An electrochemical sensor based on 2D Zn-MOFs and 2D C-Ti3C2Tx composite materials for rapid and direct detection of various foodborne pathogens.

Rapid screening for foodborne pathogens is crucial for food safety. A rapid and one-step electrochemical sensor has been developed for the detection of Escherichia coli (E. coli), Staphylococcus aureus (S. aureus) and Salmonella typhimurium (S. typhimurium). Through the construction of aptamer/two-dimensional carboxylated Ti3C2Tx (2D C-Ti3C2Tx)/two-dimensional Zn-MOF (2D Zn-MOF) composites, the recognition elements, signal tags, and signal amplifiers are integrated on the electrode surface. Pathogens are selectively captured using the aptamer, which increases the impedance of the electrode surface，leads to a decrease in the 2D Zn-MOF current. Bacteria can be rapidly quantified using a one-step detection method and the replacement of aptamers. The detection limits for E. coli, S. aureus, and S. typhimurium are 6, 5, and 5 CFU·mL-1, respectively. The sensor demonstrated reliable detection capabilities in real-sample testing. Therefore, the one-step sensor based on the 2D Zn-MOF and 2D C-Ti3C2Tx has significant application value in the detection of foodborne pathogens.

Ultrasensitive carbon nanotube-bridged MXene conductive network arrays for one-step homogeneous electrochemical immunosensing of tumor markers.

Developing non-passivating and fully integrated electrode arrays for point-of-care testing of carcinoembryonic antigen (CEA) is crucial, as the serum level of CEA is closely associated with colorectal cancer. Herein, we propose a simple, low-cost, and eco-friendly template-assisted filtration method for the scalable preparation of carbon nanotube-bridged Ti3C2Tx MXene (MX@CNT) electrode arrays with a conductive network. Furthermore, we fabricate a homogeneous electrochemical (HEC) sensor for CEA detection by integrating a magnetic-bead-based alkaline phosphatase-linked immunoassay (MB-aElisa), which enables the in-situ generation of the electroactive substance 1-naphthol (1-NP). Benefiting from the unique electrochemical characteristics of a MX@CNT electrode array, such as ultra-low background signal and superior electrocatalytic activity towards the hydrolyzed 1-NP, the MB-aElisa-based HEC sensor specifically measures CEA within a detection range spanning from 0.005 to 1.0 ng mL-1, achieving a detection limit of 1.6 pg mL-1. Subsequently, this biosensing prototype is successfully utilized for the detection of CEA in serum specimens obtained from colorectal cancer patients. More importantly, the integration of MB-aElisa with a MX@CNT electrode array not only marks a significant advancement but also enables the creation of a one-step homogeneous electrochemical immunosensing platform, serving as a paradigm for the highly sensitive and selective measurement of trace tumor markers in complex biological samples.

Automatic gait events detection with inertial measurement units: healthy subjects and moderate to severe impaired patients.

BACKGROUND: Recently, the use of inertial measurement units (IMUs) in quantitative gait analysis has been widely developed in clinical practice. Numerous methods have been developed for the automatic detection of gait events (GEs). While many of them have achieved high levels of efficiency in healthy subjects, detecting GEs in highly degraded gait from moderate to severely impaired patients remains a challenge. In this paper, we aim to present a method for improving GE detection from IMU recordings in such cases. METHODS: We recorded 10-meter gait IMU signals from 13 healthy subjects, 29 patients with multiple sclerosis, and 21 patients with post-stroke equino varus foot. An instrumented mat was used as the gold standard. Our method detects GEs from filtered acceleration free from gravity and gyration signals. Firstly, we use autocorrelation and pattern detection techniques to identify a reference stride pattern. Next, we apply multiparametric Dynamic Time Warping to annotate this pattern from a model stride, in order to detect all GEs in the signal. RESULTS: We analyzed 16,819 GEs recorded from healthy subjects and achieved an F1-score of 100%, with a median absolute error of 8 ms (IQR [3-13] ms). In multiple sclerosis and equino varus foot cohorts, we analyzed 6067 and 8951 GEs, respectively, with F1-scores of 99.4% and 96.3%, and median absolute errors of 18 ms (IQR [8-39] ms) and 26 ms (IQR [12-50] ms). CONCLUSIONS: Our results are consistent with the state of the art for healthy subjects and demonstrate a good accuracy in GEs detection for pathological patients. Therefore, our proposed method provides an efficient way to detect GEs from IMU signals, even in degraded gaits. However, it should be evaluated in each cohort before being used to ensure its reliability.

