Drug resistance revealed by in silico deep mutational scanning and mutation tracker (preprint)

As COVID-19 enters its fifth year, it continues to pose a significant global health threat, with the constantly mutating SARS-CoV-2 virus challenging drug effectiveness. A comprehensive understanding of virus-drug interactions is essential for predicting and improving drug effectiveness, especially in combating drug resistance during the pandemic. In response, the Path Laplacian Transformer-based Prospective Analysis Framework (PLFormer-PAF) has been proposed, integrating historical data analysis and predictive modeling strategies. This dual-strategy approach utilizes path topology to transform protein-ligand complexes into topological sequences, enabling the use of advanced large language models for analyzing protein-ligand interactions and enhancing its reliability with factual insights garnered from historical data. It has shown unparalleled performance in predicting binding affinity tasks across various benchmarks, including specific evaluations related to SARS-CoV-2, and assesses the impact of virus mutations on drug efficacy, offering crucial insights into potential drug resistance. The predictions align with observed mutation patterns in SARS-CoV-2, indicating that the widespread use of the Pfizer drug has lead to viral evolution and reduced drug efficacy. PLFormer-PAF's capabilities extend beyond identifying drug-resistant strains, positioning it as a key tool in drug discovery research and the development of new therapeutic strategies against fast-mutating viruses like COVID-19.

A study on how to improve users' perceived playfulness in and continuance intention with VR technology to paint in virtual natural landscapes.

COVID-19 has adversely affected public access to public green spaces. As a means of interacting with nature, parks and green spaces are an important aspect of residents' daily lives. In this study, the focus is on new digital solutions, such as the experience of painting in virtual natural settings through virtual reality technologies. This study examines factors that affect the user's perceived playfulness and continuance intention to paint in a virtual environment. A total of 732 valid samples were collected through a questionnaire survey, and a theoretical model was developed through structural equation model by analyzing attitude, perceived behavioral control, behavioral intention, continuance intention, and perceived playfulness. Results demonstrate that perceived novelty and perceived sustainability increase the positive attitude of users toward VR painting functions, whereas perceived interactivity and aesthetics have no impact on it within VR painting context. As users use VR painting, they are more concerned about time and money than equipment compatibility. This makes resource facilitating conditions a more influential factor for perceived behavior control than technology facilitating conditions.

The clinical significance of macrolide resistance in pediatric Mycoplasma pneumoniae infection during COVID-19 pandemic.

Background: Mycoplasma pneumoniae (MP) is a commonly occurring pathogen causing community-acquired pneumonia (CAP) in children. The global prevalence of macrolide-resistant MP (MRMP) infection, especially in Asian regions, is increasing rapidly. However, the prevalence of MRMP and its clinical significance during the COVID-19 pandemic is not clear. Methods: This study enrolled children with molecularly confirmed macrolide-susceptible MP (MSMP) and MRMP CAP from Beijing Children's Hospital Baoding Hospital, Capital Medical University between August 2021 and July 2022. The clinical characteristics, laboratory findings, chest imaging presentations, and strain genotypes were compared between patients with MSMP and MRMP CAP. Results: A total of 520 hospitalized children with MP-CAP were enrolled in the study, with a macrolide resistance rate of 92.7%. Patients with MRMP infection exhibited more severe clinical manifestations (such as dyspnea and pleural effusion) and had a longer hospital stay than the MSMP group. Furthermore, abnormal blood test results (including increased LDH and D-dimer) were more common in the MRMP group (P<0.05). Multilocus variable-number tandem-repeat analysis (MLVA) was performed on 304 samples based on four loci (Mpn13-16), and M3562 and M4572 were the major types, accounting for 74.0% and 16.8% of the strains, respectively. The macrolide resistance rate of M3562 strains was up to 95.1%. Conclusion: The prevalence of MRMP strains in hospitalized CAP patients was extremely high in the Baoding area, and patients infected with MRMP strains exhibited more severe clinical features and increased LDH and D-dimer. M3562 was the predominant resistant clone.

De-black-boxing health AI: demonstrating reproducible machine learning computable phenotypes using the N3C-RECOVER Long COVID model in the All of Us data repository.

