Observational studies indicate frequent associations between systemic lupus erythematosus (SLE) and various hematologic disorders, yet causal inferences are limited by confounding and reverse causality. We therefore applied a bidirectional Mendelian randomization (MR) design to assess potential genetic causal associations of SLE with specific hematologic conditions.

We used European-ancestry GWAS summary statistics for SLE (5,201 cases, 9,066 controls) and five hematologic outcomes (vitamin B12 deficiency anemia (B12DA), myelodysplastic syndrome (MDS), immune thrombocytopenia (ITP), agranulocytosis (AGC), iron deficiency anemia (IDA)) from FinnGen. The primary analysis used inverse-variance weighting, supplemented by MR-Egger and weighted median methods, with comprehensive sensitivity analyses, including heterogeneity tests, pleiotropy assessment, and leave-one-out analysis.

Bidirectional MR analysis revealed that genetically predicted SLE increased the risk of B12DA (odds ratio (OR) = 1.08, P < 0.001), and genetically predicted B12DA was associated with an increased risk of SLE (OR = 2.22, P = 1.6 × 10−29). The MDS → SLE association was nominally significant (P = 0.023) but did not survive Bonferroni correction (P < 0.005) and was inconsistent across MR methods. No significant genetic associations were found between SLE and ITP, AGC, or IDA in either direction (all P > 0.005).

This bidirectional MR study provides genetic evidence that SLE increases the risk of B12DA, whereas the reverse direction (B12DA → SLE) should be interpreted cautiously because it was based on only five instruments and was not supported by the Steiger directionality test. No robust genetic associations were found for ITP, AGC, IDA, or MDS. Clinically, monitoring B12DA in SLE patients may be warranted, although screening recommendations await prospective validation.

Emerging diseases are those that appear in a population for the first time or previously existed, but rapidly increase in incidence or geographic range. During the coronavirus disease 2019 (COVID-19) pandemic, ethnopharmacological practices became prominent in Nigeria because herbal remedies were affordable, locally accessible, and supported by longstanding cultural trust. However, despite broad public use and some regulatory progress, evidence for the safety, efficacy, standardization, and clinical application of these remedies remains limited. This narrative review examines the integration of ethnopharmacology into Nigeria’s COVID-19 response, evaluates its strengths, constraints, and lessons, and considers future pathways for incorporating ethnopharmacological practices into epidemic preparedness and response strategies. It reviews literature and regulatory information published from 2015 to 2025, focusing on herbal remedies used during the pandemic, regulatory listing processes, and key research gaps. Common remedies were derived from Zingiber officinale, Allium sativum, Citrus limon, Curcuma longa, and Garcinia kola. The National Agency for Food and Drug Administration and Control in Nigeria accelerated toxicological testing and listed 14 local herbal medicines, including Pax Herbal Cugzin, Niprimune, and IHP Detox Tea. This established a baseline safety for human consumption but did not validate therapeutic efficacy against severe acute respiratory syndrome coronavirus 2, which requires rigorous clinical trials. Although some formulations showed promising findings in preliminary clinical, in silico, and in vitro studies, the evidence was constrained by small pilot samples, absence of multiphase randomized controlled trials, insufficient in vivo pharmacokinetic data, and difficulties in isolating active compounds necessary for batch-to-batch consistency. Strengthening Nigeria’s resilience to future epidemics will require sustained investment in scientific research infrastructure, toxicogenomic and metabolomic studies, robust intellectual property frameworks for indigenous knowledge, and transparent public communication to reduce misinformation and product misuse.

High-throughput transcriptomic technologies have made differential gene expression analysis a cornerstone of cancer research by generating extensive lists of differentially expressed genes across tumor types and conditions. However, such lists provide limited biological insight without functional and mechanistic interpretation. This review examines the transition from descriptive gene expression profiles to a mechanistic understanding of cancer biology. We discuss integrative approaches that place expression changes within signaling pathways, transcriptional regulatory networks, and protein–protein interaction networks, thereby helping to identify functional modules and candidate upstream regulators. We emphasize the context-dependent nature of gene expression, which is shaped by genetic alterations, epigenetic landscapes, microenvironmental signals, and cellular heterogeneity. We also examine methodological advances, including gene set enrichment analysis, network-based modeling, and integration of genomic, epigenomic, proteomic, metabolomic, and single-cell transcriptomic data. Case studies across cancer types illustrate how mechanistic analyses can reveal context-specific transcriptional programs associated with oncogenic signaling, tumor suppression, metabolic reprogramming, epithelial–mesenchymal transition, and tumor–immune interactions. We highlight potential translational applications, including candidate biomarker discovery, prioritization of druggable targets, rational design of combination therapies, and investigation of therapeutic resistance. Finally, we discuss current challenges and emerging technologies, such as spatial transcriptomics and clustered regularly interspaced short palindromic repeats (CRISPR)-based perturbation screens, that are advancing the field toward dynamic, systems-level models of tumor biology. Integrating computational analyses with experimental validation can help translate transcriptomic data into clinically relevant hypotheses for precision oncology and personalized cancer therapy.

The modern intensive care unit (ICU) inundates clinicians with large volumes of data, leading to cognitive overload and a gap between data availability and actionable insight. While artificial intelligence (AI) promises a solution, its clinical adoption is limited by systemic barriers, including algorithmic bias, a lack of trust, and validation failures. This paper argues that a design philosophy that envisions AI as an autonomous decision-maker, rather than an integrated collaborative tool, has hindered its clinical adoption. We propose an alternative: a collaborative framework designed to augment the intensivist’s expertise by offloading specific cognitive burdens. This framework redefines AI’s purpose as managing data-intensive tasks, illustrated through four collaborative example roles: a synthesizer to create coherent clinical narratives, a sentinel for proactive deterioration surveillance, a simulator to forecast patient responses to interventions, and a stratifier to identify meaningful subphenotypes within complex syndromes. By delegating these computational tasks, this collaborative model frees clinicians to focus on complex synthesis, nuanced judgment, and compassionate communication. Realizing this vision requires a deliberate translational pathway focused on robust data infrastructure, human-centered design, and rigorous validation through prospective clinical trials. Ultimately, the successful integration of AI in critical care depends not on replacing clinicians but on empowering them, creating a more functional ICU in which technology supports the delivery of safer, more precise, and more humane care.

Atrial fibrillation and chronic kidney disease (CKD) frequently coexist, increasing thromboembolic and bleeding risks. This is a narrative review of pathophysiology and clinical evidence for anticoagulation strategies in CKD patients. Direct oral anticoagulants are preferred in CKD stages 1–4. Recent data suggest that the efficacy of apixaban and rivaroxaban is comparable to that of warfarin in end-stage renal disease. In advanced CKD, anticoagulation should be tailored with close monitoring.