A Predictive Nomogram for Predicting Improved Clinical Outcome Probability in Patients with COVID-19 in Zhejiang Province,China

The aim of this research was to develop a quantitative method for clinicians to predict the probability of improved prognosis in patients with coronavirus disease 2019(COVID-19).Data on 104 patients admitted to hospital with laboratory-confirmed COVID-19 infection from 10 January 2020 to 26 February 2020 were collected.Clinical information and laboratory findings were collected and compared between the outcomes of improved patients and non-improved patients.The least absolute shrinkage and selection operator(LASSO)logistics regression model and two-way stepwise strategy in the multivariate logistics regression model were used to select prognostic factors for predicting clinical outcomes in COVID-19 patients.The concordance index(C-index)was used to assess the discrimination of the model,and internal validation was performed through bootstrap resampling.A novel predictive nomogram was constructed by incorporating these features.Of the 104 patients included in the study(median age 55 years),75(72.1%)had improved short-term outcomes,while 29(27.9%)showed no signs of improvement.There were numerous differences in clinical characteristics and laboratory findings between patients with improved outcomes and patients without improved outcomes.After a multi-step screening process,prognostic factors were selected and incorporated into the nomogram construction,including immunoglobulin A(IgA),C-reactive protein(CRP),creatine kinase(CK),acute physiology and chronic health evaluation II(APACHE II),and interaction between CK and APACHE II.The C-index of our model was 0.962(95%confidence interval(CI),0.931-0.993)and still reached a high value of 0.948 through bootstrapping validation.A predictive nomogram we further established showed close performance compared with the ideal model on the calibration plot and was clinically practical according to the decision curve and clinical impact curve.The nomogram we constructed is useful for clinicians to predict improved clinical outcome probability for each COVID-19 patient,which may facilitate personalized counselling and treatment.

Simultaneous wireless information and power transfer transmitting architecture based on asynchronous space-time-coding digital metasurface[Invited]

Simultaneous wireless information and power transfer(SWIPT)architecture is commonly applied in wireless sensors or Internet of Things(IoT)devices,providing both wireless power sources and communication channels.However,the traditional SWIPT transmitter usually suffers from cross-talk distortion caused by the high peak-to-average power ratio of the input signal and the reduction of power amplifier efficiency.This paper proposes a SWIPT transmitting architecture based on an asynchronous space-time-coding digital metasurface(ASTCM).High-efficiency simultaneous transfer of information and power is achieved via energy distribution and information processing of the wireless monophonic signal reflected from the metasurface.We demonstrate the feasibility of the proposed method through theoretical derivations and experimental verification,which is therefore believed to have great potential in wireless communications and the IoT devices.

Manipulating the Gut Microbiome to Alleviate Steatotic Liver Disease:Current Progress and Challenges

The prevalence of metabolic-dysfunction-associated steatotic liver disease(MASLD)is alarmingly high;it is estimated to affect up to a quarter of the global population,making it the most common liver disorder worldwide.MASLD is characterized by excessive hepatic fat accumulation and is commonly associated with comorbidities such as obesity,dyslipidemia,and insulin resistance;however,it can also manifest in lean individuals.Therefore,it is crucial to develop effective therapies for this complex condition.Currently,there are no approved medications for MASLD treatment,so there is a pressing need to investigate alternative approaches.Extensive research has characterized MASLD as a multifaceted disease,frequently linked to metabolic disorders that stem from dietary habits.Evidence suggests that changes in the gut microbiome play a fundamental role in the development and progression of MASLD from simple steatosis to steatohepatitis and even hepatocellular carcinoma(HCC).In this review,we critically examine the literature on the emerging field of gut-microbiota-based therapies for MASLD and metabolicdysfunction-associated steatohepatitis(MASH),including interventions such as fecal microbiota transplantation(FMT),probiotics,prebiotics,short-chain fatty acids,antibiotics,metabolic pathway targeting,and immune checkpoint kinase blockade.