Machine learning (ML)-driven computable phenotypes are among the most challenging to share and reproduce. Despite this difficulty, the urgent public health considerations around Long COVID make it especially important to ensure the rigor and reproducibility of Long COVID phenotyping algorithms such that they can be made available to a broad audience of researchers. As part of the NIH Researching COVID to Enhance Recovery (RECOVER) Initiative, researchers with the National COVID Cohort Collaborative (N3C) devised and trained an ML-based phenotype to identify patients highly probable to have Long COVID. Supported by RECOVER, N3C and NIH's All of Us study partnered to reproduce the output of N3C's trained model in the All of Us data enclave, demonstrating model extensibility in multiple environments. This case study in ML-based phenotype reuse illustrates how open-source software best practices and cross-site collaboration can de-black-box phenotyping algorithms, prevent unnecessary rework, and promote open science in informatics.

Exploration of patients' preference for modalities of care among peritoneal dialysis patients in Singapore: a single-center experience.

INTRODUCTION: Peritoneal dialysis (PD) is home-based dialysis therapy and therefore a suitable modality for kidney failure patients, particularly, during the COVID-19 pandemic. The present study examined patients' preferences for different PD-related services. METHODS: This was a cross-sectional survey study. Anonymized data from PD patients followed up at a single center in Singapore were collected using an online platform. The study focused on telehealth services, home visits, and monitoring of quality-of-life (QoL). RESULTS: A total of 78 PD patients responded to the survey. The majority of participants were Chinese (76%), married (73%), and between 45 and 65 years old (45%). The in-person visit was preferred over teleconsultation for consultation with nephrologists (68% versus 32%), counseling for kidney disease and dialysis by renal coordinators (59%), whereas the telehealth service was favored over in-person visit for dietary counseling (60%) and medication counseling (64%). Most participants (81%) preferred medication delivery over self-collection, and the acceptable turnaround time was 1 week. Sixty percent would like to have a regular home visit, but 23% refused such visits. The preferred frequency of home visits was one-to-three visits within the first 6 months (74%) and then 6 monthly for subsequent visits (40%). The majority of participants (87%) agreed with QoL monitoring, and the preferred frequency of monitoring varied between 6 monthly (45%) and yearly (40%). Participants also indicated three key areas in research to improve QoL, such as the development of artificial kidneys, portable PD devices, and simplification of PD procedure. Participants also would like to see improvement in two main areas of PD services, such as delivery service for PD solutions and social (instrumental, informational, and emotional) support. CONCLUSIONS: Most PD patients preferred in-person visits with nephrologists or renal coordinators; however, they favored telehealth services with dieticians and pharmacists. PD patients also welcomed home visit service and QoL monitoring. Future studies should confirm these findings.

Engineering highly efficient NIR-II FRET platform for Background-Free homogeneous detection of SARS-CoV-2 neutralizing antibodies in whole blood.

Förster or fluorescence resonance energy transfer (FRET) enables to probe biomolecular interactions, thus playing a vital role in bioassays. However, conventional FRET platforms suffer from limited sensitivity due to the low FRET efficiency and poor anti-interference of existing FRET pairs. Here we report a NIR-II (1000-1700 nm) FRET platform with extremely high FRET efficiency and exceptional anti-interference capability. This NIR-II FRET platform is established based on a pair of lanthanides downshifting nanoparticles (DSNPs) by employing Nd3+ doped DSNPs as an energy donor and Yb3+ doped DSNPs as an energy acceptor. The maximum FRET efficiency of this well-engineered NIR-II FRET platform reaches up to 92.2%, which is much higher than most commonly used ones. Owing to the all-NIR advantage (λex = 808 nm, λem = 1064 nm), this highly efficient NIR-II FRET platform exhibits extraordinary anti-interference in whole blood, and thus enabling background-free homogeneous detection of SARS-CoV-2 neutralizing antibodies in clinical whole blood sample with high sensitivity (limit of detection = 0.5 µg/mL) and specificity. This work opens up new opportunities for realizing highly sensitive detection of various biomarkers in biological samples with severe background interference.

