Cholera in children in Karachi from 1990 through 1995: A study of cases admitted to a tertiary care hospital

Although cholera is an endemic disease in Bangladesh, India and other countries, it was never a significant cause of gastroenteritis in Pakistan before 1988. Since then, cases of cholera are identified each year, both in adults and children in Pakistan. In order to see the contribution of Vibrio cholerae as a cause of gastroenteritis in children, we reviewed the cases of cholera admitted in the pediatric ward of the Aga Khan University Hospital, Karachi, Pakistan. Of 4346 children hospitalized with gastroenteritis during 1990 through 1995, 348 children (8%) were confirmed to have cholera. The youngest child with cholera was seven days old. The mean age was 31 +/- 34 months. The cases of cholera were received from all over the city. Most cases were due to Vibrio cholerae Ogawa biotype ELTOR but the new strain, i.e., Vibrio cholerae 0139 was isolated in 14% cases in 1994. The sensitivity of Vibrio cholerae has also changed. In 1994, the organisms were resistant to commonly recommended antibiotics, i.e., tetracycline, ampicillin and erythrocin but sensitive to ceftrioxone, cefixime, ofloxacin and nalidixic acid. Adequate measures to improve hygiene and sanitation and supply of safe potable water is needed to prevent any future epidemic of cholera in the city

Urinary tract infection

Over two years, 9892 mid-stream urine samples from patients attending the Aga Khan University Hospital, Karachi were cultured. Significant bacterial growth was seen in 23.5% samples. Further iden­tification of these organisms Eevealed 40% of E.coli, 16% Pseudomonas aeruginosa, 11% Klebsiella aerogenes, 5.0% Enterobacter sp., 13% Protdus sp., 4.0% Serratia liquifaciens, 1.0% Acinetobacter sp., 3.0% Citrobacter sp., 4.0% Enterococci, 0.5% Staphylococcus aureus. Results of sensitivity tests performed with antibiotics Ampicillin, Cotrimoxa.zole, Nitrofurantoin, Nalidixic acid, Gentamicin, Amikacin, Pipemedic acid, Cefotaxime, Azactain and Carbenicillin did not reveal any distinct patter

Therapeutic implications of ofloxacin in the treatment of typhoid fever caused by multiply resistant Salmonella typhi

The antibiotic of choice for the treatment of typhoid fever in most parts of the world is still chloramphenicol. Ampicillin and cotrimoxazole have been used in recent years. Selection of antimicrobials for therapy has been complicated by the emergence of Salmonella typhi strains resistant to the above mentioned antibiotics. Blood and/or bone marrow cultures of 30 adult patients grew S. typhi that was resistant to chloramphenicol, ampicillin and cotrimoxazole. However, these strains were sensitive to cefotaxime, ceftrioxone, aztreonam and ofloxacin. Ofloxacin 400 mg twice a day was given orally to these patients for 14 days. All patients recovered with no untoward side effect. We concluded that ofloxacin can be used as a drug of choice for typhoid fever, in those adult patients who are infected with S. typhi resistant to chloramphenicol, ampicillin and cotrimoxazole

Secreted Phospholipase A2 Involvement in Neurodegeneration: Differential Testing of Prosurvival and Anti-Inflammatory Effects of Enzyme Inhibition

There is increased interest in the contribution of secreted phospholipase A2 (sPLA2) enzymes to neurodegenerative diseases. Systemic treatment with the nonapeptide CHEC-9, a broad spectrum uncompetitive inhibitor of sPLA2, has been shown previously to inhibit neuron death and aspects of the inflammatory response in several models of neurodegeneration. A persistent question in studies of sPLA2 inhibitors, as for several other anti-inflammatory and neuroprotective compounds, is whether the cell protection is direct or due to slowing of the toxic aspects of the inflammatory response. To further explore this issue, we developed assays using SY5Y (neuronal cells) and HL-60 (monocytes) cell lines and examined the effects of sPLA2 inhibition on these homogeneous cell types in vitro. We found that the peptide inhibited sPLA2 enzyme activity in both SY5Y and HL-60 cultures. This inhibition provided direct protection to SY5Y neuronal cells and their processes in response to several forms of stress including exposure to conditioned medium from HL-60 cells. In cultures of HL-60 cells, sPLA2 inhibition had no effect on survival of the cells but attenuated their differentiation into macrophages, with regard to process development, phagocytic ability, and the expression of differentiation marker CD36, as well as the secretion of proinflammatory cytokines TNF-α and IL-6. These results suggest that sPLA2 enzyme activity organizes a cascade of changes comprising both cell degeneration and inflammation, processes that could theoretically operate independently during neurodegenerative conditions. The effectiveness of sPLA2 inhibitor CHEC-9 may be due to its ability to affect both processes in isolation. Testing potential anti-inflammatory/neuroprotective compounds with these human cell lines and their conditioned media may provide a useful screening tool prior to in vivo therapeutic applications

Identification of undiagnosed atrial fibrillation patients using a machine learning risk prediction algorithm and diagnostic testing (PULsE-AI): Study protocol for a randomised controlled trial.

