Honey and Nigella sativa against COVID-19 in Pakistan (HNS-COVID-PK): A multicenter placebo-controlled randomized clinical trial.

Until now, no specific and effective treatment exists for coronavirus disease 2019 (COVID-19). Since honey and Nigella sativa (HNS) have established antiviral, antibacterial, antiinflammatory, antioxidant, and immunomodulatory properties, we tested their efficacy for this disease in a multicenter, placebo-controlled, and randomized clinical trial at four medical care facilities in Pakistan. RT-PCR confirmed COVID-19 adults showing moderate or severe disease were enrolled in the trial. Patients were randomly assigned in a 1:1 ratio to receive either honey (1 g kg-1 day-1 ) and Nigella sativa seeds (80 mg kg-1 day-1 ) or a placebo for up to 13 days along with standard care. The outcomes included symptoms' alleviation, viral clearance, and 30-day mortality in the intention-to-treat population. Three hundred and thirteen patients, 210 with moderate and 103 with severe disease, underwent randomization from April 30 to July 29, 2020. Among the moderate cases, 107 were assigned to HNS, whereas 103 were assigned to the placebo group. Among the severe cases, 50 were given HNS, and 53 were given the placebo. HNS resulted in ~50% reduction in time taken to alleviate symptoms as compared to placebo (moderate cases: 4 vs. 7 days, Hazard Ratio [HR]: 6.11; 95% Confidence Interval [CI]: 4.23-8.84, p < 0.0001 and for severe cases: 6 vs. 13 days, HR: 4.04; 95% CI: 2.46-6.64; p < 0.0001). HNS also cleared the virus earlier than placebo in both moderate cases (6 vs. 10 days, HR: 5.53; 95% CI: 3.76-8.14, p < 0.0001) and severe cases (8.5 vs. 12 days, HR: 4.32; 95% CI: 2.62-7.13, p < 0.0001). HNS further led to a better clinical score on day 6 with normal activity resumption in 63.6% vs. 10.9% among moderate cases (OR: 0.07; 95% CI: 0.03-0.13, p < 0.0001) and hospital discharge in 50% versus 2.8% in severe cases (OR: 0.03; 95% CI: 0.01-0.09, p < 0.0001). In severe cases, the mortality rate was less than 1/4th in the HNS group than in placebo (4% vs. 18.87%, OR: 0.18; 95% CI: 0.02-0.92, p = 0.029). No HNS-related adverse effects were observed. HNS, compared with placebo, significantly improved symptoms, expedited viral load clearance, and reduced mortality in COVID-19 patients. This trial was registered on April 15, 2020 with ClinicalTrials.gov Identifier: NCT04347382.

COVID-19: Management.

Coronavirus disease (COVID-19) has grasped the world including Pakistan. Clinical features of this disease are variable, ranging from asymptomatic to critical disease. In this unprecedented global war, the Pakistan Chest Society has written a guideline for quick review for the specialists providing care to suspected or confirmed patients. This review highlights the approach to a patient with COVID-19, including definition of the various syndromes of the disease, the abnormal laboratory parameters and outlines the therapeutic measures which are currently under investigation.

Simulation training using cadaver sheep chest in pleuroscopy - A step towards skills enhancement.

OBJECTIVE: For assessing the use of if simulation training on animal cadavers as a useful tool for training in pleuroscopy. METHODS: The email-based cross-sectional study was conducted in December 2014 at Shaikh Zayed Hospital, Federal Postgraduate Medical Institute, Lahore, Pakistan, and comprised respiratory physicians and trainees after their participation in a two-day hands-on training course on pleuroscopy and pleural medical procedures. The responses were analysed and the responses of physicians and trainees were compared. RESULTS: Of the 44 individuals who attended the course and were contacted through emails, 38(86.4%) responded, including 20(52.6%) physicians and 18(47.3%) trainees. All the 38(100%) subjects uniformly accepted the utility of simulation training in enhancing education, improving skill, and improving confidence by repeated practice, and felt that the inclusion of animal models for learning fundamental pleuroscopic procedures can help a lot in teaching. CONCLUSIONS: Animal cadavers can be used as an effective teaching tool for pleuroscopy training.

A machine-learning-enabled smart neckband for monitoring dietary intake.

