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Background: Due to high prevalence of Diabetes Mellitus in India, Diabetic nephropathy is on the rise. Diabetic Nephropathy is the leading cause of End stage renal disease and the major factor responsible for renal transplantation. Grey scale ultrasound can detect morphologic renal parenchymal changes late in the disease but the functional haemodynamic changes can be detected early on, by the means of doppler indices such as Resistive Index (RI) and Pulsatility Index (PI). Hence, application of renal doppler can aid in the diagnosis of early nephropathic changes to allow early medical intervention, therefore improving disease prognosis. Methods: Renal doppler ultrasonography was performed in 64 diabetic individuals, in the main renal artery and at the interlobar arteries of both kidneys in the upper pole, interpolar region, and lower pole. RI and PI were obtained for each artery and averaged. The correlation between the Mean RI and Mean PI, each with HbA1c and serum creatinine was checked for every patient using relevant statistical tests. Results: Mean RI and PI in study participants were above the normal range. RI and PI both showed a statistically significant positive correlation with serum creatinine, HbA1c as well as the age of the patient and duration of diabetes mellitus (p

Background: Chronic obstructive pulmonary disease (COPD) is the most prevalent long-term respiratory condition. Patients with COPD experience detrimental effects of COVID-19 infection. Objective: To figure out whether COPD is a risk factor influencing the progression of COVID-19 and to explore the clinical value of laboratory biomarkers to assess the severity of COVID-19 in patients with COPD comorbidity. Methods: In total, 1572 participants aged 35 to 70 years were enrolled to a tertiary hospital in Bangladesh between March 2022 and October 2022. Participants were categorized into four groups: (1) control, (2) COPD, (3) COVID-19, and (4) COVID-19 with COPD, and blood levels of clinical laboratory markers were assessed to analyze how these markers differ among the study groups. Results: COVID-19 patients with COPD had a significantly lower level of sodium (131.81 ± 2.8 mmol/L) and calcium (1.91 ± 0.28 mmol/L), and a significantly higher level of NT-proBNP (568.45 ± 207.40 pg/mL), bilirubin (1.34 ± 0.54 mg/dL), fibrinogen (577.27 ± 145.24 mg/dL), D-dimer (2.97 ± 2.25 μg/mL), C-reactive protein (71.08 ± 62.42 mg/L), interleukin-6 (166.47 ± 174.39 pg/mL), and procalcitonin (0.25 ± 0.30 ng/mL) compared to other study groups patients (p < 0.0001). In addition, the GOLD 4 group demonstrated significantly altered clinical parameters among COVID-19 patients with COPD. Furthermore, NT-proBNP, interleukin 6, D-dimer, C-reactive protein, and fibrinogen demonstrated excellent diagnostic performance in predicting disease severity among the COVID-19 patients with COPD, with a cut-off value of 511.2 pg/mL, 51.375 pg/mL, 1.645 μg/mL, 40.2 mg/L, and 510 mg/dL, respectively. Our results also indicate that inflammatory markers had significant positive correlations with the biochemical and coagulation markers in the COVID-19 patients suffering with COPD (p < 0.0001). Conclusions: NT-proBNP, interleukin 6, D-dimer, C-reactive protein, and fibrinogen are the most potential parameters for differentiating severe cases of COVID-19.

Background: An estimated 70 million people worldwide are thought to be affected by some severity of infertility. Ultrasound, MRI, and invasive procedures like venography and vasography are the usual investigations taken for the evaluation of infertility. Ultrasound is the preferred initial modality due to its availability and non-invasive nature. In our study, we evaluated the usefulness of advanced ultrasound techniques like Doppler and Elastography and compared the results with biochemical parameters of semen analysis. Methods: A comparative cross-sectional study was conducted over a period of one year, on men between the ages of 22 and 45, with normal or abnormal sperm analysis reports, who presented with inability to conceive. The testicular parenchyma resistive index, shear value and shear ratio were recorded. The mean values of the data and their standard deviations were assessed and compared by Student’s t-test. The correlation of semen parameters with RI and shear wave indices was tested by One way ANOVA test, with a p value

The researcher was interested in studying a crucial aspect of the systematic world music culture. He highlights the analysis of the harmonic construction in the musical compositions of the artist Khalil Ismail, where harmony is a significant Western musical science, a pillar of the structure of instrumental and vocal music.It depends on the compositions of vertical sounds performed simultaneously, as tones coincide and are sometimes dissonant to give sound resonance that enriches musical work and attracts recipients. Hence, the researcher has many questions, including: Can the composition of the writings of the artist Khalil Ismail determine in an objective research framework? Is there a study on this subject in some detail? which leads the researcher to search for answers.However, no previous studies have had found in this regard; The researcher is therefore convinced that there is an urgent need to write this research and study the all-important part of his harmony construction.

