Understanding user engagement in mobile health applications from a privacy management perspective.

Mobile health applications (mHealth apps) have surged in popularity for their role in promoting knowledge exchange and providing emotional support among health consumers. However, this enhanced social connectivity via these apps has led to an escalation in privacy breaches, potentially hindering user engagement. Drawing upon the communication privacy management theory, this study proposes a moderated mediation model to link social privacy concerns to user engagement in mHealth apps. An online survey involving 1149 mHealth app users was conducted in China to empirically validate the proposed model. Results indicated that social privacy concerns were negatively related to user engagement in mHealth apps, and perceived privacy of the app partially mediated this relationship. Moreover, perceived control positively moderated the indirect relationship between social privacy concerns and user engagement via perceived privacy. Specifically, the negative impact of social privacy concerns on perceived privacy was mitigated for users who reported higher levels of perceived control, indicating that when users feel more in control of their personal data, they are less affected by concerns over social privacy. Theoretically, this study has the potential to help scholars understand user engagement in mHealth apps from a privacy management perspective. Practically, the results of this study could assist mobile app providers and health professionals in devising evidence-based strategies to enhance social engagement and promote effective and sustainable use of mHealth apps among health consumers.

Paxlovid reduces the 28-day mortality of patients with COVID-19: a retrospective cohort study.

PURPOSE: In this study, we aim to explore the efficacy of paxlovid on reducing mortality of COVID-19 patients in clinical setting, especially whether paxlovid modifies the risk of death in these severe and critical patients. METHODS: Our retrospective cohort study was conducted on the medical records of patients, consecutively admitted for COVID-19 to five hospitals in Chongqing, China from Dec 8, 2022 to Jan 20, 2023. Based on whether patients received paxlovid during their hospitalization, patients were grouped as paxlovid group and non-paxlovid group. We used 1:1 ratio propensity score matching (PSM) in our study to adjust for confounding factors and differences between groups. Statistical analysis were performed by SPSS 23.0. The differences in 28-day mortality between these two groups and its influencing factors were the main results we focused on. RESULTS: There were 1018 patients included in our study cohort. With 1:1 ratio PSM, each of the paxlovid group and non-paxlovid group included 237 patients. The results showed that patients using paxlovid have a lower 28-day mortality in overall population either before PSM (OR 0.594, 95% CI 0.385-0.917, p = 0.019) or after PSM (OR 0.458, 95% CI 0.272-0.774, p = 0.003) with multivariable adjusted logistic regression models. Meanwhile, in severe subgroup, it showed similar findings.With paxlovid treatment, it showed a significantly lower 28-day mortality in severe subgroup both before PSM (28% vs.41%, p = 0.008) and after PSM (19% vs.32%, p = 0.007). CONCLUSION: Paxlovid can significantly reduce the risk of 28-day mortality in overall population and severe subgroup patients.This study distinguished the severe subgroup patients with COVID-19 who benefit more from paxlovid treatment.

Explainable and generalizable AI-driven multiscale informatics for dynamic system modelling.

Ultra-precision machining requires system modelling that both satisfies explainability and conforms to data fidelity. Existing modelling approaches, whether based on data-driven methods in present artificial intelligence (AI) or on first-principle knowledge, fall short of these qualities in high-demanding industrial applications. Therefore, this paper develops an explainable and generalizable 'grey-box' AI informatics method for real-world dynamic system modelling. Such a grey-box model serves as a multiscale 'world model' by integrating the first principles of the system in a white-box architecture with data-fitting black boxes for varying hyperparameters of the white box. The physical principles serve as an explainable global meta-structure of the real-world system driven by physical knowledge, while the black boxes enhance local fitting accuracy driven by training data. The grey-box model thus encapsulates implicit variables and relationships that a standalone white-box model or black-box model fails to capture. Case study on an industrial cleanroom high-precision temperature regulation system verifies that the grey-box method outperforms existing modelling methods and is suitable for varying operating conditions.

Recursive partitioning analysis for survival stratification and early imaging prediction of molecular biomarker in glioma patients.