Handheld NIR-to-NIR Platform for on-site evaluating protective neutralizing antibody against SARS-CoV-2 ancestral strain and Omicron variant after vaccination or infection

Lateral flow assays (LFAs) are promising points-of-care tests, playing a vital role in diseases screening, diagnosis and surveillance. However, development of portable, cheap, and smart LFAs platform for sensitive and accurate quantification of disease biomarkers in complex media is challenging. Here, a cheap handheld device was developed to realize on-site detection of disease biomarkers by Nd3+/Yb3+ co-doped near-infrared (NIR)-to-NIR downconversion nanoparticles (DCNPs) based LFA. Its sensitivity is at least 8-fold higher for detecting NIR light signal from Nd3+/Yb3+ co-doped nanoparticles than conventional expensive InGaAs camera based detection platform. Additionally, we enhance NIR quantum yield of Nd3+/Yb3+ co-doped nanoparticles up to 35.5% via simultaneous high dopant of sensitizer ions Nd3+ and emitter ions Yb3+. Combination of NIR-to-NIR handheld detection device and ultra-bright NIR emitting NaNbF4:Yb60%@NaLuF4 nanoparticle probe allows the detection sensitivity of SARS-CoV-2 ancestral strain and Omicron variants specific neutralizing antibodies LFA up to the level of commercial enzyme linked immunosorbent assay kit. Furthermore, by this robust method, enhanced neutralizing antibodies against SARS-CoV-2 ancestral strain and Omicron variants are observed in healthy participants with Ad5-nCoV booster on top of two doses of inactivated vaccine. This NIR-to-NIR handheld platform provides a promising strategy for on-site evaluating protective humoral immunity after SARS-CoV-2 vaccination or infection.

Clinical observation of neutralizing antibody DXP-604 for treatment critical COVID-19 patients infected with SARS-CoV-2 Delta variant.

From January to March 2022, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Delta (B.1.617.2) infection was prevalent in Yuzhou and Zhengzhou. DXP-604 is a broad-spectrum antiviral monoclonal antibody, which has excellent viral neutralization ability in vitro and a long half-life in vivo, with good biosafety and tolerability. Preliminary results showed that DXP-604 can accelerate recovery from Coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 Delta variant in hospitalized patients with mild to moderate clinical symptoms. However, the efficacy of DXP-604 has not been fully studied in high-risk severe patients. Here, we prospectively enrolled 27 high-risk patients, two groups were divided, in addition to receiving standard of care (SOC), 14 of them additionally received the neutralizing antibody DXP-604 therapy, and another 13 intensive care unit (ICU) patients simultaneously underwent SOC as a control group matched for age, gender, and clinical type. The results revealed lower C-reactive protein, interleukin-6, lactic dehydrogenase and neutrophil counts, and higher lymphocyte and monocyte counts from Day 3 post-DXP-604 treatment compared with SOC treatment. Besides, thoracic CT images showed improvements in lesion areas and degrees, along with changes in blood inflammatory factors. Moreover, DXP-604 reduced the invasive mechanical ventilation and mortality of high-risk SARS-CoV-2 infected patients. The ongoing clinical trials of DXP-604 neutralizing antibody will clarify its utility as a new attractive countermeasure for high-risk COVID-19.

Telehealth Policy, Practice, and Education: a Position Statement of the Society of General Internal Medicine.

Telehealth services, specifically telemedicine audio-video and audio-only patient encounters, expanded dramatically during the COVID-19 pandemic through temporary waivers and flexibilities tied to the public health emergency. Early studies demonstrate significant potential to advance the quintuple aim (patient experience, health outcomes, cost, clinician well-being, and equity). Supported well, telemedicine can particularly improve patient satisfaction, health outcomes, and equity. Implemented poorly, telemedicine can facilitate unsafe care, worsen disparities, and waste resources. Without further action from lawmakers and agencies, payment will end for many telemedicine services currently used by millions of Americans at the end of 2024. Policymakers, health systems, clinicians, and educators must decide how to support, implement, and sustain telemedicine, and long-term studies and clinical practice guidelines are emerging to provide direction. In this position statement, we use clinical vignettes to review relevant literature and highlight where key actions are needed. These include areas where telemedicine must be expanded (e.g., to support chronic disease management) and where guidelines are needed (e.g., to prevent inequitable offering of telemedicine services and prevent unsafe or low-value care). We provide policy, clinical practice, and education recommendations for telemedicine on behalf of the Society of General Internal Medicine. Policy recommendations include ending geographic and site restrictions, expanding the definition of telemedicine to include audio-only services, establishing appropriate telemedicine service codes, and expanding broadband access to all Americans. Clinical practice recommendations include ensuring appropriate telemedicine use (for limited acute care situations or in conjunction with in-person services to extend longitudinal care relationships), that the choice of modality be done through patient-clinician shared decision-making, and that health systems design telemedicine services through community partnerships to ensure equitable implementation. Education recommendations include developing telemedicine-specific educational strategies for trainees that align with accreditation body competencies and providing educators with protected time and faculty development resources.