Optimized Antibiotic Resistance Genes Monitoring Scenarios Promote Sustainability of Urban Water Cycle.

Dissemination of antibiotic resistance genes (ARGs) in urban water bodies has become a significant environmental and health concern. Many approaches based on real-time quantitative PCR (qPCR) have been developed to offer rapid and highly specific detection of ARGs in water environments, but the complicated and time-consuming procedures have hindered their widespread use. Herein, we developed a facile one-step approach for rapid detection of ARGs by leveraging the trans-cleavage activity of Cas12a and recombinase polymerase amplification (RPA). This efficient method matches the sensitivity and specificity of qPCR and requires no complex equipment. The results show a strong correlation between the prevalence of four ARG markers (ARGs: sul1, qnrA-1, mcr-1, and class 1 integrons: intl1) in tap water, human urine, farm wastewater, hospital wastewater, municipal wastewater treatment plants (WWTPs), and proximate natural aquatic ecosystems, indicating the circulation of ARGs within the urban water cycle. Through monitoring the ARG markers in 18 WWTPs in 9 cities across China during both peak and declining stages of the COVID epidemic, we found an increased detection frequency of mcr-1 and qnrA-1 in wastewater during peak periods. The ARG detection method developed in this work may offer a useful tool for promoting a sustainable urban water cycle.

A method for calculating fall risk parameters from discrete stride time series regardless of sensor placement.

BACKGROUND: To complement traditional clinical fall risk assessments, research is oriented towards adding real-life gait-related fall risk parameters (FRP) using inertial sensors fixed to a specific body position. While fixing the sensor position can facilitate data processing, it can reduce user compliance. A newly proposed step detection method, Smartstep, has been proven to be robust against sensor position and real-life challenges. Moreover, FRP based on step variability calculated from stride times (Standard deviation (SD), Coefficient of Variance (Cov), fractal exponent, and sample entropy of stride duration) proved to be useful to prospectively predict the fall risk. RESEARCH QUESTIONS: To evaluate whether Smartstep is convenient for calculating FRP from different sensor placements. METHODS: 29 elderly performed a 6-minute walking test with IMU placed on the waist and the wrist. FRP were computed from step-time estimated from Smartstep and compared to those obtained from foot-mounted inertial sensors: precision and recall of the step detection, Root mean square error (RMSE) and Intraclass Correlation Coefficient (ICC) of stride durations, and limits of agreement of FRP. RESULTS: The step detection precision and recall were respectively 99.5% and 95.9% for the waist position, and 99.4% and 95.7% for the wrist position. The ICC and RMSE of stride duration were 0.91 and 54 ms respectively for both the waist and the hand position. The limits of agreement of Cov, SD, fractal exponent, and sample entropy of stride duration are respectively 2.15%, 25 ms, 0.3, 0.5 for the waist and 1.6%, 16 ms, 0.23, 0.4 for the hand. SIGNIFICANCE: Robust against the elderly's gait and different body locations, especially the wrist, this method can open doors toward ambulatory measurements of steps, and calculation of different discrete stride-related falling risk indicators.

Simultaneous multiple target detection platform based on vertical flow immunoassay.

