The relationship between modified Graeb score and intraventricular hematoma volume with Glasgow outcome scale and modified Rankin scale in intraventricular hemorrhage of brain: a comparative study

Background: Intraventricular hemorrhage (IVH) is an acute neurosurgical condition. The aim of this study was to identify the relationship between modified Graeb score (mGS) and intraventricular hematoma volume with Glasgow outcome scale (GOS) and modified Rankin scale (mRS).Methods: This is a Quasi-experimental study conducted in the department of neurosurgery, Chittagong Medical College Hospital, Chittagong, Bangladesh during the period from 24 July 2018 to 23 July 2019. After a detailed history and clinical examination, 150 patients were selected for this study. The study participants were divided into two major groups- external ventricular drainage (EVD) and conservative; both groups consisted of 44 patients. Written informed consent were taken from the participants. Data were analyzed using statistical package for the social sciences (SPSS) software.Results: Overall mean age was around 60 years with an age range from 15-85 years. More than three fourth of the patients in both groups were from the age group of >50 years (73.83%). There were no differences between EVD and conservative groups regarding medical comorbidities. Most prevalent comorbidity among the patients of both groups’ hypertension, followed by diabetes and previous ischemic stroke. Overall the most frequent symptoms in the studied patients were vomiting, followed by loss of consciousness, headache and convulsion. There were no significant differences between the two groups regarding presenting symptoms. The mean Glasgow coma scale (GCS) score level was significantly lower in the patients with EVD than their counterpart from 1st post-operative day to 8th post-operative day. However, within-group comparison shows that the GCS score was significantly increased from 1st day to 8th day in both groups of patients.Conclusions: These findings can be used to identify patients in whom an EVD may provide measurable outcomes benefit with respect to patient mortality and help guide neurosurgical decision-making in particular patient subgroups with acute IVH

Ischemic Strokes: Observations from a Hospital Based Stroke Registry in Bangladesh.

Background. Stroke is an important morbidity for low and middle income countries like Bangladesh. We established the first stroke registry in Bangladesh. Methods. Data was collected from stroke patients who were admitted in Department of Neurology of BIRDEM with first ever stroke, aged between 30 and 90 years. Patients with intracerebral hemorrhage, subarachnoid and subdural hemorrhage, and posttrauma features were excluded. Results. Data was gathered from 679 stroke patients. Mean age was 60.6 years. Almost 68% of patients were male. Small vessel strokes were the most common accounting for 45.4% of all the patients followed by large vessel getting affected in 32.5% of the cases. Only 16 (2.4%) died during treatment, and 436 (64.2%) patients had their mRS score of 3 to 5. Age greater than 70 years was associated with poor outcome on discharge [OR 1.79 (95% CI: 1.05 to 3.06)] adjusting for gender, duration of hospital stay, HDL, and pneumonia. Age, mRS, systolic blood pressure, urinary tract infection, pneumonia, and stroke severity explained the Barthel score. Conclusion. Mortality was low but most of patient had moderate to severe disability at discharge. Age, mRS, systolic blood pressure, urinary tract infection, pneumonia, and stroke severity influenced the Barthel score

Motion Artifacts Correction from Single-Channel EEG and fNIRS Signals using Novel Wavelet Packet Decomposition in Combination with Canonical Correlation Analysis

The electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals, highly non-stationary in nature, greatly suffers from motion artifacts while recorded using wearable sensors. This paper proposes two robust methods: i) Wavelet packet decomposition (WPD), and ii) WPD in combination with canonical correlation analysis (WPD-CCA), for motion artifact correction from single-channel EEG and fNIRS signals. The efficacy of these proposed techniques is tested using a benchmark dataset and the performance of the proposed methods is measured using two well-established performance matrices: i) Difference in the signal to noise ratio ({\Delta}SNR) and ii) Percentage reduction in motion artifacts ({\eta}). The proposed WPD-based single-stage motion artifacts correction technique produces the highest average {\Delta}SNR (29.44 dB) when db2 wavelet packet is incorporated whereas the greatest average {\eta} (53.48%) is obtained using db1 wavelet packet for all the available 23 EEG recordings. Our proposed two-stage motion artifacts correction technique i.e. the WPD-CCA method utilizing db1 wavelet packet has shown the best denoising performance producing an average {\Delta}SNR and {\eta} values of 30.76 dB and 59.51%, respectively for all the EEG recordings. On the other hand, the two-stage motion artifacts removal technique i.e. WPD-CCA has produced the best average {\Delta}SNR (16.55 dB, utilizing db1 wavelet packet) and largest average {\eta} (41.40%, using fk8 wavelet packet). The highest average {\Delta}SNR and {\eta} using single-stage artifacts removal techniques (WPD) are found as 16.11 dB and 26.40%, respectively for all the fNIRS signals using fk4 wavelet packet. In both EEG and fNIRS modalities, the percentage reduction in motion artifacts increases by 11.28% and 56.82%, respectively when two-stage WPD-CCA techniques are employed.Comment: 25 pages, 10 figures and 2 table

