A bagging approach for improved predictive accuracy of intradialytic hypotension during hemodialysis treatment.

The primary objective of this study is to enhance the prediction accuracy of intradialytic hypotension in patients undergoing hemodialysis. A significant challenge in this context arises from the nature of the data derived from the monitoring devices and exhibits an extreme class imbalance problem. Traditional predictive models often display a bias towards the majority class, compromising the accuracy of minority class predictions. Therefore, we introduce a method called UnderXGBoost. This novel methodology combines the under-sampling, bagging, and XGBoost techniques to balance the dataset and improve predictive accuracy for the minority class. This method is characterized by its straightforward implementation and training efficiency. Empirical validation in a real-world dataset confirms the superior performance of UnderXGBoost compared to existing models in predicting intradialytic hypotension. Furthermore, our approach demonstrates versatility, allowing XGBoost to be substituted with other classifiers and still producing promising results. Sensitivity analysis was performed to assess the model's robustness, reinforce its reliability, and indicate its applicability to a broader range of medical scenarios facing similar challenges of data imbalance. Our model aims to enable medical professionals to provide preemptive treatments more effectively, thereby improving patient care and prognosis. This study contributes a novel and effective solution to a critical issue in medical prediction, thus broadening the application spectrum of predictive modeling in the healthcare domain.

Tubulin engineering by semi-synthesis reveals that polyglutamylation directs detyrosination.

Microtubules, a critical component of the cytoskeleton, carry post-translational modifications (PTMs) that are important for the regulation of key cellular processes. Long-lived microtubules, in neurons particularly, exhibit both detyrosination of α-tubulin and polyglutamylation. Dysregulation of these PTMs can result in developmental defects and neurodegeneration. Owing to a lack of tools to study the regulation and function of these PTMs, the mechanisms that govern such PTM patterns are not well understood. Here we produce fully functional tubulin carrying precisely defined PTMs within its C-terminal tail. We ligate synthetic α-tubulin tails-which are site-specifically glutamylated-to recombinant human tubulin heterodimers by applying a sortase- and intein-mediated tandem transamidation strategy. Using microtubules reconstituted with these designer tubulins, we find that α-tubulin polyglutamylation promotes its detyrosination by enhancing the activity of the tubulin tyrosine carboxypeptidase vasohibin/small vasohibin-binding protein in a manner dependent on the length of polyglutamyl chains. We also find that modulating polyglutamylation levels in cells results in corresponding changes in detyrosination, corroborating the link between the detyrosination cycle to polyglutamylation.

PREDICTING VISUAL OUTCOME AFTER SURGERY IN PATIENTS WITH IDIOPATHIC EPIRETINAL MEMBRANE USING A NOVEL CONVOLUTIONAL NEURAL NETWORK.

PURPOSE: To develop a deep convolutional neural network that enables the prediction of postoperative visual outcomes after epiretinal membrane surgery based on preoperative optical coherence tomography images and clinical parameters to refine surgical decision making. METHODS: A total of 529 patients with idiopathic epiretinal membrane who underwent standard vitrectomy with epiretinal membrane peeling surgery by two surgeons between January 1, 2014, and June 1, 2020, were enrolled. The newly developed Heterogeneous Data Fusion Net was introduced to predict postoperative visual acuity outcomes (improvement ≥2 lines in Snellen chart) 12 months after surgery based on preoperative cross-sectional optical coherence tomography images and clinical factors, including age, sex, and preoperative visual acuity. The predictive accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve of the convolutional neural network model were evaluated. RESULTS: The developed model demonstrated an overall accuracy for visual outcome prediction of 88.68% (95% CI, 79.0%-95.7%) with an area under the receiver operating characteristic curve of 97.8% (95% CI, 86.8%-98.0%), sensitivity of 87.0% (95% CI, 67.9%-95.5%), specificity of 92.9% (95% CI, 77.4%-98.0%), precision of 0.909, recall of 0.870, and F1 score of 0.889. The heatmaps identified the critical area for prediction as the ellipsoid zone of photoreceptors and the superficial retina, which was subjected to tangential traction of the proliferative membrane. CONCLUSION: The novel Heterogeneous Data Fusion Net demonstrated high accuracy in the automated prediction of visual outcomes after weighing and leveraging multiple clinical parameters, including optical coherence tomography images. This approach may be helpful in establishing personalized therapeutic strategies for epiretinal membrane management.

