Climate change is altering the distribution, intensity, and timing of environmental exposures relevant to neurological health and cancer, while also disrupting prevention and healthcare services. This narrative, concept-oriented review integrates evidence on extreme heat, ambient air pollution and wildfire smoke, environmental contaminants, climate-sensitive infectious disease, psychosocial stress, and health system disruption. It introduces the Climate Neuro-Cancer Axis (CNCA) as a hypothesis-generating framework for investigating potential links among climate-sensitive exposures, neural and neuroimmune processes, and cancer risk or outcomes. The review distinguishes established hazards such as the carcinogenicity of outdoor air pollution from plausible biological pathways and hypotheses that remain insufficiently tested. Current evidence supports action to reduce harmful exposures and strengthen climate-resilient health systems, but does not establish a single causal pathway linking climate change, nervous system dysfunction, and carcinogenesis, nor does it demonstrate that all relevant exposures act synergistically. The framework therefore generates testable questions rather than presenting a validated syndrome or clinical model. For environmental management, priorities include integrated exposure monitoring, pollution prevention, heat risk reduction, safe chemical management, continuity of cancer and neurological care during extreme events, and equitable surveillance. Future research should use longitudinal, multi-exposure designs, transparent methods, realistic exposure levels, and explicit evaluation of environmental and social effect modifiers. Pharmacological agents should not be used for climate-associated cancer prevention without disease-specific evidence and guideline support.
Wound complications remain a frequent and consequential part of surgical treatment for breast cancer. After mastectomy with axillary lymph node dissection, women may develop seroma, surgical site infection, skin flap necrosis or dehiscence, and the same chest wall may later be exposed to radiotherapy. Honey, particularly sterilised medical-grade preparations, has re-entered wound care because of its antimicrobial, antibiofilm and immunomodulatory properties, and it is sometimes proposed as a low-cost dressing for women treated for breast cancer. Whether this proposal is justified for the post-mastectomy wound has not previously been examined critically. This narrative review evaluated literature first published between 2000 and 2025, identified through structured searches of biomedical and open scholarly databases with citation chasing, and appraised it qualitatively for design, product characterisation, outcome validity and transferability to the mastectomy and axillary wound. The evidence was organised into five domains: the biology of the post-mastectomy wound, the composition and mechanisms of honey, clinical trials in surgical wounds of other types, studies in breast-cancer-specific wounds, and safety. No controlled clinical study has evaluated topical honey on the closed or complicated wound after mastectomy with axillary dissection. The closest evidence comes from small trials in other closed surgical incisions, from infected post-operative wounds, from radiation dermatitis after breast surgery, where findings are conflicting and largely negative for symptom control, and from malignant fungating breast wounds, where honey performed similarly to silver. Mechanistic data are consistent and plausible for bacterial control, moisture balance and re-epithelialisation, but they do not address seroma, the commonest complication. Product heterogeneity, small samples, unblinded assessment and selective publication limit confidence throughout. Topical medical-grade honey may be a reasonable option for selected superficial, open or infected wounds after breast cancer surgery when standard care has been considered, but routine prophylactic use on mastectomy incisions is not supported. Adequately powered randomised trials using characterised products, standardised wound outcomes and patient-reported measures are required before firmer recommendations can be made.
Colorectal cancer (CRC) is the most frequently diagnosed cancer in Romania, according to the most recent GLOBOCAN 2022 estimates. We developed a homogeneous Markov-chain model describing the treatment-stage transitions of a cohort of 100 CRC patients undergoing cytostatic (chemotherapy) treatment at a hospital in Sibiu, Romania, distributed across four states of decreasing severity (S₁–S₄), with S₄ representing patients no longer requiring cytostatic treatment. Transition probabilities, estimated from the observed data conditioned on Body Mass Index via Bayes' formula, define an absorbing Markov chain: numerical evolution of the state distribution over 100 treatment doses shows the cohort converging steadily toward S₄, with over 99% of patients reaching this state by the 100th dose. We derive semi-empirical exponential approximations that closely reproduce this recursion in closed form for two of the three transient states across the full dose range, and for the third once early transient effects have decayed (n≳30), providing readers with an accessible pedagogical bridge from matrix recursion to closed-form approximation. Building on these empirically grounded results, we additionally propose, as methodological frameworks rather than empirically validated findings, two forward-looking extensions: a quality-of-life-weighted formulation compatible with the existing transition matrix, and a constrained-optimization framework identifying an optimal dose count n₀ that balances treatment benefit against accelerating patient toxicity and financial cost, illustrated numerically with hypothetical weights. As a single-center study with a modest sample size (N=100) and no external validation of the estimated transition probabilities, the results should be read as an approximate, illustrative picture of Romanian clinical practice rather than a nationally representative or externally validated estimate. The present work provides a transparent, reproducible modeling framework - including a didactic Appendix - intended both as a practical tool and as a teaching resource for graduate students in mathematical epidemiology.
