Diagnostic Utility of High Fluorescence Cells in Detecting Malignant Effusions.

Background: Most modern haematology analysers have a dedicated body fluid mode for cell counts of body fluids. Many analysers also count the number of high fluorescence cells (HF cells). HF cells have a large nuclear size and emit high fluorescence when stained with fluorescent dyes. Due to their large nuclear size, Malignant cells are counted as HF cells. Aims and Objectives: We aim to determine the diagnostic utility of HF cells in predicting the presence of malignant cells in serous effusions. Materials and Methods: HF cell counts were done on 209 serous fluid samples using the body fluid mode of Mindray BC-6800 plus haematology analyser. Papanicilaou-stained smears of all samples were examined for the presence of malignant cells by a panel of cytopathologists. ROC curve analysis was done to determine the sensitivity and specificity of HF cells in malignant effusions. Results: Out of 209 samples, malignant cells were found by microscopy in 97 cases (46.4%). The absolute number and percentage of HF cells were significantly higher (P < 0.001) in malignant effusions (HF# = 24.9 cells/ul, HF% = 10.4%) when compared to non-malignant samples (HF# = 4.95 cells/ul, HF% = 5.76%). ROC curve analysis determined an optimal cut-off of ≥30 HF cells/ul (sensitivity = 73.91, specificity = 55.66%) for the prediction of malignant cells. Conclusion: HF cells in serous effusions can be a helpful tool to aid the pathologist, but it is not an ideal screening test due to its low sensitivity (67.74%) and negative likelihood ratio (0.5) at a cut-off of ≥30 HF cells/ul. However, due to high specificity of 83.18% at a cut-off of ≥72 HF cells/ul, a meticulous search for malignant cells should be done on microscopy.

Association of Interictal Epileptiform Discharges with Sleep and Anti-Epileptic Drugs.

BACKGROUND: The presence of interictal epileptiform discharges (IEDs) in electroencephalogram (EEG) is diagnostic of epilepsy. Latent IEDs are activated during sleep. Anti-epileptic drugs (AEDs) improve sleep. AEDs, sleep, and IEDs may interact and affect epilepsy management. PURPOSE: To explore the occurrence of IEDs and its association with sleep and AED status in suspected patients of epilepsy. METHODS: EEG records were collected of suspected patients of epilepsy who reported to the electrophysiology laboratory of a tertiary care hospital during 1 year. The anthropometric details, clinical presentations, and AED status of the patients were recorded from the EEG records. Patients were divided into 2 categories based on whether AEDs had been started prior to the EEG evaluation (category-I) or not (category-II). The occurrences of IEDs in EEG recordings in both categories were analyzed. RESULTS: In 1 year, 138 patients were referred for diagnostic EEG evaluation. One-hundred-two patients fulfilled the inclusion criteria, of which 57 patients (53%) belonged to category-I and 45 patients (47%) belonged to category-II. Incidence of IEDs, suggestive of definite diagnosis of epilepsy in category-I was 88% and in category-II was 69%, and this difference was statistically significant (p = 0.02). CONCLUSION: The increased proportion of IEDs in category-I patients may be due to high clinical suspicion or compounding interaction of AEDs and sleep. More extensive studies are required to delineate the complex interaction of AEDs, sleep, and IEDs so that judicious yet prompt management of epilepsy can be carried out.

FFT transformed quantitative EEG analysis of short term memory load.

BACKGROUND: The EEG is considered as building block of functional signaling in the brain. The role of EEG oscillations in human information processing has been intensively investigated. PURPOSE: To study the quantitative EEG correlates of short term memory load as assessed through Sternberg memory test. METHODS: The study was conducted on 34 healthy male student volunteers. The intervention consisted of Sternberg memory test, which runs on a version of the Sternberg memory scanning paradigm software on a computer. Electroencephalography (EEG) was recorded from 19 scalp locations according to 10-20 international system of electrode placement. EEG signals were analyzed offline. To overcome the problems of fixed band system, individual alpha frequency (IAF) based frequency band selection method was adopted. The outcome measures were FFT transformed absolute powers in the six bands at 19 electrode positions. RESULTS: Sternberg memory test served as model of short term memory load. Correlation analysis of EEG during memory task was reflected as decreased absolute power in Upper alpha band in nearly all the electrode positions; increased power in Theta band at Fronto-Temporal region and Lower 1 alpha band at Fronto-Central region. Lower 2 alpha, Beta and Gamma band power remained unchanged. CONCLUSION: Short term memory load has distinct electroencephalographic correlates resembling the mentally stressed state. This is evident from decreased power in Upper alpha band (corresponding to Alpha band of traditional EEG system) which is representative band of relaxed mental state. Fronto-temporal Theta power changes may reflect the encoding and execution of memory task.