Mineral Trioxide Aggregate in Aggressive Dental Resorption: A Case Report

The study was carried out to evaluate the clinical efficacy of Mineral trioxide aggregate (MTA) in arresting dental resorption and as a regenerative material especially for growth of bone and periodontal ligament. Tooth no 25 having Aggressive Dental Resorption (simultaneous presentation of apical and lateral perforating resorption) with discharging sinus and co-existing oral communication through periodontal pocket was treated with MTA. After thorough debridement and disinfection of the root canal, complete obturation of the root canal system was done with MTA and evaluated thereafter. Follow up examinations up to a period of 1 year could not reveal resolution of any of the preoperative signs and symptoms i.e. discharging sinus, periodontal pocket healing and mobility; also did not show radiographic evidence of arrest of resorption and bone or periodontal tissue formation. Clinical efficacy of MTA in arresting dental resorption with subsequent repair found questionable. However, Shorter period of disinfection, co-existence of oral communication with the resorptive defects through periodontium and non surgical treatment approach all or any one of these may be the concern for the failure. Keywords: Resorption, Perforation, MTA.DOI: 10.3329/bsmmuj.v2i1.3711 BSMMU J 2009; 2(1): 42-4

Central venous catheter use in severe malaria: time to reconsider the World Health Organization guidelines?

<p>Abstract</p> <p>Background</p> <p>To optimize the fluid status of adult patients with severe malaria, World Health Organization (WHO) guidelines recommend the insertion of a central venous catheter (CVC) and a target central venous pressure (CVP) of 0-5 cmH₂O. However there are few data from clinical trials to support this recommendation.</p> <p>Methods</p> <p>Twenty-eight adult Indian and Bangladeshi patients admitted to the intensive care unit with severe <it>falciparum </it>malaria were enrolled in the study. All patients had a CVC inserted and had regular CVP measurements recorded. The CVP measurements were compared with markers of disease severity, clinical endpoints and volumetric measures derived from transpulmonary thermodilution.</p> <p>Results</p> <p>There was no correlation between the admission CVP and patient outcome (p = 0.67) or disease severity (p = 0.33). There was no correlation between the baseline CVP and the concomitant extravascular lung water (p = 0.62), global end diastolic volume (p = 0.88) or cardiac index (p = 0.44). There was no correlation between the baseline CVP and the likelihood of a patient being fluid responsive (p = 0.37). On the occasions when the CVP was in the WHO target range patients were usually hypovolaemic and often had pulmonary oedema by volumetric measures. Seven of 28 patients suffered a complication of the CVC insertion, although none were fatal.</p> <p>Conclusion</p> <p>The WHO recommendation for the routine insertion of a CVC, and the maintenance of a CVP of 0-5 cmH₂O in adults with severe malaria, should be reconsidered.</p

STANDARDIZATION OF AGRO-TECHNIQUES FOR THE PRODUCTION OF PROCESSING QUALITY POTATO

Six experiments were carried out at the Tuber Crops Research Center of Bangladesh Agricultural Research Institute, Gazipur during the period from November 2012 t0 February 2015 with a view to developing a package of agro-techniques for maximizing process grade tuber yield and quality. The popular potato variety BARJ Alu 28 (Lady Rosetta) was used in all the experiments. ln the first experiment, planting time and dehaulming schedule were evaluated to maximize process grade tuber yield and quality. Response of different planting geometry was measured in the second experiment. In the third experiment seed tuber size and inter row spacing were standardized. Nitrogen and potassium fertilizers were evaluated to find out the optimum doses for higher process grade yield and quality in the fourth experiment. ln the 5th experiment, the treatments found most effective in experiments 2, 3 and 4 were accumulated and compared in different combinations to formulate the best agro-technique package, In the 6th experiment, the treatments found most effective in previous two experiments of nitrogen and potassium were evaluated with cowdung manure for better quality processing tubers production. Results indicated that significantly higher yield of process grade tuber was recorded in November I5 planting in combination with all the dehaulming schedules, ranged from 20.67 to 21.50 t/ha. Processing quality parameters like high specific gravity and dry matter and low reducing sugar content were significantly influenced in the former planting time under dehaulming at 90 days after planting. The chips and French fry grade tubers as well as total tuber yield were the highest at 67.5 cm x 25 cm spacing. Medium sized seed tuber found profitable for maximizing tuber yield as well as dry matter production under 25 cm intra row spacing. This combination also gave the highest yield of chips grade tuber (21.9 t/ha). Nitrogen and potassium in combination significantly increased the chips and French fry grade tuber number and weight per hill as well as their yield. Reducing sugar content was the lowest (31.97 mg/I 00 g fresh weight) at 200 kg K/ha. Dry matter content increased up to 150 kg/ha of both N and K doses and higher doses showed declining tendency. With cowdung manure, an integrated plant nutrient supply method increased yield and quality of process grade tubers. However, the quality of process products vary significantly with all other factors except planting geometry, seed size and organic matter