In general, vertical flow assay (VFA) has a disadvantage of requiring a complex analysis process that involves manually injecting various reagents (target analyte, washing buffer, detection conjugate, etc.) sequentially. However, in this study, we have developed an innovative paper-based VFA device that replaces the complex analysis process with one-step and enables the detection of multiple targets. The fabrication process of the multi-target detection VFA device is as follows: preparation and pre-treatment of the strip materials, design of strip cartridge, design of the multiple detection VFA device, optimization experiments for strip sample flow rates, determination of device analysis time, determination of device limit of detection (LOD), multiple target signal uniformity experiment, immunoglobulin G (IgG) and C-reactive protein (CRP) antigen-antibody multiple detection experiment, and data extraction and analysis method. The use of paper-based materials enables the device to be produced at cost-effective, and cartridge production allowed for uniform array formation. IgG and CRP are used to evaluate the performance of the device as common biomarkers. The device proposed in this study is currently under research. To validate multiple target detection capability of the VFA device proposed in this study, two types of antigens-antibodies (Human IgG and Human CRP) were employed. The detection limit is 0.15 µg/mL for IgG and 0.19 µg/mL for CRP in naked eye. Furthermore, it was confirmed that there is no cross-reactivity caused by the device structure through IgG and CRP antigens. In conclusion, the VFA device proposed in this study consists of a one-step analysis process, and it has been confirmed that it can detect multiple targets simultaneously.

Introducing surgical intelligence in gynecology: Automated identification of key steps in hysterectomy.

OBJECTIVE: The analysis of surgical videos using artificial intelligence holds great promise for the future of surgery by facilitating the development of surgical best practices, identifying key pitfalls, enhancing situational awareness, and disseminating that information via real-time, intraoperative decision-making. The objective of the present study was to examine the feasibility and accuracy of a novel computer vision algorithm for hysterectomy surgical step identification. METHODS: This was a retrospective study conducted on surgical videos of laparoscopic hysterectomies performed in 277 patients in five medical centers. We used a surgical intelligence platform (Theator Inc.) that employs advanced computer vision and AI technology to automatically capture video data during surgery, deidentify, and upload procedures to a secure cloud infrastructure. Videos were manually annotated with sequential steps of surgery by a team of annotation specialists. Subsequently, a computer vision system was trained to perform automated step detection in hysterectomy. Analyzing automated video annotations in comparison to manual human annotations was used to determine accuracy. RESULTS: The mean duration of the videos was 103 ± 43 min. Accuracy between AI-based predictions and manual human annotations was 93.1% on average. Accuracy was highest for the dissection and mobilization step (96.9%) and lowest for the adhesiolysis step (70.3%). CONCLUSION: The results of the present study demonstrate that a novel AI-based model achieves high accuracy for automated steps identification in hysterectomy. This lays the foundations for the next phase of AI, focused on real-time clinical decision support and prediction of outcome measures, to optimize surgeon workflow and elevate patient care.

Rapid and sensitive detection of nucleic acids using an RAA-CRISPR/Cas12b one-pot detection assay (Rcod).

Rapid, sensitive and specific methods are crucial for nucleic acid detection. CRISPR/Cas12b has recently been widely used in nucleic acid detection. However, due to its thermophagic property, DNA isothermal recombinase-aided amplification (RAA) and subsequent CRISPR/Cas12b detection require two separate reactions, which is cumbersome and inconvenient and may cause aerosol pollution. In this study, we propose an RAA-CRISPR/Cas12b one-pot detection assay (Rcod) for Bordetella pertussis detection without additional amplification product transfer steps. The time from sample processing to response time was less than 30 min using nucleic acid extraction-free method, and the sensitivity reached 0.2 copies/µL. In this system, Alicyclobacillus acidoterrestris Cas12b protein (AacCas12b) exhibited strong and specific trans-cleavage activity at a constant temperature of 37 °C, while the cis-cleavage activity was weak. This characteristic reduces the interference of AacCas12b with nucleic acids in the system. Compared with real-time PCR, our Rcod system detected B. pertussis in 221 clinical samples with a sensitivity and specificity of 97.96 % and 99.19 %, respectively, with nucleic acid extraction-free method. The rapid, sensitive and specific Rcod system provides ideas for the establishment of CRISPR-based one-step nucleic acid detection and may aid the development of reliable point-of-care nucleic acid tests. IMPORTANCE: Pertussis is an acute respiratory infection caused by B. pertussis that is highly contagious and potentially fatal, and early diagnosis is essential for the treatment of whooping cough. In this study, we found that AacCas12b has high and strongly specific trans-cleavage activity at lower temperatures. A RAA-CRISPR/Cas12b one-step detection platform (Rcod) without interference with amplification was developed. In addition, the combination of Rcod and nucleic acid extraction-free method can quickly and accurately detect the qualitative detection of B. pertussis, and the detection results are visualized, which makes the pathogen nucleic acid detection and analysis process simpler, and provides a new method for the rapid clinical diagnosis of B. pertussis.

