negligible matric interference using a high-salt buffer. Validation of Direct FO-BLI using six paired sera and whole blood from epileptic patients showed excellent agreement with ultra-performance liquid chromatography. Being automated and able to achieve high throughput, Direct FO-BLI proved itself to be more effective for integration into the clinic by delivering CBZ values from whole blood within minutes.

Optical Sensor Using Space-Domain Active Fiber Cavity Ringdown Technique

A novel active fiber cavity ringdown (FCRD) technique using frequency-shifted interferometry (FSI) is proposed for the first time. Using this scheme, external parameters can be monitored in the space domain by measuring the ringdown distance instead of ringdown time. A bidirectional erbium-doped fiber amplifier (Bi-EDFA) is employed to compensate the inherent cavity loss for achieving higher sensitivity. And two band-pass filters are used to reduce the amplified spontaneous emission (ASE) noise of the Bi-EDFA. Compared with the well-known time-domain active FCRD scheme, our proposed method enables us to avoid using pulsed laser needed in time-domain active FCRD, it uses continuous-wave laser to inject into the fiber cavity and stabilize the optical power in the fiber cavity, which can suppress gain fluctuations of the EDFA and thus improve the detecting precision. Moreover, this novel method enables us to use differential detection method for further reducing the ASE noise, and thus eliminating the baseline drift of ringdown signal. A magnetic field sensor was developed as a proof-of-concept demonstration. The experimental results demonstrate that the proposed sensor with a sensitivity of 0.01537 (1/km·Gs) was achieved. This is the highest magnetic field sensitivity compared to the time-domain active FLRD method. Due to the reduced ASE noise, the stability of the proposed sensing system was also greatly improved.

smBEVO: A computer vision approach to rapid baseline correction of single-molecule time series

Single-molecule time series inform on the dynamics of molecular mechanisms that are occluded in ensemble-averaged measures. Amplitude-based methods and hidden Markov models (HMMs) frequently used for interpreting these time series require removal of low frequency drift that can be difficult to completely avoid in real world experiments. Current approaches for drift correction primarily involve either tedious manual assignment of the baseline or unsupervised frameworks such as infinite HMMs coupled with baseline nodes that are computationally expensive and unreliable. Here, we develop an image-based method for baseline correction using techniques from computer vision such as lane detection and active contours. The approach is remarkably accurate and efficient, allowing for rapid analysis of single-molecule time series contaminated with nearly any type of slow baseline drift.

Automated Medical Care: Bradycardia Detection and Cardiac Monitoring of Preterm Infants

Background and Objectives: Prematurity of birth occurs before the 37th week of gestation and affects up to 10% of births worldwide. It is correlated with critical outcomes; therefore, constant monitoring in neonatal intensive care units or home environments is required. The aim of this work was to develop solutions for remote neonatal intensive supervision systems, which should assist medical diagnosis of premature infants and raise alarm at cardiac abnormalities, such as bradycardia. Additionally, the COVID-19 pandemic has put a worldwide stress upon the medical staff and the management of healthcare units. Materials and Methods: A traditional medical diagnosing scheme was set up, implemented with the aid of powerful mathematical operators. The algorithm was tailored to the infants’ personal ECG characteristics and was tested on real ECG data from the publicly available PhysioNet database “Preterm Infant Cardio-Respiratory Signals Database”. Different processing problems were solved: noise filtering, baseline drift removal, event detection and compression of medical data using the à trous wavelet transform. Results: In all 10 available clinical cases, the bradycardia events annotated by the physicians were correctly detected using the RR intervals. Compressing the ECG signals for remote transmission, we obtained compression ratios (CR) varying from 1.72 to 7.42, with the median CR value around 3. Conclusions: We noticed that a significant amount of noise can be added to a signal while monitoring using standard clinical sensors. We tried to offer solutions for these technical problems. Recent studies have shown that persons infected with the COVID-19 disease are frequently reported to develop cardiovascular symptoms and cardiac arrhythmias. An automatic surveillance system (both for neonates and adults) has a practical medical application. The proposed algorithm is personalized, no fixed reference value being applied, and the algorithm follows the neonate’s cardiac rhythm changes. The performance depends on the characteristics of the input ECG. The signal-to-noise ratio of the processed ECG was improved, with a value of up to 10 dB.

Humidity Dependence of Commercial Thick and Thin-Film MOX Gas Sensors under UV Illumination

Enhancing the performance of a chemo-resistive gas sensor is often challenging due to environmental humidity influencing its sensitivity and baseline resistance. One of the most promising ways of overcoming this challenge is through ultraviolet (UV) illumination of the sensing material. Most research has focused on using UV with in-house developed sensors, which has limited their widespread use. In this work, we have evaluated if UV can enhance the performance of commercially available MOX-based gas sensors. The performance of five different MOX sensors has been evaluated, specifically SGX Microtech MiCS6814 (thin-film triple sensor), FIGARO TGS2620 (n-type thick film), and Alphasense VOC sensor (p-type thick film). These sensors were tested towards isobutylene gas under UV light at different wavelengths (UV-278 nm and UV-365 nm) to investigate its effect on humidity, sensitivity, baseline drift, and recovery time of each sensor. We found the response time of thin-film sensors for reducing gases was improved by 70 s under UV- 365 nm at normal operating temperatures. In addition, all the sensors were left in a dirty environment and the humid-gas testing was repeated. However, due to their robust design, the sensitivity and baseline drift of all the sensors remained the same. This indicates that UV has only limited uses with commercial gas sensors.