51 Mineral Trioxide Aggregate in Aggressive Dental Resorption: A Case Report

The study was carried out to evaluate the clinical efficacy of Mineral trioxide aggregate (MTA) in arresting dental resorption and as a regenerative material especially for growth of bone and periodontal ligament. Tooth no 25 having Aggressive Dental Resorption (simultaneous presentation of apical and lateral perforating resorption) with discharging sinus and co-existing oral communication through periodontal pocket was treated with MTA. After thorough debridement and disinfection of the root canal, complete obturation of the root canal system was done with MTA and evaluated thereafter. Follow up examinations up to a period of 1 year could not reveal resolution of any of the preoperative signs and symptoms i.e. discharging sinus, periodontal pocket healing and mobility; also did not show radiographic evidence of arrest of resorption and bone or periodontal tissue formation. Clinical efficacy of MTA in arresting dental resorption with subsequent repair found questionable. However, Shorter period of disinfection, co-existence of oral communication with the resorptive defects through periodontium and non surgical treatment approach all or any one of these may be the concern for the failure

Evaluation of Morphology of Premature Ventricular Contraction on 12-Lead Electrocardiogram

Background-Evaluation of different morphology of premature ventricular contraction (PVC) in 12-lead ECG might reflect the presence or absence of myocardial diseases and determine PVC foci. It is important for ablation procedure and it can help in pre-procedural planning and potentially may improve ablation outcome.Methods and Results-In this study, 12-lead Electrocardiogram (ECG) of 50 patients with or without structural cardiac diseases, who had experienced PVC, were obtained. PVC QRS duration, contour, pattern, unifocal or multifocaland different morphology in various lead were evaluated. PVC-QRS morphology of 50 ECGs showed QRSd d” 140ms was 60%, &gt;140ms was 24%, &gt;160ms was 16%. QRS notching &lt;40ms was 42%, &gt;40ms was 16%, smooth contour was 42%. The morphology of PVCs in lead V1, RBBB morphology was 36%, LBBB morphology was 64%; in lead V1 &amp; V2, high r 8%, low r 4%. QRS wave polarity in lead I negative (QS, Qr, or rS wave pattern) 28%, positive (R-wave pattern) 52%; in lead II, III, avF, positive 76%. Of these RR’ or Rr’ pattern 20%, R pattern 56%. Negative 24%. QRS transition in chest lead, 16% transition occur at V4 –V5, 48% at V3-V4, 4% at V2-V3, 36% at V1-V2 level. The pattern of PVCs were bigeminy 24%, trigeminy 6%, couplet 4%, salvos 12%, R on T 2%, VT 6%. Of the 32 PVCs originating from the RVOT, 8 were classified as of free-wall origin, 24 of septal, 14 of left, 26 of right, 4 of proximal, and 2 of distal origin. Of the 6 PVCs originating from the LVOT, 4 were originated from the LVOT close to the left coronary cusp and 2 were originated from the LVOT close to the right coronary cusp. Of the 12 PVCs originated from LV fascicle, 12 of posterior fascicle origin and none from anterior fascicle origin.Conclusion-12-lead ECG is a simple, inexpensive and noninvasive tool to detect PVCs and facilitate their localization. By evaluating morphology of PVC, we can also predict the structural and functional state of heart.Bangladesh Heart Journal 2016; 31(2) : 75-79</jats:p

Daily Prediction and Multi-Step Forward Forecasting of Reference Evapotranspiration Using LSTM and Bi-LSTM Models

Precise forecasting of reference evapotranspiration (ET0) is one of the critical initial steps in determining crop water requirements, which contributes to the reliable management and long-term planning of the world’s scarce water sources. This study provides daily prediction and multi-step forward forecasting of ET0 utilizing a long short-term memory network (LSTM) and a bi-directional LSTM (Bi-LSTM) model. For daily predictions, the LSTM model’s accuracy was compared to that of other artificial intelligence-based models commonly used in ET0 forecasting, including support vector regression (SVR), M5 model tree (M5Tree), multivariate adaptive regression spline (MARS), probabilistic linear regression (PLR), adaptive neuro-fuzzy inference system (ANFIS), and Gaussian process regression (GPR). The LSTM model outperformed the other models in a comparison based on Shannon’s entropy-based decision theory, while the M5 tree and PLR models proved to be the lowest performers. Prior to performing a multi-step-ahead forecasting, ANFIS, sequence-to-sequence regression LSTM network (SSR-LSTM), LSTM, and Bi-LSTM approaches were used for one-step-ahead forecasting utilizing the past values of the ET0 time series. The results showed that the Bi-LSTM model outperformed other models and that the sequence of models in ascending order in terms of accuracies was Bi-LSTM > SSR-LSTM > ANFIS > LSTM. The Bi-LSTM model provided multi-step (5 day)-ahead ET0 forecasting in the next step. According to the results, the Bi-LSTM provided reasonably accurate and acceptable forecasting of multi-step-forward ET0 with relatively lower levels of forecasting errors. In the final step, the generalization capability of the proposed best models (LSTM for daily predictions and Bi-LSTM for multi-step-ahead forecasting) was evaluated on new unseen data obtained from a test station, Ishurdi. The model’s performance was assessed on three distinct datasets (the entire dataset and the first and the second halves of the entire dataset) derived from the test dataset between 1 January 2015 and 31 December 2020. The results indicated that the deep learning techniques (LSTM and Bi-LSTM) achieved equally good performances as the training station dataset, for which the models were developed. The research outcomes demonstrated the ability of the developed deep learning models to generalize the prediction capabilities outside the training station

