Antimicrobial resistance is recognised as one of the top threats healthcare is bound to face in the future. There have been various attempts to preserve the efficacy of existing antimicrobials, develop new and efficient antimicrobials, manage infections with multi-drug resistant strains, and improve patient outcomes, resulting in a growing mass of routinely available data, including electronic health records and microbiological information that can be employed to develop individualised antimicrobial stewardship. Machine learning methods have been developed to predict antimicrobial resistance from whole-genome sequencing data, forecast medication susceptibility, recognise epidemic patterns for surveillance purposes, or propose new antibacterial treatments and accelerate scientific discovery. Unfortunately, there is an evident gap between the number of machine learning applications in science and the effective implementation of these systems. This narrative review highlights some of the outstanding opportunities that machine learning offers when applied in research related to antimicrobial resistance. In the future, machine learning tools may prove to be superbugs’ kryptonite. This review aims to provide an overview of available publications to aid researchers that are looking to expand their work with new approaches and to acquaint them with the current application of machine learning techniques in this field.

Breast cancer is a significant health issue affecting women worldwide, and accurately detecting lymph node metastasis is critical in determining treatment and prognosis. While traditional diagnostic methods have limitations and complications, artificial intelligence (AI) techniques such as machine learning (ML) and deep learning (DL) offer promising solutions for improving and supplementing diagnostic procedures. Current research has explored state-of-the-art DL models for breast cancer lymph node classification from radiological images, achieving high performances (AUC: 0.71–0.99). AI models trained on clinicopathological features also show promise in predicting metastasis status (AUC: 0.74–0.77), whereas multimodal (radiomics + clinicopathological features) models combine the best from both approaches and also achieve good results (AUC: 0.82–0.94). Once properly validated, such models could greatly improve cancer care, especially in areas with limited medical resources. This comprehensive review aims to compile knowledge about state-of-the-art AI models used for breast cancer lymph node metastasis detection, discusses proper validation techniques and potential pitfalls and limitations, and presents future directions and best practices to achieve high usability in real-world clinical settings.

The clonal hematopoiesis of indeterminate potential (CHIP) is a term used to describe individuals who have detectable somatic mutations in genes commonly found in individuals with hematologic cancers but without any apparent evidence of such conditions. The mortality rate in individuals with CHIP is remarkably higher than the influence ascribed to hematologic malignancies, and it is plausible that cardiovascular diseases (CVD) could elucidate the apparent disparity. Studies have shown that the most frequently altered genes in CHIP are associated with the increased incidence of CVDs, type 2 diabetes mellitus (T2DM) and myeloid malignancies, as well as obesity. Additionally, multiple research studies have confirmed that obesity is also independently associated with these conditions, particularly the development and progression of atherosclerotic CVD. Considering the shared pathogenetic mechanisms of obesity and CHIP, our objective in this review was to investigate both preclinical and clinical evidence regarding the correlation between obesity and CHIP and the resulting implications of this interaction on the pathophysiology of CVDs and malignancies. The pro-inflammatory condition induced by obesity and CHIP enhances the probability of developing both diseases and increases the likelihood of developing CVDs, T2DM and malignancies, suggesting that a dangerous vicious loop may exist. However, it is vital to conduct additional research that will suggest targeted treatment options for obese individuals with CHIP in order to reduce harmful effects connected to these conditions.

Objectives: To determine whether plasma concentrations of anandamide (AEA) and 2-arachidonoylglycerol (2-AG) are elevated in adults with prediabetes, we explored their association with tissue advanced glycation end-products (AGEs) and assessed the influence of Mediterranean diet adherence. Methods: This cross-sectional single-centre study included 92 adults with prediabetes and 86 age-/sex-matched normoglycaemic controls. Anthropometry, blood pressure, biochemical indices, and skin autofluorescence-derived AGEs were measured. Serum AEA and 2-AG were quantified by competitive ELISA, while Mediterranean diet adherence was assessed using the Mediterranean Diet Serving Score (MDSS). Results: Prediabetes was associated with higher AEA (p = 0.004) but not 2-AG (p = 0.520). Also, AEA correlated positively with AGE values (r = 0.36; p = 0.002) and increased across AGE-based cardiovascular risk categories. In multivariable models, both prediabetes status and AGE burden independently predicted AEA. Participants achieving MDSS ≥ 14 exhibited lower AEA (p = 0.038); 2-AG remained unaffected. Finally, the multivariable analysis confirmed that both prediabetes (β = 11.9; p = 0.005) and AGE values (β = 0.25; p = 0.003) are positively associated with plasma AEA levels, independent of age, sex, BMI, and fasting plasma glucose levels. Conclusions: Circulating AEA, but not 2-AG, is elevated in prediabetes and independently linked to cumulative AGE burden, suggesting early endocannabinoid activation contributes to cardiometabolic risk. High adherence to a Mediterranean diet may mitigate this dysregulation.

Overnutrition is a predominant issue in contemporary society, increasing rapidly despite considerable progress in our comprehension of nutrition, the health consequences of different food categories, and the dangers linked to excessive calorie consumption. The pathways connecting obesity to associated disorders are intricate, although research has consistently identified oxidative stress as a principal facilitator of the progression of many diseases. In this paper, the synthesis of various reactive species at the molecular level is studied, and the influence of diet on their production is assessed, with a thorough examination of the cellular mechanisms involved. Furthermore, the correlation between oxidative stress and the development of cardiometabolic diseases is explored, highlighting the most recent and relevant research in the field.

The aim of this study was to explore adherence to the Consolidated Standards of Reporting Trials (CONSORT) reporting standards in abstracts of randomized controlled trials on glaucoma. A cross-sectional observational study was conducted on the aforementioned abstracts, indexed in MEDLINE/PubMed between the years 2017 and 2021. In total, 302 abstracts met the inclusion criteria and were further analyzed. The median score of CONSORT-A items was 8 (interquartile range, 7–10) out of 17 (47.0%). Most analyzed studies were conducted in a single center (80.5%) and the abstracts were predominantly structured (95.0%). Only 20.5% of the abstracts adequately described the trial design, while randomization and funding were described by 6.0% of the abstracts. Higher overall scores were associated with structured abstracts, a multicenter setting, statistically significant results, funding by industry, a higher number of participants, and having been published in journals with impact factors above four (p < 0.001, respectively). The results of this study indicate a suboptimal adherence to CONSORT-A reporting standards, especially in particular items such as randomization and funding. Since these factors could contribute to the overall quality of the trials and further translation of trial results into clinical practice, an improvement in glaucoma research reporting transparency is needed.