A Phase II randomized controlled trial of oral prednisolone in early diffuse cutaneous systemic sclerosis (PRedSS).

OBJECTIVES: Although the painful and disabling features of early diffuse cutaneous SSc (dcSSc) have an inflammatory basis and could respond to corticosteroids, corticosteroids are a risk factor for scleroderma renal crisis. Whether or not they should be prescribed is therefore highly contentious. Our aim was to examine safety and efficacy of moderate-dose prednisolone in early dcSSc. METHODS: PRedSS set out as a Phase II, multicentre, double-blind randomized controlled trial, converted to open-label during the Covid-19 pandemic. Patients were randomized to receive either prednisolone (∼0.3 mg/kg) or matching placebo (or no treatment during open-label) for 6 months. Co-primary endpoints were the HAQ Disability Index (HAQ-DI) and modified Rodnan skin score (mRSS) at 3 months. Over 20 secondary endpoints included patient reported outcome measures reflecting pain, itch, fatigue, anxiety and depression, and helplessness. Target recruitment was 72 patients. RESULTS: Thirty-five patients were randomized (17 prednisolone, 18 placebo/control). The adjusted mean difference between treatment groups at 3 months in HAQ-DI score was -0.10 (97.5% CI: -0.29, 0.10), P = 0.254, and in mRSS -3.90 (97.5% CI: -8.83, 1.03), P = 0.070, both favouring prednisolone but not significantly. Patients in the prednisolone group experienced significantly less pain (P = 0.027), anxiety (P = 0.018) and helplessness (P = 0.040) than control patients at 3 months. There were no renal crises, but sample size was small. CONCLUSION: PRedSS was terminated early primarily due to the Covid-19 pandemic, and so was underpowered. Therefore, interpretation must be cautious and results considered inconclusive, indicating the need for a further randomized trial. TRIAL REGISTRATION: ClinicalTrials.gov, https://clinicaltrials.gov, NCT03708718.

Secure Bluetooth Communication in Smart Healthcare Systems: A Novel Community Dataset and Intrusion Detection System.

Smart health presents an ever-expanding attack surface due to the continuous adoption of a broad variety of Internet of Medical Things (IoMT) devices and applications. IoMT is a common approach to smart city solutions that deliver long-term benefits to critical infrastructures, such as smart healthcare. Many of the IoMT devices in smart cities use Bluetooth technology for short-range communication due to its flexibility, low resource consumption, and flexibility. As smart healthcare applications rely on distributed control optimization, artificial intelligence (AI) and deep learning (DL) offer effective approaches to mitigate cyber-attacks. This paper presents a decentralized, predictive, DL-based process to autonomously detect and block malicious traffic and provide an end-to-end defense against network attacks in IoMT devices. Furthermore, we provide the BlueTack dataset for Bluetooth-based attacks against IoMT networks. To the best of our knowledge, this is the first intrusion detection dataset for Bluetooth classic and Bluetooth low energy (BLE). Using the BlueTack dataset, we devised a multi-layer intrusion detection method that uses deep-learning techniques. We propose a decentralized architecture for deploying this intrusion detection system on the edge nodes of a smart healthcare system that may be deployed in a smart city. The presented multi-layer intrusion detection models achieve performances in the range of 97-99.5% based on the F1 scores.

Hybrid surgical approach excision of gastrointestinal stromal tumor (GIST): A case report of GIST at an unusual location and review of the literature.

Gastrointestinal stromal tumors are the most common malignant subepithelial lesions involving the gastrointestinal tract. Surgical techniques have been the mainstay of treatment, however, in recent times hybrid surgeries are being introduced yielding better clinical outcomes.

Speech as a Biomarker for COVID-19 Detection Using Machine Learning.

The use of speech as a biomedical signal for diagnosing COVID-19 is investigated using statistical analysis of speech spectral features and classification algorithms based on machine learning. It is established that spectral features of speech, obtained by computing the short-time Fourier Transform (STFT), get altered in a statistical sense as a result of physiological changes. These spectral features are then used as input features to machine learning-based classification algorithms to classify them as coming from a COVID-19 positive individual or not. Speech samples from healthy as well as "asymptomatic" COVID-19 positive individuals have been used in this study. It is shown that the RMS error of statistical distribution fitting is higher in the case of speech samples of COVID-19 positive speech samples as compared to the speech samples of healthy individuals. Five state-of-the-art machine learning classification algorithms have also been analyzed, and the performance evaluation metrics of these algorithms are also presented. The tuning of machine learning model parameters is done so as to minimize the misclassification of COVID-19 positive individuals as being COVID-19 negative since the cost associated with this misclassification is higher than the opposite misclassification. The best performance in terms of the "recall" metric is observed for the Decision Forest algorithm which gives a recall value of 0.7892.