The Effect of Using Augmented Reality Technology in Takeaway Food Packaging to Improve Young Consumers' Negative Evaluations

This paper examines the use of augmented reality technology in the design of packaging for takeaway food to assist in marketing. The research is divided into three studies for progressive investigation and analysis. Study 1 collected 375,859 negative evaluations of food delivery from the Internet and explored the main reasons that may have impacted the user's evaluation by Latent Dirichlet Allocation topic modeling. Study 2 evaluated the effectiveness of augmented reality packaging by surveying 165 subjects and comparing it with traditional packaging. We conducted a survey of 1603 subjects in Study 3 and used the technology incentive model (TIM) to analyze how augmented reality technology positively impacts food delivery marketing. It has been established that packaging will influence the negative perception of consumers about buying and eating takeout food. Specifically, augmented reality technology can improve negative evaluations by providing a more conducive user experience than traditional packaging. According to our findings, augmented reality technology has improved the consumers' perception of interaction, perceived vividness, and novelty experience, and achieved the aim of promoting takeaway food retail by improving negative evaluations posted by users.

Digital transformation for crisis preparedness: service employees' perspective

PurposeDigital transformation (DT) has had a profound impact on how services are delivered, but its effects on service frontline employees in crisis have yet to be examined. Using conservation of resources theory, the purpose of this study is to empirically test the overall effects of DT within service organisations on service employees' beliefs with respect to crisis preparedness, life satisfaction and customer orientation. It also examines the moderating effects of crisis-related anxiety and job experience on these relationships.Design/methodology/approachThis study's hypotheses were tested quantitatively with an online survey of N = 592 frontline service employees working in hospitality and retail services organisation during the crisis of the COVID-19 pandemic. Structural equation modelling was used to analyse the data. A post-hoc study of customer-facing supervisors (N = 268) was conducted to validate the study findings and establish generalisability.FindingsDT predicts service employees' beliefs regarding crisis preparedness. In turn, crisis preparedness increases life satisfaction and customer orientation. Moreover, crisis-related anxiety negatively moderates the relationship between DT and crisis preparedness. Post hoc analyses validate the results derived from service employees' data. Surprisingly, there is no significant relationship between crisis preparedness and life satisfaction for supervisors/managers with low job experience.Originality/valueThis study makes an empirical contribution to the service management literature by examining the impact of DT on service employees' beliefs with respect to crisis preparedness that subsequently influences their life satisfaction and ability to remain customer oriented during a crisis. It highlights an important intersection between technology and service work in terms of a transformative impact of DT on service employee outcomes during crises.

The relationship between high density lipoprotein cholesterol and sepsis: A clinical and genetic approach.

