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A 50-year-old man underwent intubation for hypercarbic respiratory failure. Multiple pale, rubbery, branching casts were removed by means of bronchoscopy, and the patient ultimately received a diagnosis of plastic bronchitis.

Objectives Considering the therapeutic potential of honey and Nigella sativa (HNS) in coronavirus disease 2019 (COVID-19) patients, the objective of the study is defined to evaluate the prophylactic role of HNS. Trial design The study is a randomized, placebo-controlled, adaptive clinical trial with parallel group design, superiority framework with an allocation ratio of 1:1 among experimental (HNS) and placebo group. An interim analysis will be done when half of the patients have been recruited to evaluate the need to adapt sample size, efficacy, and futility of the trial. Participants All asymptomatic patients with hospital or community based COVID-19 exposure will be screened if they have had 4 days exposure to a confirmed case. Non-pregnant adults with significant exposure level will be enrolled in the study High-risk exposure (<6 feet distance for >10min without face protection) Moderate exposure (<6 feet distance for >10min with face protection) Subjects with acute or chronic infection, COVID-19 vaccinated, and allergy to HNS will be excluded from the study. Recruitment will be done at Shaikh Zayed Post-Graduate Medical Institute, Ali Clinic and Doctors Lounge in Lahore (Pakistan). Intervention and comparator In this clinical study, patients will receive either raw natural honey (0.5 g) and encapsulated organic Nigella sativa seeds (40 mg) per kg body weight per day or empty capsule with and 30 ml of 5% dextrose water as a placebo for 14 days. Both the natural products will be certified for standardization by Government College University (Botany department). Furthermore, each patient will be given standard care therapy according to version 3.0 of the COVID-19 clinical management guidelines by the Ministry of National Health Services of Pakistan. Main outcomes Primary outcome will be Incidence of COVID-19 cases within 14 days of randomisation. Secondary endpoints include incidence of COVID-19-related symptoms, hospitalizations, and deaths along with the severity of COVID-19-related symptoms till 14 th day of randomization. Randomisation Participants will be randomized into experimental and control groups (1:1 allocation ratio) via the lottery method. There will be stratification based on high risk and moderate risk exposure. Blinding (masking) Quadruple blinding will be ensured for the participants, care providers and outcome accessors. Data analysts will also be blinded to avoid conflict of interest. Site principal investigator will be responsible for ensuring masking. Numbers to be randomised (sample size) 1000 participants will be enrolled in the study with 1:1 allocation. Trial Status The final protocol version 1.4 was approved by institutional review board of Shaikh Zayed Post-Graduate Medical Complex on February 15, 2021. The trial recruitment was started on March 05, 2021, with a trial completion date of February 15, 2022. Trial registration Clinical trial was registered on February 23, 2021, www.clinicaltrials.gov with registration ID NCT04767087 . Full protocol The full protocol is attached as an additional file, accessible from the Trials website (Additional file 1). With the intention of expediting dissemination of this trial, the conventional formatting has been eliminated; this Letter serves as a summary of the key elements of the full protocol. The study protocol has been reported in accordance with the Standard Protocol Items: Recommendations for Clinical Interventional Trials (SPIRIT) guidelines.

Introduction: Ventilator-associated events (VAEs) are objective measures as defined by the Centers for Disease Control and Prevention (CDC). To reduce VAEs, some hospitals have started patients on higher baseline positive end-expiratory pressure (PEEP) to avoid triggering VAE criteria due to respiratory fluctuations. Methods: At our institution, VAEs were gathered from January 2014 through December 2019. Using the CDC-defined classifications, VAEs were split into two groups to separate patients with hypoxemia only (VAC) and those with hypoxemia and evidence of inflammation or infection (IVAC-plus). We used the geometric distribution to calculate the daily event probability before and after the protocol implementation. A probability threshold was used to determine if the days between events was exceeded during the post-protocol period. Results: A total of 306 VAEs were collected over the study period. Of those, 155 were VACs and 107 were IVAC-plus events during the pre-protocol period. After implementing the protocol, 24 VACs and 20 IVAC-plus events were reported. There was a non-significant decrease in daily event probabilities in both the VAC and IVAC-plus groups (0.083 vs. 0.068 and 0.057 vs. 0.039, respectively). Conclusion: We concluded a starting PEEP of 8 cmH 2 O is unlikely to be an effective intervention at reducing the probability of a VAE. Until specific guidelines by the CDC are established, hospitals should consider alternative methods to reduce VAEs.

