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In today’s digital world, the Internet is an essential component of communication networks. It provides a platform for quickly exchanging information among communicating parties. There is a risk of unauthorized persons gaining access to our sensitive information while it is being transmitted. Cryptography is one of the most effective and efficient strategies for protecting our data and it are utilized all around the world. The efficiency of a cryptography algorithm is determined by a number of parameters, one of which is the length of the key. For cryptography, key (public/private) is an essential part. To provide robust security, RSA takes larger key size. If we use larger key size, the processing performance will be slowed. As a result, processing speed will decrease and memory consumption will increase. Due to this, cryptographic algorithms with smaller key size and higher security are becoming more popular. Out of the cryptographic algorithms, Elliptic Curve Cryptography (ECC) provides equivalent level of safety which RSA provides, but it takes smaller key size. On the basis of key size, our work focused on, studied, and compared the efficacy in terms of security among the well-known public key cryptography algorithms, namely ECC (Elliptic Curve Cryptography) and RSA (Rivets Shamir Adelman).

The measurement of glucose concentration is a fundamental daily care for diabetes patients, and therefore, its detection with accuracy is of prime importance in the field of health care. In this study, the fabrication of an electrochemical sensor for glucose sensing was successfully designed. The electrode material was fabricated using polyaniline and systematically characterized using scanning electron microscopy, high-resolution transmission electron microscopy, X-ray diffraction, Fourier transform infrared spectroscopy, and UV-visible spectroscopy. The polyaniline nanofiber-modified electrode showed excellent detection ability for glucose with a linear range of 10 μM to 1 mM and a detection limit of 10.6 μM. The stability of the same electrode was tested for 7 days. The electrode shows high sensitivity for glucose detection in the presence of interferences. The polyaniline-modified electrode does not affect the presence of interferences and has a low detection limit. It is also cost-effective and does not require complex sample preparation steps. This makes it a potential tool for glucose detection in pharmacy and medical diagnostics.

Thoracic diseases, including pneumonia, tuberculosis, lung cancer, and others, pose significant health risks and require timely and accurate diagnosis to ensure proper treatment. Thus, in this research, a model for thorax disease classification using Chest X-rays is proposed by considering deep learning model. The input is pre-processed by resizing, normalizing pixel values, and applying data augmentation to address the issue of imbalanced datasets and improve model generalization. Significant features are extracted from the images using an Enhanced Auto-Encoder (EnAE) model, which combines a stacked auto-encoder architecture with an attention module to enhance feature representation and classification accuracy. To further improve feature selection, we utilize the Chaotic Whale Optimization (ChWO) Algorithm, which optimally selects the most relevant attributes from the extracted features. Finally, the disease classification is performed using the novel Improved Swin Transformer (IMSTrans) model, which is designed to efficiently process high-dimensional medical image data and achieve superior classification performance. The proposed EnAE + ChWO+IMSTrans model for thorax disease classification was evaluated using extensive Chest X-ray datasets and the Lung Disease Dataset. The proposed method demonstrates enhanced Accuracy, Precision, Recall, F-Score, MCC and MAE of 0.964, 0.977, 0.9845, 0.964, 0.9647, and 0.184 respectively indicating the reliable and efficient solution for thorax disease classification.

Unintended pregnancy among female sex workers (FSW) is a pressing reproductive health concern attributable to risky sexual behaviors, healthcare inequities and poor negotiation powers with male sex partners. However, evidence is scarce on the prevalence and determinants of unintended pregnancies among FSW, which is crucial for enhancing reproductive healthcare. This analysis aims to measure the prevalence of lifetime unintended pregnancies and their associated factors. A cross-sectional study was conducted on 327 FSW in Jashore (a border belt district of Bangladesh) from September 2022 to March 2023. Participants were recruited through take-all sampling. Data were collected on the lifetime history of unintended pregnancies and other relevant variables through face-to-face interviews. Chi-square statistic was used to compare the characteristics of FSW reporting unintended pregnancies. To assess the net association of factors associated with unintended pregnancy, multiple logistic regression was applied. The lifetime prevalence of unintended pregnancies was reported at 75.8% (95%CI: 71.0-80.1). Among those who reported unintended pregnancies, 37.1% (95%CI: 30.8-43.8) had no education, 39.9% (95%CI: 32.8-47.5) were 25-34 years old, 49.6% (95%CI: 39.3-59.9) were currently married and 62.9% (95%CI: 49.7-74.4) earned ≤10,000 BDT per month compared to those who did not report lifetime unintended pregnancies. The likelihood of unintended pregnancies was significantly higher among those who reported having sex with non-transactional male sex partners (AOR: 2.4, 95%CI: 1.1-5.3, p = 0.036) than those who never had sex with any non-transactional male sex partner. The likelihood was also higher among those who reported rape in their lifetime (AOR: 2.0, 95%CI: 1.0-3.8, p = 0.037) and who self-reported mental health problems (AOR: 2.1, 95%CI: 1.0-4.2, p = 0.045) within the past year, compared to their counterparts. This study highlights the considerable prevalence and associated determinants of unintended pregnancies among FSW in Jashore. These determinants need to be considered to strengthen reproductive healthcare interventions and policies for FSW. Reproductive health of FSW cannot be improved unless these factors are addressed in the ongoing interventions.

