Objective: To determine the frequency of micro-organisms causing sepsis as well as to determine the antibiotic susceptibility and resistance of microorganisms isolated in a medical intensive care unit.Materials and methods: This is a cross-sectional analysis of 802 patients from a medical intensive care unit (ICU) of Shifa International Hospital, Islamabad, Pakistan over a one-year period from August 2015 to August 2016. Specimens collected were from blood, urine, endotracheal secretions, catheter tips, tissue, pus swabs, cerebrospinal fluid, ascites, bronchoalveolar lavage (BAL), and pleural fluid. All bacteria were identified by standard microbiological methods, and antibiotic sensitivity/resistance was performed using the disk diffusion technique, according to Clinical and Laboratory Standards Institute (CLSI) guidelines. Data was collected using a critical care unit electronic database and data analysis was done by using  the Statistical Package for Social Sciences (SPSS), version 20 (IBM SPSS Statistics, Armonk, NY).Results: Gram-negative bacteria were more frequent as compared to gram-positive bacteria. Most common bacterial isolates were Acinetobacter (15.3%), Escherichia coli (15.3%), Pseudomonas aeruginosa (13%), and Klebsiella pneumoniae (10.2%), whereas Enterococcus (7%) and methicillin-resistant staphylococcus aureus (MRSA) (6.2%) were the two most common gram-positive bacteria. For Acinetobacter, colistin was the most effective antibiotic (3% resistance). For E.coli, colistin (0%), tigecycline (0%), amikacin (7%), and carbapenems (10%) showed low resistance. Pseudomonas aeruginosa showed low resistance to colistin (7%). For Klebsiella pneumoniae, low resistance was seen for tigecycline (0%) and minocycline (16%). Overall, ICU mortality was 31.3%, including miscellaneous cases.Conclusion: Gram-negative infections, especially by multidrug-resistant organisms, are on the rise in ICUs. Empirical antibiotics should be used according to the local unit specific data. Constant evaluation of current practice on basis of trends in multidrug resistance and antibiotic consumption patterns are essential.

Carpal tunnel syndrome (CTS) is the most common compressive entrapment neuropathy caused by the compression of the median nerve at the wrist space known as the carpal tunnel. The epidemiologic factors related to CTS include genetic, medical, social, vocational, and demographic factors. The common symptoms experienced include pain, paresthesia, and numbness in the median nerve distribution. If left untreated, it can lead to irreversible median nerve damage, causing a loss of hand function. Body mass index (BMI) has been attributed as a risk factor for the development of CTS.We planned to determine the frequency of obesity among CTS patients in the neurophysiology department of a tertiary care center in Islamabad, Pakistan. The survey was designed as a cross-sectional descriptive study from March 2016 to August 2016 using a consecutive nonprobability sampling technique. A total of 112 patients with a mean age of 54 ± 5 years were included in the study. In the study population, 39 patients (35 percent) were males and 73 were females (65 percent). Based on BMI, 74 patients (66 percent) had a normal weight and 38 (34 percent) were obese. The frequency of obesity in our study was 34 percent, excluding the other comorbid conditions, which is quite high. Targeted therapy in those with CTS should also include weight reduction measures because obesity poses a cause-and-effect relationship for both the severity and the pathogenesis of CTS.

Patients with spinal abnormalities infrequently present with intradural intramedullary bleeding. The more common causes include spinal trauma, arteriovenous malformations and saccular aneurysms of spinal arteries. On occasion, spinal cord tumors either primary or metastatic may cause intramedullary bleed with ependymoma of the conus medullaris. Spinal nerve sheath tumors such as schwannomas only rarely cause intradural intramedullary bleed, especially in the absence of spinal cord or nerve root symptoms. We report a case of spinal intradural schwannoma presenting with acute onset of quadriparesis. Cerebral angiography studies were negative but magnetic resonance imaging (MRI) of the spine revealed a large hemorrhagic tumor in the thoracolumbar junction. However, we suggest that the patients with intradural intramedullary bleed should be evaluated for underlying spine disease.

ObjectiveTo predict changes in the quality of life scores of hemodialysis patients for the coming month and the development of an early warning system using machine learningMethodsIt was a prospective cohort study (one-month duration) at the dialysis center of a tertiary care hospital in Pakistan. The study started on 1st October 2016. About 78 patients have been enrolled till now. Bachelor of Medicine and Bachelor of Surgery (MBBS) qualified doctors administered a proforma with demographics and the validated Urdu version of World Health Organization Quality Of Life-BREF (WHOQOL-BREF). It was to be repeated after one month to the same patient by the same investigator. Simple statistics were computed using SPSS version 24 (IBM Corp., Armonk, NY) while machine learning was performed using R (version 3.0) and Orange (version 3.1).ResultsUsing machine learning algorithms, two models (classification tree and Naïve Bayes) were generated to predict an increase or decrease of 5% in a patient’s WHOQOL-BREF score over one month. The classification tree was selected as the most accurate model with an area under curve (AUC) of 83.3% (accuracy: 81.9%) for the prediction of 5% increase in QOL and an AUC of 76.2% (accuracy: 81.8%) for the prediction of 5% decrease in QOL over the coming month. The factors associated with an increase of QOL by 5% or more over the next month included younger age (<19 years) and higher iron sucrose doses (>278mg/month). Drops in psychological, physical, and social domain scores lead to a decrease of 5% or more in QOL scores over the following month.ConclusionAn early warning system, dialysis data interpretation for algorithmic-prediction on quality of life (DIAL) was built for the early detection of deteriorating QOL scores in the hemodialysis population using machine learning algorithms. The model pointed out that working on psychological and environmental domains, in particular, may prevent the drop in QOL scores from occurring. DIAL, if implemented on a larger scale, is expected to help patients in terms of ensuring a better QOL and in reducing the financial burden in the long term.