Prediction of treatment outcome in neovascular age-related macular degeneration using a novel convolutional neural network.

While prognosis and risk of progression are crucial in developing precise therapeutic strategy in neovascular age-related macular degeneration (nAMD), limited predictive tools are available. We proposed a novel deep convolutional neural network that enables feature extraction through image and non-image data integration to seize imperative information and achieve highly accurate outcome prediction. The Heterogeneous Data Fusion Net (HDF-Net) was designed to predict visual acuity (VA) outcome (improvement ≥ 2 line or not) at 12th months after anti-VEGF treatment. A set of pre-treatment optical coherence tomography (OCT) image and non-image demographic features were employed as input data and the corresponding 12th-month post-treatment VA as the target data to train, validate, and test the HDF-Net. This newly designed HDF-Net demonstrated an AUC of 0.989 (95% CI 0.970-0.999), accuracy of 0.936 [95% confidence interval (CI) 0.889-0.964], sensitivity of 0.933 (95% CI 0.841-0.974), and specificity of 0.938 (95% CI 0.877-0.969). By simulating the clinical decision process with mixed pre-treatment information from raw OCT images and numeric data, HDF-Net demonstrated promising performance in predicting individualized treatment outcome. The results highlight the potential of deep learning to simultaneously process a broad range of clinical data to weigh and leverage the complete information of the patient. This novel approach is an important step toward real-world personalized therapeutic strategy for typical nAMD.

Supermode Hybridization: A Material-Independent Route toward Record Schottky Detection Sensitivity Using <0.05 µm3 Amorphous Absorber Volume.

Schottky photodetectors are attractive for CMOS-compatible photonic integrated circuits, but the inability to simultaneously optimize the metal emitter thickness for photon absorption and hot carrier emission limits the detection efficiency and sensitivity. Here, we propose and experimentally demonstrate a supermode hybridization waveguiding effect that can overcome the trade-off. By introducing structural asymmetry into coupled plasmonic nanostructures, hybridization-induced field enhancement can help ultrathin metal emitters to achieve greater optical absorption than bulk counterparts. Despite the use of amorphous materials with higher transport losses, our hybridized Schottky detectors demonstrate higher responsivity per device volume compared to crystalline-based and unhybridized Schottky designs with broadband (1.5-1.6 µm) and athermal (15-100 °C) behavior as well as record sensitivity of -55 dBm that approaches Ge counterparts that are 36 times larger. The hybridization effect can be utilized across diverse nanomaterial platforms to facilitate light-matter interaction, paving the way toward backend-compatible, chip-integrated photonics with greater manufacturing flexibility.

Monolithic Plasmonic Waveguide Architecture for Passive and Active Optical Circuits.

Guided-wave plasmonic circuits are promising platforms for sensing, interconnection, and quantum applications in the subdiffraction regime. Nonetheless, the loss-confinement trade-off remains a collective bottleneck for plasmonic-enhanced optical processes. Here, we report a unique plasmonic waveguide architecture that can alleviate such trade-off and improve the efficiencies of plasmonic-based emission, light-matter-interaction, and detection simultaneously. Specifically, record experimental attributes such as normalized Purcell factor approaching 104, 10 dB amplitude modulation with <1 dB insertion loss and fJ-level switching energy, and photodetection sensitivity and internal quantum efficiency of -54 dBm and 6.4% respectively have been realized within our amorphous-based, coupled-mode plasmonic structure. The ability to support multiple optoelectronic phenomena while providing performance gains over existing plasmonic and dielectric counterparts offers a clear path toward reconfigurable, monolithic plasmonic circuits.

An antifouling peptide-based biosensor for determination of Streptococcus pneumonia markers in human serum.

