Journal of Advances in Medical and Pharmaceutical Sciences

Artificial Intelligence and Multimodal Data Approaches for Diagnosis, Prognosis, and Treatment Decision Support in Diffuse Large B-Cell Lymphoma and Acute Myeloid Leukaemia: A Systematic Review

Sat, 12 Sep 2026 00:00:00 +0000

Aims: To synthesise and critically appraise evidence on artificial intelligence (AI) and multimodal data approaches for diagnosis, prognosis, and treatment decision support in diffuse large B-cell lymphoma (DLBCL) and acute myeloid leukaemia (AML), and to compare how disease biology shapes model design and clinical readiness.

Study Design: Systematic review with narrative synthesis.

Place and Duration of Study: PubMed/MEDLINE, Embase, Scopus, Web of Science Core Collection, IEEE Xplore, and the Cochrane Library were searched from database inception to 29 July 2026.

Methodology: English-language original studies involving human participants, clinical images, human-derived samples, or patient-derived datasets were eligible. Two reviewers independently screened records, assessed full texts, and extracted study characteristics, data modalities, analytical methods, validation approaches, and performance measures. PROBAST+AI domains were used to guide appraisal of eligible patient-level prediction models; exploratory multi-omics, molecular-subtyping, biomarker-discovery, and segmentation studies underwent structured descriptive appraisal. TRIPOD+AI informed assessment of reporting completeness. Owing to substantial clinical and methodological heterogeneity, findings were synthesised narratively.

Results: Twenty-eight studies were included: 22 on DLBCL and six on AML. DLBCL research was dominated by PET/CT radiomics, deep imaging, digital pathology, and imaging-clinical-molecular fusion. Internally evaluated diagnostic models reported area under the curve values as high as 0.999, whereas externally validated prognostic models achieved values of 0.66-0.71 and showed inconsistent incremental improvement over the International Prognostic Index. AML studies mainly integrated genomic, transcriptomic, epigenomic, single-cell, proteomic, metabolic, and functional drug-response data. Prognostic models reported concordance indices of 0.72-0.81 and time-dependent area under the curve values of 0.795-0.899. The most frequent methodological concerns were retrospective sampling, high-dimensional modelling in modest cohorts, incomplete calibration reporting, and limited independent validation. Treatment-response models in both diseases remained retrospective or exploratory, and none demonstrated improved outcomes through AI-guided treatment allocation.

Conclusion: AI and multimodal data approaches show greatest maturity for prognostic stratification, but diagnostic replacement and treatment selection remain unproven. Prospective, multicentre clinical-impact validation is the principal requirement for translation into routine care.

Mechanism-Based Pharmacotherapy of Obesity: From Pathophysiology to Precision and Next-Generation Therapeutics

Sattar J. Abood, Waleed K. Abdulsahib — Tue, 29 Sep 2026 00:00:00 +0000

Obesity is a chronic, relapsing, and highly heterogeneous disease driven by genetic susceptibility, neuroendocrine and reward circuitry, gut–brain signalling, adipose-tissue dysfunction, inflammation, metabolic adaptation, and interacting environmental and behavioural influences. As these mechanisms have become better understood, the therapeutic landscape has expanded accordingly, and the rapid emergence of incretin-based and multi-pathway pharmacotherapies has pushed treatment expectations well beyond modest weight loss toward clinically meaningful improvement in obesity itself and its downstream complications.

This review draws together obesity pathophysiology and its molecular therapeutic targets with established pharmacotherapies, emerging agents, drug-repurposing opportunities, precision treatment, and next-generation drug discovery to build a mechanism-based, clinically oriented synthesis. Current and investigational strategies are evaluated in terms of efficacy, safety, tolerability, durability, body composition, complication-specific benefit, and translational maturity, with particular attention to glucagon-like peptide-1 (GLP-1)–based therapies, dual and triple receptor agonists, amylin-containing combinations, oral and engineered long-acting agents, and strategies that extend beyond appetite suppression.

Substantial progress notwithstanding, several questions remain unresolved, including long-term treatment persistence and maintenance, weight regain after treatment interruption, preservation of lean mass and physical function, accessibility, heterogeneity of treatment response, optimal sequencing and combination strategies, and complication-guided drug selection. Phenotype- and mechanism-informed prescribing, tissue-directed drug delivery, modulation of energy expenditure, and novel gut–brain pathways could address some of these limitations, although several of these approaches remain at an early stage of translational development. Progress will therefore hinge not simply on achieving greater weight reduction, but on translating mechanistic advances into treatments that deliver durable clinical benefit with acceptable safety, feasibility, and functional outcomes — pushing obesity pharmacotherapy toward a more precise, complication-informed, and longitudinal model of disease management tailored to each patient's biology and clinical needs.