The increasing need for precise dietary monitoring across various health scenarios has led to innovations in wearable sensing technologies. However, continuously tracking food and fluid intake during daily activities can be complex. In this study, we present a machine-learning-powered smart neckband that features wireless connectivity and a comfortable, foldable design. Initially considered beneficial for managing conditions such as diabetes and obesity by facilitating dietary control, the device's utility extends beyond these applications. It has proved to be valuable for sports enthusiasts, individuals focused on diet control, and general health monitoring. Its wireless connectivity, ergonomic design, and advanced classification capabilities offer a promising solution for overcoming the limitations of traditional dietary tracking methods, highlighting its potential in personalized healthcare and wellness strategies.

Comparison of frequency of undiagnosed chronic obstructive pulmonary disease in current or former tobacco smokers having ischaemic heart disease.

BACKGROUND AND OBJECTIVES: This study compares the frequency of undiagnosed chronic obstructive pulmonary disease (COPD) in tobacco smokers suffering from ischaemic heart disease (IHD) and analyses the association of COPD severity with status, type and duration of smoking. METHODS: An analytical cross-sectional study conducted in current and former cigarette, hookah and combined cigarette and hookah smokers with IHD to detect previously undiagnosed COPD through spirometry. RESULTS: Among 124 males with IHD, majority [74 (59.7%)] were former smokers and were in the age ranged between 42 to 78 years. All had dyspnoea up to grade 4 and 64 (51.6%) also reported chronic cough and sputum production. According to type of smoking, 64 (51.6%) smoked cigarettes, 30 (24.2%) smoked hookah and 30 (24.2%) smoked both hookah and cigarettes. Forty-seven (37.9%) were found to have COPD, 24 (37.5%) among cigarette smokers, 12 (40%) among hookah smokers, while 11 (36.7%) were from cigarette and hookah smokers. Duration of smoking, its type and magnitude had no association with severity of COPD. CONCLUSIONS: The frequency of undiagnosed COPD is high in smokers with IHD. Hookah and combined hookah and cigarette smokers are almost as susceptible to develop COPD as are cigarette smokers.

General perceptions and practices of smokers regarding tobacco-related issues and hazards.

OBJECTIVE: To assess the perceptions and practices related to tobacco consumption and its hazards among cigarette smokers seeking medical attention. METHODS: The cross-sectional study included 180 active smokers who were either hospitalised in medical or chest ward or attending pulmonary OPD at Shaikh Zayed Hospital, Lahore, between January and July 2010. Patients having altered sensorium were excluded from the study. A questionnaire was filled by the consulting participants. SPSS version 15 and chi-square tests were used for statistical analyses. RESULTS: A total of 180 questionnaires were distributed among 118 (65.6%) in-house and 62 (34.4%) active smokers in the out-patients department. These included 154 (85.6%) males and 26 (14.4%) females, with a mean age of 53.84 +/- 17.35 years, and with varying pack years (mean 26.44 +/- 19.89). Apart from cigarettes, 42 (74%) were also smoking other types of tobacco. Attempt to quit smoking was made by 92 (51.1%) and 151 (83.9%) were willing to give up. Majority of them 130 (72%) had at least two more smokers in the family. Most of the patients 169 (93.9%) presented with illnesses directly related to smoking. Besides, 127 (70.6%) patients had visited their general practitioners within the preceding year and a positive history of hospitalisation was found in 101 (56.1%). Regarding the knowledge of smoking hazards, only 5 (2.8%) could name three organs that may dysfunction due to smoking. While 162 (90%) had poor perception regarding the estimated number of chemicals in a cigarette, 120 (66.7%) knew the smoking's association with cancer. CONCLUSION: A high proportion of urban population consumes tobacco and most are poorly informed about the traumatic effects of its consumption. The continuing habits of patients with a history of seeking medical attention suggest that healthcare providers are missing opportunities for quit-smoking counselling.

Res-SE-ConvNet: A Deep Neural Network for Hypoxemia Severity Prediction for Hospital In-Patients Using Photoplethysmograph Signal.

Determining the severity level of hypoxemia, the scarcity of saturated oxygen (SpO2) in the human body, is very important for the patients, a matter which has become even more significant during the outbreak of Covid-19 variants. Although the widespread usage of Pulse Oximeter has helped the doctors aware of the current level of SpO2 and thereby determine the hypoxemia severity of a particular patient, the high sensitivity of the device can lead to the desensitization of the care-givers, resulting in slower response to actual hypoxemia event. There has been research conducted for the detection of severity level using various parameters and bio-signals and feeding them in a machine learning algorithm. However, in this paper, we have proposed a new residual-squeeze-excitation-attention based convolutional network (Res-SE-ConvNet) using only Photoplethysmography (PPG) signal for the comfortability of the patient. Unlike the other methods, the proposed method has outperformed the standard state-of-art methods as the result shows 96.5% accuracy in determining 3 class severity problems with 0.79 Cohen Kappa score. This method has the potential to aid the patients in receiving the benefit of an automatic and faster clinical decision support system, thus handling the severity of hypoxemia.