We report a peptide-based sensor that involves a multivalent interaction with L-ascorbate 6-phosphate lactonase (UlaG), a protein marker of Streptococcus pneumonia. By integrating the antifouling feature of the sensor, we significantly improved the signal-to-noise ratio of UlaG detection. The antifouling surface was fabricated via electrodeposition using an equivalent mixture of 4-amino-N,N,N-trimethylanilinium and 4-aminobenzenesulfonate. This antifouling layer not only effectively reduces the non-specific adsorption on the biosensor but also decreases the charge transfer resistance (Rct) of the screen-printed carbon electrode. The aniline-modified S7 peptide, an UlaG-binding peptide, was pre-synthesized and further electrochemically modified to bind onto the antifouling layer. Bio-electrochemical analysis confirms that the antifouling S7-peptide sensor binds strongly to the UlaG with a dissociation constant (Kd) = 0.5 nM. This strong interaction can be attributed to a multivalent interaction between the biosensor and the heximeric form of UlaG. To demonstrate the potential for clinical application, further detection of Streptococcus pneumonia from 50 to 5×104 CFU/mL were successfully performed in 25% human serum.

Carbon-doped SnS2 nanostructure as a high-efficiency solar fuel catalyst under visible light.

Photocatalytic formation of hydrocarbons using solar energy via artificial photosynthesis is a highly desirable renewable-energy source for replacing conventional fossil fuels. Using an L-cysteine-based hydrothermal process, here we synthesize a carbon-doped SnS2 (SnS2-C) metal dichalcogenide nanostructure, which exhibits a highly active and selective photocatalytic conversion of CO2 to hydrocarbons under visible-light. The interstitial carbon doping induced microstrain in the SnS2 lattice, resulting in different photophysical properties as compared with undoped SnS2. This SnS2-C photocatalyst significantly enhances the CO2 reduction activity under visible light, attaining a photochemical quantum efficiency of above 0.7%. The SnS2-C photocatalyst represents an important contribution towards high quantum efficiency artificial photosynthesis based on gas phase photocatalytic CO2 reduction under visible light, where the in situ carbon-doped SnS2 nanostructure improves the stability and the light harvesting and charge separation efficiency, and significantly enhances the photocatalytic activity.

Ultrahigh Responsivity and Detectivity Graphene-Perovskite Hybrid Phototransistors by Sequential Vapor Deposition.

In this work, graphene-methylammonium lead iodide (MAPbI3) perovskite hybrid phototransistors fabricated by sequential vapor deposition are demonstrated. Ultrahigh responsivity of 1.73 × 107 A W-1 and detectivity of 2 × 1015 Jones are achieved, with extremely high effective quantum efficiencies of about 108% in the visible range (450-700 nm). This excellent performance is attributed to the ultra-flat perovskite films grown by vapor deposition on the graphene sheets. The hybrid structure of graphene covered with uniform perovskite has high exciton separation ability under light exposure, and thus efficiently generates photocurrents. This paper presents photoluminescence (PL) images along with statistical analysis used to study the photo-induced exciton behavior. Both uniform and dramatic PL intensity quenching has been observed over entire measured regions, consistently demonstrating excellent exciton separation in the devices.

Wave propagation in bianisotropic metamaterials: angular selective transmission.

We investigate the basic features of wave propagation in bianisotropic metamaterials characterized by asymmetric magnetoelectric tensors with zero diagonal elements. The wave propagation is described by a biquadratic dispersion relation with two elliptically polarized eigenwaves. In particular, the bianisotropic media may possess a hybrid character of the elliptic and hyperbolic dispersions. For a wave incident from vacuum onto a bianisotropic medium, there exist an ordinary and an inversion critical angle, leading to angular selective transmission. A standard and a complementary type of angular selective transmissions are illustrated with the incidence of Gaussian beams based on Fourier integral formulation.

The use of Chitosan to enhance photodynamic inactivation against Candida albicans and its drug-resistant clinical isolates.