Patient Characteristics and Treatment Outcomes among Patients with Drug-Resistant Tuberculosis in Meru County, Kenya

Muthuri Geoffrey Koome, Kubai Patrick Kinyua, Kithinji Dorothy Kagendo — Sat, 19 Sep 2026 00:00:00 +0000

Background: Drug-resistant tuberculosis (DR-TB) remains a public health challenge in Kenya despite advances in diagnosis and shorter, all-oral treatment regimens.

Aim: This study assessed patient characteristics influencing DR-TB treatment outcomes in Meru County, Kenya.

Methods: A retrospective descriptive study reviewed 201 de-identified records of patients diagnosed with DR-TB and enrolled for treatment between January 2020 and December 2023. Patient characteristics and treatment outcomes were summarised using descriptive statistics. Chi-square tests assessed associations, and multivariable binary logistic regression identified independent predictors of treatment outcomes at p < 0.05.

Results: Overall treatment success was 95%. Unfavourable outcomes comprised 3% loss to follow-up and 2% deaths. Most patients were adults, predominantly male, and most had pulmonary DR-TB. Education level was independently associated with favourable treatment outcomes (OR = 2.82; 95% CI: 1.04–7.65; p = 0.041). Age, gender, occupation, marital status, and alcohol use were not statistically significant independent predictors.

Conclusions: DR-TB treatment outcomes in Meru County were predominantly favourable in this retrospective sample. Education level was the only patient characteristic independently associated with treatment outcome. Patient education and support tailored to literacy levels may help sustain treatment success. Further research should examine behavioural, clinical, socioeconomic, and health-system factors that may contribute to variation in DR-TB outcomes.

Breast Cancer Awareness and Breast Self-Examination Practices among Female Students at Al-Mashreq University: A Cross-Sectional Study

Sat, 19 Sep 2026 00:00:00 +0000

Background: Breast cancer remains the most common cancer among women worldwide and a major cause of preventable cancer mortality, particularly in resource-constrained settings where delayed presentation is common. Breast self-examination (BSE) does not replace clinical assessment or mammography, but breast self-awareness may promote early help-seeking among young women. This study assessed breast cancer awareness and BSE practices among female students at Al-Mashreq University.

Methods: A descriptive cross-sectional study was conducted among 215 female students at Al-Mashreq University, Jeddah Center, Saudi Arabia. Data were collected using a structured self-administered questionnaire covering sociodemographic characteristics, breast cancer risk factors, warning signs, screening knowledge, attitudes, BSE practice, and barriers. Descriptive statistics summarised participant characteristics and responses. Chi-square tests assessed associations between knowledge level and sociodemographic variables. Statistical significance was set at p<0.05.

Results: Most participants were aged 20 to <25 years (148/215, 68.8%), single (206/215, 95.8%), and had no family history of breast cancer (169/215, 78.6%). Overall, 115 students (53.5%) had good knowledge, 65 (30.2%) moderate knowledge, and 35 (16.3%) poor knowledge. Although 179 students (83.3%) had heard of BSE, only 69 (32.1%) practised it, and only 27 (12.6%) reported monthly practice. The most common barriers were lack of knowledge (82/215, 38.1%), forgetfulness (53/215, 24.7%), and fear of discovering an abnormality (40/215, 18.6%). Knowledge level was significantly associated with age, college, academic year, and family history of breast cancer.

Conclusion: Female students showed acceptable breast cancer awareness but inadequate BSE practice. Structured, practical, and culturally sensitive breast health education is needed to convert awareness into sustained early-detection behaviour.

Integrative Transcriptomic and Network Pharmacology Analysis Reveals Candidate Molecular Associations and Hypothesis-Generating Drug-Gene Relationships in Major Depressive Disorder

Laura Gomes Libório, Sophia Massesine Pimentel, Sara Laíse Cordeiro — Sat, 19 Sep 2026 00:00:00 +0000

Aims: To characterize transcriptomic alterations associated with major depressive disorder (MDD) in postmortem human brain tissue and integrate differential gene-expression findings with protein-protein interaction, functional-enrichment, and drug-gene interaction analyses.