BACKGROUND: Glioma is the most common primary brain tumor with high mortality and disability rates. Recent studies have highlighted the significant prognostic consequences of subtyping molecular pathological markers using tumor samples, such as IDH, 1p/19q, and TERT. However, the relative importance of individual markers or marker combinations in affecting patient survival remains unclear. Moreover, the high cost and reliance on postoperative tumor samples hinder the widespread use of these molecular markers in clinical practice, particularly during the preoperative period. We aim to identify the most prominent molecular biomarker combination that affects patient survival and develop a preoperative MRI-based predictive model and clinical scoring system for this combination. METHODS: A cohort dataset of 2,879 patients was compiled for survival risk stratification. In a subset of 238 patients, recursive partitioning analysis (RPA) was applied to create a survival subgroup framework based on molecular markers. We then collected MRI data and applied Visually Accessible Rembrandt Images (VASARI) features to construct predictive models and clinical scoring systems. RESULTS: The RPA delineated four survival groups primarily defined by the status of IDH and TERT mutations. Predictive models incorporating VASARI features and clinical data achieved AUC values of 0.85 for IDH and 0.82 for TERT mutations. Nomogram-based scoring systems were also formulated to facilitate clinical application. CONCLUSIONS: The combination of IDH-TERT mutation status alone can identify the most distinct survival differences in glioma patients. The predictive model based on preoperative MRI features, supported by clinical assessments, offers a reliable method for early molecular mutation prediction and constitutes a valuable scoring tool for clinicians in guiding treatment strategies.

Tetraniliprole resistance in field-collected populations of Tuta absoluta (Lepidoptera: Gelechiidae) from China: Baseline susceptibility, cross-resistance, inheritance, and biochemical mechanism.

Tuta absoluta is one of the most destructive and invasive insect pests throughout the world. It feeds on numerous solanaceous plant species and has developed resistance to most types of popular insecticides. Tetraniliprole is a novel diamide chemical agent that acts as a modulator of the ryanodine receptor. To establish T. absoluta susceptibility to tetraniliprole and to understand potential mechanisms of resistance, we monitored 18 field populations of T. absoluta collected from northern China. One field-evolved resistant population, Huailai (HL), showed moderate resistance to tetraniliprole (36.2-fold) in comparison with susceptible strain YN-S. Assays of cross-resistance, synergism, metabolic enzyme activity, and inheritance of resistance were performed with YN-S strain and HL population. The latter displayed 12.2- and 6.7-fold cross-resistance to chlorantraniliprole and flubendiamide, respectively, but little cross-resistance to broflanilide (1.6-fold), spinosad (2.1-fold), metaflumizone (1.5-fold), or indoxacarb (2.8-fold). Genetic analyses revealed that tetraniliprole resistance in HL population was autosomal, incompletely dominant, and polygenic. Piperonyl butoxide was found to significantly increase tetraniliprole toxicity, and enzymatic activities of P450 monooxygenase and glutathione S-transferase were significantly higher in HL than YN-S population. These results enhance our knowledge of the inheritance and mechanism of tetraniliprole resistance, enabling future optimization of resistance management strategies.

Baseline of susceptibility, risk assessment, biochemical mechanism, and fitness cost of resistance to dimpropyridaz, a novel pyridazine pyrazolecarboxamide insecticide, in Bemisia tabaci from China.

Bemisia tabaci is one of the most destructive agricultural insect pests around the world, and it has developed high levels of resistance to most pesticides. Dimpropyridaz, a novel insecticide developed by BASF, displays excellent activity against piercing-sucking insect pests. In this study, baseline of susceptibility showed all tested field populations of B. tabaci are susceptible to dimpropyridaz. After continuous selection with dimpropyridaz in the lab, a B. tabaci strain (F12) developed 2.2-fold higher level of resistance compared with a susceptible MED-S strain, and the realized heritability (h2) was estimated as 0.0518. The F12 strain displayed little cross-resistance to afidopyropen, cyantraniliprole, sulfoxaflor, or abamectin, and significantly increased activity of cytochrome P450 monooxygenase (P450). The fitness cost of dimpropyridaz resistance was evident in F12 strain, which had a relative fitness of 0.95 and significantly lower fecundity per female compared with MED-S strain. Taken together, B. tabaci displays high susceptibility to dimpropyridaz in the field, and low risk of developing resistance to dimpropyridaz under successive selection pressure. Little cross-resistance to popular insecticides was found, and fitness cost associated dimpropyridaz resistance was observed. Higher activity of cytochrome P450 in the F12 strain, may be involved in the process of detoxifying dimpropyridaz in whitefly.