Trends and outcomes of chronic coronary total occlusion-related ventricular tachyarrhythmias.

Our study aims to establish trends and frequencies of ventricular tachyarrhythmia (VTA) among patients with chronic coronary total occlusion (CCTO). We identified CCTO hospitalizations with and without VTA using the National Inpatient Sample. A total of 911,579 CCTO-related hospitalizations were identified, with 92,450 (10.1%) encounters associated with VTA. The CCTO-VTA cohort showed higher all-cause mortality (adjusted odds ratio [aOR] = 4.45, P < 0.001), longer hospital stays (6.8 vs 4.6 days; P < 0.001), and higher hospital charges ($117,382 vs $75,419; P < 0.001) compared to the CCTO non-VTA group. Rates and odds of cardiogenic shock (aOR = 4.19), venous thromboembolism (aOR = 2.09), respiratory failure (aOR = 2.85), and requirement of mechanical ventilation (aOR = 4.23) were higher in the CCTO-VTA group (P < 0.001). Over time, there was an increase in VTA (9.2% in 2010 to 12.1% in 2014) and all-cause mortality (7.5% in 2010 to 12.4% in 2014; P < 0.001). Trends in VTA among patients with CCTO increased by 4.8% for undergoing percutaneous coronary intervention and by 2.5% for undergoing both percutaneous coronary intervention and coronary artery bypass grafting (P < 0.001). Occurrence of VTA among CCTO patients is associated with worse outcomes and higher resource utilization.

Sickle cell disease-associated arrhythmias and in-hospital outcomes: Insights from the National Inpatient Sample.

BACKGROUND: The frequency and temporal trend in the prevalence of arrhythmias and associated in-hospital outcomes in patients with sickle cell disease (SCD) have never been quantified. METHODS: Our study cohort of SCD patients and sub-types of arrhythmias were derived from the 2010-2014 National Inpatient Sample using relevant diagnostic codes. The frequency and trends of arrhythmia and odds of inpatient mortality were measured. RESULTS: A total of 891 450 hospitalized SCD patients were identified, of which, 55 616 (6.2%) patients experienced arrhythmias. The SCD cohort with arrhythmia demonstrated higher all-cause mortality (2.7% vs 0.4%; adjusted OR 2.53, 95% CI 2.15-2.97, P < .001), prolonged hospital stays (6.9 vs 5.0 days) and higher hospital charges ($53 871 vs $30 905) relative to those without arrhythmias (P < .001).The frequency of supraventricular arrhythmia (AFib, SVT, and AF) and ventricular arrhythmia (VFib and VT) were 1893 and 362 per 100 000 SCD-related admissions, respectively. Unspecified arrhythmias (4126) were seen most frequently followed by AFib (1622) per 100 000 SCD-related admissions. From 2010 to 2014, the frequency of any arrhythmias and atrial fibrillation in hospitalized SCD patients relatively increased by 29.6% and 38.5%, respectively. There was nearly a twofold (2.4% in 2010 to 5.0% in 2014) increase in the frequency of arrhythmia among patients aged <18 years. The frequency of arrhythmias in hospitalized male and female SCD patients relatively increased by 28.8% and 31.4%, respectively (P trend < .001). CONCLUSIONS: The frequency of arrhythmias among SCD patients is on the rise with worse hospitalization outcomes, including higher in-hospital mortality and higher resource utilization as compared to those without arrhythmias.

Molecular phenotyping of a UK population: defining the human serum metabolome.