Study Design: Exploratory in silico transcriptomic and network pharmacology study based on secondary analysis of a publicly available RNA-sequencing dataset.

Place and Duration of Study: Data were obtained from the NCBI Gene Expression Omnibus (GSE101521). Analyses were conducted using R and publicly available molecular-interaction and functional-annotation resources.

Methodology: Postmortem dorsolateral prefrontal cortex RNA-sequencing data from 29 controls, 9 MDD non-suicide cases (MDD-NS), and 21 MDD suicide cases (MDD-S) were analyzed with DESeq2. A likelihood-ratio test (LRT) evaluated global three-group differences. A separate Wald contrast characterized CON-versus-MDD-NS differences. Seven prespecified transcriptomic candidates were carried forward to STRING and DGIdb analyses. Formal enrichment statistics were calculated for the expanded STRING network and interpreted separately from the original transcriptomic seeds.

Results: The LRT identified HSPA6, IL1B, CCL4L2, CXCL8, SERPINH1, CCL2, and DMBT1L1 among the highest-ranked FDR-significant signals (adjusted P = 5.01 × 10⁻5 to 0.0316). In the exploratory CON-versus-MDD-NS contrast, HSPA6, IL1B, CXCL8, SERPINH1, and DMBT1L1 showed large positive log2FC estimates, indicating lower relative expression in MDD-NS; these pairwise findings are reported as nominal, unadjusted signals. The expanded STRING network comprised 16 nodes and 77 edges (12 expected; PPI enrichment P < 1.0 × 10⁻16). DGIdb returned 143 records, 131 (91.6%) of which involved CXCL8 or IL1B, indicating substantial target-specific annotation density.

Conclusion: The data support exploratory molecular associations involving stress-response and immune/chemokine-related genes. Network expansion and database coverage materially influenced downstream enrichment and drug-gene results. The findings do not establish causal mechanisms, validated biomarkers, or therapeutic efficacy and require covariate-adjusted replication and experimental validation.

Reversing the Expected Pattern: Atypical Socio-demographic and Social-media predictors of Self-medication among Community Pharmacy Clients in Southern Nigeria

Sat, 26 Sep 2026 00:00:00 +0000

Background: The international literature consistently reports self-medication to be more prevalent among younger, less-educated and lower-income individuals, with gender patterns that are more variable but generally skewed toward higher self-medication among women.

Objective: To characterise the socio-demographic and informational predictors of self-medication among community pharmacy clients in Yenagoa Local Government Area (LGA), Bayelsa State, Nigeria, and to examine the extent to which these predictors depart from the directionality typically reported elsewhere.

Methods: A cross-sectional descriptive survey of 554 adult community pharmacy clients was conducted using stratified random sampling and a structured questionnaire. Multiple linear regression was used to assess the joint and relative contributions of age, gender, education and monthly income to the level of self-medication; independent-samples t-tests assessed the influence of knowledge and attitude on practice; descriptive statistics characterised information sources.

Results: Age, gender, education and monthly income jointly explained 83.1% of the variance in the level of self-medication (R² = 0.831, F(4,327) = 396.581, p < 0.05). Contrary to the directionality typically reported in the international and Nigerian literature, self-medication in this sample increased with age (β = 0.487) rather than decreasing, was more prevalent among men (β = 0.587, the strongest predictor) rather than women, and increased with both income (β = 0.133) and education (β = 0.062) rather than being concentrated among poorer or less-educated groups. Respondents with poor self-medication knowledge self-medicated significantly more than those with good knowledge (mean = 3.21 versus 1.44; mean difference = 1.77, 95% CI 1.55–1.98, p < 0.001), and respondents with a positive attitude self-medicated significantly more than those with a negative attitude. Social media was the leading information source underlying self-medication (33.9%), ahead of personal knowledge (31.5%) and professional advice (17.4%).

Conclusion: In this population, self-medication is not concentrated among the socio-economically disadvantaged, as is commonly assumed, but rather among older, better-resourced and better-educated men whose information environment is dominated by social media rather than professional consultation. This reversal challenges the assumption that self-medication interventions should be targeted primarily at low-resource or low-literacy groups, and instead points to a resourced, socially mediated behaviour that requires a different intervention logic.