Cancer is a therapeutically challenging and genomically complicated disease. Pioneering studies have uncovered multifaceted aspects of cancer, ranging from intra- and inter-tumor heterogeneity, drug resistance, and genetic/epigenetic mutations. Loss of apoptosis is another critical aspect that makes cancer cells resistant to death. A substantial fraction of mechanistic information gleaned from cutting-edge studies has enabled researchers to develop near-to-complete resolution of the apoptotic pathway. Within the exciting frontiers of apoptosis, TRAIL (tumor necrosis factor-related apoptosis-inducing ligand) has garnered phenomenal appreciation by interdisciplinary researchers principally because of its unique capability to target cancer cells. TRAIL-based monotherapies and combinatorial therapies have reached phase II and phase III clinical trials. Rapidly upgrading the list of clinical trials substantiates the clinically valuable role of TRAIL-based therapeutics in cancer therapy. However, there is a growing concern about the poor bioavailability and rapid clearance of TRAIL-based therapeutics. Excitingly, the charismatic field of nanotechnology offers solutions for different problems, and we have witnessed remarkable breakthroughs in the efficacy of TRAIL-based therapeutics using nanotechnological approaches. In this review, we have attempted to provide a summary about different nanotechnologically assisted delivery methods for TRAIL-based therapeutics in cell culture studies and animal model studies for the inhibition/prevention of cancer.

This study proposes a reproducible hybrid computational model for the explainable classification of territorial vulnerability using heterogeneous tabular data. The approach integrates fuzzy logic and extreme gradient boosting in a two-stage architecture that balances interpretability and predictive performance. First, a fuzzy transformation is applied to construct interpretable risk and resilience indicators based on multi-source administrative indicators. The analytical dataset was formed by integrating 11 heterogeneous administrative sources into a single matrix of 166 territorial units and 76 features. The model was evaluated on a stratified 75/25 split of the training and test sets using the F1 score, ROC-AUC, precision, recall, and integrated quality criterion. Experimental results show that the proposed Fuzzy-XGBoost framework achieved an F1 score of 0.7333 on the test dataset, an ROC-AUC of 0.8291, and an Integrated Score of 0.768, outperforming the strongest baseline and improving recall in highly vulnerable areas. Furthermore, probabilistic threshold optimization identified an operating point at τ = 0.35, reducing the number of missed high-risk cases while maintaining acceptable specificity. The results demonstrate that fuzzy feature expansion combined with gradient boosting provides an efficient and interpretable solution for tabular risk classification and decision support problems under heterogeneity and uncertainty.

Background/Objectives: Clinicians’ expectations of artificial intelligence (AI) for periodontal care in Central Asia are largely unknown. We surveyed dentists and physicians in Kazakhstan on preliminary requirements for a future decision-support system and explored patients’ self-reported periodontal signs. Methods: Cross-sectional online surveys (July–September 2026; non-probability recruitment) included 33 dentists, 91 primary health care physicians and medical specialists, and 455 adult patients whose periodontal eligibility was not clinically verified; the patient questionnaire did not address AI. Analyses used Wilson 95% confidence intervals (CI), exact tests with Holm adjustment and exploratory logistic regression. Results: Dentists prioritised history-taking (81.8%) and early gingivitis diagnosis (69.7%). Autonomy choices were spread across five clinician-controlled configurations (low autonomy most frequent, 30.3%); no autonomous option was offered. Lack of a legal framework and liability insurance was the most cited barrier (60.6%). Physicians and specialists selected early gingivitis screening (80.2%) but cited insufficient competence (53.8%). Self-reported signs occurred in 49.0% of patients. Associations with high stress (adjusted odds ratio 2.11; 95% CI 1.14–3.89) and family history (1.61; 1.02–2.55) lost significance after excluding 204 responses collected via an inter-university platform. Conclusions: Clinician responses suggest preliminary requirements; patient associations remain exploratory. Instrument and clinical outcome validation should precede system design.

Background/Objectives: Artificial intelligence (AI) tools for periodontal diagnosis are proliferating, yet little is known about what clinicians and patients in Central Asia expect from such systems. We elicited requirements for an AI-enabled periodontal decision-support system from three stakeholder groups in Kazakhstan and examined which patient-reported risk factors are associated with self-reported signs of periodontal disease. Methods: A cross-sectional online survey (July–September 2026) enrolled 33 dentists, 91 primary health care (PHC) physicians and medical specialists, and 455 patients with periodontal disease. Proportions were reported with Wilson 95% confidence intervals (CI); groups were compared with Fisher’s exact, χ², Mann–Whitney and Kruskal–Wallis tests with effect sizes; multivariable logistic regression identified factors associated with self-reported periodontal signs. Results: Dentists prioritised automation of history-taking (81.8%) and visual gingival assessment (72.7%), and named early gingivitis diagnosis as the stage where AI is most needed (69.7%). Only human-controlled autonomy levels were chosen; the most frequently cited barrier was the absence of a legal framework and liability insurance (60.6%), and dentists with >5 years of experience more often demanded mandatory verification or a prohibition of full autonomy (exact p = 0.045; Cramér’s V = 0.54). PHC physicians considered early gingivitis screening their most effective contribution (80.2%) but cited insufficient competence as the main barrier (53.8%). Self-reported periodontal signs were present in 49.0% of patients and were independently associated with high stress (adjusted odds ratio 2.11; 95% CI 1.14–3.89) and a positive family history (1.61; 1.02–2.55); six symptom items did not form a reliable scale (Cronbach’s α = 0.56). Conclusions: Stakeholders in Kazakhstan favour a low-autonomy, clinician-verified AI system focused on early gingivitis detection, interprofessional data sharing and clearly regulated accountability. These requirements provide an evidence base for the design and governance of periodontal AI in emerging health systems.