Antioxidant and antineoplastic activities of methanolic extract of Kaempferia galanga Linn. Rhizome against Ehrlich ascites carcinoma cells

As sources of natural products, medicinal plants bear a great interest for researcher in recent decades and this interest has increased considerably in finding naturally occurring antioxidant and antineoplastic compounds. Kaempferia galangal Linn., is an important member of medicinal flora available in Bangladesh and used traditionally for the prevention of numerous diseases. The present study was designed to investigate the antioxidant and antineoplastic activities of methanol extract of Kaempferia galanga rhizome (MEKGR). In vitro models and MTT assays were used to determine the antioxidant and in vitro antineoplastic properties of MEKGR. Antineoplastic effect of MEKGR against Ehrlich ascites carcinoma (EAC) were assessed in vivo by evaluating the viable tumor cell count, survival time, body weight gain due to tumor burden, observing morphological changes and nuclear damage of EAC cells by fluorescence microscope and estimating hematological profiles of experimental mice. Chemical composition was also analyzed by GC–MS. Treatment with MEKGR significantly (p < 0.05) reduced viable EAC cells and weight gain and increased life span. MEKGR restored all hematological parameters, such as RBC, WBC, hemoglobin (Hb%) of EAC-bearing mice towards normal level. Membrane blebbing, chromatin condensation, nuclear fragmentations were observed after treatment with MEKGR. MEKGR exhibited strong antioxidant activity. TPC (Total phenolic content) and TFC (Total flavonoid content) were found strongly correlated (P < 0.05) with antioxidant activities of MEKGR. 2-Propenoic acid, phthalic acid, palmitic acid, sandaracopimaradiene, oleic acid, octadecanoic acid, 2-[2-(4-nonylphenoxy) ethoxy] ethanol and glycidyl stearate were identified as the major constituents of MEKGR by GC–MS analysis. The overall findings of this study suggest that MEKGR may provide a natural source of antioxidant and antineoplastic activities. Keywords: Kaempferia galanga L. rhizome, Antioxidant activity, Anticancer activity, Apoptosis, GC–MS analysi

Knowledge, Attitude and Practice of Hypercholesterolemic Type 2 Diabetic Subjects on Dyslipidemia

This study was undertaken to assess the knowledge, attitude and practice (KAP) of hypercholesterolemic type 2 diabetic subjects on dyslipidemia and to analyze the influence of some demographic and socioeconomic factors on the level of KAP.It was a descriptive cross-sectional survey. One hundred eleven newly diagnosed type 2 diabetic subjects (male 61%, female 39%, age 45±9 years, BMI 24±4.8 Kg/m2) with hypercholesterolemia (fasting plasma total cholesterol >200 mg/dl) were selected from the out patient department of BIRDEM by purposive sampling method. Data were collected by a pre-designed, pretested, interviewer-administered questionnaire. Three categories were defined on the basis of the score obtained by each subject namely low, medium and high as follows: knowledge-score 60%; attitude-score 80%; and practice-score 70% respectively. The levels of knowledge were low in 42%, medium in 35% and high in 23% of the study subjects. The corresponding attitude levels were low in 1%, medium in 31% and high in 68%, and the levels of practice were low in 80%, medium in 14% and high in 6% of the subjects. The knowledge score was higher in secondary and graduate (53.4±8.9%, and 54.9±10.1%) groups compared to illiterate-primary group (48.9±9.9%). Practice score of illiterate-primary group (34.5±16.8%) was lower than secondary and graduate (43.1±13.9% and 46.7±18.1%) groups, but they did not differ on attitude. The various income groups did not differ on knowledge. Attitude score of high-income group (78.7±8.4%) was better than low-income group (70.9±11.8%). Practice score in high-income group (44.7±16.0%) was better than medium income and low-income groups (31.3±14.5% and 28.6±15.0%). Knowledge and practice score in Bangladeshi hypercholesterolemic type 2 diabetic subjects are not satisfactory although they have fairly good attitude levels. Education and income status are the major determinants of knowledge, attitude and practice regarding dyslipidemia in diabetes. A coordinated policy is required to promote knowledge and attitude on healthy lifestyle and to translate those into practice. Ibrahim Med. Coll. J. 2011; 5(2): 37-4

