BackgroundObesity is a multifaceted disease characterized by an abnormal accumulation of adipose tissue. Growing evidence has proposed microbiota-derived metabolites as a potential factor in the pathophysiology of obesity and related metabolic conditions over the last decade. As one of the essential metabolites, butyrate affects several host cellular mechanisms related to appetite sensations and weight control. However, the effects of butyrate on obesity in humans have yet to be studied. Thus, the present study was aimed to evaluate the effects of sodium butyrate (SB) supplementation on the expression levels of peroxisome proliferator activated-receptor (PPAR) gamma coactivator-1α (PGC-1α), PPARα and uncoupling protein 1 (UCP1) genes, serum level of glucagon-like peptide (GLP1), and metabolic parameters, as well as anthropometric indices in obese individuals on a weight loss diet.MethodsThis triple-blind randomized controlled trial (RCT) will include 50 eligible obese subjects aged between 18 and 60 years. Participants will be randomly assigned into two groups: 8 weeks of SB (600 mg/day) + hypo-caloric diet or placebo (600 mg/day) + hypo-caloric diet. At weeks 0 and 8, distinct objectives will be pursued: (1) PGC-1α, PPARα, and UCP1 genes expression will be evaluated by real-time polymerase chain reaction; (2) biochemical parameters will be assayed using enzymatic methods; and (3) insulin and GLP1 serum level will be assessed by enzyme-linked immunosorbent assay kit.DiscussionNew evidence from this trial may help fill the knowledge gap in this realm and facilitate multi-center clinical trials with a substantially larger sample size.Trial registrationIranian Registry of Clinical Trials: IRCT20190303042905N2. Registered on 31 January 2021.

BackgroundFrailty is expressed by a reduction in physical capacity, mobility, muscle strength, and endurance. (Pre-)frailty is present in up to 42% of the older surgical population, with an increased risk for peri- and postoperative complications. Consequently, these patients often suffer from a delayed or limited recovery, loss of autonomy and quality of life, and a decrease in functional and cognitive capacities. Since frailty is modifiable, prehabilitation may improve the physiological reserves of patients and reduce the care dependency 12 months after surgery.MethodsPatients ≥ 70 years old scheduled for elective surgery or intervention will be recruited in this multicenter, randomized controlled study, with a target of 1400 participants with an allocation ratio of 1:1. The intervention consists of (1) a shared decision-making process with the patient, relatives, and an interdisciplinary and interprofessional team and (2) a 3-week multimodal, individualized prehabilitation program including exercise therapy, nutritional intervention, mobility or balance training, and psychosocial interventions and medical assessment. The frequency of the supervised prehabilitation is 5 times/week for 3 weeks. The primary endpoint is defined as the level of care dependency 12 months after surgery or intervention.DiscussionPrehabilitation has been proven to be effective for different populations, including colorectal, transplant, and cardiac surgery patients. In contrast, evidence for prehabilitation in older, frail patients has not been clearly established. To the best of our knowledge, this is currently the largest prehabilitation study on older people with frailty undergoing general elective surgery.Trial registrationClinicalTrials.gov NCT04418271. Registered on 5 June 2020. Universal Trial Number (UTN): U1111-1253-4820

BackgroundPromoting of local healthcare planning is crucial for assisting public health nurses in improving community health inequities. However, there is no effective educational program for developing relevant skills and knowledge among these nurses. Therefore, this study aims to assess the feasibility of a newly developed web-based self-learning program to promote the involvement of public health nurses in the local healthcare planning process.MethodsA pilot randomized control trial randomly allocated eligible public health nurses to intervention and control wait-list groups [1:1]. The former will be exposed to six web-based learning modules from July to October 2021. After collecting post-test data, the wait-list group will be exposed to the same modules to ensure learning equity. The primary outcome will be evaluated by implementing a validated and standardized scale designed to measure public health policy competencies at the baseline and post-intervention, while secondary outcome will be measured on an action scale to demonstrate the necessity of healthcare activities. The third outcome will be the knowledge and skills related to local healthcare planning by public health nurses. The participants will provide feedback through free descriptions on the trial feasibility and a web-based self-learning program to identify improvement points for continual refinement.DiscussionThe results will provide suggestions in preparation for a future definitive randomized controlled trial. This will provide preliminary data for an intervention aimed at improving relevant competencies among public health nurses who are tasked with resolving health inequities in their respective communities through local health planning.Trial registrationThe protocol for this study was registered with the University Hospital Medical Information Network Clinical Trials Registry and approved by the International Committee of Medical Journal Editors (No. UMIN000043628, March 23, 2021).

BackgroundInterstitial lung diseases (ILD), such as idiopathic pulmonary fibrosis (IPF) and non-specific interstitial pneumonia (NSIP), and chronic obstructive pulmonary disease (COPD) are severe, progressive pulmonary disorders with a poor prognosis. Prompt and accurate diagnosis is important to enable patients to receive appropriate care at the earliest possible stage to delay disease progression and prolong survival. Artificial intelligence-assisted lung auscultation and ultrasound (LUS) could constitute an alternative to conventional, subjective, operator-related methods for the accurate and earlier diagnosis of these diseases. This protocol describes the standardised collection of digitally-acquired lung sounds and LUS images of adult outpatients with IPF, NSIP or COPD and a deep learning diagnostic and severity-stratification approach.MethodsA total of 120 consecutive patients (≥ 18 years) meeting international criteria for IPF, NSIP or COPD and 40 age-matched controls will be recruited in a Swiss pulmonology outpatient clinic, starting from August 2022. At inclusion, demographic and clinical data will be collected. Lung auscultation will be recorded with a digital stethoscope at 10 thoracic sites in each patient and LUS images using a standard point-of-care device will be acquired at the same sites. A deep learning algorithm (DeepBreath) using convolutional neural networks, long short-term memory models, and transformer architectures will be trained on these audio recordings and LUS images to derive an automated diagnostic tool. The primary outcome is the diagnosis of ILD versus control subjects or COPD. Secondary outcomes are the clinical, functional and radiological characteristics of IPF, NSIP and COPD diagnosis. Quality of life will be measured with dedicated questionnaires. Based on previous work to distinguish normal and pathological lung sounds, we estimate to achieve convergence with an area under the receiver operating characteristic curve of > 80% using 40 patients in each category, yielding a sample size calculation of 80 ILD (40 IPF, 40 NSIP), 40 COPD, and 40 controls.DiscussionThis approach has a broad potential to better guide care management by exploring the synergistic value of several point-of-care-tests for the automated detection and differential diagnosis of ILD and COPD and to estimate severity.Trial registration Registration: August 8, 2022. ClinicalTrials.gov Identifier: NCT05318599.