We report the synthesis of Fe3O4/graphene (Fe3O4/Gr) nanocomposite for highly selective and highly sensitive peroxide sensor application. The nanocomposites were produced by a modified co-precipitation method. Further, structural, chemical, and morphological characterization of the Fe3O4/Gr was investigated by standard characterization techniques, such as X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscope (TEM) and high-resolution TEM (HRTEM), Fourier transform infrared (FTIR), and X-ray photoelectron spectroscopy (XPS). The average crystal size of Fe3O4 nanoparticles was calculated as 14.5 nm. Moreover, nanocomposite (Fe3O4/Gr) was employed to fabricate the flexible electrode using polymeric carbon fiber cloth or carbon cloth (pCFC or CC) as support. The electrochemical performance of as-fabricated Fe3O4/Gr/CC was evaluated toward H2O2 with excellent electrocatalytic activity. It was found that Fe3O4/Gr/CC-based electrodes show a good linear range, high sensitivity, and a low detection limit for H2O2 detection. The linear range for the optimized sensor was found to be in the range of 10–110 μM and limit of detection was calculated as 4.79 μM with a sensitivity of 0.037 µA μM−1 cm−2. The cost-effective materials used in this work as compared to noble metals provide satisfactory results. As well as showing high stability, the proposed biosensor is also highly reproducible.

One of the most widespread metabolic diseases, Type-2 Diabetes Mellitus (T2DM) is defined by high blood sugar levels brought on by decreased insulin secretion, reduced insulin action, or both. Due to its cost-effectiveness and eco-friendliness, plant-mediated green synthesis of nanomaterials has become more and more popular. The aim of the study is to synthesize AgNPs, their characterizations and further in-vitro and in-vivo studies. Several methods were used to morphologically characterise the AgNPs. The AgNPs were crystalline, spherical, and clustered, with sizes ranging from 20 to 50 nm. AgNPs were found to contain various functional groups using Fourier transform infrared spectroscopy. This study focuses on the green-synthesis of AgNPs from Fagonia cretica ( F. cretica ) leaves extract to evaluate their synthesized AgNPs for in-vitro and in-vivo anti-diabetic function. For the in-vivo tests, 20 male Balb/C albino-mice were split up into four different groups. Anti-diabetic in-vivo studies showed significant weight gain and a decrease in all biochemical markers (pancreas panel, liver function panel, renal function panel, and lipid profile) in Streptozotocin (STZ)-induced diabetic mice. In vitro anti-diabetic investigations were also conducted on AgNPs, comprising α-amylase, α-glucosidase inhibitions, and antioxidant assays. AgNPs showed antioxidant activity in both the DPPH and ABTS assays. The research showed that the isolated nanoparticles have powerful antioxidant and enzyme inhibitory properties, especially against the main enzymes involved in T2DM.

Choosing the appropriate career path poses a significant hurdle for students, especially when time is constrained. This research addresses the challenge of career prediction by introducing a method that integrates additional attributes, refines feature prioritization, and streamlines feature selection to enhance prediction precision. The key objectives of this study are to pinpoint pertinent features, accurately rank them, and enhance prediction accuracy by eliminating non-essential features. To accomplish these aims, three methodologies are employed: Feature Fusion and Normalization (FFN) for precise data identification, Average Feature Ranking (AFR) utilizing a blend of Random Forest (RF) and Linear Regression (LR) for feature prioritization, and Improved Prediction with Weighted Characteristics (PWF) which integrates Principal Component (PC) analysis for feature reduction. The prediction performance is assessed using a hybrid Multilayer Perceptron (MLP) classifier with 5-fold cross-validation. The outcomes reveal that the hybrid approach yields a superior feature set for prediction. The top twelve ranked features are determined by averaging each feature’s RF scores and coefficients. The achieved accuracy (ACC), precision (P), recall (R), and F1 scores stand at 87%, 87%, 86%, and 86%, respectively, with an Area Under the Receiver Operating Characteristic Curve (AUC-ROC) value of 92%. These findings underscore the efficacy of the proposed hybrid learning technique in accurately forecasting career trajectories.