Molecule Engineering Metal-Organic Framework-Based Organic Photoelectrochemical Transistor Sensor for Ultrasensitive Bilirubin Detection.

The functionalization of metal-organic frameworks (MOFs) with organic small molecules by in situ postsynthetic modification has garnered considerable attention. However, the precise engineering of recognition sites using this method remains rarely explored in optically controlled bioelectronics. Herein, employing the Schiff base reaction to embed the small molecule (THBA) into a Zr-MOF, we fabricated a hydroxyl-rich MOF on the surface of titanium dioxide nanorod arrays (U6H@TiO2 NRs) to develop light-sensitive gate electrodes with tailored recognition capabilities. The U6H@TiO2 NR gate electrodes were integrated into organic photoelectrochemical transistor (OPECT) sensing systems to tailor a sensitive device for bilirubin (I-Bil) detection. In the presence of I-Bil, coordination effects, hydrogen bonding, and π-π interactions facilitated strong binding between U6H@TiO2 NRs and the target I-Bil. The electron-donating property of I-Bil influenced the gate voltage, enabling precise control of the channel status and modulation of the channel current. The OPECT device exhibited exceptional analytical performance toward I-Bil with wide linearity ranging from 1 × 10-16 to 1 × 10-9 M and a low limit detection of 0.022 fM. Leveraging the versatility of small molecules for boosting the functionalization of materials, this work demonstrates the great potential of the small molecule family for OPECT bioanalysis and holds promise for the advancement of OPECT sensors.

Reg2 treatment is protective but the induced Reg2 autoantibody is destructive to the islets in NOD mice.

Regenerating family protein 2 (Reg2) is a trophic factor which stimulates ß-cell replication and resists islet destruction. However, Reg2 also serves as an islet autoantigen, which makes it complicated to judge the effectiveness in treating diabetes. How Reg2 treatment behaves in non-obese diabetic (NOD) mice is to be investigated. NOD mice were treated with recombinant Reg2 protein, Complete Freund's adjuvant (CFA) + PBS and CFA+Reg2 vaccinations, CFA+PBS- and CFA+Reg2-immunized antisera, and single chain variable fragment (scFv)-Reg2 and mIgG2a-Reg2 antibodies. Glycemic level, bodyweight, serum Reg2 antibody titer, glucose tolerance, and insulin secretion were determined. Islet morphological characteristics, insulitis, cell apoptosis, islet cell components, and T cell infiltration were analyzed by histological examinations. The autoantigenicity of constructed Reg2C and Reg2X fragments was determined in healthy BALB/c mice, and the bioactivity in stimulating cell proliferation and survival was assessed in insulinoma MIN6 cells. Reg2 administration alleviated diabetes in NOD mice with improved glucose tolerance and insulin secretion but elevated serum Reg2 autoantibodies. Histomorphometry showed reduced inflammatory area, TUNEL signal and CD8 + T cell infiltration, and increased ß-cell proportion in support of the islet-protective effect of Reg2 treatment. CFA+PBS and CFA+Reg2 immunizations prevented diabetic onset and alleviated insulitis while injections of the antisera offered mild protections. Antibody treatments accelerated diabetic onset without increasing the overall incidence. Reg2C fragment depletes antigenicity, but reserves protective activity in streptozotocin (STZ)-treated MIN6 cells. In conclusion, Reg2 treatment alleviates type 1 diabetes (T1D) by preserving islet ß-cells, but induces Reg2 autoantibody production which poses a potential risk of accelerating diabetic progression.

Joint diffusion: mutual consistency-driven diffusion model for PET-MRI co-reconstruction.

