Predictors of Clinical Outcome in Women with Pelvic Organ Prolapse Who Underwent Transvaginal Mesh Reconstruction Surgery.

Background and Objectives: To identify the predictors of clinical outcomes in women with pelvic organ prolapse (POP) who underwent transvaginal reconstruction surgery, especially with transobturator mesh fixation or sacrospinous mesh fixation. Materials and Methods: All women with POP who underwent transvaginal reconstruction surgery, especially with transobturator mesh fixation or sacrospinous mesh fixation, were reviewed. Results: Between January 2011 and May 2019, a total of 206 consecutive women were reviewed, including 68 women receiving POP reconstruction with transobturator mesh fixation and 138 women who underwent POP reconstruction with sacrospinous mesh fixation. The least experienced surgeon (hazard ratio = 804.6) and advanced stage of cystocele (hazard ratio = 8.80) were the predictors of POP recurrence, especially those women with stage 4 of cystocele. Young age (hazard ratio = 0.94) was a predictor for mesh extrusion, especially those women with age ≤67 years. Follow-up interval (odds ratio = 1.03, p = 0.02) was also an independent predictor of mesh extrusion. High maximum flow rate (Qmax, hazard ratio = 1.03) was the sole predictor of postoperative stress urinary incontinence, especially those women with Qmax ≥19.2 mL/s. Preoperative overactive bladder syndrome (hazard ratio = 3.22) were a predictor for postoperative overactive bladder syndrome. In addition, overactive bladder syndrome rate improved after surgery in the sacrospinous group (p = 0.0001). Voiding dysfunction rates improved after surgery in both sacrospinous and transobturator groups. Conclusions: Predictors of clinical outcome in women who underwent transvaginal POP mesh reconstruction are identified. The findings can serve as a guide for preoperative consultation of similar procedures.

Natural polyketide 6-pentyl-2H-pyrone-2-one and its synthetic analogues efficiently prevent marine biofouling.

Biofouling is a widespread phenomenon in oceans worldwide. With increasing human development and activities in open and coastal waters, and due to the environmental impact of AF organotins and copper-based paint, the demand for nontoxic antifouling (AF) paints is increasing. Various bioassays for antimicrobial activity, anti-biofilm formation and anti-barnacle settlement were established to evaluate the possibility of using marine natural products as AF agents. A series of natural products, isolated from the marine-derived fungi Trichoderma atroviride and T. reesei, were evaluated for their AF activity. One pyrone-type compound (1) demonstrated significant inhibitory activities toward barnacle cyprid settlement. Furthermore, a series of pyrone analogues (S1-S6) were synthesized, and their bioactivities were evaluated in the established systems. The results showed that compounds S5 and S6 exhibited a broad spectrum of bioactivities, such as anti-barnacle settlement, anti-biofilm formation and antimicrobial activities.

Hybrid light field head-mounted display using time-multiplexed liquid crystal lens array for resolution enhancement.

In recent years, head-mounted display technologies have greatly advanced. In order to overcome the accommodation-convergence conflict, light field displays reconstruct three-dimensional (3D) images with a focusing cue but sacrifice resolution. In this paper, a hybrid head-mounted display system that is based on a liquid crystal microlens array is proposed. By using a time-multiplexed method, the display signals can be divided into light field and two-dimensional (2D) modes to show comfortable 3D images with high resolution compensated by the 2D image. According to the experimental results, the prototype supports a 12.28 ppd resolution in the diagonal direction, which reaches 82% of the traditional virtual reality (VR) head-mounted display (HMD).

Using the TrueLoo Smart Device to Record Toileting Sessions in Older Adults: Retrospective Validation and Acceptance Study.