Daily Prediction and Multi-Step Forward Forecasting of Reference Evapotranspiration Using LSTM and Bi-LSTM Models

Precise forecasting of reference evapotranspiration (ET0) is one of the critical initial steps in determining crop water requirements, which contributes to the reliable management and long-term planning of the world’s scarce water sources. This study provides daily prediction and multi-step forward forecasting of ET0 utilizing a long short-term memory network (LSTM) and a bi-directional LSTM (Bi-LSTM) model. For daily predictions, the LSTM model’s accuracy was compared to that of other artificial intelligence-based models commonly used in ET0 forecasting, including support vector regression (SVR), M5 model tree (M5Tree), multivariate adaptive regression spline (MARS), probabilistic linear regression (PLR), adaptive neuro-fuzzy inference system (ANFIS), and Gaussian process regression (GPR). The LSTM model outperformed the other models in a comparison based on Shannon’s entropy-based decision theory, while the M5 tree and PLR models proved to be the lowest performers. Prior to performing a multi-step-ahead forecasting, ANFIS, sequence-to-sequence regression LSTM network (SSR-LSTM), LSTM, and Bi-LSTM approaches were used for one-step-ahead forecasting utilizing the past values of the ET0 time series. The results showed that the Bi-LSTM model outperformed other models and that the sequence of models in ascending order in terms of accuracies was Bi-LSTM &gt; SSR-LSTM &gt; ANFIS &gt; LSTM. The Bi-LSTM model provided multi-step (5 day)-ahead ET0 forecasting in the next step. According to the results, the Bi-LSTM provided reasonably accurate and acceptable forecasting of multi-step-forward ET0 with relatively lower levels of forecasting errors. In the final step, the generalization capability of the proposed best models (LSTM for daily predictions and Bi-LSTM for multi-step-ahead forecasting) was evaluated on new unseen data obtained from a test station, Ishurdi. The model’s performance was assessed on three distinct datasets (the entire dataset and the first and the second halves of the entire dataset) derived from the test dataset between 1 January 2015 and 31 December 2020. The results indicated that the deep learning techniques (LSTM and Bi-LSTM) achieved equally good performances as the training station dataset, for which the models were developed. The research outcomes demonstrated the ability of the developed deep learning models to generalize the prediction capabilities outside the training station.</jats:p

Daily Prediction and Multi-Step Forward Forecasting of Reference Evapotranspiration Using LSTM and Bi-LSTM Models

Precise forecasting of reference evapotranspiration (ET0) is one of the critical initial steps in determining crop water requirements, which contributes to the reliable management and long-term planning of the world&rsquo;s scarce water sources. This study provides daily prediction and multi-step forward forecasting of ET0 utilizing a long short-term memory network (LSTM) and a bi-directional LSTM (Bi-LSTM) model. For daily predictions, the LSTM model&rsquo;s accuracy was compared to that of other artificial intelligence-based models commonly used in ET0 forecasting, including support vector regression (SVR), M5 model tree (M5Tree), multivariate adaptive regression spline (MARS), probabilistic linear regression (PLR), adaptive neuro-fuzzy inference system (ANFIS), and Gaussian process regression (GPR). The LSTM model outperformed the other models in a comparison based on Shannon&rsquo;s entropy-based decision theory, while the M5 tree and PLR models proved to be the lowest performers. Prior to performing a multi-step-ahead forecasting, ANFIS, sequence-to-sequence regression LSTM network (SSR-LSTM), LSTM, and Bi-LSTM approaches were used for one-step-ahead forecasting utilizing the past values of the ET0 time series. The results showed that the Bi-LSTM model outperformed other models and that the sequence of models in ascending order in terms of accuracies was Bi-LSTM &gt; SSR-LSTM &gt; ANFIS &gt; LSTM. The Bi-LSTM model provided multi-step (5 day)-ahead ET0 forecasting in the next step. According to the results, the Bi-LSTM provided reasonably accurate and acceptable forecasting of multi-step-forward ET0 with relatively lower levels of forecasting errors. In the final step, the generalization capability of the proposed best models (LSTM for daily predictions and Bi-LSTM for multi-step-ahead forecasting) was evaluated on new unseen data obtained from a test station, Ishurdi. The model&rsquo;s performance was assessed on three distinct datasets (the entire dataset and the first and the second halves of the entire dataset) derived from the test dataset between 1 January 2015 and 31 December 2020. The results indicated that the deep learning techniques (LSTM and Bi-LSTM) achieved equally good performances as the training station dataset, for which the models were developed. The research outcomes demonstrated the ability of the developed deep learning models to generalize the prediction capabilities outside the training station