Highly Sensitive Fiber Crossbar Sensors Enabled by Second-Order Synergistic Effect of Air Capacitance and Equipotential Body.

Fiber crossbars, an emerging electronic device, have become the most promising basic unit for advanced smart textiles. The demand for highly sensitive fiber crossbar sensors (FCSs) in wearable electronics is increased. However, the unique structure of FCSs presents challenges in replicating existing sensitivity enhancement strategies. Aiming at the sensitivity of fiber crossbar sensors, a second-order synergistic strategy is proposed that combines air capacitance and equipotential bodies, resulting in a remarkable sensitivity enhancement of over 20 times for FCSs. This strategy offers a promising avenue for the design and fabrication of FCSs that do not depend on intricate microstructures. Furthermore, the integrative structure of core-sheath fibers ensures a robust interface, leading to a low hysteresis of only 2.33% and exceptional stability. The outstanding capacitive response performance of FCSs allows them to effectively capture weak signals such as pulses and sounds. This capability opens up possibilities for the application of FCSs in personalized health management, as demonstrated by wireless monitoring systems based on pulse signals.

Environmental assessment of fabric wet processing from gate-to-gate perspective: Comparative study of weaving and materials.

Textile industry has yet to be developed beyond low efficiency, high resources consumption, and toxic emissions, with wet processing process a dominant contributor to resources consumption and pollution emissions. Recognition of the environmental impact of the representative wet processing is essential to achieve eco-friendly development of textile industry. Using Life Cycle Assessment (LCA), this study addressed the environmental impacts of wet processing of woven/knitted cotton and polyester fabrics from 4 textile enterprises in China by deploying gate to gate system boundary. One ton of grey cloth was chosen as the functional unit. Eighteen midpoint impact categories and three endpoint impact categories were assessed via ReCiPe 2016 v1.1 (H) method. The results indicated "dyeing unit" as the dominant unit for all the impact categories at the midpoint, which was mainly derived from electricity consumed by cotton wet processing and detergents used in polyester wet processing. Among 4 different fabric wet processing, woven polyester wet processing exhibited the highest impact, while the least impact was assigned to knitted cotton. In the midpoint categories of water use, dyeing unit was also the major contributor in wet processing of knitted cotton (41.20 m3) and knitted polyester (44.70 m3). Pretreatment accounted for an overwhelming percentage of water use in woven cotton (48.00 %) and woven polyester (56.00 %). Woven polyester wet processing was also the most energy-intensive and resource-consuming industry among all scenarios, with a 3.37-fold higher fossil resource scarcity per ton of fabric compared with woven cotton. The results recommend measures for cleaner production in the wet processing.

Highly Sensitive Capacitive Fiber Pressure Sensors Enabled by Electrode and Dielectric Layer Regulation.

Capacitive pressure sensors play an important role in the field of flexible electronics. Despite significant advances in two-dimensional (2D) soft pressure sensors, one-dimensional (1D) fiber electronics are still struggling. Due to differences in structure, the theoretical research of 2D sensors has difficulty guiding the design of 1D sensors. The multiple response factors of 1D sensors and the capacitive response mechanism have not been explored. Fiber sensors urgently need a tailor-made theoretical research and development path. In this regard, we established a fiber pressure-sensing platform using a coaxial wet spinning process. Aiming at the two problems of the soft electrode modulus and dielectric layer thickness, the conclusions are drawn from three aspects: model analysis, experimental verification, and formula derivation. It makes up some theoretical blanks of capacitive fiber pressure sensors. Through the self-regulation of these two factors without a complex structural design, the sensitivity can be significantly improved. This provides a great reference for the design and development of fiber pressure sensors. Besides, taking advantage of the scalability and easy integration of 1D electronics, multipoint sensors prepared by fibers have verified their application potential in health monitoring, human-machine interface, and motion behavior analysis.

Highly Sensitive Fiber Pressure Sensors over a Wide Pressure Range Enabled by Resistive-Capacitive Hybrid Response.

Soft capacitive pressure sensors with high performance are becoming increasingly in demand in the emerging flexible wearable field. While capacitive fiber pressure sensors have achieved high sensitivity, their sensitivity range is limited to low-pressure levels. As fiber sensors typically require preloading and fixation, this narrow range of high sensitivity poses a challenge for practical applications. To overcome this limitation, the study proposes resistive-capacitive hybrid response fiber pressure sensors (HFPSs) with three-layer core-sheath structures. The trigger and sensitivity enhancement mechanisms of the hybrid response are determined through model analysis and experimental verification. By adjustment of the sensitivity enhancement range of the hybrid response, the sensitivity attenuation of HFPSs is alleviated significantly. The obtained results demonstrate that HFPSs have excellent characteristics such as fast response, low hysteresis, wide response frequency, small signal drift, and good durability. The hybrid response enhances the practical sensitivity of HFPSs for various applications. With enhanced sensitivity, HFPSs can effectively monitor pulse signals at preloads ranging from 0 to 22.7 kPa. This wide range of preloads improves the fault tolerance of pulse monitoring and expands the potential application scenarios of fiber pressure sensors.

