Breast reconstruction following mastectomy or sectorectomy significantly impacts the quality of life and psychological well-being of breast cancer patients. Since its inception in the 1950s, artificial intelligence (AI) has gradually entered the medical field, promising to transform surgical planning, intraoperative guidance, postoperative care, and medical research. This article examines AI applications in breast reconstruction, supported by recent studies. AI shows promise in enhancing imaging for tumor detection and surgical planning, improving microsurgical precision, predicting complications such as flap failure, and optimizing postoperative monitoring. However, challenges remain, including data quality, safety, algorithm transparency, and clinical integration. Despite these shortcomings, AI has the potential to revolutionize breast reconstruction by improving preoperative planning, surgical precision, operative efficiency, and patient outcomes. This review provides a foundation for further research as AI continues to evolve and clinical trials expand its applications, offering greater benefits to patients and healthcare providers.

Basal cell carcinoma (BCC) is considered to be the most common cancer in humans. It has a slow growth rhythm, and for this reason, metastases are rare. For our retrospective study, we selected 180 patients from those who underwent surgery for a variety of skin tumours between January 2019 and August 2022 and whose histopathological examination revealed basal cell carcinoma. All surgeries were performed by plastic surgeons at the “St. John” hospital in Bucharest. The aim of this article is to provide observational data regarding BCC—in terms of histopathology and diagnostic and therapeutic management and to integrate these data into the current knowledge of this pathology.

We successfully prepared a non-enzymatic sensor based on a graphene-thiophene composite for histamine detection. The self-assembling properties of the thiophene onto Au support and the high electrical conductivity of graphene encouraged the choice of this type of composite. The composite was deposited via electrochemical polymerization onto the Au layer of a screen-printed microelectrode. The electropolymerization and electrochemical detection of histamine were both achieved by cyclic voltammetry. Two types of electrolytes were used for the electrochemical detection: (a) phosphate buffer solution (PBS), which showed low-intensity redox peaks for histamine; and (b) trichloroacetic acid (TCA) 0.01 M, which showed improved results over PBS and did not damage the microelectrode. For the concentration range of 100–200 mg/kg, the sensor shows a linear regression pattern for the oxidation peak fitted on the equation I_pa = 123.412 + 0.49933 $\times$x, with R² = 0.94178. The lowest limit of detection was calculated to be 13.8 mg/kg and the limit of quantification was calculated at 46 mg/kg. These results are important since by monitoring the amount of histamine in a food product, early onset of spoilage can be easily detected, thus reducing foodborne poisoning and food waste (by recycling products that are still edible).

Gout is a metabolic condition, common to animals and humans, issuing from the excessive accumulation of end products of proteins degradation. In this study, histopathological and cytological examinations, combined with Raman spectroscopy, have been performed to investigate tissue samples from reptiles, chickens, and humans, presenting lesions produced by uric acid accumulation. As a result of classic processing and staining techniques commonly used in the anatomopathological diagnosis, uric acid crystals lose their structural characteristics, thus making difficult a precise diagnostic. Therefore, complementary diagnostic methods, such as Raman spectroscopy, are needed. This study compares from several perspectives the above mentioned diagnostic methods, concluding that Raman spectroscopy provides highlights in the diagnosis of gout in humans and animals, also adding useful information to differential diagnosis of lesions.