This article explores the possible role of Montelukast in management of SARS-CoV-2 infection after reviewing the available literature and further uses computational docking to estimate the effects of Montelukast on the main protease inhibitor site of SARS-CoV-2.Methodology: In this study, we used molecular docking to estimate the direct effects of Montelukast on the main protease (Mpro) inhibitor site of the SARS-CoV-2. While other studies have been performed on the homology models, we obtained the Mpro crystalized structure, A-chain (304 amino acid residues) from protein data bank (PDB code 5REK) for this analysisResults:The best docked Montelukast conformer had a mfscore of -71.68 and was seen to be making multiple hydrogen bonds with the neighbouring residues (T24, T24, T26, S46) with the closest bond with T24 (Distance= 1.71 angstrom). Important finding was its hydrogen bond with H41 and hydrophobic interactions with C145 as these residues for important members of the active catalytics site.Conclusion:The computational model which was used against the crystalized Mpro structure suggested a possible inhibitory role of Montelukast in binding to the Mpro catalytic site which may modulate and inhibit the viral replication.

Abstract Objectives: To assess the burden of sleep disorders in the elderly, and the effects of various co-morbidities linked with sleep disorders. Method: The longitudinal cross-sectional study was conducted in different outpatient departments at a tertiary care centre in Islamabad, Pakistan, from June 2014 to June 2015, and comprised patients of either gender aged 60 years or above. Pittsburgh sleep quality index and Epworth sleepiness scale were used to measure the quality and patterns of sleep and daytime sleepiness in the elderly. Data was analysed using SPSS 21. Results: Of the 1000 subjects, 638(63.8%) were males, and 362(36.2%) were females. The overall mean age was 66.96±7.05 years. Epworth sleepiness scale >10 was found in 265(26.5%) subjects, while Pittsburgh sleep quality index score in 516(51.6%) was >5. Sleep quality score in 578(57.8%) women was statistically significant compared to 478(47.8%) males (p<0.05). Conclusions: There was a significant burden of sleep-related disorders in the subjects. Key Words: Sleep disorders, ESS, PSQI, Pakistan, Elderly.

ObjectivesThe objective of this exploratory study was to find out the correlation of femoral vein diameter (FVD) to central venous pressure (CVP) measurements and to derive a prediction equation to help ascertain the fluid volume status in a critical patient.Patients and methodsThis was a single-centered prospective cohort study designed and conducted by the critical care department of Shifa International hospital in Islamabad, Pakistan. Patients were enrolled from the medical and surgical intensive care units. The inclusion criteria consisted of patients > 18 years of age, and an intrathoracic central venous catheterization (CVC) in place for producing CVP waveform through the transducer. Patients having contraindications to CVP placement and those unable to lie supine were excluded from the study. Critical Care fellows with sufficient training in performing venous ultrasonography measured the FVD. They were blinded to the CVP values of the same patients.ResultsThe study included 108 patients. Among these 70/108 (64.8%) were males. Mean age was 53.85 (SD=16.74). The CVP and femoral vein diameter were measured in all patients. Mean CVP was 9.89 cmH2O (SD=3.46) and mean femoral vein diameter was 0.92 cm (SD=0.27). Multiple regression was used to generate a prediction model. FVD, age and sex of the patient were used as predictor variables to predict CVP diameter. The model was statistically significant with a p-value of < 0.000 and an F-value of 104.806. R-squared value for this model came out to be 0.744, thus the model was able to explain about 74.4% of the variance in the values observed for CVP. When controlled for age and sex, FVD was found highly correlated with CVP diameter with a p-value of < 0.000. A regression equation was derived that can be used to generate predicted values of CVP in millimeters of mercury with an R-square of 0.745 if FVD in centimeters is provided; CVP (cmH2O) = -0.039 + 10.718* FVD.ConclusionsFVD was found highly correlated to CVP measurements and it suggests an alternate non-invasive method of ascertaining the volume status in the critically ill.

BackgroundThe current Novel Coronavirus (SARS-CoV-2) pandemic is the third major outbreak of the 21st century which emerged in December 2019 from Wuhan, China. At present there are no known treatments or vaccines to cure or prevent the illness.ObjectiveThe objective of this study was to explore a list of potential drugs (herbal and antivirals) for their role in inhibiting activity and or replication of SARS-CoV-2 by using molecular docking onto the crystal structures of key viral proteins.MethodologyIn this study, we used molecular docking to estimate the binding affinities of 3699 drugs on the potential active sites of the 6 main SARS-CoV-2 proteins (Papain like protease, Main protease, ADP Ribose phosphatase, Spike protein, NSP-9 and NSP-10 to 16 complex). While other studies have mostly been performed on the homology models, we obtained the most recently submitted crystal structures of all 6 proteins from the protein data bank for this analysis.ResultsOur results showed the top ligands as Theasinensin A, Epigallocatechin, Theaflavin, Theasinensin A, Epigallocatechin and Favipiravir showing the highest binding affinities against papain-like protease, ADP ribose phosphatase, main protease, spike protein, RNA replicase (NSP-9) and methyl-transferase (NSP-16) respectively.ConclusionWe show that the compounds from our list with the greatest inhibitory potential against SARS-CoV-2 activity or replication include Theasinensin A, Epigallocatechin-3-gallate, Theaflavin, Favipiravir, Curucumin, Quercetin, Mitoxantrone, Amentoflavone, Colistin, Cimicifugic acid, Theaflavin, Silymarin and Chebulagic. We recommend further wet-lab and clinical testing of these compounds to further explore their role against SARS-CoV-2.