Sepsis accounts for one in three hospital deaths. Higher concentrations of high-density lipoprotein cholesterol (HDL-C) are associated with apparent protection from sepsis, suggesting a potential therapeutic role for HDL-C or drugs, such as cholesteryl ester transport protein (CETP) inhibitors that increase HDL-C. However, these beneficial clinical associations might be due to confounding; genetic approaches can address this possibility. We identified 73,406 White adults admitted to Vanderbilt University Medical Center with infection; 11,612 had HDL-C levels, and 12,377 had genotype information from which we constructed polygenic risk scores (PRS) for HDL-C and the effect of CETP on HDL-C. We tested the associations between predictors (measured HDL-C, HDL-C PRS, CETP PRS, and rs1800777) and outcomes: sepsis, septic shock, respiratory failure, and in-hospital death. In unadjusted analyses, lower measured HDL-C concentrations were significantly associated with increased risk of sepsis (p = 2.4 × 10-23 ), septic shock (p = 4.1 × 10-12 ), respiratory failure (p = 2.8 × 10-8 ), and in-hospital death (p = 1.0 × 10-8 ). After adjustment (age, sex, electronic health record length, comorbidity score, LDL-C, triglycerides, and body mass index), these associations were markedly attenuated: sepsis (p = 2.6 × 10-3 ), septic shock (p = 8.1 × 10-3 ), respiratory failure (p = 0.11), and in-hospital death (p = 4.5 × 10-3 ). HDL-C PRS, CETP PRS, and rs1800777 significantly predicted HDL-C (p < 2 × 10-16 ), but none were associated with sepsis outcomes. Concordant findings were observed in 13,254 Black patients hospitalized with infections. Lower measured HDL-C levels were significantly associated with increased risk of sepsis and related outcomes in patients with infection, but a causal relationship is unlikely because no association was found between the HDL-C PRS or the CETP PRS and the risk of adverse sepsis outcomes.

Professional identity, job satisfaction, and intention to stay among clinical nurses during the prolonged COVID-19 pandemic: A mediation analysis.

AIM: This study aims to examine the relationship between professional identity and job satisfaction and their impact on intention to stay among clinical nurses in China during the prolonged COVID-19 pandemic. METHODS: A cross-sectional survey was conducted from April 30 to May 25, 2021, in China. Data were collected using professional identity, job satisfaction, and intention to stay questionnaires from 1425 clinical nurses. A single mediation analysis was utilized to test the interrelationship among the variables, and the STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) checklist as a reporting guide. RESULTS: Nurses indicated a medium level of professional identity, job satisfaction, and intention to stay, with mean scores of 3.85, 3.25, and 3.47, respectively. The professional identity displayed positive indirect effect on nurses' intention to stay through job satisfaction (indirect effect = 0.498, 95% CI [0.439, 0.558]). CONCLUSION: Cultivating professional identity among nurses can increase their job satisfaction and ultimately enhance the intention to stay in the nursing profession. However, this study also suggests paying more attention to job satisfaction to keep nursing retention. These may be helpful to retain the nursing workforce.

Feasibility Study on Browser-Based Federated Machine Learning (FML) Architecture for Medical Application

Traditional approaches to Artificial Intelligence (AI) based medical image classification requires huge amounts of data sets to be stored in a centralized server for analysis and training. In medical applications, data privacy and ownership may pose a challenge. In addition, costs incurred by data transfer and cloud server may pose a challenge to implementing a large dataset. This work studies the feasibility of a decentralized, browser-based Artificial Intelligence (AI) federated machine learning (FML) architecture. The proposed work studies the feasibility of bringing training and inference to the browser, hence removing the need to transfer raw data to a centralized server. If feasible, the system allows practitioners to compress and upload their pre-trained model to the server instead of raw data. This allows medical practitioners to update the model without the need to reveal their raw data. A sandbox system was implemented by applying transfer learning on MobileNet V3 and was tested with chest X-ray image datasets from COVID-19, viral pneumonia, and normal patients to simulate medical usage environment. The training speed, model performance and inference speed were tested on a PC browser and mobile phone with various levels of network throttling and image degradation. © 2022 IEEE.

Management for stroke intelligent early warning empowered by big data.

Global aging population, especially with the global pandemic outbreak of the Corona Virus Disease 2019 (COVID-19), has endangered human health security. Digital information technology through big data empowerment and intelligent application is widely considered a key element to solve the problems. Stroke is a life-threaten disorder. We studied individual health management and disease risk perception using human health assessment model and make full use of wearable wireless sensor, Internet of Things, big data, and Artificial Intelligence for potential risk monitoring and real-time stroke warning. We proposed an effective method of monitoring, early warning and rescue to improve the stroke treatment. The result shows that the health management empowered by big data can generate new opportunities and ideas to solve early detection and warning of stroke.