Phenotyping of 1,200 'healthy' adults from the UK has been performed through the investigation of diverse classes of hydrophilic and lipophilic metabolites present in serum by applying a series of chromatography-mass spectrometry platforms. These data were made robust to instrumental drift by numerical correction; this was prerequisite to allow detection of subtle metabolic differences. The variation in observed metabolite relative concentrations between the 1,200 subjects ranged from less than 5 % to more than 200 %. Variations in metabolites could be related to differences in gender, age, BMI, blood pressure, and smoking. Investigations suggest that a sample size of 600 subjects is both necessary and sufficient for robust analysis of these data. Overall, this is a large scale and non-targeted chromatographic MS-based metabolomics study, using samples from over 1,000 individuals, to provide a comprehensive measurement of their serum metabolomes. This work provides an important baseline or reference dataset for understanding the 'normal' relative concentrations and variation in the human serum metabolome. These may be related to our increasing knowledge of the human metabolic network map. Information on the Husermet study is available at http://www.husermet.org/. Importantly, all of the data are made freely available at MetaboLights (http://www.ebi.ac.uk/metabolights/).

Hybrid mesh for nasal airflow studies.

The accuracy of the numerical result is closely related to mesh density as well as its distribution. Mesh plays a very significant role in the outcome of numerical simulation. Many nasal airflow studies have employed unstructured mesh and more recently hybrid mesh scheme has been utilized considering the complexity of anatomical architecture. The objective of this study is to compare the results of hybrid mesh with unstructured mesh and study its effect on the flow parameters inside the nasal cavity. A three-dimensional nasal cavity model is reconstructed based on computed tomographic images of a healthy Malaysian adult nose. Navier-Stokes equation for steady airflow is solved numerically to examine inspiratory nasal flow. The pressure drop obtained using the unstructured computational grid is about 22.6 Pa for a flow rate of 20 L/min, whereas the hybrid mesh resulted in 17.8 Pa for the same flow rate. The maximum velocity obtained at the nasal valve using unstructured grid is 4.18 m/s and that with hybrid mesh is around 4.76 m/s. Hybrid mesh reported lower grid convergence index (GCI) than the unstructured mesh. Significant differences between unstructured mesh and hybrid mesh are determined highlighting the usefulness of hybrid mesh for nasal airflow studies.

The administration of entacapone prevents L-dopa-induced dyskinesia when added to dopamine agonist therapy in MPTP-treated primates.

More continuous delivery of l-3,4-dihydroxyphenylalanine (l-dopa) achieved by combination with the catechol-O-methyl transfer (COMT) inhibitor entacapone reduces the onset of dyskinesia in MPTP-treated common marmosets compared with pulsatile l-dopa regimens. We now investigate whether l-dopa delivery also influences dyskinesia induction when added to dopamine agonist treatment. Drug-naive 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine hydrochloride (MPTP)-treated common marmosets were treated with ropinirole twice daily (BID) for 14 days which reversed motor disability and increased locomotor activity with minimal dyskinesia. Ropinirole treatment was continued but some animals also received l-dopa BID or four times daily (QID) with and without entacapone or vehicle for a further 16 days. Continuing ropinirole treatment alone maintained a similar reversal of motor deficits and low levels of dyskinesia for the first 14 days and the second 16 days. The addition of l-dopa BID or QID without entacapone produced only a minor further reversal of motor deficits, but significantly increased the intensity of dyskinesia. In contrast, the addition of l-dopa BID or QID with entacapone also produced some further improvement in motor function with the combination of entacapone and l-dopa BID significantly improving motor disability compared to l-dopa alone, but no further increase in dyskinesia intensity was observed compared with ropinirole alone treatment. The results show that combined treatment with l-dopa and entacapone has a marked effect on dyskinesia induction even when therapy has been introduced with a dopamine agonist.

Antiparkinsonian activity of L-propyl-L-leucyl-glycinamide or melanocyte-inhibiting factor in MPTP-treated common marmosets.

The neuropeptide melanocyte-inhibiting factor (MIF) or L-propyl-L-leucyl-glycinamide (PLG) has been reported in some studies to improve the motor signs of Parkinson's disease (PD) and in rodent models of PD. In this study of oral and intravenous MIF in N-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-lesioned marmosets, a wide range of doses of MIF administered alone (0.25, 1, 2, 5, 10, 20 mg/kg orally) did not increase locomotor activity, relieve motor disability, or induce dyskinesias. When MIF (1.0 and 5.0 mg/kg orally or 10 and 20 mg/kg intravenously) was administered concomitantly with levodopa/benserazide, no significant differences in motor function or dyskinesias were observed compared with levodopa/benserazide alone. The results of this first study of MIF in the marmoset MPTP model provide no encouragement for the reinvestigation of MIF in the clinical management of the motor signs of PD.