Background: Cancer represents a major public health burden in Romania. Using GLOBOCAN 2020 estimates, this study provides an updated empirical examination of the relationships among cancer incidence, mortality, rank, cumulative risk, and selected age-standardised indicators, with the aim of providing an empirical quantitative description of the Romanian cancer profile and comparing selected characteristics with global patterns. Methods: GLOBOCAN 2020 estimates for Romania were systematically analysed across 35 cancer sites. The relationships between the number of new cases (Nc) and the number of deaths (Nd) were investigated using linear and log-transformed/power-law regressions. Additional empirical models were applied to describe the dependence of incidence and mortality on cancer rank through a derived severity parameter, and selected results were compared with World 2020 estimates. Results: Romania recorded an estimated 98,886 new cancer cases and 54,486 cancer-related deaths in 2020. Lung cancer ranked first for both incidence and mortality, whereas breast and prostate cancers showed high incidence with comparatively lower Nd/Nc ratios. A linear relationship constrained through the origin yielded Nd=0.5565Nc, while the log-transformed analysis produced a power-law relationship characterised by α=1.01291 and k=0.4372. Rank-based modelling showed a strong concentration of incidence and mortality among a limited number of leading cancer sites. Comparisons with World 2020 estimates highlighted differences in the relative distribution of major cancers and in selected empirical parameters. Conclusion: The proposed empirical framework provides a quantitative description of the incidence–mortality structure of cancer in Romania in 2020 and generates interpretable, empirically grounded parameters that may facilitate comparisons across cancer sites, countries, and future GLOBOCAN datasets. Because the analysis is based on estimated cross-sectional data, the observed relationships should be interpreted as empirical associations rather than as direct evidence of biological or clinical causation. Further validation using longitudinal cancer-registry data and uncertainty analysis would strengthen the predictive and comparative value of the proposed models.
Background: Triple-negative breast cancer (TNBC) remains a clinically challenging breast cancer subtype, motivating the investigation of repurposed compounds and rational combinations. Metformin and caffeine affect distinct cellular processes, but their quantitative interaction in BRCA1-altered TNBC cells is insufficiently characterised. Methods: HCC1937 BRCA1-altered TNBC cells were exposed to metformin and caffeine individually or in sequential combinations. MTT reduction after 48 h was used as a proxy for metabolically active cell mass. Concentration-response relationships were fitted using a four-parameter logistic model, and drug interactions were evaluated using the Bliss independence model and expressed as excess-over-Bliss (ΔBliss) values. Results: Caffeine and metformin reduced MTT signal in a concentration-dependent manner. The estimated caffeine IC₅₀ was 8.6 mM. Metformin did not reach 50% inhibition within the tested 0-20 mM range; its estimated IC₅₀ was approximately 22.0 mM and therefore represents an extrapolated value. In the sequence using 5 mM metformin followed by caffeine, the apparent caffeine IC₅₀ shifted to approximately 1.8 mM. The combination of 0.5 mM caffeine with 5 mM metformin produced 35% observed inhibition versus 28% expected under Bliss independence (ΔBliss = +0.07), whereas other tested pairs were consistent with Bliss independence or showed below-Bliss responses. Conclusion: Metformin and caffeine produced concentration-dependent reductions in MTT-derived metabolic activity in HCC1937 cells, and their interaction varied across the tested concentrations and treatment sequences. The findings support further evaluation using broader concentration matrices, replicate-level interaction statistics, orthogonal viability or cell-death assays, and exposure conditions with greater translational relevance.
