An Empirical Study on Diabetes Depression over Distress Evaluation Using Diagnosis Statistical Manual and Chi-Square Method

Investigating the effect of the educational intervention based on the Health Belief Model on the knowledge and beliefs of Yemeni teachers in the use of breast cancer screening: a randomized controlled trial study

BACKGROUND

Breast cancer (BC) is the most prevalent cancer among women. Teachers play a crucial role in promoting healthy behaviors, including breast cancer screening (BCS). This study aimed to assess the impact of an Health Belief Model (HBM)-based educational intervention on BCS uptake, knowledge, and beliefs among female Yemeni teachers in Klang Valley, Malaysia.

METHODS

A cluster-randomized controlled trial was conducted with 180 participants from 12 schools, randomly assigned to intervention or control groups. The intervention group participated in a 90-minute educational session, with follow-up assessments at baseline, and at 1, 3, and 6 months' post-intervention, using validated Arabic questionnaires. Data analysis was performed using SPSS version 22.0, with Generalized Estimating Equations (GEE) applied to assess differences within and between groups over time. Statistical significance was set at P < 0.05.

RESULTS

At baseline, there were no significant differences between groups. Post-intervention, the intervention group showed significantly higher rates of breast self-examination (BSE) and clinical breast examination (CBE) compared to the control group, with adjusted odds ratios (AOR) of 17.51 (CI: 8.22-37.29) for BSE and 2.75 (CI: 1.42-5.32) for CBE. Over six months, BSE performance in the intervention group increased, with AORs improving from 11.01 (CI: 5.05-24.04) to 18.55 (CI: 8.83-38.99). Similarly, CBE uptake rose from 1.60 (CI: 1.02-2.52) to 2.27 (CI: 1.44-3.58). Secondary outcomes revealed significant gains in knowledge and beliefs in the intervention group, including increased confidence in performing BSE and reduced perceived barriers.

CONCLUSIONS

The HBM-based educational intervention effectively enhanced BCS uptake, improved knowledge, and decreased barriers to BCS among Yemeni teachers in Malaysia, highlighting the potential of targeted educational programs to promote cancer screening behaviors in underserved populations.

CLINICAL TRIAL REGISTRATION

Retrospectively registered, ANZCTR (ACTRN12618000173291). Registered on February 02, 2018.

Machine Learning Techniques for Antimicrobial Resistance Prediction of Pseudomonas Aeruginosa from Whole Genome Sequence Data

AIM

Due to the growing availability of genomic datasets, machine learning models have shown impressive diagnostic potential in identifying emerging and reemerging pathogens. This study aims to use machine learning techniques to develop and compare a model for predicting bacterial resistance to a panel of 12 classes of antibiotics using whole genome sequence (WGS) data of Pseudomonas aeruginosa.

METHOD

A machine learning technique called Random Forest (RF) and BioWeka was used for classification accuracy assessment and logistic regression (LR) for statistical analysis.

RESULTS

Our results show 44.66% of isolates were resistant to twelve antimicrobial agents and 55.33% were sensitive. The mean classification accuracy was obtained ≥98% for BioWeka and ≥96 for RF on these families of antimicrobials. Where ampicillin was 99.31% and 94.00%, amoxicillin was 99.02% and 95.21%, meropenem was 98.27% and 96.63%, cefepime was 99.73% and 98.34%, fosfomycin was 96.44% and 99.23%, ceftazidime was 98.63% and 94.31%, chloramphenicol was 98.71% and 96.00%, erythromycin was 95.76% and 97.63%, tetracycline was 99.27% and 98.25%, gentamycin was 98.00% and 97.30%, butirosin was 99.57% and 98.03%, and ciprofloxacin was 96.17% and 98.97% with 10-fold-cross validation. In addition, out of twelve, eight drugs have found no false-positive and false-negative bacterial strains.

CONCLUSION

The ability to accurately detect antibiotic resistance could help clinicians make educated decisions about empiric therapy based on the local antibiotic resistance pattern. Moreover, infection prevention may have major consequences if such prescribing practices become widespread for human health.

Automated Detection of Alzheimer’s via Hybrid Classical Quantum Neural Networks

Deep Neural Networks have offered numerous innovative solutions to brain-related diseases including Alzheimer’s. However, there are still a few standpoints in terms of diagnosis and planning that can be transformed via quantum Machine Learning (QML). In this study, we present a hybrid classical–quantum machine learning model for the detection of Alzheimer’s using 6400 labeled MRI scans with two classes. Hybrid classical–quantum transfer learning is used, which makes it possible to optimally pre-process complex and high-dimensional data. Classical neural networks extract high-dimensional features and embed informative feature vectors into a quantum processor. We use resnet34 to extract features from the image and feed a 512-feature vector to our quantum variational circuit (QVC) to generate a four-feature vector for precise decision boundaries. Adam optimizer is used to exploit the adaptive learning rate corresponding to each parameter based on first- and second-order gradients. Furthermore, to validate the model, different quantum simulators (PennyLane, qiskit.aer and qiskit.basicaer) are used for the detection of the demented and non-demented images. The learning rate is set to 10−4 for and optimized quantum depth of six layers, resulting in a training accuracy of 99.1% and a classification accuracy of 97.2% for 20 epochs. The hybrid classical–quantum network significantly outperformed the classical network, as the classification accuracy achieved by the classical transfer learning model was 92%. Thus, a hybrid transfer-learning model is used for binary detection, in which a quantum circuit improves the performance of a pre-trained ResNet34 architecture. Therefore, this work offers a method for selecting an optimal approach for detecting Alzheimer’s disease. The proposed model not only allows for the automated detection of Alzheimer’s but would also speed up the process significantly in clinical settings.

Improving the Survival Time of Multiagents in Social Dilemmas through Neurotransmitter-Based Deep Q-Learning Model of Emotions

In multiagent systems, social dilemmas often arise whenever there is a competition over the limited resources. The major challenge is to establish cooperation among intelligent virtual agents for solving the situations of social dilemmas. In humans, personality and emotions are the primary factors that lead them toward a cooperative environment. To make agents cooperate, they have to become more like humans, that is, believable. Therefore, we hypothesize that emotions according to the personality give birth to believability, and if believability is introduced into agents through emotions, it improves their survival rate in social dilemma situations. The existing researches have introduced different computational models to introduce emotions in virtual agents, but they lack emotions through neurotransmitters. We have proposed a neurotransmitters-based deep Q-learning computational model in multiagents that is a suitable choice for emotion modeling and, hence, believability. The proposed model regulates the agents' emotions by controlling the virtual neurotransmitters (dopamine and oxytocin) according to the agent's personality. The personality of the agent is introduced using OCEAN model. To evaluate the proposed system, we simulated a survival scenario with limited food resources in different experiments. These experiments vary the number of selfish agents (higher neuroticism personality trait) and the selfless agents (higher agreeableness personality trait). Experimental results show that by adding the selfless agents in the scenario, the agents develop cooperation, and their collective survival time increases. Thus, to resolve the social dilemma problems in virtual agents, we can make agents believable through the proposed neurotransmitter-based emotional model. This proposed work may help in developing nonplayer characters (NPCs) in games.