Monoclonal antibody: a cell specific immunotherapy to treat cancer

Fundamentally, the therapy technique which is utilized in malignancy immunotherapy, monoclonal antibodies (mAb), is one of them, and it is used extensively as a treatment for the disease. To achieve more successful treatment, novel combination treatments and treatment procedures must be created. The purpose of this study is the improvement of mAb treatment and detail late advance and new limits, particularly in cancer therapy. With various keywords, we searched Google Scholar, PubMed, and Scopus for monoclonal antibody therapy as an alternate form of chemotherapy. The number of patients who received each therapy regimen, and the recovery rate are all displayed in this study, also a comparative study between monotherapy and chemotherapy. The result showed that rituximab had a greater overall response rate than other drugs, at 68%. In the combination treatment group (monotherapy+chemotherapy), 100% of patients had adverse events, compared to 84.2 percent in the monotherapy group. The pharmaceutical industry's fastest-growing medications, monoclonal antibodies are increasingly being examined in Clinical trials as stand-alone treatments or in conjunction with other therapies. It has a promising future since it will provide better tailored therapy and combination therapy for the treatment of cancer

Antibiotic resistance situation in Dhaka, Bangladesh: a review

Antibiotic resistance is global trouble and in the megacities, it is causing more rapidly due to the misuse and overuse of antibiotics. This systematic evaluation used to be carried out to summarize the contemporary day kingdom of affairs of antibiotic resistance in Dhaka, to emerge as aware of gaps in close observation, and to prink tips primarily based on honesty and surely on the findings. Google scholar, PubMed, and Bangladeshi journals online have been searched for the use of applicable key phrases to select articles connected to antibiotic resistance in Dhaka, Bangladesh published between 2004 to 2020. The resistance of a bacterium to a given drug was once added as the median resistance and interquartile fluctuate. Forty-one articles have been blanketed in this systematic review. Antimicrobial susceptibility trying out used to be once as quickly as carried out via disk diffusion approach in 97.56% of studies, at the equal time as the clinical and laboratory standards institute suggestions had been accompanied in 92.68%. Data concerning the susceptibility attempting out method and furnish of sickness (hospital/community) had been absent in 12.19%, 10.52%, and 90.24% of the research, respectively. An excessive prevalence of resistance used to be detected in most examined pathogens, and many of the normal first-line pills have been the most importantly ineffective. Resistance to carbapenems was once low in most cases. An excessive incidence of resistance to most antibiotics used to be detected, alongside necessary gaps in surveillance and facts gaps in the methodological data of the show up to be up

Particle swarm optimization based LSTM networks for water level forecasting : a case study on Bangladesh river network

Floods are one of the most catastrophic natural disasters. Water level forecasting is an essential method of avoiding floods and disaster preparedness. In recent years, models for predicting water levels have been developed using artificial intelligence techniques like the artificial neural network (ANN). It has been demonstrated that more advanced and sequenced-based deep learning techniques, like long short-term memory (LSTM) networks, are superior at forecasting hydrological data. However, historically, most LSTM hyperparameters were based on experience, which typically did not produce the best outcomes. The Particle Swarm Optimization (PSO) method was utilized to adjust the LSTM hyperparameter to increase the capacity to learn data sequence characteristics. Utilizing water level observation data from stations along Bangladesh's Brahmaputra, Ganges, and Meghna rivers, the model was utilized to estimate flood dynamics. The Nash Sutcliffe efficiency (NSE) coefficient, root mean square error (RMSE), and MAE were used to assess the model's performance, where PSO-LSTM model outperforms the ANN, PSO-ANN, and LSTM models in predicting water levels in all stations. The PSO-LSTM model provides improved prediction accuracy and stability and improves water level forecasting accuracy at varying lead times. The findings may aid in sustainable flood risk mitigation in the study region in the future

Improving spatial agreement in machine learning-based landslide susceptibility mapping

