Objective: This study aimed to investigate the current status and performance of machine learning (ML) approaches in providing reproducible treatment response predictions. Methods: This systematic review was conducted in accordance with the PRISMA statement and the CHARMS checklist. We searched PubMed, Cochrane Library, Web of Science, Scopus, and EBSCO databases for cohort studies that derived and/or validated ML models focused on predicting rheumatoid arthritis (RA) treatment response. We extracted data and critically appraised studies based on the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) and Prediction Model Risk of Bias Assessment Tool (PROBAST) guidelines. Results: From 210 unduplicated records identified by the literature search, we retained 29 eligible studies. Of these studies, 10 developed a predictive model and reported a mean adherence to the TRIPOD guidelines of 45.6 % (95 % CI: 38.3–52.8 %). The remaining 19 studies not only developed a predictive model but also validated it externally, with a mean adherence of 42.9 % (95 % CI: 39.1–46.6 %). Most of the articles had an unclear risk of bias (41.4 %), followed by a high risk of bias, which was present in 37.9 %. Conclusions: In recent years, ML methods have been increasingly used to predict treatment response in RA. Our critical appraisal revealed unclear and high risk of bias in most of the identified models, suggesting that researchers can do more to address the risk of bias and increase transparency, including the use of calibration measures and reporting methods for handling missing data. Funding: None.

The search for new cancer treatments from traditional medicine involves developing studies to understand at the molecular level different cell signaling pathways involved in cancer development. In this work, we present a model of the PI3K/Akt/mTOR pathway, which plays a key role in cell cycle regulation and is related to cell survival, proliferation, and growth in cancer, as well as resistance to antitumor therapies, so finding drugs that act on this pathway is ideal to propose a new adjuvant treatment. The aim of this work was to model, simulate and predict in silico using the Big Data-Cellulat platform the possible targets in the PI3K/Akt/mTOR pathway on which the Opuntia joconostle extract acts, as well as to indicate the concentration range to be used to find the mean lethal dose in in vitro experiments on breast cancer cells. The in silico results show that, in a cancer cell, the activation of JAK and STAT, as well as PI3K and Akt is related to the effect of cell proliferation, angiogenesis, and inhibition of apoptosis, and that the extract of O. joconostle has an antiproliferative effect on breast cancer cells by inhibiting cell proliferation, regulating the cell cycle and inhibiting apoptosis through this signaling pathway . In vitro it was demonstrated that the extract shows an antiproliferative effect, causing the arrest of cells in the G2/M phase of the cell cycle. Therefore, it is concluded that the use of in silico tools is a valuable method to perform virtual experiments and discover new treatments. The use of this type of model supports in vitro experimentation, reducing the costs and number of experiments in the real laboratory.

Citrulline (C₆H₁₃N₃O₃) is an amino acid found in the body as a zwitterion. This means its carboxylic and amine groups can act as Lewis donors to chelate metal cations. In addition, citrulline possesses a terminal ureido group on its aliphatic chain, which also appears to coordinate. Here, two new mixed complexes of citrulline were made with 1,10-phenanthroline and 2,2′-bipyridine. These compounds, once dissolved in water, gave aquo-complexes that were subject to DFT studies and in vitro toxicity studies on cancer cell lines (HeLa, MDA-MB-231, HCT 15, and MCF7) showed promising results. Docking studies with DNA were also conducted, indicating potential anticancer properties.

Research into muscle tissue pathologies offers great opportunities for new pharmaceutical agents. Current therapies, including corticosteroids and immunosuppressants, have limited efficacy and significant adverse effects. In this context, steroidal hydrazone compound 4d was investigated for its ability to promote muscle growth and regeneration as a potential anabolic and regenerative modulator. Flow cytometry analysis showed that 4d significantly increases cell populations in S phase, indicating a strong proliferative stimulus in pathways regulated by TNF-α, AKT, MAFbX, and SMAD2/3. Molecular docking studies showed that 4d shares strong interactions with the known MasR agonist (EP-2825), exhibiting a higher predicted binding affinity. Furthermore, 4d demonstrated the ability to interact with ACVR1/2A receptors, mimicking the binding profiles of known antagonists and potentially inhibiting myostatin/SMAD signaling. Taken together, experimental and computational evidence supports a dual-mechanistic model in which 4d promotes muscle proliferation and regeneration by (1) activating the MasR–PI3K/AKT/mTOR axis and (2) inhibiting the ACVR1/2A–SMAD pathway, counteracting the action of myostatin. These findings position compound 4d as a promising therapeutic candidate against muscle wasting disorders, including cancer-related cachexia, by inducing a robust and multifactorial anabolic response.