Explore the vast range of titles available.

Premenstrual Syndrome (PMS) and Premenstrual Dysphoric Disorder (PMDD) have been identified as potential risk factors for various mental health issues, such as suicidal ideation and attempts. However, few studies have examined this association among Bangladeshi university students. This study aimed to examine the potential associations between PMDD and its co-existence with depression, anxiety, stress, and various suicidal behaviors, including suicidal ideation and attempts. A cross-sectional survey involving 516 university students was conducted between September and October 2023. The survey was carried out in person and employed a structured questionnaire that comprised demographic information; the Depression, Anxiety, and Stress Scale (DASS-21); and the Premenstrual Symptoms Screening Tool (PSST), a 19-item screening tool for premenstrual symptoms. Multivariable logistic regression analyses were conducted to examine the relationships between variables. In the present study, participants with PMDD reported a prevalence of 38.8% for past-year suicidal ideation and 28.6% for suicide attempts. Through logistic regression analysis, we found a significant association between moderate/severe PMS and PMDD and a higher likelihoods of reporting suicidal ideation (AOR =  4.73; 95% CI 2.08-10.73 and AOR =  5.42; 95% CI 2.02-10.52) and suicide attempts (AOR =  3.77; 95% CI 1.36-10.50 and AOR =  4.07; 95% CI 1.22-15.56). The association between suicidal behaviors and PMS/PMDD was mediated by co-existing conditions such as depression, anxiety, and stress. A notable proportion of individuals diagnosed with PMDD reported experiencing suicidal ideation or engaging in suicide attempts at some point in their lives. The findings of this research support the importance of conducting regular assessments of suicidal risk among women experiencing moderate to severe premenstrual disturbance. Furthermore, it is crucial to integrate mental health screenings and implement psychosocial interventions specifically designed for women diagnosed with PMS or PMDD and those with co-existing depression, anxiety, and stress alongside PMS/PMDD.

Suicidal behaviours among students pose a significant public health concern, with mental health problems being well-established risk factors. However, the association between food insecurity (FIS) and suicidal behaviours remains understudied, particularly in Bangladesh. This study aimed to investigate the relationship between FIS and suicidal behaviours among Bangladeshi university students. A cross-sectional survey using convenience sampling was conducted between August 2022 and September 2022. Information related to socio-demographics, mental health problems, FIS and related events and suicidal behaviours were collected. Chi-squared tests and multivariable logistic regression models, both unadjusted and adjusted, were employed to examine the relationship between FIS and suicidal behaviour. Six public universities in Bangladesh. This study included 1480 students from diverse academic disciplines. A substantial proportion of respondents experienced FIS, with 75·5 % reporting low or very low food security. Students experiencing FIS had a significantly higher prevalence of suicidal ideation, plans and attempts compared with food-secure students (18·6 % . 2·8 %, 8·7 % . 0·8 % and 5·4 % . 0·3 %, respectively; all  < 0·001). In addition, students who have personal debt and participate in food assistance programmes had a higher risk of suicidal behaviours. This study highlights the association between FIS and suicidal behaviours among university students. Targeted mental health screening, evaluation and interventions within universities may be crucial for addressing the needs of high-risk students facing FIS.

Interleukin-10, a highly effective cytokine recognized for its anti-inflammatory properties, plays a critical role in the immune system. In addition to its well-documented capacity to mitigate inflammation, IL-10 can unexpectedly demonstrate pro-inflammatory characteristics under specific circumstances. The presence of both aspects emphasizes the vital need to identify the IL-10-induced peptide. To mitigate the drawbacks of manual identification, which include its high cost, this study introduces StackIL10, an ensemble learning model based on stacking, to identify IL-10-inducing peptides in a precise and efficient manner. Ten Amino-acid-composition-based Feature Extraction approaches are considered. The StackIL10, stacking ensemble, the model with five optimized Machine Learning Algorithm (specifically LGBM, RF, SVM, Decision Tree, KNN) as the base learners and a Logistic Regression as the meta learner was constructed, and the identification rate reached 91.7%, MCC of 0.833 with 0.9078 Specificity. Experiments were conducted to examine the impact of various enhancement techniques on the correctness of IL-10 Prediction. These experiments included comparisons between single models and various combinations of stacking-based ensemble models. It was demonstrated that the model proposed in this study was more effective than singular models and produced satisfactory results, thereby improving the identification of peptides that induce IL-10.