Dual-signal and one-step monitoring of Staphylococcus aureus in milk using hybridization chain reaction based fluorescent sensor.

Food-borne pathogens in dairy products that was contaminated from raw ingredients or improper food handling can cause a major threaten to human health. Here, to construct the pathogens detection, a dual-signal readout fluorescent switching sensor was designed for one-step determination of Staphylococcus aureus (S. aureus), which was a marker of food contamination. Graphene oxide (GO) was used as a fluorescence quencher, while fluorophore-labeled hairpin DNA was used as a donor, resulting in fluorescence resonance energy transfer (FRET) from the fluorophore to GO (signal off). Enzyme-free hybridization chain reaction could generate remarkable signal amplification, which avoided the nonspecific desorption caused by any enzymatic proteins in GO surface. With the strong binding ability of aptamer to S. aureus, a long bifluorescent molecules-labeled double-stranded DNA product was formed, bringing in dual-signal readout responses (signal on). Consequently, a reliable, sensitive and selective sensor was obtained for one-step quantification of S. aureus concentration from 10 to 108 CFU/mL with a detection limit of 1 CFU/mL. Furthermore, satisfactory stability, reproducibility, specificity and good recovery efficiency in milk samples revealed that the proposed sensor could be served as a prospective tool for food safety analysis.

Typing of cancer cells by microswimmer based on Co-Fe-MOF for one-step simultaneously detect multiple biomarkers.

Capturing, identifying, and counting CTCs cancer cells that have escaped from the tumor and wandered into the bloodstream is a major challenge. We proposed a noval microswimmer dual-mode aptamer (electrochemical and fluorescent)-(Mapt-EF) homogeneous sensor with active capture/controlled release double signaling molecule/separation and release cell based on the Co-Fe-MOF nanomaterial for simultaneous one-step detection of multiple biomarkers protein tyrosine kinase-7 (PTK7), Epithelial cell adhesion molecule (EpCAM), and mucin-1 (MUC1) for diagnosis of multiple cancer cell types. The Co-Fe-MOF is a nano-enzyme capable of catalyzing the decomposition of hydrogen peroxide to release bubbles of oxygen, producing a driving force to conduct hydrogen peroxide through the liquid, and has the capacity to self-decompose during the catalytic process. Phosphoric acid is present in the aptamer chains of PTK7, EpCAM, and MUC1, and the aptamer chains are adsorbed to the surface of the Mapt-EF homogeneous sensor in the form of a gated switch to inhibit the catalytic decomposition activity of hydrogen peroxide. The Mapt-EF homogeneous sensor has the capability to actively target biomarkers that can be entrained by oxygen bubbles without being degraded. The detection time of the sensor was 20 min, the detection limits were 9.6 fg/mL, 8.4 fg/mL and 7.7 fg/mL with the linear range was 0-20 pg/mL, respectively. The Mapt-EF homogeneous sensor has high detection sensitivity, and its detection limit can reach the level of single cell at the lowest. The Mapt-EF homogeneous sensor has great application potential in clinical detection and analysis of tumor cells.

Semi-supervised approach to identify steps, shoes, and gender of older adults under semi-naturalistic conditions using a waist-worn inertial sensor.

An emerging source of information to recognize individuals' characteristics are the walking pattern-related parameters. The elderly can be one of the populations that can benefit most from recognition-based applications, which may help to increase their possibilities of living independently at home. Approaches have been mostly focused on gait events' identification or assessment; nonetheless, such information can also be used to obtain seniors' characteristics that depend on physiological or environmental factors. These factors can be useful to provide a customized assistance based on contextual information. In this paper, we propose a method focused on seniors, to detect steps, and to recognize gender and type of shoes by using only the initial foot contact (IC) data obtained from inertial sensors during semi-controlled walking. Data were collected from 20 older adults who walked at self-speed in a natural environment. The method consists of first clustering the IC using k-means; then, a trained recurrent neural network recognizes gender, type of shoes, and the step phases (IC and other phases); to finally conduct step detection (SD) using a ruled-based method. The method recognizes gender and the type of shoes with an accuracy of 93% and 83.07%, respectively, whereas there were not misrecognitions of the step phases. SD achieved a mean absolute percentage error equal to 0.64%. The good results show that the method is appropriate for users' characteristics recognition applications without depending on assumptions based on individualities. Likewise, the method can be useful to monitor physical activity or systems aimed to keep safe older adults.