Drug-resistant Candida infection is a major health concern among immunocompromised patients. Antimicrobial photodynamic inactivation (PDI) was introduced as an alternative treatment for local infections. Although Candida (C.) has demonstrated susceptibility to PDI, high doses of photosensitizer (PS) and light energy are required, which may be harmful to eukaryotic human cells. This study explores the capacity of chitosan, a polycationic biopolymer, to increase the efficacy of PDI against C. albicans, as well as fluconazole-resistant clinical isolates in planktonic or biofilm states. Chitosan was shown to effectively augment the effect of PDI mediated by toluidine blue O (TBO) against C. albicans that were incubated with chitosan for 30 min following PDI. Chitosan at concentrations as low as 0.25% eradicated C. albicans; however, without PDI treatment, chitosan alone did not demonstrate significant antimicrobial activity within the 30 min of incubation. These results suggest that chitosan only augmented the fungicidal effect after the cells had been damaged by PDI. Increasing the dosage of chitosan or prolonging the incubation time allowed a reduction in the PDI condition required to completely eradicate C. albicans. These results clearly indicate that combining chitosan with PDI is a promising antimicrobial approach to treat infectious diseases.

Negative refraction and backward wave in pseudochiral mediums: illustrations of Gaussian beams.

We investigate the phenomena of negative refraction and backward wave in pseudochiral mediums, with illustrations of Gaussian beams. Due to symmetry breaking intrinsic in pseudochiral mediums, there exist two elliptically polarized eigenwaves with different wave vectors. As the chirality parameter increases from zero, the two waves begin to split from each other. For a wave incident from vacuum onto a pseudochiral medium, negative refraction may occur for the right-handed wave, whereas backward wave may appear for the left-handed wave. These features are illustrated with Gaussian beams based on Fourier integral formulations for the incident, reflected, and transmitted waves. Negative refraction and backward wave are manifest, respectively, on the energy flow in space and wavefront movement in time.

Photodynamic inactivation of chlorin e6-loaded CTAB-liposomes against Candida albicans.

BACKGROUND AND OBJECTIVES: Antimicrobial photodynamic inactivation (PDI) is a promising therapeutic modality for the treatment of local infections. To increase the efficacy of PDI, chlorine e6 (Ce6) was encapsulated in cationic CTAB-liposomes composed of various ratios of dimyristoyl-sn-glycero-phosphatidylcholine (DMPC) and the cationic surfactant, cetyltrimethyl ammonium bromide (CTAB). The PDI efficacy of the liposomal-Ce6 was assessed in vitro against susceptible and drug-resistant clinical isolates of Candida albicans (C. albicans) as well as in infected burn wounds. STUDY DESIGN/MATERIALS AND METHODS: Ce6 was encapsulated in CTAB-liposomes by the film hydration method. Particle size distribution and zeta potential of the cationic liposomes were measured using a Zetasizer Nano-ZS. UV-visible spectra were used to measure lipid/Ce6 (L/C) ratio and drug entrapment efficiency while differential scanning calorimetry (DSC) was used to study the thermotropic behavior of DMPC liposomes upon CTAB addition. In vivo PDI efficacy was carried out in an infected burn wound using a rat model. RESULTS: The increase in zeta potential and a shift in the phase transition temperature (Tm ) upon CTAB addition confirmed its entrapment within the lipid bilayers of the liposome. Meanwhile, the CTAB addition did not affect the Ce6 entrapment efficiency and physical attributes of the liposomes. In vitro studies showed that the PDI effect of the Ce6-loaded CTAB-liposomes was dependent on the lipid to Ce6 molar ratio (L/C), particle size and the concentration of CTAB in the liposomes. The lower L/C ratio and smaller liposomes exerted significantly higher PDI effects. In addition, an increase in the CTAB to lipid ratio led to a significant increase in the PDI effect of Ce6 against susceptible and drug-resistant clinical isolates of C. albicans after light illumination. CONCLUSIONS: Our results indicate that a low L/C ratio, high positive charge, and small particle size of CTAB-liposomes significantly enhances their PDI efficacy against C. albicans.