We propose a privacy-preserving ensemble infused enhanced Deep Neural Network (DNN) based learning framework in this paper for Internet-of-Things (IoT), edge, and cloud convergence in the context of healthcare. In the convergence, edge server is used for both storing IoT produced bioimage and hosting DNN algorithm for local model training. The cloud is used for ensembling local models. The DNN-based training process of a model with a local dataset suffers from low accuracy, which can be improved by the aforementioned convergence and Ensemble Learning. The ensemble learning allows multiple participants to outsource their local model for producing a generalized final model with high accuracy. Nevertheless, Ensemble Learning elevates the risk of leaking sensitive private data from the final model. The proposed framework presents a Differential Privacy-based privacy-preserving DNN with Transfer Learning for a local model generation to ensure minimal loss and higher efficiency at edge server. We conduct several experiments to evaluate the performance of our proposed framework.

The adsorption method using waste bagasse has been examined to remove 2-chlorophenol (2-CP) from aqueous solutions at room temperature. The adsorption of 2-chlorophenol by bagasse carbon could be studied in batches by changing the contact time, operating temperature, pH of the solution, initial concentration, adsorbent dose, and particle size. It took three hours to reach equilibrium. The Langmuir model correctly predicted the adsorption equilibrium data for 2-chlorophenol-sorbent systems in the concentration range that was examined. When the pH was lower, getting rid of 2-CP from surfaces was easier. Studies of desorption show that chemisorption is an important part of the adsorption process. J. Asiat. Soc. Bangladesh, Sci. 48(1-2): 83-95, June-December 2022

Machine Learning (ML) algorithms can effectively perform analytics and inferences for building smart applications, such as early detection of diseases in the Industrial Internet of Things (IIoT) and smart healthcare systems. The main components of ML, including training and testing phases, can be decomposed into microservices to improve service quality, along with fast implementation and integration with the edge and cloud services. However, the execution of ML in an edge-cloud environment introduces privacy risks to data owners (e.g., patients). In this paper, we present a privacy-preserving machine learning (ML) framework by leveraging microservice technology for safeguarding healthcare IIoT systems. More specifically, we develop a microservice-based distributed privacy-preserving technique using Differential Privacy (DP) and Radial Basis Function Network (RBFN) to balance between privacy protection and model performance in edge networks. We conduct extensive experiments to evaluate the performance of the proposed technique. The results revealed that DP has a significant influence on the model’s performance and achieves more than 90% accuracy with an epsilon value over 0.4, enhancing data protection and analytics through the implementation of microservices.

The study included the effect of different doses of Ritan A, both in: cream Ritan A topical, and Ritan A drug oral forms. The study included the effect of cream in histopathological lesions of the skin dorsal region and the effect of the drug in causing distortions morphological and histopathological lesions in the liver and kidney of pregnant mice Mus musculus and their fetuses and the extent of its influence in the appearance of congenital malformations macroscopic and influence on the histopathological lesions of the liver and kidney of embryos . Histological examination of the skin of mice treated cream Ritan A showed number of histopathological lesions represented the expansion of the skin layer and the dermis and the occurrence of necrosis of nuclear, and the histological examination of the liver and kidney of pregnant females were treated with a drug Ritan A oral are necrosis and hemorrhage and infiltration of lymphocytes and swelling of the epithelial cells with appearance desquamation of renal tubule epithelium and contraction of renal glomeruli and damage to the glomeruli in the Overdose and lethal dose groups

This paper proposes a blockchain-based Federated Learning (FL) framework with Intel Software Guard Extension (SGX)-based Trusted Execution Environment (TEE) to securely aggregate local models in Industrial Internet-of-Things (IIoTs). In FL, local models can be tampered with by attackers. Hence, a global model generated from the tampered local models can be erroneous. Therefore, the proposed framework leverages a blockchain network for secure model aggregation. Each blockchain node hosts an SGX-enabled processor that securely performs the FL-based aggregation tasks to generate a global model. Blockchain nodes can verify the authenticity of the aggregated model, run a blockchain consensus mechanism to ensure the integrity of the model, and add it to the distributed ledger for tamper-proof storage. Each cluster can obtain the aggregated model from the blockchain and verify its integrity before using it. We conducted several experiments with different CNN models and datasets to evaluate the performance of the proposed framework.

Methotrexate is commonly used in the treatment of inflammatory disorders and malignancies. Although it is an effective therapeutic agent, it may have serious adverse effects in both therapeutic dose and overdose. Here, we report a case of a 54-year-old lady with polyarthritis, who was prescribed 2 tablets of methotrexate (10 mg) orally once a week. However, mistakenly the patient started taking the drug daily. After 12 days of starting methotrexate, she presented with widespread skin lesions, sore mouth and dysphagia. A diagnosis of methotrexate induced erythema multiforme was made. Laboratory investigations revealed pancytopenia. She was managed conservatively. Although rare, methotrexate overdose should be considered along with other possibilities in a patient with erythema multiforme. This case report also focuses on the rational use of drugs as well as the importance of good communication between health care professionals and patients. BIRDEM Med J 2023; 13(1): 52-55

Due to the rising awareness of privacy and security in machine learning applications, federated learning (FL) has received widespread attention and applied to several areas, e.g., intelligence healthcare systems, IoT-based industries, and smart cities. FL enables clients to train a global model collaboratively without accessing their local training data. However, the current FL schemes are vulnerable to adversarial attacks. Its architecture makes detecting and defending against malicious model updates difficult. In addition, most recent studies to detect FL from malicious updates while maintaining the model's privacy have not been sufficiently explored. This paper proposed blockchain-based federated learning with SMPC model verification against poisoning attacks for healthcare systems. First, we check the machine learning model from the FL participants through an encrypted inference process and remove the compromised model. Once the participants' local models have been verified, the models are sent to the blockchain node to be securely aggregated. We conducted several experiments with different medical datasets to evaluate our proposed framework.