Fast and visual detection of nucleic acids using a one-step RPA-CRISPR detection (ORCD) system unrestricted by the PAM.

CRISPR-Cas12a (Cpf1) is widely used for pathogen detection. However, most Cas12a nucleic acid detection methods are limited by a PAM sequence requirement. Moreover, preamplification and Cas12a cleavage are separate. Here, we developed a one-step RPA-CRISPR detection (ORCD) system unrestricted by the PAM sequence with high sensitivity and specificity that offers one-tube, rapid, and visually observable detection of nucleic acids. In this system, Cas12a detection and RPA amplification are performed simultaneously, without separate preamplification and product transfer steps, and 0.2 copies/µL of DNA and 0.4 copies/µL of RNA can be detected. In the ORCD system, the activity of Cas12a is the key to the nucleic acid detection; specifically, reducing Cas12a activity increases the sensitivity of ORCD assay detection of the PAM target. Furthermore, by combining this detection technique with a nucleic acid extraction-free method, our ORCD system can be used to extract, amplify and detect samples within 30 min, as verified with tests of 82 Bordetella pertussis clinical samples with a sensitivity and specificity of 97.30% and 100% compared with PCR. We also tested 13 SARS-CoV-2 samples with RT-ORCD, and the results were consistent with RT-PCR.

Wheel drive-based DNA sensing system for highly specific and rapid one-step detection of MiRNAs at the attomolar level.

With the use of DNA as building blocks, a variety of microRNA amplification-based sensing systems have been developed. Nevertheless, ultrasensitive, selective and rapid detection of microRNAs with a high signal-to-background ratio and point mutation discrimination ability remains a challenge. Herein, we propose a novel wheel drive-based DNA sensing system (NWDS) based on a self-assembled, self-quenched nanoprobe (SQP) to conduct highly specific and ultrasensitive one-step measurement of microRNAs. In this work, a signalling recognition DNA hairpin (DH) sequence with a self-complementary stem domain of 14 base pairs was used, which contained three functional regions, namely a recognition region for the target miRNA-21, a sticky region with 9 complementary nucleotides to the 3'terminus of a DNA wheel (DW) and a region for the hybridization with a quenching DNA primer (DP). The SQP was ingeniously self-assembled at room temperature by the DH and DP, which was capable of eliminating unwanted background signals. MiRNA-21 was employed as a target model to specifically activate the SQP, leading to specific hybridization between the HP and DW. With the assistance of a polymerase, an SQP-based wheel driving took place to induce hybridization/polymerization displacement cycles, initiating target recycling and DP displacement. As a result, a large amount of the newly formed hybrid SQP/DW accumulated to generate a substantially enhanced fluorescence signal. In this way, the newly proposed NWDS exhibits ultrasensitivity with a detection limit of 5.62 aM across a wide linear dynamic response range up to 200 nM, excellent selectivity with the capability to discriminate homologous miRNAs and one-base, two-base and three-base mismatched sequences, and an outstanding analytical performance in complex systems. In addition, the significant simultaneous advantages of one-step operation, rapid detection within 15 min and a high signal-to-background ratio of 26 offer a unique opportunity to promote the early diagnosis of cancer-related diseases and molecular biological analysis.

Quantification of MicroRNAs or Viral RNAs with Microelectrode Sensors Enabled by Electrochemical Signal Amplification.