The current study explores metaverse adoption among higher education institutions (HEIs) in the light of a theoretical framework to empower future perspectives of the metaverse as a learning platform. Even though this technology was just recently introduced to the higher education sector, very few attempts have been made to evaluate its impact. The purpose of this research is to analyze the elements that influence the continuous intention (CI) to utilize the metaverse technology in learning. The technology acceptance model (TAM) and the self-determination theory (SDT) are both included in this study. A questionnaire was developed and distributed to students attending private universities in order to obtain the data that was needed for the proposed model. Using a hybrid approach that consists of partial least squares structural equation modeling (PLS-SEM) and an artificial neural network (ANN) model, which combines a linear PLS model with compensation and a nonlinear ANN model without compensation, the effect of CI on using the metaverse as a learning platform is investigated. This approach was chosen because it contains both of these types of models. When it comes to explaining the use of metaverse technology among students attending higher education institutions in Egypt, the research findings suggested that autonomy and perceived usefulness (PU) are major determinants. Nevertheless, the continuing intention was unaffected by the perceived ease of use (PEOU) of the product. Furthermore, according to the data provided by the ANN model, the most significant predictors are relatedness, PEOU, autonomy, and PU. It has been determined that the results obtained from the PLS-SEM and ANN modes are identical. Additionally, both theoretical and practical implications are discussed in this article.

The objective of this study was to explore the knowledge and attitudes regarding seafood safety and consumption, along with preferences and environmental issues in coastal Bangladesh. A cross-sectional, consumer-based survey was conducted in Bangladesh from September to November 2023, targeting 1100 participants aged 18 years and older across seven coastal districts. Convenience sampling and in-person interviews were used for the data collection. The average knowledge and attitude scores toward seafood safety and consumption were 48.2% and 63.5%, respectively. Several factors influenced seafood safety and consumption knowledge, including age, education level, family size, religion, and residence in coastal areas (all P < 0.05). In contrast, attitudes toward seafood safety and consumption were shaped by education level, family size, employment status, seafood allergies, and history of seafood poisoning (all P < 0.05). The most commonly consumed seafood was rupchanda, followed by shrimp. Most participants consumed seafood for its health benefits, with no significant seasonal impact on seafood consumption. Overfishing and climate change were recognised as the most alarming environmental dangers identified by the participants. Coastal communities in Bangladesh have demonstrated moderate attitudes, but relatively low knowledge of seafood safety and consumption. Targeted educational programmes, including community workshops on safe handling and storage, school-based programmes on marine conservation, and digital campaigns via SMS/social media, are needed to improve seafood safety knowledge, while promoting sustainable consumption practices is crucial for addressing environmental concerns like overfishing. Additionally, improving market accessibility and highlighting the health advantages of seafood can drive more informed and healthier consumption choices.

Machine Learning has a significant impact on different aspects of science and technology including that of medical researches and life sciences. Diabetes Mellitus, more commonly known as diabetes, is a chronic disease that involves abnormally high levels of glucose sugar in blood cells and the usage of insulin in the human body. This article has focused on analyzing diabetes patients as well as detection of diabetes using different Machine Learning techniques to build up a model with a few dependencies based on the PIMA dataset. The model has been tested on an unseen portion of PIMA and also on the dataset collected from Kurmitola General Hospital, Dhaka, Bangladesh. The research is conducted to demonstrate the performance of several classifiers trained on a particular country’s diabetes dataset and tested on patients from a different country. We have evaluated decision tree, K-nearest neighbor, random forest, and Naïve Bayes in this research and the results show that both random forest and Naïve Bayes classifier performed well on both datasets.

Curculigo orchioides (C. orchioides), a traditionally valued medicinal plant, has been utilized for centuries in the management of various ailments, but its full spectrum of therapeutical potentials remains underexplored. This study aimed to perform GC-MS profiling of bioactive phytochemicals as well as to evaluate the antioxidant and anti-diabetic properties of the ethanolic root extract of C. orchioides (ERCO) through an integrative approach combining in vitro , in vivo , and in silico methods. Phytochemical screening confirmed the presence of some major bioactive phytochemical groups including alkaloids, flavonoids, tannins, saponins, and steroids which are well-known for their pharmacological relevance. Antioxidant activity was demonstrated through high levels of total phenolic content (TPC), total flavonoid content (TFC), total tannin content (TTC) determined as 44.055 mg GAE/gm, 0.6768 mg QE/gm, and 103.375 mg TAE/gm of dry weight extract, respectively, along with notable ferric reducing antioxidant power (FRAP). Anti-diabetic potential was supported by significant in vitro inhibition of pancreatic α -amylase and α -glucosidase enzymes, with IC 50 values of 84.17 μg/mL and 36.33 μg/mL, respectively. In vivo studies in alloxan-induced diabetic mice further validated the extract’s substantial blood glucose-reduction abilities (47.28% and 52.11% at the dose of 100 mg/kg and 200 mg/kg body-weight, respectively), indicating the potential for blood sugar regulation. GC-MS profiling confirmed the presence of 23 major phytochemicals, which were subjected to molecular docking studies against human glutathione peroxidase, peroxiredoxin 5, Catalase, sulfonylurea receptor 1 (SUR1), α-amylase, and α-glucosidase. Among them, 2-epoxy-3,4-dihydroxycyclohexano[a]pyrene (CID: 41322) emerged as a lead compound, exhibiting strong binding affinities for both α-amylase (−9.1 kcal/mol) and α-glucosidase (−8.8 kcal/mol). ADMET predictions and stable molecular dynamics simulation outcomes further underscored its drug-likeness. Collectively, these findings position ERCO as a promising source of natural antioxidants and anti-diabetic agents, while identifying 2-epoxy-3,4-dihydroxycyclohexano[a]pyrene as a potential therapeutic lead. This investigation provides a foundation for future drug development, and, further experimental validations, isolation of active compounds, and subsequent clinical studies are required to validate its safety and efficacy.