Despite yielding considerable degrees of accuracy in landslide predictions, the outcomes of different landslide susceptibility models are prone to spatial disagreement; and therefore, uncertainties. Uncertainties in the results of various landslide susceptibility models create challenges in selecting the most suitable method to manage this complex natural phenomenon. This study aimed to propose an approach to reduce uncertainties in landslide prediction, diagnosing spatial agreement in machine learning-based landslide susceptibility maps. It first developed landslide susceptibility maps of Cox’s Bazar district of Bangladesh, applying four machine learning algorithms: K-Nearest Neighbor (KNN), Multi-Layer Perceptron (MLP), Random Forest (RF), and Support Vector Machine (SVM), featuring hyperparameter optimization of 12 landslide conditioning factors. The results of all the four models yielded very high prediction accuracy, with the area under the curve (AUC) values range between 0.93 to 0.96. The assessment of spatial agreement of landslide predictions showed that the pixel-wise correlation coefficients of landslide probability between various models range from 0.69 to 0.85, indicating the uncertainty in predicted landslides by various models, despite their considerable prediction accuracy. The uncertainty was addressed by establishing a Logistic Regression (LR) model, incorporating the binary landslide inventory data as the dependent variable and the results of the four landslide susceptibility models as independent variables. The outcomes indicated that the RF model had the highest influence in predicting the observed landslide locations, followed by the MLP, SVM, and KNN models. Finally, a combined landslide susceptibility map was developed by integrating the results of the four machine learning-based landslide predictions. The combined map resulted in better spatial agreement (correlation coefficients range between 0.88 and 0.92) and greater prediction accuracy (0.97) compared to the individual models. The modelling approach followed in this study would be useful in minimizing uncertainties of various methods and improving landslide predictions

National-scale flood risk assessment using GIS and remote sensing-based hybridized deep neural network and fuzzy analytic hierarchy process models : a case of Bangladesh

Assessing flood risk is challenging due to complex interactions among flood susceptibility, hazard, exposure, and vulnerability parameters. This study presents a novel flood risk assessment framework by utilizing a hybridized deep neural network (DNN) and fuzzy analytic hierarchy process (AHP) models. Bangladesh was selected as a case study region, where limited studies examined flood risk at a national scale. The results exhibited that hybridized DNN and fuzzy AHP models can produce the most accurate flood risk map while comparing among 15 different models. About 20.45% of Bangladesh are at flood risk zones of moderate, high, and very high severity. The northeastern region, as well as areas adjacent to the Ganges–Brahmaputra–Meghna rivers, have high flood damage potential, where a significant number of people were affected during the 2020 flood event. The risk assessment framework developed in this study would help policymakers formulate a comprehensive flood risk management system

Efficient Multi-site Data Movement Using Constraint Programming for Data Hungry Science

For the past decade, HENP experiments have been heading towards a distributed computing model in an effort to concurrently process tasks over enormous data sets that have been increasing in size as a function of time. In order to optimize all available resources (geographically spread) and minimize the processing time, it is necessary to face also the question of efficient data transfers and placements. A key question is whether the time penalty for moving the data to the computational resources is worth the presumed gain. Onward to the truly distributed task scheduling we present the technique using a Constraint Programming (CP) approach. The CP technique schedules data transfers from multiple resources considering all available paths of diverse characteristic (capacity, sharing and storage) having minimum user's waiting time as an objective. We introduce a model for planning data transfers to a single destination (data transfer) as well as its extension for an optimal data set spreading strategy (data placement). Several enhancements for a solver of the CP model will be shown, leading to a faster schedule computation time using symmetry breaking, branch cutting, well studied principles from job-shop scheduling field and several heuristics. Finally, we will present the design and implementation of a corner-stone application aimed at moving datasets according to the schedule. Results will include comparison of performance and trade-off between CP techniques and a Peer-2-Peer model from simulation framework as well as the real case scenario taken from a practical usage of a CP scheduler.Comment: To appear in proceedings of Computing in High Energy and Nuclear Physics 200