BACKGROUND: Because of the relationship between independent living and activities of daily living, care teams spend significant time managing assisted living residents' toileting problems. Recently, the TrueLoo was developed as a connected toilet seat to automatically log and monitor toileting sessions. OBJECTIVE: This study aimed to demonstrate the validity of the TrueLoo to (1) record and identify toileting sessions with regard to stool and urine events; (2) compare the results with the person-reported, standard-of-care methods; and (3) establish metrics of user acceptability and ease of use in a assisted living facility population. METHODS: We used two phases: (1) initial development of the TrueLoo algorithms to accurately identify urine and stool events and (2) evaluation of the algorithms against person-reported, standard-of-care methods commonly used in assisted living facilities. Phase 2 analyzed data over a 3-day period from 52 devices. Participants' age ranged from 63 to 101 (mean 84, SD 9.35) years. Acceptability and ease-of-use data were also collected. RESULTS: Regarding the development of the TrueLoo algorithm for urine assessment, sensitivity and specificity of 96% and 85% were observed when evaluating a gold-standard labeled data set, respectively (F1-score=0.95). For stool, sensitivity and specificity of 90% and 79% were observed, respectively (F1-score=0.85). Regarding the TrueLoo algorithm in assisted living settings, classification performance statistics for urine assessment revealed sensitivity and specificity of 84% and 94%, respectively (F1-score=0.90), and for stool, 92% and 98%, respectively (F1-score=0.91). Throughout the study, 46 person-reported instances of urine were documented, compared with 630 recorded by the TrueLoo. For stool events, 116 person-reported events were reported, compared with 153 by the TrueLoo. This indicates that person-reported events were captured 7% (46/630) of the time for urine and 76% (116/153) of the time for stool. Overall, 45% (32/71) of participants said that the new toilet seat was better than their previous one, 84% (60/71) reported that using the TrueLoo was easy, and 99% (69/71) said that they believed the system could help aging adults. Over 98% (69/71) of participants reported that they would find alerts related to their health valuable and would be willing to share this information with their doctor. When asked about sharing information with caregivers, 66% (46/71) reported that they would prefer the TrueLoo to send information and alerts to their caregiver, as opposed to the participant having to personally communicate those details. CONCLUSIONS: The TrueLoo accurately recorded toileting sessions compared with standard-of-care methods, successfully establishing metrics of user acceptability and ease of use in assisted living populations. While additional validation studies are warranted, data presented in this paper support the use of the TrueLoo in assisted living settings as a model of event monitoring during toileting.

Metasurface-empowered snapshot hyperspectral imaging with convex/deep (CODE) small-data learning theory.

Hyperspectral imaging is vital for material identification but traditional systems are bulky, hindering the development of compact systems. While previous metasurfaces address volume issues, the requirements of complicated fabrication processes and significant footprint still limit their applications. This work reports a compact snapshot hyperspectral imager by incorporating the meta-optics with a small-data convex/deep (CODE) deep learning theory. Our snapshot hyperspectral imager comprises only one single multi-wavelength metasurface chip working in the visible window (500-650 nm), significantly reducing the device area. To demonstrate the high performance of our hyperspectral imager, a 4-band multispectral imaging dataset is used as the input. Through the CODE-driven imaging system, it efficiently generates an 18-band hyperspectral data cube with high fidelity using only 18 training data points. We expect the elegant integration of multi-resonant metasurfaces with small-data learning theory will enable low-profile advanced instruments for fundamental science studies and real-world applications.

Redox-Active High-Performance Polyimides as Versatile Electrode Materials for Organic Lithium- and Sodium-Ion Batteries.

Organic electrode materials for rechargeable batteries show great promise for improving the storage capacity, reducing production costs, and minimizing environmental impact toward sustainability. In this study, we report a series of newly synthesized arylamine-based polyimides, TPPA-PIs, with three different bridge functionalizations on the imide rings and isomeric constituents that can work as versatile battery electrodes. As a lithium-ion battery cathode, a maximum energy density of 248 Wh kg-1 with high voltage operation up to 4.0 V can be achieved. As a lithium-ion battery anode, the TPPA-PIs showed a reversible storage capacity of 806 mA h g-1 at 100 mA g-1 current density with good rate capability up to a current density of 2000 mA g-1. Moreover, when applied as sodium-ion battery anodes, TPPA-PIs delivered an optimum specific capacity of up to 218 mA h g-1 after 50 cycles at a 50 mA g-1 current density and revealed a long cycling stability up to 1000 cycles under a high current density of 1000 mA g-1. More importantly, these electrochemical performances of TPPA-PIs are among the best compared with other reported polymer-based electrodes. The mechanistic studies show that both bridge functionalization on the imide units and isomerism impact the electrochemical performance by regulating their intrinsic properties such as charge storage behavior, ion diffusivity, and activation energy. We believe that such a detailed study of the structural design to electrochemical performance of these polymeric electrodes will offer insights into materials development and optimization for next-generation multifunctional energy storage devices in a wide range of applications.

Deep Ensemble Feature Network for Gastric Section Classification.

In this paper, we propose a novel deep ensemble feature (DEF) network to classify gastric sections from endoscopic images. Different from recent deep ensemble learning methods, which need to train deep features and classifiers individually to obtain fused classification results, the proposed method can simultaneously learn the deep ensemble feature from arbitrary number of convolutional neural networks (CNNs) and the decision classifier in an end-to-end trainable manner. It comprises two sub networks, the ensemble feature network and the decision network. The former sub network learns the deep ensemble feature from multiple CNNs to represent endoscopic images. The latter sub network learns to obtain the classification labels by using the deep ensemble feature. Both sub networks are optimized based on the proposed ensemble feature loss and the decision loss which guide the learning of deep features and decisions. As shown in the experimental results, the proposed method outperforms the state-of-the-art deep learning, ensemble learning, and deep ensemble learning methods.