S2C-DeLeNet: A parameter transfer based segmentation-classification integration for detecting skin cancer lesions from dermoscopic images.

Dermoscopic images ideally depict pigmentation attributes on the skin surface which is highly regarded in the medical community for detection of skin abnormality, disease or even cancer. The identification of such abnormality, however, requires trained eyes and accurate detection necessitates the process being time-intensive. As such, computerized detection schemes have become quite an essential, especially schemes which adopt deep learning tactics. In this paper, a convolutional deep neural network, S2C-DeLeNet, is proposed, which (i) Performs segmentation procedure of lesion based regions with respect to the unaffected skin tissue from dermoscopic images using a segmentation sub-network, (ii) Classifies each image based on its medical condition type utilizing transferred parameters from the inherent segmentation sub-network. The architecture of the segmentation sub-network contains EfficientNet-B4 backbone in place of the encoder and the classification sub-network bears a 'Classification Feature Extraction' system which pulls trained segmentation feature maps towards lesion prediction. Inside the classification architecture, there have been designed, (i) A 'Feature Coalescing Module' in order to trail and mix each dimensional feature from both encoder and decoder, (ii) A '3D-Layer Residuals' block to create a parallel pathway of low-dimensional features with high variance for better classification. After fine-tuning on a publicly accessible dataset, a mean dice-score of 0.9494 during segmentation is procured which beats existing segmentation strategies and a mean accuracy of 0.9103 is obtained for classification which outperforms conventional and noted classifiers. Additionally, the already fine-tuned network demonstrates highly satisfactory results on other skin cancer segmentation datasets while cross-inference. Extensive experimentation is done to prove the efficacy of the network for not only dermoscopic images but also different medical modalities; which can show its potential in being a systematic diagnostic solution in the field of dermatology and possibly more.

Pulmonary embolism, thrombocytopenia, and antiphospholipid syndrome.

This case report describes a young non-smoker male masquerading under the diagnosis of community acquired pneumonia who was found to have respiratory symptoms attributed to bilateral pulmonary emboli. There was also evidence of thrombocytopenia and proximal deep venous thrombosis of right lower limb. He underwent further investigations and was found to have positive anti-cardiolipin antibodies, lupus anticoagulant and prolonged activated partial thromboplastin time due to antiphospholipid antibody syndrome. This article highlights the importance of consideration of earlier diagnosis in younger patients with congenital thrombophilias that carries potential for prevention and treatment of life threatening thromboembolic manifestations.

National Registry of Interstitial Lung Disease from Pakistan.

Introduction Interstitial lung disease (ILD) is a heterogeneous group of over 200 parenchymal lung diseases with a myriad of etiologies. Interstitial lung disease registries from around the world show varying prevalence and incidence of these diseases. The aim of this study was to determine the epidemiology and characteristics of ILD in Pakistan. Methods This web-based registry, which is the first multicenter registry of ILD from Pakistan, recruited patients from 10 centers of five major cities between January 2016 and March 2019. Results A total of 744 patients were enrolled in the registry. The five most frequent ILDs were idiopathic pulmonary fibrosis (IPF) 34.4%, hypersensitivity pneumonitis (HP) - 17.7%, idiopathic nonspecific interstitial pneumonitis (iNSIP) - 16.8%, connective tissue disease-associated ILD (CTD-ILD) - 16.3%, and sarcoidosis - 9.1%. Conclusion Idiopathic pulmonary fibrosis is the most prevalent ILD in Pakistan, followed by HP and iNSIP. An ongoing prospective registry with longitudinal follow-up will help us further elaborate on the clinical characteristics, treatment, and survival outcome of patients with ILD.

An AIDS patient with multiple opportunistic infections.

A patient was diagnosed having advanced acquired immunodeficiency syndrome (AIDS) who suffered from various pulmonary and extrapulmonary infective complications. A number of opportunistic infections were diagnosed and prompt treatment was initiated. Due to his low CD4+ lymphocyte count that carries a higher morbidity and mortality, he experienced clinical worsening even with treatment and could not survive through this Black Death.