Moringa oleifera, a perennial tree of Indian origin, is cultivated in several tropical and sub-tropical countries because of its ability to grow under unfavourable conditions such as poor soil, lower requirement of water and managemental practices. The leaves and pods of the plant are highly rich in essential nutrients such as protein, vitamin, essential amino acids, macro and micro elements in addition to the presence of diverse nutraceutical molecules such as antioxidant, flavonoids, isothiocyanates, phenolics etc. The presence of bioactive principles in different parts of the Moringa plant prompted the people to use it as part of traditional medicines for the cure of several human ailments such as diabetes, intestinal worms, hyperlipidaemia, high blood pressure, muscle spasm, constipation, ringworm, etc. With the growing health consciousness coupled with phobia against modern chemical based therapeutic, there is increasing demands for plant sourced nutraceuticals. Evidently, this gives opportunity for development of Moringa based product for welfare of human society. The current review made an effort to summarize the research advancement on different aspects of Moringa oleifera focussing on taxonomy, cultivation, nutritional attributes, therapeutic values, and value-added products of this divine tree. SAARC J. Agric., 20(2): 1-15 (2022)

At present one-third of Bangladesh's population is under the age of thirty. Additionally, Bangladesh's working-age population (15–59%) is currently bigger than the non-working-age population (>15 and 60+), which has the potential to propel economic growth with a focus on our youth. Although there is a lot of interest in Bangladeshi adolescents, there is relatively little thorough information available about their situation. This research was conducted to offer insightful information about our youngsters. The youth's self-identities and life goals were first observed, together with their level of education and skill readiness, and then their engagement in earning activities. Then, a survey was conducted to determine how free they felt to choose. Their political stance was then examined. From the analysis, it can be said that Bangladeshi youth are still mostly untapped and are not at all prepared to take advantage of the prospects presented by the twenty-first century. This is especially true for young women and those from underprivileged socioeconomic backgrounds. Keywords: ; ; Goals of life; Preparedness for future; 4IR.

Higher plants were used as a bioindicator of environmental toxicity to estimate the severe problems related to the health of living organisms and the environment. Allium cepa plant was used to evaluate the DNA damage caused by heavy metal exposure, the roots of A.cepa plant. They were treated with four concentrations (5, 10, 20 and 25 ppm) for each of the metals Cadmium, zinc, copper and lead. At the same time, concentrations (50, 100, 200 and 500 ppm) were used for the preservative (sodium benzoate). The comet assay, a sensitive and suitable test for assessing DNA damage caused by chemical exposure, was used in this study. The Comet's six characteristics were measured: Head intensity, Head DNA%, Tail length, Tail intensity, Tail DNA% and Tail moment. The results showed that the metals are causing the DNA damage of meristematic cells of the roots of the A. cepa plant, depending on the tail length from most to least effective Cadmium> zinc > sodium benzoate > copper > lead > wastewater. I consider that it is not necessary to write down these values. The results of this study confirm that the meristematic cells of the roots of A. cepa are a suitable model for detecting DNA damage analyzed by the comet assay. Keywords: Toxic metals - Bio-indicator - Single cell gel electrophoresis (SCGE)

Phylogenetic and molecular perspective of plant identification can reveal the plant composition of areas. Many new species have been identified based on morphological characters in different area of Kurdistan, but few have been identified through DNA barcoding method. In this study, Internal transcribed spacers 1 and 2 (ITS1 and ITS2) were used to identify some species in Ashdagh Mountain. The identified taxa were belonged to different families and clades and not recorded in any studies. The nucleotide sequences of identified species were 98-100% identical with species in the genebank database. The phylogenetic analysis showed diversity in phylogenetic relationships among coexisting species. In addition, the existing of Chara sp. in the acidic stream of Awa spi is a new record and not observed before. The finding of this study show the importance of using molecular and phylogenetic technique to identify and understand phylogenetic relationship among coexisting species because Kurdistan is a hot spot and need more emphasis. In addition, new species occurrence data has been added to flora of Kurdistan.

Androgen insensitivity syndrome is a rare disorder of sex development that results from genetic mutations affecting the androgen receptor. Recently, we encountered a case of a 13-year-old individual who had been raised as female and sought medical attention for primary amenorrhea, which led to the discovery of partial androgen insensitivity syndrome. Early detection and gonad removal are necessary to mitigate the risk of cancer. Additional management steps such as corrective surgery and psychological support can also be valuable.