CDK: A novel high-performance transfer feature technique for early detection of osteoarthritis.

Knee osteoarthritis (OA) is a prevalent condition causing significant disability, particularly among the elderly, necessitating advancements in diagnostic methodologies to facilitate early detection and treatment. Traditional OA diagnosis, relying on radiography and physical exams, faces limitations in accuracy and objectivity. This underscores the need for more advanced diagnostic methods, such as machine learning (ML) and deep learning (DL), to improve OA detection and classification. This research introduces a novel ensemble learning approach for image data feature extraction which ingeniously combines the strengths of 2 advanced (ML) models with a (DL) method to substantially improve the accuracy of OA detection from radiographic images. This innovative strategy aims to address the limitations of traditional diagnostic tools by leveraging the enhanced sensitivity and specificity of combined ML and DL models. The methodology deployed in this study encompasses the application of 10 ML models to a comprehensive publicly available Kaggle dataset with a total of 3615 samples of knee X-ray images. Through rigorous k-fold cross-validation and meticulous hyperparameter optimization, we also included evaluation metrics like accuracy, receiver operating characteristic, precision, recall, and F1-score to assess our models' performance effectively. The proposed novel CDK (convolutional neural network, decision tree, K-nearest classifier) ensemble approach for feature extraction is designed to synergize the predictive capabilities of individual models, thereby significantly improving the detection accuracy of OA indicators within radiographic images. We applied several ML and DL approaches to the newly created feature set to evaluate performance. The CDK ensemble model outperformed state-of-the-art studies with a high-performance score of 99.72% accuracy. This remarkable achievement underscores the model's exceptional capability in the early detection of OA, highlighting its superiority in comparison to existing methods.

A Novel Deep Learning Approach for Forecasting Myocardial Infarction Occurrences with Time Series Patient Data.

Myocardial Infarction (MI) commonly referred to as a heart attack, results from the abrupt obstruction of blood supply to a section of the heart muscle, leading to the deterioration or death of the affected tissue due to a lack of oxygen. MI, poses a significant public health concern worldwide, particularly affecting the citizens of the Chittagong Metropolitan Area. The challenges lie in both prevention and treatment, as the emergence of MI has inflicted considerable suffering among residents. Early warning systems are crucial for managing epidemics promptly, especially given the escalating disease burden in older populations and the complexities of assessing present and future demands. The primary objective of this study is to forecast MI incidence early using a deep learning model, predicting the prevalence of heart attacks in patients. Our approach involves a novel dataset collected from daily heart attack incidence Time Series Patient Data spanning January 1, 2020, to December 31, 2021, in the Chittagong Metropolitan Area. Initially, we applied various advanced models, including Autoregressive Integrated Moving Average (ARIMA), Error-Trend-Seasonal (ETS), Trigonometric seasonality, Box-Cox transformation, ARMA errors, Trend and Seasonal (TBATS), and Long Short Time Memory (LSTM). To enhance prediction accuracy, we propose a novel Myocardial Sequence Classification (MSC)-LSTM method tailored to forecast heart attack occurrences in patients using the newly collected data from the Chittagong Metropolitan Area. Comprehensive results comparisons reveal that the novel MSC-LSTM model outperforms other applied models in terms of performance, achieving a minimum Mean Percentage Error (MPE) score of 1.6477. This research aids in predicting the likely future course of heart attack occurrences, facilitating the development of thorough plans for future preventive measures. The forecasting of MI occurrences contributes to effective resource allocation, capacity planning, policy creation, budgeting, public awareness, research identification, quality improvement, and disaster preparedness.

Unveiling the Anthelminthic Potential of Merremia vitifolia Stem through in Vitro and in Silico Approach.

This study aimed to assess the anthelmintic activity of methanol extracts from Merremia vitifolia stems using a combination approach encompassing experimental, in vitro, and in silico evaluations. Despite the well-recognized pharmacological properties of M. vitifolia, its potential as an anthelmintic agent remained unexplored. This plant's anthelmintic potential was assessed on adult earthworms (Pheretima posthuma), revealing a dose-dependent reduction in spontaneous motility leading to paralysis and eventual mortality. The most effective dose of M. vitifolia (200 mg/ml) for anthelmintic effects on Pheretima posthuma was identified. Complementary in silico investigations were also conducted, employing Autodock PyRx 0.8 for docking studies of reported M. vitifolia compounds. Notably, quercetin emerged as a promising candidate with superior binding energies against ß-tubulin (-8.3 Kcal/mol). Moreover, this comprehensive research underlines the anthelmintic potential of Merremia vitifolia stem extract and highlights quercetin as a noteworthy compound for further investigation in the quest for novel anthelmintic agents.