Atrial fibrillation (AF) is associated with an increased risk of stroke, enhanced stroke severity, and other comorbidities. However, AF is often asymptomatic, and frequently remains undiagnosed until complications occur. Current screening approaches for AF lack either cost-effectiveness or diagnostic sensitivity; thus, there is interest in tools that could be used for population screening. An AF risk prediction algorithm, developed using machine learning from a UK dataset of 2,994,837 patients, was found to be more effective than existing models at identifying patients at risk of AF. Therefore, the aim of the trial is to assess the effectiveness of this risk prediction algorithm combined with diagnostic testing for the identification of AF in a real-world primary care setting. Eligible participants (aged ≥30 years and without an existing AF diagnosis) registered at participating UK general practices will be randomised into intervention and control arms. Intervention arm participants identified at highest risk of developing AF (algorithm risk score ≥ 7.4%) will be invited for a 12‑lead electrocardiogram (ECG) followed by two-weeks of home-based ECG monitoring with a KardiaMobile device. Control arm participants will be used for comparison and will be managed routinely. The primary outcome is the number of AF diagnoses in the intervention arm compared with the control arm during the research window. If the trial is successful, there is potential for the risk prediction algorithm to be implemented throughout primary care for narrowing the population considered at highest risk for AF who could benefit from more intensive screening for AF. Trial Registration: NCT04045639

Detecting undiagnosed atrial fibrillation in UK primary care: Validation of a machine learning prediction algorithm in a retrospective cohort study

Aims To evaluate the ability of a machine learning algorithm to identify patients at high risk of atrial fibrillation in primary care. Methods A retrospective cohort study was undertaken using the DISCOVER registry to validate an algorithm developed using a Clinical Practice Research Datalink (CPRD) dataset. The validation dataset included primary care patients in London, England aged ≥30 years from 1 January 2006 to 31 December 2013, without a diagnosis of atrial fibrillation in the prior 5 years. Algorithm performance metrics were sensitivity, specificity, positive predictive value, negative predictive value (NPV) and number needed to screen (NNS). Subgroup analysis of patients aged ≥65 years was also performed. Results Of 2,542,732 patients in DISCOVER, the algorithm identified 604,135 patients suitable for risk assessment. Of these, 3.0% (17,880 patients) had a diagnosis of atrial fibrillation recorded before study end. The area under the curve of the receiver operating characteristic was 0.87, compared with 0.83 in algorithm development. The NNS was nine patients, matching the CPRD cohort. In patients aged ≥30 years, the algorithm correctly identified 99.1% of patients who did not have atrial fibrillation (NPV) and 75.0% of true atrial fibrillation cases (sensitivity). Among patients aged ≥65 years (n = 117,965), the NPV was 96.7% with 91.8% sensitivity. Conclusions This atrial fibrillation risk prediction algorithm, based on machine learning methods, identified patients at highest risk of atrial fibrillation. It performed comparably in a large, real-world population-based cohort and the developmental registry cohort. If implemented in primary care, the algorithm could be an effective tool for narrowing the population who would benefit from atrial fibrillation screening in the United Kingdom

Repeated Mechanical Endovascular Thrombectomy for Recurrent Large Vessel Occlusion: A Multicenter Experience

BACKGROUND AND PURPOSE: Mechanical thrombectomy (MT) is now the standard of care for large vessel occlusion (LVO) stroke. However, little is known about the frequency and outcomes of repeat MT (rMT) for patients with recurrent LVO. METHODS: This is a retrospective multicenter cohort of patients who underwent rMT at 6 tertiary institutions in the United States between March 2016 and March 2020. Procedural, imaging, and outcome data were evaluated. Outcome at discharge was evaluated using the modified Rankin Scale. RESULTS: Of 3059 patients treated with MT during the study period, 56 (1.8%) underwent at least 1 rMT. Fifty-four (96%) patients were analyzed; median age was 64 years. The median time interval between index MT and rMT was 2 days; 35 of 54 patients (65%) experienced recurrent LVO during the index hospitalization. The mechanism of stroke was cardioembolism in 30 patients (56%), intracranial atherosclerosis in 4 patients (7%), extracranial atherosclerosis in 2 patients (4%), and other causes in 18 patients (33%). A final TICI recanalization score of 2b or 3 was achieved in all 54 patients during index MT (100%) and in 51 of 54 patients (94%) during rMT. Thirty-two of 54 patients (59%) experienced recurrent LVO of a previously treated artery, mostly the pretreated left MCA (23 patients, 73%). Fifty of the 54 patients (93%) had a documented discharge modified Rankin Scale after rMT: 15 (30%) had minimal or no disability (modified Rankin Scale score ≤2), 25 (50%) had moderate to severe disability (modified Rankin Scale score 3-5), and 10 (20%) died. CONCLUSIONS: Almost 2% of patients treated with MT experience recurrent LVO, usually of a previously treated artery during the same hospitalization. Repeat MT seems to be safe and effective for attaining vessel recanalization, and good outcome can be expected in 30% of patients

Lipidomic analysis of variation in response to simvastatin in the Cholesterol and Pharmacogenetics Study

Statins are commonly used for reducing cardiovascular disease risk but therapeutic benefit and reductions in levels of low-density lipoprotein cholesterol (LDL-C) vary among individuals. Other effects, including reductions in C-reactive protein (CRP), also contribute to treatment response. Metabolomics provides powerful tools to map pathways implicated in variation in response to statin treatment. This could lead to mechanistic hypotheses that provide insight into the underlying basis for individual variation in drug response. Using a targeted lipidomics platform, we defined lipid changes in blood samples from the upper and lower tails of the LDL-C response distribution in the Cholesterol and Pharmacogenetics study. Metabolic changes in responders are more comprehensive than those seen in non-responders. Baseline cholesterol ester and phospholipid metabolites correlated with LDL-C response to treatment. CRP response to therapy correlated with baseline plasmalogens, lipids involved in inflammation. There was no overlap of lipids whose changes correlated with LDL-C or CRP responses to simvastatin suggesting that distinct metabolic pathways govern statin effects on these two biomarkers. Metabolic signatures could provide insights about variability in response and mechanisms of action of statins