Composting is a sustainable way of transforming organic waste into valuable organic fertilizers which have the potential to act as soil conditioners by controlling various biological processes. The prime objective of the current study was to determine the influencing factors behind the intent of home food waste composting, by employing the combined model of Theory of Planned Behavior (TPB) and Dualistic Passion Model (DMP). The combined model showed a higher predictive ability in comparison to the individual TPB model. The fit statistic of the integrated model was deemed good, and 65% of the variance for home composting intention was explained. Using a face-to-face questionnaire survey, a total of 203 valid responses were gathered from home gardeners and tested via a unique two-step methodology: the PLS-SEM and the artificial neural network (ANN). The results revealed that the composting intention can be significantly influenced by attitude, subjective norms, and perceived behavioral control. The study also confirmed the positive effect of harmonious passion and the negative effect of obsessive passion on the intention of food waste composting. Furthermore, the hybrid method produced more reliable results because HP was found to be the most important variable in both ANN and PLS-SEM results, while PBC was observed to be the second most important variable in ANN and the fourth most important in PLS-SEM. The results of the current study not only highlight the importance of passion in determining food waste composting intention in Dhaka, Bangladesh, but also provide helpful information for designing effective, sustainable tactics for encouraging residents to compost food waste at home.

SocialMedia Marketing (SMM) has become a mainstream promotional scheme. Almost every business promotes itself through social media, and an educational institution is no different. The users’ responses to social media posts are crucial to a successful promotional campaign. An adverse reaction leaves a long-term negative impact on the audience, and the conversion rate falls. This is why selecting the content to share on social media is one of the most effective decisions behind the success of a campaign. This paper proposes a Data-Driven Promotional Management System (DPMS) for universities to guide the selection of appropriate content to promote on social media, which is more likely to obtain positive user reactions. The main objective of DPMS is to make effective decisions for Social Media Marketing (SMM). The novel DPMS uses a well-engineered and optimized BiLSTM network, classifying users’ sentiments about different university divisions, with a stunning accuracy of 98.66%. The average precision, recall, specificity, and F1-score of the DPMS are 98.12%, 98.24%, 99.39%, and 98.18%, respectively. This innovative Promotional Management System (PMS) increases the positive impression by 68.75%, reduces the adverse reaction by 31.25%, and increases the conversion rate by 18%. In a nutshell, the proposed DPMS is the first promotional management system for universities. It demonstrates significant potential for improving the brand value of universities and for increasing the intake rate.

Globally, studies have shown conflicting results regarding the association of blood groups with SARS CoV-2 infection. To observe the association between ABO blood groups and the presentation and outcomes of confirmed COVID-19 cases. This was a prospective cohort study of patients with mild-to-moderately severe COVID-19 infections who presented in the COVID-19 unit of Dhaka Medical College Hospital and were enrolled between 01 June and 25 August, 2020. Patients were followed up for at least 30 days after disease onset. We grouped participants with A-positive and A-negative blood groups into group I and participants with other blood groups into group II. The cohort included 438 patients; 52 patients were lost to follow-up, five died, and 381 completed the study. The prevalence of blood group A [144 (32.9%)] was significantly higher among COVID-19 patients than in the general population (p < 0.001). The presenting age [mean (SD)] of group I [42.1 (14.5)] was higher than that of group II [38.8 (12.4), p = 0.014]. Sex (p = 0.23) and co-morbidity (hypertension, p = 0.34; diabetes, p = 0.13) did not differ between the patients in groups I and II. No differences were observed regarding important presenting symptoms, including fever (p = 0.72), cough (p = 0.69), and respiratory distress (p = 0.09). There was no significant difference in the median duration of symptoms in the two group (12 days), and conversion to the next level of severity was observed in 26 (20.6%) and 36 patients (13.8%) in group I and II, respectively. However, persistent positivity of RT-PCR at 14 days of initial positivity was more frequent among the patients in group I [24 (19%)] than among those in group II [29 (11.1%)]. The prevalence of blood group A was higher among COVID-19 patients. Although ABO blood groups were not associated with the presentation or recovery period of COVID-19, patients with blood group A had delayed seroconversion.