[Development of acute kidney injury prognostic model for critically ill patients based on MIMIC-III database].

OBJECTIVE: To investigate the risk factors affecting the prognosis of patients with acute kidney injury (AKI) in the intensive care unit (ICU) based on the Medical Information Mart for Intensive Care III (MIMIC-III) database, and to establish a prognostic model for AKI. METHODS: Patients (aged ≥ 18 years) with acute renal failure, admitted to the ICU for the first time, and had complete hospital records (the RIFLE diagnostic criteria were used in the database, and the diagnosis was expressed as AKI in this article) were screened from MIMIC-III database according to diagnostic codes. Patients were divided into two groups based on survival state at discharge, and the general information, underlying diseases, injury factors, vital signs and laboratory indicators within 24 hours after AKI, related intervention and prognostic indicators were analyzed. Univariate and multivariate Logistic regression analysis were used to determine the risk factors affecting mortality in patients with AKI and established a prediction model. The receiver operator characteristic curve (ROC curve) was used to analyze the predictive value of the prediction model for the prognosis of AKI patients. RESULTS: There were 4 554 patients with AKI included and 862 died, with mortality of 18.93%. Univariate Logistic regression analysis was performed for factors that might be associated with death in AKI patients, and the results showed that age, hypertension, lymphoma, metastatic carcinoma, vancomycin, aspirin, coagulation abnormalities, cardiac arrest, sepsis or septic shock, invasive mechanical ventilation, white blood cell count (WBC), platelet count (PLT), K+, blood urea nitrogen (BUN), total bilirubin (TBil), renal replacement therapy (RRT) and length of stay (LOS) were independent risk factors [odds ratio (OR) and 95% confidence interval (95%CI) were 1.002 (1.001-1.003), 0.764 (0.618-0.819), 1.749 (1.112-2.752), 2.606 (1.968-3.451), 1.779 (1.529-2.071), 0.689 (0.563-0.842), 1.871 (1.590-2.201), 2.468 (1.209-5.036), 2.610 (2.226-3.060), 2.154 (1.853-2.505), 1.105 (1.009-1.021), 0.998 (0.997-0.998), 1.132 (1.057-1.212), 1.008 (1.006-1.011), 1.061 (1.049-1.073), 2.142 (1.793-2.997), 0.805 (0.778-1.113), all P < 0.05]. Further binary Logistic regression analysis showed that lymphoma, metastatic cancer, vancomycin, cardiac arrest, sepsis or septic shock, coagulation dysfunction, invasive mechanical ventilation, increased BUN, increased TBil, increased or decreased blood K+ and increased WBC were independent risk factors for death [ß values were 0.636, 1.005, 0.207, 0.894, 0.787, 0.346, 0.686, 0.006, 0.051, 0.085, and 0.009; OR and 95%CI were 1.889 (1.177-3.031), 2.733 (2.027-3.683), 1.229 (1.040-1.453), 2.445 (1.165-5.133), 2.197 (1.850-2.610), 1.413 (1.183-1.689), 1.987 (1.688-2.338), 1.006 (1.003-1.009), 1.052 (1.039-1.065), 1.089 (1.008-1.176), and 1.009 (1.004-1.015), respectively, all P < 0.05]. The Hosmer-Lemeshow test showed that the AKI prognostic model was able to fit the observed data well (P = 0.604). ROC curve analysis showed that the area under ROC curve (AUC) of the AKI prognostic model was 0.716 (95%CI was 0.697-0.735), when the cut-off value was 0.320, the sensitivity was 71.9%, the specificity was 60.1%, the positive likelihood ratio was 1.80, and the negative likelihood ratio was 0.47. CONCLUSIONS: The prognostic prediction model of AKI in critically ill patients established and based on the MIMIC-III database may have practical significance for prognostic risk assessment of AKI and later intervention.

Evaluation of antimicrobial peptide LL-37 for treatment of Staphylococcus aureus biofilm on titanium plate.