Objective. Positron Emission Tomography and Magnetic Resonance Imaging (PET-MRI) systems can obtain functional and anatomical scans. But PET suffers from a low signal-to-noise ratio, while MRI are time-consuming. To address time-consuming, an effective strategy involves reducing k-space data collection, albeit at the cost of lowering image quality. This study aims to leverage the inherent complementarity within PET-MRI data to enhance the image quality of PET-MRI.Approach. A novel PET-MRI joint reconstruction model, termed MC-Diffusion, is proposed in the Bayesian framework. The joint reconstruction problem is transformed into a joint regularization problem, where data fidelity terms of PET and MRI are expressed independently. The regular term, the derivative of the logarithm of the joint probability distribution of PET and MRI, employs a joint score-based diffusion model for learning. The diffusion model involves the forward diffusion process and the reverse diffusion process. The forward diffusion process adds noise to transform a complex joint data distribution into a known joint prior distribution for PET and MRI simultaneously, resembling a denoiser. The reverse diffusion process removes noise using a denoiser to revert the joint prior distribution to the original joint data distribution, effectively utilizing joint probability distribution to describe the correlations of PET and MRI for improved quality of joint reconstruction.Main results. Qualitative and quantitative improvements are observed with the MC-Diffusion model. Comparative analysis against LPLS and Joint ISAT-net on the ADNI dataset demonstrates superior performance by exploiting complementary information between PET and MRI. The MC-Diffusion model effectively enhances the quality of PET and MRI images.Significance. This study employs the MC-Diffusion model to enhance the quality of PET-MRI images by integrating the fundamental principles of PET and MRI modalities and leveraging their inherent complementarity. Furthermore, utilizing the diffusion model to learn the joint probability distribution of PET and MRI, thereby elucidating their latent correlation, facilitates a more profound comprehension of the priors obtained through deep learning, contrasting with black-box prior or artificially constructed structural similarities.

Broadband mid-infrared photodetectors utilizing two-dimensional van der Waals heterostructures with parallel-stacked pn junctions.

Optimizing the width of depletion region is a key consideration in designing high performance photovoltaic photodetectors, as the electron-hole pairs generated outside the depletion region cannot be effectively separated, leading to a negligible contribution to the overall photocurrent. However, currently reported photovoltaic mid-infrared photodetectors based on two-dimensional heterostructures usually adopt a single pn junction configuration, where the depletion region width is not maximally optimized. Here, we demonstrate the construction of a high performance broadband mid-infrared photodetector based on a MoS2/b-AsP/MoS2npn van der Waals heterostructure. The npn heterojunction can be equivalently represented as two parallel-stacked pn junctions, effectively increasing the thickness of the depletion region. Consequently, the npn device shows a high detectivity of 1.3 × 1010cmHz1/2W-1at the mid-infrared wavelength, which is significantly improved compared with its single pn junction counterpart. Moreover, it exhibits a fast response speed of 12 µs, and a broadband detection capability ranging from visible to mid-infrared wavelengths.

Activatable NIR Fluorescence Probe for Epinephrine Detection and Bioimaging Based on Anionic Heptamethine Cyanine.

Epinephrine (EP) is an essential catecholamine in the human body. Currently, most EP detection methods are not suitable for in vivo detection due to material limitations. An organic small molecule fluorescent probe based on a chemical cascade reaction for the detection of EP was designed. Anionic heptamethine cyanine dye was selected as a fluorescent dye because of its NIR fluorescence emission with excellent biocompatibility. The secondary amine of EP nucleophilically attacks the carbonate of the probe with its stronger nucleophilicity and further undergoes intramolecular nucleophilic cyclization to release the fluorophore. Other substances containing only primary amines or no ß-OH lack reaction competitiveness due to their weaker nucleophilicity or inability to undergo further cyclization. The fluorescence recovery of the probe was linearly related to the EP concentration of 2-75 µmol/L. The detection limit was 0.4 µmol/L. The recovery rate was 94.78-111.32%. Finally, we successfully achieved bioimaging of EP in living cells and EP analogue in nematodes.

Configurable dual-topological-interface-states induced reflection in hybrid multilayers consisting of a Ge2Sb2Te5 film.