Quantification of circulating microRNAs (miRNAs) or viral RNAs is of great significance because of their broad relevance to human health. Currently, quantitative reverse transcription polymerase chain reaction (qRT-PCR), as well as microarray and gene sequencing, are considered mainstream techniques for miRNA identification and quantitation and the gold standard for SARS-CoV2 detection in the COVID-19 pandemic. However, these laboratory techniques are challenged by the low levels and wide dynamic range (from aM to nM) of miRNAs in a physiological sample, as well as the difficulty in the implementation in point-of-care settings. Here, we describe a one-step label-free electrochemical sensing technique by assembling self-folded multi-stem DNA-redox probe structure on gold microelectrodes and introducing a reductant, tris(2-carboxyethyl) phosphine hydrochloride (TCEP), in the detection buffer solution to achieve ultrasensitive detection with a detection limit of 0.1 fM that can be further improved if needed.

Microfluidic biosensor for one-step detection of multiplex foodborne bacteria ssDNA simultaneously by smartphone.

As a major threat to food safety due to their pathogenicity, foodborne bacteria have received much attention. In this paper, we present a one-step and wash-free microfluidic biosensor platform by smartphone for simultaneous multiple foodborne bacteria target single-stranded DNA (ssDNA) detection. This technology is based on the fluorescence resonance energy transfer (FRET) between the graphene oxide (GO) and fluorescence molecules modified capture ssDNA of the target bacteria ssDNA (ctDNA) which were coated on the microfluidic chips. The fluorescence recovery was recorded by a smartphone fluorescent detector. With an optimal analytical performance, the platform realized the detection of four kinds of bacteria ssDNA simultaneously within 5 min, with the limits of detection (LODs) of 0.17, 0.18, 0.27, and 0.17 nM, respectively. And the throughput analysis of trace amounts of foodborne bacteria ssDNA in milk and water samples were successfully detected. This one-step and wash-free microfluidic biosensor can be used as a tool for food safety analysis.

Ultrasensitive Detection of C-Reactive Protein by a Novel Nanoplasmonic Immunoturbidimetry Assay.

Nanotechnology has attracted much attention, and may become the key to a whole new world in the fields of food, agriculture, building materials, machinery, medicine, and electrical engineering, because of its unique physical and chemical properties, including high surface area and outstanding electrical and optical properties. The bottom-up approach in nanofabrication involves the growth of particles, and we were inspired to propose a novel nanoplasmonic method to detect the formation of nanoparticles in real time. This innovative idea may contribute to the promotion of nanotechnology development. An increase in nanometer particle size leads to optical extinction or density (OD)-value changes in our nanosensor chip at a specific wavelength measured in a generic microplate reader. Moreover, in applying this method, an ultrasensitive nanoplasmonic immunoturbidimetry assay (NanoPITA) was carried out for the high-throughput quantification of hypersensitive C-reactive protein (CRP), a well-known biomarker of cardiovascular, inflammatory, and tumor diseases. The one-step detection of the CRP concentration was completed in 10 min with high fidelity, using the endpoint analysis method. The new NanoPITA method not only produced a linear range from 1 ng/mL to 500 ng/mL CRP with the detection limit reduced to 0.54 ng/mL, which was an improvement of over 1000 times, with respect to regular immunoturbidity measurement, but was also effective in blood detection. This attractive method, combined with surface plasmon resonance and immunoturbidimetry, may become a new technology platform in the application of biological detection.

One-step detection of alpha fetal protein based on gold microelectrode through square wave voltammetry.

The detection of tumor markers in blood samples with high efficiency and sensitivity is in urgent need. In this work, a one-step quantitative detection assay for alpha fetal protein (AFP) based on gold microelectrode which is denoted as AuµE through square wave voltammetry using [Fe(CN)6]3-/4- as mediator was developed. As the biorecognition element of the assay, sulfydryl-modified AFP aptamer could be directly conjugated onto the surface of the AuµE, which could capture AFP with high specificity, and this attachment would cause the decrease of the capacitive current of the cyclic voltammetry due to the reduction of the active area of the electrodes. Under the optimized conditions, the AuµE aptasensor exhibited a linear detection range for AFP from 10-10 to 10-7 g/mL (S = 7.6 nA/dec, R2 = 0.991), and the detection limit is 2.5 × 10-11 g/mL. The AuµEs aptasensor demonstrates good selectivity against other types of proteins and small molecules, and has good reproducibility. The real blood samples were used for detection of AFP using the AuµEs aptasensor, the results agree well with those provided by the hospital through electrochemiluminescence method. Herein, the proposed one-step detection assay has a great application potential in point-of-care clinical diagnostics.

