The emergence of multidrug resistant (MDR) bacteria has become a major global health concern due to the reduced effectiveness of conventional antibiotics. The present study evaluated the synergistic antibacterial activity of Azadirachta indica (neem) bark extract combined with Curcuma longa (turmeric) rhizome extract against selected multidrug resistant bacterial isolates. Ethanolic extracts of neem bark and turmeric rhizome were prepared and tested individually as well as in combination (1:1 ratio) using the disc diffusion method on Mueller-Hinton agar. The antibacterial activity was assessed against Escherichia coli, Pseudomonas aeruginosa, Bacillus cereus, and Staphylococcus aureus. Chloramphenicol was used as the positive control, while ethanol served as the negative control. The combined extract demonstrated enhanced antibacterial activity compared to the individual extracts, indicating a synergistic effect between neem and turmeric. Maximum inhibition was observed against S. aureus (20 mm), followed by B. cereus (18 mm), E. coli (10 mm), and P. aeruginosa (8 mm). Individual extracts showed comparatively lower inhibition zones. The results suggest that the synergistic interaction of phytochemicals such as curcumin, flavonoids, tannins, and azadirachtin may contribute to the improved antibacterial effect. This study highlights the potential of combined medicinal plant extracts as natural alternative antimicrobial agents against multidrug resistant pathogens.

The emergence of multidrug resistant (MDR) bacteria has become a major global health concern due to the reduced effectiveness of conventional antibiotics. The present study evaluated the synergistic antibacterial activity of Azadirachta indica (neem) bark extract combined with Curcuma longa (turmeric) rhizome extract against selected multidrug resistant bacterial isolates. Ethanolic extracts of neem bark and turmeric rhizome were prepared and tested individually as well as in combination (1:1 ratio) using the disc diffusion method on Mueller-Hinton agar. The antibacterial activity was assessed against Escherichia coli, Pseudomonas aeruginosa, Bacillus cereus, and Staphylococcus aureus. Chloramphenicol was used as the positive control, while ethanol served as the negative control. The combined extract demonstrated enhanced antibacterial activity compared to the individual extracts, indicating a synergistic effect between neem and turmeric. Maximum inhibition was observed against S. aureus (20 mm), followed by B. cereus (18 mm), E. coli (10 mm), and P. aeruginosa (8 mm). Individual extracts showed comparatively lower inhibition zones. The results suggest that the synergistic interaction of phytochemicals such as curcumin, flavonoids, tannins, and azadirachtin may contribute to the improved antibacterial effect. This study highlights the potential of combined medicinal plant extracts as natural alternative antimicrobial agents against multidrug resistant pathogens.

Epileptic seizure detection is a critical task in neurological diagnosis, where timely identification can significantly improve patient outcomes. This work presents a hybrid deep learning model that combines Convolutional Neural Networks (CNN) with Bidirectional Long Short-Term Memory (Bi-LSTM) networks for analyzing EEG signals. The CNN component captures spatial characteristics of brain activity, while the Bi-LSTM layer models temporal dependencies in both forward and backward directions. The proposed model is evaluated using the Bonn EEG dataset, achieving an accuracy of 96.09%. The results indicate that the hybrid approach performs better than conventional machine learning techniques such as Support Vector Machines and Random Forests, making it suitable for automated seizure detection systems.

Marriage is an important institution among women folks and marriage bring joy, peace, stability, focus and sense of belonging in womanhood. When delay occur it brings psychological trauma to women. Delayed marriage among women generally refers to women marrying at a later age than what is traditionally expected in each society and this varies by culture. Across many parts of the world, the average age of first marriage has been rising. This shift is linked to social, economic, and cultural changes rather than a single cause. Delayed marriage among women of reproductive age is influenced by several factors including social, economic, cultural, psychological, spiritual and personal factors. This study aimed to Assess the Determinants of Delayed Marriage Among Women of Reproductive Age in South-South Nigeria. This was a cross-sectional study involving 250 women. Participants’ age is between 18 to 47 years. A well-structured questionnaire was administered to participants. The study lasted for a period of 2 months. Statistical analysis was done using SPSS version 25.0 and p < 0.05 was significant. The results revealed that majority (36%) of the participants were between 29-34 years old, 76% had tertiary education, 40% are unemployed, 60% residence in Urban areas, 60% are not in a relationship. Several factors were responsible for the delay in marriage including: financial instability 80%, 80% is delayed due to economic responsibilities, 80% is due to career development, 76% due to cultural influence, 68% is due to psychological influence, 80% influenced by family expectations, 80% due to social pressure, 76% due to personal factor, 68% due to desire for independence, and 68% is due to previous relationship experiences. This delay in marriage is due to social, economic, financial, personal, psychological, cultural, career development, and desire for independence.

Precision oncology increasingly depends on integrating heterogeneous evidence across molecular profiling, medical imaging, and clinical records, yet robust deployment is limited by data fragmentation across hospitals, missing modalities, batch effects, privacy constraints, and weak mechanistic interpretability. We propose a causal and explainable federated multimodal learning framework for cancer prediction and target discovery that fuses multi-omics, radiology or digital pathology imaging, longitudinal EHR features, and CRISPR dependency signals. The system trains across sites without centralizing raw data using federated optimization with secure aggregation and optional differential privacy, and is designed to remain reliable under non-IID site heterogeneity and structured missingness. To move beyond correlational risk scoring, we introduce a causal layer that encodes structural assumptions for treatment response and survival, supports counterfactual prediction, and applies invariant learning style regularization to improve transportability. For clinical safety, the framework outputs calibrated uncertainty and multi-level explanations, including modality contribution reporting, feature attributions over genes, imaging regions, and EHR variables, and causal what-if narratives for treatment changes and gene perturbations. We define a fully public experimental protocol using TCGA and CPTAC for multi-omics and outcomes, TCIA for imaging domain shift evaluation, and DepMap for CRISPR based dependency mapping and pathway level target rationale. This work provides an end-to-end, reproducible blueprint for privacy-preserving, mechanism-aware cancer AI, enabling benchmark driven validation prior to prospective multi-hospital deployment.