Objective dyspnea evaluation on COVID-19 patients learning from exertion-induced dyspnea scores

Objective: Dyspnea is one of the most common symptoms for many pulmonary diseases including COVID-19. Clinical assessment of dyspnea is mainly performed by subjective self-report, which has limited accuracy and is challenging for continuous monitoring. The objective of this research study is to determine if dyspnea progression in COVID patients can be assessed using a non-invasive wearable sensor and if the findings are comparable to a learning model of physiologically induced dyspnea on healthy subjects. Methods: Non-invasive wearable respiratory sensors were employed to retrieve continuous respiratory characteristics with user comfort and convenience. Overnight (~16h) respiratory waveforms were collected on 12 COVID-19 patients, and a benchmark on 13 healthy subjects with exertion-induced dyspnea were also performed for blind comparison. The learning model was built from the respiratory features with self report on 32 healthy subjects under exertion and airway blockage. Results: High similarity between dyspnea on COVID patients and physiologically induced dyspnea on healthy subjects was established. COVID patients have consistently high objective dyspnea scores in comparison with normal breathing of healthy subjects. We also exhibited continuous dyspnea scoring capability for 12-16 hours on patients. Conclusion: This paper validates the viability to use our objective dyspnea scoring for clinical dyspnea assessment on COVID patients. Significance: The proposed system can help the identification of dyspneic exacerbation in conditions such as COVID, leading to early intervention and possibly improving their outcome. This approach can be potentially applied to other pulmonary disorders such as asthma, emphysema, and pneumonia

Creation of a novel algorithm to identify patients with Becker and Duchenne muscular dystrophy within an administrative database and application of the algorithm to assess cardiovascular morbidity

BACKGROUND: Outcome analyses in large administrative databases are ideal for rare diseases such as Becker and Duchenne muscular dystrophy. Unfortunately, Becker and Duchenne do not yet have specific International Classification of Disease-9/-10 codes. We hypothesised that an algorithm could accurately identify these patients within administrative data and improve assessment of cardiovascular morbidity. METHODS: Hospital discharges (n=13,189) for patients with muscular dystrophy classified by International Classification of Disease-9 code: 359.1 were identified from the Pediatric Health Information System database. An identification algorithm was created and then validated at three institutions. Multi-variable generalised linear mixed-effects models were used to estimate the associations of length of stay, hospitalisation cost, and 14-day readmission with age, encounter severity, and respiratory disease accounting for clustering within the hospital. RESULTS: The identification algorithm improved identification of patients with Becker and Duchenne from 55% (code 359.1 alone) to 77%. On bi-variate analysis, left ventricular dysfunction and arrhythmia were associated with increased cost of hospitalisation, length of stay, and mortality (p<0.001). After adjustment, Becker and Duchenne patients with left ventricular dysfunction and arrhythmia had increased length of stay with rate ratio 1.4 and 1.2 (p<0.001 and p=0.004) and increased cost of hospitalization with rate ratio 1.4 and 1.4 (both p<0.001). CONCLUSIONS: Our algorithm accurately identifies patients with Becker and Duchenne and can be used for future analysis of administrative data. Our analysis demonstrates the significant effects of cardiovascular disease on length of stay and hospitalisation cost in patients with Becker and Duchenne. Better recognition of the contribution of cardiovascular disease during hospitalisation with earlier more intensive evaluation and therapy may help improve outcomes in this patient population

Azithromycin and survival in Streptococcus pneumoniae pneumonia: A retrospective study