Field based assessment of a tri-axial accelerometers validity to identify steps and reliability to quantify external load.

Background: The monitoring of accelerometry derived load has received increased attention in recent years. However, the ability of such measures to quantify training load during sport-related activities is not well established. Thus, the current study aimed to assess the validity and reliability of tri-axial accelerometers to identify step count and quantify external load during several locomotor conditions including walking, jogging, and running. Method: Thirty physically active college students (height = 176.8 ± 6.1 cm, weight = 82.3 ± 12.8 kg) participated. Acceleration data was collected via two tri-axial accelerometers (Device A and B) sampling at 100 Hz, mounted closely together at the xiphoid process. Each participant completed two trials of straight-line walking, jogging, and running on a 20 m course. Device A was used to assess accelerometer validity to identify step count and the test-retest reliability of the instrument to quantify the external load. Device A and Device B were used to assess inter-device reliability. The reliability of accelerometry-derived metrics Impulse Load (IL) and Magnitude g (MAG) were assessed. Results: The instrument demonstrated a positive predictive value (PPV) ranging between 96.98%-99.41% and an agreement ranging between 93.08%-96.29% for step detection during all conditions. Good test-retest reliability was found with a coefficient of variation (CV) <5% for IL and MAG during all locomotor conditions. Good inter-device reliability was also found for all locomotor conditions (IL and MAG CV < 5%). Conclusion: This research indicates that tri-axial accelerometers can be used to identify steps and quantify external load when movement is completed at a range of speeds.

Step Detection Accuracy and Energy Expenditure Estimation at Different Speeds by Three Accelerometers in a Controlled Environment in Overweight/Obese Subjects.

Our aim was to compare three research-grade accelerometers for their accuracy in step detection and energy expenditure (EE) estimation in a laboratory setting, at different speeds, especially in overweight/obese participants. Forty-eight overweight/obese subjects participated. Participants performed an exercise routine on a treadmill with six different speeds (1.5, 3, 4.5, 6, 7.5, and 9 km/h) for 4 min each. The exercise was recorded on video and subjects wore three accelerometers during the exercise: Sartorio Xelometer (SX, hip), activPAL (AP, thigh), and ActiGraph GT3X (AG, hip), and energy expenditure (EE) was estimated using indirect calorimetry for comparisons. For step detection, speed-wise mean absolute percentage errors for the SX ranged between 9.73-2.26, 6.39-0.95 for the AP, and 88.69-2.63 for the AG. The activPALs step detection was the most accurate. For EE estimation, the ranges were 21.41-15.15 for the SX, 57.38-12.36 for the AP, and 59.45-28.92 for the AG. All EE estimation errors were due to underestimation. All three devices were accurate in detecting steps when speed exceeded 4 km/h and inaccurate in EE estimation regardless of speed. Our results will guide users to recognize the differences, weaknesses, and strengths of the accelerometer devices and their algorithms.

One-step homogeneous micro-orifice resistance immunoassay for detection of chlorpyrifos in orange samples.

In this work, a one-step homogeneous micro-orifice resistance immunoassay has been proposed for chlorpyrifos detection by integrating functionalized polystyrene (PS) microsphere probes with particle counting technology. The particle counter is highly sensitive and accurate for detecting the state of PS microspheres, where the particles of different states exhibit significant differences in resistance. The state of the functionalized PS microspheres is altered from dispersed to aggregated during the antigen-antibody recognition. Based on the degree of aggregation of the functionalized PS microsphere probes, chlorpyrifos can be quantitatively detected through the competitive immune response between PS antibodies and PS complete antigens. This one-step homogeneous micro-orifice resistance immunoassay simplified the procedures and greatly increased the sensitivity of detection, which has been successfully applied to detect chlorpyrifos in orange samples within 0.5 h, with the detection limit of 0.058 ng/mL.