Ischemic Strokes: Observations from a Hospital Based Stroke Registry in Bangladesh

Background. Stroke is an important morbidity for low and middle income countries like Bangladesh. We established the first stroke registry in Bangladesh. Methods. Data was collected from stroke patients who were admitted in Department of Neurology of BIRDEM with first ever stroke, aged between 30 and 90 years. Patients with intracerebral hemorrhage, subarachnoid and subdural hemorrhage, and posttrauma features were excluded. Results. Data was gathered from 679 stroke patients. Mean age was 60.6 years. Almost 68% of patients were male. Small vessel strokes were the most common accounting for 45.4% of all the patients followed by large vessel getting affected in 32.5% of the cases. Only 16 (2.4%) died during treatment, and 436 (64.2%) patients had their mRS score of 3 to 5. Age greater than 70 years was associated with poor outcome on discharge [OR 1.79 (95% CI: 1.05 to 3.06)] adjusting for gender, duration of hospital stay, HDL, and pneumonia. Age, mRS, systolic blood pressure, urinary tract infection, pneumonia, and stroke severity explained the Barthel score. Conclusion. Mortality was low but most of patient had moderate to severe disability at discharge. Age, mRS, systolic blood pressure, urinary tract infection, pneumonia, and stroke severity influenced the Barthel score

Thermal Change Index-Based Diabetic Foot Thermogram Image Classification Using Machine Learning Techniques

Diabetes mellitus (DM) can lead to plantar ulcers, amputation and death. Plantar foot thermogram images acquired using an infrared camera have been shown to detect changes in temperature distribution associated with a higher risk of foot ulceration. Machine learning approaches applied to such infrared images may have utility in the early diagnosis of diabetic foot complications. In this work, a publicly available dataset was categorized into different classes, which were corrobo-rated by domain experts, based on a temperature distribution parameter-the thermal change index (TCI). We then explored different machine-learning approaches for classifying thermograms of the TCI-labeled dataset. Classical machine learning algorithms with feature engineering and the convolutional neural network (CNN) with image enhancement techniques were extensively investigated to identify the best performing network for classifying thermograms. The multilayer perceptron (MLP) classifier along with the features extracted from thermogram images showed an accuracy of 90.1% in multi-class classification, which outperformed the literature-reported performance metrics on this dataset. 2022 by the authors. Licensee MDPI, Basel, Switzerland.Funding: This research was funded by Qatar National Research Fund (QNRF), International Research Collaboration Co-Fund (IRCC)-Qatar University and University Kebangsaan Malaysia with grant number NPRP12S-0227-190164, IRCC-2021-001 and DPK-2021-001 respectively.Scopu

Thermal Change Index-Based Diabetic Foot Thermogram Image Classification Using Machine Learning Techniques

Diabetes mellitus (DM) can lead to plantar ulcers, amputation and death. Plantar foot thermogram images acquired using an infrared camera have been shown to detect changes in temperature distribution associated with a higher risk of foot ulceration. Machine learning approaches applied to such infrared images may have utility in the early diagnosis of diabetic foot complications. In this work, a publicly available dataset was categorized into different classes, which were corroborated by domain experts, based on a temperature distribution parameter&mdash;the thermal change index (TCI). We then explored different machine-learning approaches for classifying thermograms of the TCI-labeled dataset. Classical machine learning algorithms with feature engineering and the convolutional neural network (CNN) with image enhancement techniques were extensively investigated to identify the best performing network for classifying thermograms. The multilayer perceptron (MLP) classifier along with the features extracted from thermogram images showed an accuracy of 90.1% in multi-class classification, which outperformed the literature-reported performance metrics on this dataset