OBJECTIVE: Streptococcus pneumoniae (SP) represents a major pathogen in pneumonia. The impact of azithromycin on mortality in SP pneumonia remains unclear. Recent safety concerns regarding azithromycin have raised alarm about this agent's role with pneumonia. We sought to clarify the relationship between survival and azithromycin use in SP pneumonia. DESIGN: Retrospective cohort. SETTING: Urban academic hospital. PARTICIPANTS: Adults with a diagnosis of SP pneumonia (January–December 2010). The diagnosis of pneumonia required a compatible clinical syndrome and radiographic evidence of an infiltrate. INTERVENTION: None. PRIMARY AND SECONDARY OUTCOME MEASURES: Hospital mortality served as the primary endpoint, and we compared patients given azithromycin with those not treated with this. Covariates of interest included demographics, severity of illness, comorbidities and infection-related characteristics (eg, appropriateness of initial treatment, bacteraemia). We employed logistic regression to assess the independent impact of azithromycin on hospital mortality. RESULTS: The cohort included 187 patients (mean age: 67.0±8.2 years, 50.3% men, 5.9% admitted to the intensive care unit). The most frequently utilised non-macrolide antibiotics included: ceftriaxone (n=111), cefepime (n=31) and moxifloxacin (n=22). Approximately two-thirds of the cohort received azithromycin. Crude mortality was lower in persons given azithromycin (5.6% vs 23.6%, p<0.01). The final survival model included four variables: age, need for mechanical ventilation, initial appropriate therapy and azithromycin use. The adjusted OR for mortality associated with azithromycin equalled 0.26 (95% CI 0.08 to 0.80, p=0.018). CONCLUSIONS: SP pneumonia generally remains associated with substantial mortality while azithromycin treatment is associated with significantly higher survival rates. The impact of azithromycin is independent of multiple potential confounders

Metal Complexes as Antifungals? From a Crowd-Sourced Compound Library to the First InVivo{In Vivo} Experiments

There are currently fewer than 10 antifungal drugs in clinical development, but new fungal strains that are resistant to most current antifungals are spreading rapidly across the world. To prevent a second resistance crisis, new classes of antifungal drugs are urgently needed. Metal complexes have proven to be promising candidates for novel antibiotics, but so far, few compounds have been explored for their potential application as antifungal agents. In this work, we report the evaluation of 1039 metal-containing compounds that were screened by the Community for Open Antimicrobial Drug Discovery (CO-ADD). We show that 20.9% of all metal compounds tested have antimicrobial activity against two representative Candida and Cryptococcus strains compared with only 1.1% of the >300,000 purely organic molecules tested through CO-ADD. We identified 90 metal compounds (8.7%) that show antifungal activity while not displaying any cytotoxicity against mammalian cell lines or hemolytic properties at similar concentrations. The structures of 21 metal complexes that display high antifungal activity (MIC ≤1.25 μM) are discussed and evaluated further against a broad panel of yeasts. Most of these have not been previously tested for antifungal activity. Eleven of these metal complexes were tested for toxicity in the Galleria mellonella moth larva model, revealing that only one compound showed signs of toxicity at the highest injected concentration. Lastly, we demonstrated that the organo-Pt(II) cyclooctadiene complex $\textbf{Pt1}$ significantly reduces fungal load in an in vivoG. mellonella infection model. These findings showcase that the structural and chemical diversity of metal-based compounds can be an invaluable tool in the development of new drugs against infectious diseases

Ipsilesional Mu Rhythm Desynchronization and Changes in Motor Behavior Following Post Stroke BCI Intervention for Motor Rehabilitation

Loss of motor function is a common deficit following stroke insult and often manifests as persistent upper extremity (UE) disability which can affect a survivor’s ability to participate in activities of daily living. Recent research suggests the use of brain–computer interface (BCI) devices might improve UE function in stroke survivors at various times since stroke. This randomized crossover-controlled trial examines whether intervention with this BCI device design attenuates the effects of hemiparesis, encourages reorganization of motor related brain signals (EEG measured sensorimotor rhythm desynchronization), and improves movement, as measured by the Action Research Arm Test (ARAT). A sample of 21 stroke survivors, presenting with varied times since stroke and levels of UE impairment, received a maximum of 18–30 h of intervention with a novel electroencephalogram-based BCI-driven functional electrical stimulator (EEG-BCI-FES) device. Driven by spectral power recordings from contralateral EEG electrodes during cued attempted grasping of the hand, the user’s input to the EEG-BCI-FES device modulates horizontal movement of a virtual cursor and also facilitates concurrent stimulation of the impaired UE. Outcome measures of function and capacity were assessed at baseline, mid-therapy, and at completion of therapy while EEG was recorded only during intervention sessions. A significant increase in r-squared values [reflecting Mu rhythm (8–12 Hz) desynchronization as the result of attempted movements of the impaired hand] presented post-therapy compared to baseline. These findings suggest that intervention corresponds with greater desynchronization of Mu rhythm in the ipsilesional hemisphere during attempted movements of the impaired hand and this change is related to changes in behavior as a result of the intervention. BCI intervention may be an effective way of addressing the recovery of a stroke impaired UE and studying neuromechanical coupling with motor outputs.Clinical Trial Registration:ClinicalTrials.gov, identifier NCT02098265