The increasing complexity of healthcare data demands predictive models that are both accurate and interpretable. This study presents the Clinical Predictive Fusion Network (CPFN). This adaptive ensemble learning framework integrates Logistic Regression, Random Forest, and Support Vector Machine classifiers through a validation-driven weighted fusion strategy. The model’s adaptive weighting enables it to learn the relative reliability of base classifiers across multimodal patient datasets. CPFN was evaluated using 10-fold stratified cross-validation on disease-specific (cardiology, neurology, diabetes, pulmonology, and oncology) and a synthetically fused multi-disease dataset, achieving up to 93.0 ± 0.4% accuracy on individual datasets and 95.5 ± 0.3% on the combined dataset. Other metrics included a recall of 92.0 ± 0.5%, F1-score of 92.5 ± 0.4%, and ROC-AUC ranging from 0.95 to 0.975 (95% CI, bootstrap 1000 resamples). These results demonstrate that CPFN maintains consistent and generalizable performance across heterogeneous data sources. The model’s transparent fusion design and detailed pseudocode enhance reproducibility and clinical applicability, positioning CPFN as a scalable, data-driven decision-support framework for next-generation predictive healthcare systems.

There is increasing evidence of the post-COVID-19 suffering and decreased quality of life in the COVID-19 patients. This study aimed to assess the quality of life and associated factors of COVID-19 patients at one month after discharge from the hospital. This was a cross-sectional study that was conducted at the post-covid clinic of Dhaka Medical College Hospital (DMCH) where RT–PCR-confirmed adult COVID-19 recovered patients were enrolled one month after discharge from the same hospital. They were consecutively selected from January 01 to May 30. A pretested semi-structured questionnaire was used for the data collection for clinical variables. The generic multi-attributable utility instrument EQ-5D-5L was used for assessing health-related quality of life (HRQoL). A total of 563 patients were enrolled in the study. The patients had a mean age with standard deviation (±SD) of 51.18 (±13.49) years and 55.95% were male. The mean (SD) EQ-5D-5L index score and EQ-VAS scores were 0.78 (±0.19) and 70.26 (±11.13), respectively. Overall, 45.77%, 50.99%, 52.79%, 55.14% and 62.16% had problems (slight to extreme) in the mobility, self-care, usual activities, pain/discomfort and anxiety/depression dimensions, respectively. Patients aged ≥60 years had significant problem in mobility (odds ratio [OR] 3.24, 95% confidence interval [CI]: 1.07–9.77). Female participants were 5.50 times (95% CI: 2.22–13.62) more likely to have problems in their usual activities. In comparison to urban area, living in a peri-urban setting was significantly associated with problems in mobility (OR 1.89, 95% CI: 1.13–3.20), pain/discomfort (OR 1.82, 95% CI: 1.04–3.12) and anxiety/depression (OR 2.16, 95% CI: 1.22–3.84). Comorbid patients were 1.75 times (95% CI: 1.07–2.85) more likely to report problems in the pain/discomfort dimension. Presence of symptom(s) was associated with problems in self-care (OR 3.27, 95%CI: 1.31–8.18), usual-activity (OR 3.08, 95%CI: 1.21–7.87), pain/discomfort dimensions (OR 2.75, 95%CI: 1.09–6.96) and anxiety/depression (OR 3.35, 95%CI: 1.35–8.30). Specific management strategies should be planned to address the factors associated with low health-related quality of life in post-acute care of COVID-19 patients.

In the present study, a highly selective and sensitive electrochemical sensing platform for the detection of dopamine was developed with CuO nanoparticles embedded in N-doped carbon nanostructure (CuO@NDC). The successfully fabricated nanostructures were characterized by standard instrumentation techniques. The fabricated CuO@NDC nanostructures were used for the development of dopamine electrochemical sensor. The reaction mechanism of a dopamine on the electrode surface is a three-electron three-proton process. The proposed sensor’s performance was shown to be superior to several recently reported investigations. Under optimized conditions, the linear equation for detecting dopamine by differential pulse voltammetry is Ipa (μA) = 0.07701 c (μM) − 0.1232 (R2 = 0.996), and the linear range is 5-75 μM. The limit of detection (LOD) and sensitivity were calculated as 0.868 μM and 421.1 μA/μM, respectively. The sensor has simple preparation, low cost, high sensitivity, good stability, and good reproducibility.