Zingiber roseum Rosc. rhizome: A rich source of hepatoprotective polyphenols.

Zingiber roseum is native to Bangladesh and widely used in folk medicine. This present study was designed to assess the ameliorative potential of Zingiber roseum rhizome extract in carbon tetrachloride (CCl4) induced hepatotoxicity in mice model. Seven phenolic compounds were identified and quantified by HPLC analysis in the plant extract, including quercetin, myricetin, catechin hydrate, trans-ferulic acid, trans-cinnamic acid, (-) epicatechin, and rosmarinic acid. Hepatotoxicity was induced by administrating a single intraperitoneal injection of CCl4 (10 mL/kg) on 7th day of treatment. The results revealed that plant extract at all doses (100, 200 and 400 mg/kg) significantly reduced (p < 0.05) the elevated serum aspartate aminotransferase (AST), alanine aminotransferase (ALT) and alkaline phosphatase (ALP) concentrations, and these effects were comparable to that of standard drug silymarin. Histopathological examination also revealed the evidence of recovery from CCL4 induced cellular damage when pretreated with Z. roseum rhizome extract. The in-vivo hepatoprotective effects were further investigated by the in-silico study of the aforementioned compounds with liver-protective enzymes such as superoxide dismutase (SOD), peroxiredoxin, and catalase. The strong binding affinities (ranging from -7.3359 to -9.111 KCal/mol) between the phenolic compounds (except trans-cinnamic acid) and oxidative stress enzymes inhibit ROS production during metabolism. The compounds were also found non-toxic in computational prediction, and a series of biological activities like antioxidant, anticarcinogen, cardio-protectant, hepato-protectant have been detected.

Characterization of Vibrio spp. in environmental water samples collected from flood prone areas of Bangladesh and their antibiotic resistance profile.

Last cholera epidemic has been recorded in Bangladesh between 1992-1993, while few sporadic localized outbreaks have been reported as recent as 2005. Serotype O1 of Vibrio cholera is considered as the principal causative agent which transmits through contaminated drinking water resulting that epidemic. Therefore, the objective of this research was to isolate V. cholera in 3 different water sources; River, pond and tube-well, in 5 different locations of Gazipur, Bangladesh, and to analyze their antibiogram study. A total of 45 water samples were randomly collected for the isolation and identification of Vibrio spp. Samples are then serially diluted in alkaline peptone water and streak on Thiosulfate Citrate Bile Salt Sucrose-TCBS agar for quantification of V. spp. For V. cholera isolation water samples were first enriched in nutrient broth at 37 °C for 16 hours followed by cultivation in selective media; TCBS agar at 37 °C for 24 hours. Yellow colonies on TCBS agar were screed as V. cholera and was confirmed by analyzing their biochemical characteristics like Catalase, Oxidase, MR, VP, Indole, Sugar fermentation. Following isolation antibiotic sensitivity test was performed on each V. cholera isolates to determine their antibiotic sensitivity profile. The results showed, out of 45 samples 12 contained V. cholera. Tube-well water has significantly lower concentration (log CFU/mL) of V. spp. than river and pond water (P < 0.05). Bacterial concentration doesn't deviate (P > 0.05) significantly in 5 different location the sample was collected from. All the 12 isolates were sensitive to Gentamicin and ciprofloxacin (100%), while Chloramphenicol (91.67%), Sulfamethoxazole (91.67%), Azithromycin (66.67%) showed high sensitivity. Isolates showed marginal sensitivity towards Tetracycline (33.33%), and Cephalexin (16.67%) and 100% resistance against antibiotics like Vancomycin, Penicillin, Erythromycin, and Nalidixic Acid. Based on these data we recommend using tube-well water instead of river and pond water for drinking purposes. Furthermore, we suggest selective use of sensitive antimicrobials listed here for therapeutics of cholera outbreak.