Active control of induced reflection is crucial for many potential applications ranging from slowing light to biosensing devices. However, most previous approaches require patterned nanostructures to achieve controllable induced reflection, which hinders their further applications due to complicated architectures. Herein, we propose a lithography-free multilayered structure to achieve the induced reflection through the coupling of dual-topological-interface-states. The multilayers consist of two one-dimensional (1D) photonic crystals (PCs) and an Ag film separated by a Spacer, topological edge state (TES) and topological Tamm state (TTS) can be excited simultaneously and their coupling induces the reflection window. The coupled-oscillator model is proposed to mimic the coupling between the TES and TTS, and the analytical results are in good agreement with finite element method (FEM). In addition, the TES-TTS induced reflection is robust to the variation of structural parameters. By integrating an ultra-thin phase-change film of Ge2Sb2Te5 (GST) into the multilayers, the induced reflection can be switched through the phase transition of the GST film. The multipole decomposition reveals that the vanished reflection window is arising from the disappearance of TTS associated with the toroidal dipole (TD) mode.

Flexible design of chiroptical response of planar chiral metamaterials using deep learning.

Optical chirality is highly demanded for biochemical sensing, spectral detection, and advanced imaging, however, conventional design schemes for chiral metamaterials require highly computational cost due to the trial-and-error strategy, and it is crucial to accelerate the design process particularly in comparably simple planar chiral metamaterials. Herein, we construct a bidirectional deep learning (BDL) network consists of spectra predicting network (SPN) and design predicting network (DPN) to accelerate the prediction of spectra and inverse design of chiroptical response of planar chiral metamaterials. It is shown that the proposed BDL network can accelerate the design process and exhibit high prediction accuracy. The average process of prediction only takes ∼15 ms, which is 1 in 40000 compared to finite-difference time-domain (FDTD). The mean-square error (MSE) loss of forward and inverse prediction reaches 0.0085 after 100 epochs. Over 95.2% of training samples have MSE ≤ 0.0042 and MSE ≤ 0.0044 for SPN and DPN, respectively; indicating that the BDL network is robust in the inverse deign without underfitting or overfitting for both SPN and DPN. Our founding shows great potentials in accelerating the on-demand design of planar chiral metamaterials.

Direct effects of barley yellow dwarf virus on the performance, parasitoid resistance, and feeding behavior of its vector Sitobion avenae (Hemiptera: Aphididae).

BACKGROUND: The complex interaction between plant viruses and their insect vectors is the basis for the epidemiology of plant viruses. The 'Vector Manipulation Hypothesis' (VMH) was proposed to demonstrate the evolution of strategies in plant viruses to enhance their transmission to new hosts through direct effects on insect vector behavior and/or physiology. However, the aphid vectors used in previous studies were mostly obtained by feeding on virus-infected plants and as a result, it was difficult to eliminate the confounding effects of infected host plants. Furthermore, the mechanisms of the direct effects of plant viruses on insect vectors have rarely been examined comprehensively. RESULTS: We fed Sitobion avenae on an artificial diet infused with a purified suspension of Barley yellow dwarf virus (BYDV) PAV strain to obtain viruliferous aphids. We then examined their growth and reproduction performance, resistance to the parasitoid Aphidius gifuensis Ashmead, and feeding behavior. The results indicate that (1) viruliferous aphids had a shorter life span and a lower relative growth rate at the nymphal stage; (2) A. gifuensis had a lower parasitism rate, mummification rate, and emergence rate in viruliferous aphids; (3) Viruliferous aphids spent more time on non-probing and salivation behavior and had a shorter total duration of penetration and ingestion compared with healthy conspecifics. CONCLUSION: These results suggest that plant virus infection may directly alter vector fitness and behavior that improves plant virus transmission, but not vector growth. These findings highlight the mechanisms of VMH and the ecological significance of vector manipulation by plant viruses, and have implications for plant virus disease and vector management. © 2024 Society of Chemical Industry.

CD52 knockdown inhibits aerobic glycolysis and malignant behavior of NSCLC cells through AKT signaling pathway.