ABSTRACT: The antimicrobial peptide LL-37 belongs to the cathelicidin family and is one of the few human bactericidal peptides with potent antistaphylococcal activity. Staphylococcus aureus is one of the main infection bacteria in orthopedic implant therapy. Biofilm formation after bacterial infection brings more and more severe test for clinical antiinfection treatment.However, there are few studies on LL-37 in S. aureus infection of prosthesis. In this work, addition to research the antibacterial activity and the inhibitory effect on bacterial adhesion of LL-37, an in vitro model of S. aureus biofilm formation on titanium alloy surface was established to observe the inhibitory effect of LL-37.The results showed that LL-37 has a strong antibacterial effect on S. aureus in vitro, and the minimum inhibitory concentration (MIC) is about 0.62 µΜ. Moreover, LL-37 has significant impact on the adhesion of S. aureus when the concentration ≥0.16 µM and significant anti-staphylococcal biofilm effects on static biofilm models at the concentration of 0.31 to 10 µM. Additionally, LL-37 at 5 µM had a significant destructive effect on S. aureus biofilm (P < .05) that formed on the titanium alloy surface.This study further confirmed the role of LL-37 in the process of S. aureus infection, including antimicrobial activities, inhibition of bacterial adhesion, and inhibition of mature biofilm. LL-37 can significantly destroy the stable biofilm structure on the titanium alloy surface in vitro, which may provide a new way for refractory infection caused by S. aureus in titanium alloy prosthesis infection.

Rhotekin 2 silencing inhibits proliferation and induces apoptosis in human osteosarcoma cells.

Human osteosarcoma is the most frequent primary malignant of bone, and often occurs in adolescents. However, molecular mechanism of this disease remains unclear. In the present study, we found that the level of Rhotekin 2 (RTKN2) was up-regulated in osteosarcoma tissues and cell lines. In addition, silencing of RTKN2 of human osteosarcoma cell lines U2OS, inhibited proliferation, and induced G1 phase cell cycle arrest via reducing the level of the cyclin-dependent kinase 2 (CDK2). Furthermore, RTKN2 knockdown in the U2OS cells induced apoptosis by increasing the level of Bax and decreasing the level of Bcl2. These results suggested that RTKN2 is involved in the progression of human osteosarcoma, and may be a potential therapeutic target.

A study of the interactions between carboplatin and blood plasma proteins using size exclusion chromatography coupled to inductively coupled plasma mass spectrometry.

To study the carboplatin-protein interaction, a sensitive method using size exclusion chromatography coupled to inductively coupled plasma mass spectrometry (SEC-ICP-MS) was developed. The complexes formed between plasma proteins and carboplatin were monitored and identified with this method. Composite blood plasma samples from patients who were undergoing chemotherapy were analyzed, and carboplatin was found to bind plasma proteins. In addition, blank plasma samples were spiked with carboplatin and were analyzed as a time course study, and the results confirmed that carboplatin formed complexes with plasma proteins, primarily albumin and gamma-globulin. To further substantiate the study, these two proteins were incubated with carboplatin. The binding between carboplatin and these proteins was then characterized qualitatively and quantitatively. In addition to a one-to-one binding of Pt to protein, protein aggregation was observed. The kinetics of the binding process of carboplatin to albumin and gamma-globulin was also studied. The initial reaction rate constant of carboplatin binding to albumin was determined to be 0.74 M(-1) min(-1), while that for gamma-globulin was 1.01 M(-1) min(-1), which are both lower than the rate constant of the cisplatin-albumin reaction previously reported.

Arsenic speciation analysis of human urine using ion exchange chromatography coupled to inductively coupled plasma mass spectrometry.

A sensitive and robust method for the determination of seven inorganic and organic arsenic species was developed using ion exchange chromatography combined with inductively coupled plasma mass spectrometry (IC-ICP-MS). Both anion and cation exchange columns were used in a complementary fashion. Arsenite (As(III)), arsenate (As(V)), monomethylarsonic acid (MMA(V)) and dimethylarsinic acid (DMA(V)) were selectively separated by an anion exchange column using sodium hydroxide (NaOH) gradient elution, while monomethylarsonous acid (MMA(III)), dimethylarsinous acid (DMA(III)) and arsenobetaine (AsB) were separated by a cation exchange column using 70 mM nitric acid as the mobile phase. Baseline separation, high repeatability and low detection limits (0.10-0.75 ng mL(-1)) were achieved. The spiked urine samples were analyzed with this method to evaluate the matrix effect on the method. The results suggest 1-10 dilutions should be made to urine samples before sample injection for the anion exchange analysis to minimize the matrix effect. To validate the method, a new standard reference material (NIST SRM-2670a) was also analyzed. The arsenic species in NIST SRM-2670a were determined by this method, and the sum of their concentrations agreed well with the total arsenic content certified for NIST SRM-2670a. Moreover, this method was applied to measure arsenic species in urine samples from one subject living in New Jersey who drank well water contaminated with arsenic. By this method, two key arsenic metabolites, MMA(III) and DMA(III), were found to be present in these urine samples, which has previously been rarely reported.