Intractable cough due to endobronchial chondroma.

A 62-year-old man, suffering from bronchial asthma was evaluated due to intractable cough. His dyspnea was controlled but cough remained unresponsive to escalation of asthma management steps. Cough occurred in bouts, especially during night time and was occasionally productive of mucoid sputum. Other than bilateral rhonchi on chest auscultation, remaining systemic examination was unremarkable. CT chest showed a mass lesion in the bronchus intermedius that was confirmed on bronchoscopy and was removed after electrocautery snare excision. Histopathology of the lesion was consistent with endobronchial chondroma. The patient experienced a dramatic resolution of cough post tumor removal. Follow up bronchoscopy after 24 months revealed no tumor recurrence.

Sleep Apnea Event Detection from Sub-frame Based Feature Variation in EEG Signal Using Deep Convolutional Neural Network.

The topic of automatic detection of sleep apnea which is a respiratory sleep disorder, affecting millions of patients worldwide, is continuously being explored by researchers. Electroencephalogram signal (EEG) represents a promising tool due to its direct correlation to neural activity and ease of extraction. Here, an innovative approach is proposed to automatically detect apnea by incorporating local variations of temporal features for identifying the global feature variations over a broader window. An EEG data frame is divided into smaller sub-frames to effectively extract local feature variation within one larger frame. A fully convolutional neural network (FCNN) is proposed that will take each sub-frame of a single frame individually to extract local features. Following that, a dense classifier consisting of a series of fully connected layers is trained to analyze all the local features extracted from subframes for classifying the entire frame as apnea/non-apnea. Finally, a unique post-processing technique is applied which significantly improves accuracy. Both the EEG frame length and post-processing parameters are varied to find optimal detection conditions. Large-scale experimentation is executed on publicly available data of patients with varying apnea-hypopnea indices for performance evaluation of the suggested method.

A massive pleural-based desmoid tumour.

A 49-year-old Pakistani male presented with "heaviness" in his chest. Chest radiograph and computed tomography (CT) confirmed a massive left-sided pleural-based opacity. Three years ago, he was investigated for a left-sided lymphocytic, exudative pleural effusion following an episode of dengue fever. Tube thoracostomy removed 1.3 L of fluid. Pleural biopsy and bronchial washings were non-contributory. He received empirical anti-tuberculosis treatment and remained asymptomatic until this presentation. To investigate the new pleural mass, he underwent a video-assisted thoracoscopic surgery, which revealed a 2.2 kg mass in the pleural cavity involving the anterior mediastinum and chest wall and adhered to the visceral pleura. Following conversion to an open thoracotomy, the mass was completely excised, which involved non-anatomical lung resection. Histopathology and immunohistochemistry of the resected tumour were consistent for a desmoid tumour. He was followed up for 9 months with no evidence of tumour recurrence. Predominantly pleural-based desmoid tumour is rare but should be included in the differential diagnosis of spindle cell tumours.

Sewing machine needle retrieval from distal airways using flexible bronchoscope under fluoroscopy.

18-year-old lady was admitted through emergency department after she accidently aspirated a long sewing machine needle into her airways. Upon examination, she was anxious with normal vital signs and without any respiratory distress with SpO2 of 98% on air. Systemic examination was normal except slightly reduced breath sounds intensity on right lower side of chest. Her chest radiograph revealed needle in right lower lobe but it was not visualized on bronchoscopic examination of airways up to sub segmental level. Using flexible bronchoscope under fluoroscope guidance, needle was retrieved successfully from posterior basal sub segment of right lower lobe utilizing alligator biopsy forceps, without any noticeable complication.

Co-existent Ascariasis and Multiloculated Tuberculous Pleurisy Treated with Intrapleural Streptokinase.

This case describes a young male with pleuro-pulmonary tuberculosis having one-week history of fever and hemoptysis along with a multiloculated right sided exudative lymphocytic pleural effusion. His pulmonary diagnostic confusion was due to passage of adult Ascaris lumbricoides per orally but that was found to be of intestinal origin. Pleural tissue histopathology revealed necrotizing granulomatous inflammation and right upper lobe bronchial washings were positive for acid fast bacilli. His multiloculated pleural effusion was successfully resolved with intrapleural streptokinase injections via a 10 French pleural catheter. Treatment with class-I anti-tuberculous drugs led to complete clearance of remaining pleuro-pulmonary shadowing.