Malignant infiltration of pia and arachnoid mater, referred to as leptomeningeal carcinomatosis (LMC), is a rare complication of gastric carcinoma. The most common underlying malignancy in patients with LMC are leukemia, breast cancer, lymphoma, and lung cancer. We report a case of gastric adenocarcinoma that presented with LMC in the absence of overt gastrointestinal signs or symptoms. A 56-year-old Hispanic woman presented to the hospital with a three-week history of intermittent headaches and visual blurring. An initial brain imaging showed infarction in the distribution of right posterior inferior cerebellar artery (PICA) along with communicating hydrocephalus. She underwent ventriculoperitoneal (VP) shunt placement with improvement in her symptoms. Two months later she presented again with deterioration in her mental status. Imaging studies and cerebrospinal fluid (CSF) analysis confirmed the diagnosis of LMC. Further studies determined the primary tumor to be signet ring cell gastric adenocarcinoma. However, she did not have any preceding gastrointestinal symptoms. In light of the poor prognosis, the patient's family proceeded with comfort care measures. Our case portrays a rare presentation of gastric adenocarcinoma with LMC without other distant organ metastatic involvement. It also illustrates the occult nature of gastric carcinoma and signifies the importance of neurologic assessment of patients, with or at risk of gastric carcinoma. ​It also raises a theoretical concern for VP shunt as a potential conduit of malignant cells from the abdomen to the central nervous system, which may serve as an important susbtrate for future research.

Background Acute kidney injury (AKI) is common in patients with acute myocardial infarction. AKI in this setting is associated with short- and long-term adverse events. The aim of this study was to develop a simple score to predict AKI in patients presenting with acute myocardial infarction based on data available at time of admission. Methods This was a retrospective analysis of data collected as part of the Acute Coronary Treatment and Intervention Outcomes Network (ACTION) registry at a tertiary care center between 1/1/2011 and 12/31/2013. Data were collected prospectively for all patients who presented within 24 h of the onset of myocardial infarction. AKI was defined as an increase in creatinine from admission level to peak level of ≥0.3 mg/dl or by ≥50 %. Patients with history of end-stage renal disease requiring renal replacement therapy were excluded. Results Of 1107 patients included in the study, 147 (13.3 %) developed AKI. The following factors were independently associated with increased risk for AKI: cardiac arrest, decompensated heart failure on presentation, diabetes mellitus, hypertension, anemia, impaired renal function on presentation, and tachycardia on presentation. These factors were combined to form a new predictive tool. The new score showed excellent discrimination for AKI: the area under the receiver operating characteristic curve (AUROC) was 0.76 (95 % confidence interval 0.72–0.80). Conclusion A simple score using clinical and laboratory data available on admission can predict the risk of AKI in patients presenting with acute myocardial infarction.

Alzheimer’s disease effects human brain cells and results in dementia. The gradual deterioration of the brain cells results in disability of performing daily routine tasks. The treatment for this disease is still not mature enough. However, its early diagnosis may allow restraining the spread of disease. For early detection of Alzheimer’s through brain Magnetic Resonance Imaging (MRI), an automated detection and classification system needs to be developed that can detect and classify the subject having dementia. These systems also need not only to classify dementia patients but to also identify the four progressing stages of dementia. The proposed system works on an efficient technique of utilizing transfer learning to classify the images by fine-tuning a pre-trained convolutional network, AlexNet. The architecture is trained and tested over the pre-processed segmented (Grey Matter, White Matter, and Cerebral Spinal Fluid) and un-segmented images for both binary and multi-class classification. The performance of the proposed system is evaluated over Open Access Series of Imaging Studies (OASIS) dataset. The algorithm showed promising results by giving the best overall accuracy of 92.85% for multi-class classification of un-segmented images.

Flying ad-hoc networks (FANETs) are a very vibrant research area nowadays. They have many military and civil applications. Limited battery energy and the high mobility of micro unmanned aerial vehicles (UAVs) represent their two main problems, i.e., short flight time and inefficient routing. In this paper, we try to address both of these problems by means of efficient clustering. First, we adjust the transmission power of the UAVs by anticipating their operational requirements. Optimal transmission range will have minimum packet loss ratio (PLR) and better link quality, which ultimately save the energy consumed during communication. Second, we use a variant of the K-Means Density clustering algorithm for selection of cluster heads. Optimal cluster heads enhance the cluster lifetime and reduce the routing overhead. The proposed model outperforms the state of the art artificial intelligence techniques such as Ant Colony Optimization-based clustering algorithm and Grey Wolf Optimization-based clustering algorithm. The performance of the proposed algorithm is evaluated in term of number of clusters, cluster building time, cluster lifetime and energy consumption.