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Lung cancer and pulmonary tuberculosis are two significant public health problems that continue to take millions of lives each year. They may have similar symptoms and, in some cases, are diagnosed simultaneously or may have a causal relationship. In tuberculosis disease, the chronic inflammation, different produced molecules, genomic changes, and fibrosis are believed to be important factors that may promote carcinogenesis. As a reverse reaction, the development of carcinogenesis and the treatment may induce the reactivation of latent tuberculosis infection. Moreover, the recently used checkpoint inhibitors are a debatable subject since they help treat lung cancer but may lead to the reactivation of pulmonary tuberculosis and checkpoint-induced pneumonitis. Pulmonary rehabilitation is an effective intervention in post-tuberculosis patients and lung cancer patients and should be recommended to improve outcomes in these pathologies.

Melanoma is a common and aggressive tumor originating from melanocytes. The increasing incidence of cutaneous melanoma in recent last decades highlights the need for predictive biomarkers studies. Melanoma development is a complex process, involving the interplay of genetic, epigenetic, and environmental factors. Genetic aberrations include BRAF, NRAS, NF1, MAP2K1/MAP2K2, KIT, GNAQ, GNA11, CDKN2A, TERT mutations, and translocations of kinases. Epigenetic alterations involve microRNAs, non-coding RNAs, histones modifications, and abnormal DNA methylations. Genetic aberrations and epigenetic marks are important as biomarkers for the diagnosis, prognosis, and prediction of disease recurrence, and for therapeutic targets. This review summarizes our current knowledge of the genomic and epigenetic changes in melanoma and discusses the latest scientific information.

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We performed a meta-analysis of chemo-brain diagnostic, pooling sensitivities, and specificities in order to assess the accuracy of a machine-learning (ML) algorithm in breast cancer survivors previously treated with chemotherapy. We searched PubMed, Web of Science, and Scopus for eligible articles before 30 September 2022. We identified three eligible studies from which we extracted seven ML algorithms. For our data, the χ2 tests demonstrated the homogeneity of the sensitivity’s models (χ2 = 7.6987, df = 6, p-value = 0.261) and the specificities of the ML models (χ2 = 3.0151, df = 6, p-value = 0.807). The pooled area under the curve (AUC) for the overall ML models in this study was 0.914 (95%CI: 0.891–0.939) and partial AUC (restricted to observed false positive rates and normalized) was 0.844 (95%CI: 0.80–0.889). Additionally, the pooled sensitivity and pooled specificity values were 0.81 (95% CI: 0.75–0.86) and 0.82 (95% CI: 0.76–0.86), respectively. From all included ML models, support vector machine demonstrated the best test performance. ML models represent a promising, reliable modality for chemo-brain prediction in breast cancer survivors previously treated with chemotherapy, demonstrating high accuracy.