CD52 is an important functional regulator involved in the development of human cancer. In this study, the clinical significance and biological function of CD52 in the malignant behavior of non-small cell lung cancer (NSCLC) were explored. In this study, immunohistochemical (IHC) staining was performed to determine the expression pattern of CD52 in NSCLC. Loss of function assays were used to evaluate the biological functions of CD52 in NSCLC cells in vitro and in vivo. Our data indicated that the expression of CD52 was significantly elevated in NSCLC and correlated with the patient prognosis. Functionally, downregulation of CD52 expression significantly suppressed the proliferation, migration, aerobic glycolysis and tumorigenesis of NSCLC cells. Moreover, CD52 regulated aerobic glycolysis of NSCLC cells through the AKT pathway. Furthermore, aerobic glycolysis induced by 2-DG inhibited the proliferation of NSCLC cells. In conclusion, CD52 knockdown inhibited aerobic glycolysis and malignant behavior of NSCLC cells through AKT signaling pathway, which may be employed in an alternative therapeutic target for NSCLC.

Tetraniliprole risk assessment: Unveiling a hidden threat for managing a generalist herbivore.

Insecticide resistance poses a significant challenge in managing generalist herbivores such as the tobacco cutworm (TCW), Spodoptera litura. This study investigates the potential risks associated with using the novel diamide insecticide tetraniliprole to control TCW. A tetraniliprole-resistant strain was developed through twelve generations of laboratory selection, indicating an intermediate risk of resistance development. Field monitoring in China revealed a significant incidence of resistance, particularly in the Nanchang (NC) population (>100-fold). Tetraniliprole showed moderate to high cross-resistance to multiple insecticides and was autosomally inherited with incomplete dominance, controlled by multiple genes, some of which belong to the cytochrome P450 family associated with enhanced detoxification. Life table studies indicated transgenerational hormesis, stimulating TCW female fecundity and increasing population net reproduction rates (R0). These findings suggest a potential for pest resurgence under tetraniliprole use. The integrated risk assessment provides a basis for the sustainable management of TCW using tetraniliprole.

Specific plasma microRNA profiles could be potential non-invasive biomarkers for biochemical pregnancy loss following embryo transfer.

BACKGROUND: Plasma microRNAs act as biomarkers for predicting and diagnosing diseases. Reliable non-invasive biomarkers for biochemical pregnancy loss have not been established. We aim to analyze the dynamic microRNA profiles during the peri-implantation period and investigate if plasma microRNAs could be non-invasive biomarkers predicting BPL. METHODS: In this study, we collected plasma samples from patients undergoing embryo transfer (ET) on ET day (ET0), 11 days after ET (ET11), and 14 days after ET (ET14). Patients were divided into the NP (negative pregnancy), BPL (biochemical pregnancy loss), and CP (clinical pregnancy) groups according to serum hCG levels at day11~14 and ultrasound at day28~35 following ET. MicroRNA profiles at different time-points were detected by miRNA-sequencing. We analyzed plasma microRNA signatures for BPL at the peri-implantation stage, we characterized the dynamic microRNA changes during the implantation period, constructed a microRNA co-expression network, and established predictive models for BPL. Finally, the sequencing results were confirmed by Taqman RT-qPCR. RESULTS: BPL patients have distinct plasma microRNA profiles compared to CP patients at multiple time-points during the peri-implantation period. Machine learning models revealed that plasma microRNAs could predict BPL. RT-qPCR confirmed that miR-181a-2-3p, miR-9-5p, miR-150-3p, miR-150-5p, and miR-98-5p, miR-363-3p were significantly differentially expressed between patients with different reproductive outcomes. CONCLUSION: Our study highlights the non-invasive value of plasma microRNAs in predicting BPL.

The Fabrication of a Customized Surgical Template for a Miniscrew Placement Using a Fully Digitized Process.

This report presents a clinical case involving the application of a computer-aided design and manufacturing (CAD-CAM) guide to insert miniscrew anchorage at the zygomatic alveolar ridge. A 24-year-old male adult came in with overcrowded teeth and a protruding facial profile, particularly severe overcrowding in the upper teeth and moderate overcrowding in the lower teeth. The orthodontic treatment plan involved extracting four first premolars and adding a mini-implant in the upper jaw to enhance anchorage. A miniscrew was placed in the patient's left zygomatic alveolar ridge using a guide and in the right zygomatic alveolar ridge based on experience. The use of a mini-implant guide improves the accuracy of mini-implant positioning and angulation in the infrazygomatic crest zone, reduces the risk of tooth root damage, and enhances mini-implant stability.