Identifying Existing Evidence to Potentially Develop a Machine Learning Diagnostic Algorithm for Cough in Primary Care Settings: Scoping Review.

BACKGROUND: Primary care is known to be one of the most complex health care settings because of the high number of theoretically possible diagnoses. Therefore, the process of clinical decision-making in primary care includes complex analytical and nonanalytical factors such as gut feelings and dealing with uncertainties. Artificial intelligence is also mandated to offer support in finding valid diagnoses. Nevertheless, to translate some aspects of what occurs during a consultation into a machine-based diagnostic algorithm, the probabilities for the underlying diagnoses (odds ratios) need to be determined. OBJECTIVE: Cough is one of the most common reasons for a consultation in general practice, the core discipline in primary care. The aim of this scoping review was to identify the available data on cough as a predictor of various diagnoses encountered in general practice. In the context of an ongoing project, we reflect on this database as a possible basis for a machine-based diagnostic algorithm. Furthermore, we discuss the applicability of such an algorithm against the background of the specifics of general practice. METHODS: The PubMed, Scopus, Web of Science, and Cochrane Library databases were searched with defined search terms, supplemented by the search for gray literature via the German Journal of Family Medicine until April 20, 2023. The inclusion criterion was the explicit analysis of cough as a predictor of any conceivable disease. Exclusion criteria were articles that did not provide original study results, articles in languages other than English or German, and articles that did not mention cough as a diagnostic predictor. RESULTS: In total, 1458 records were identified for screening, of which 35 articles met our inclusion criteria. Most of the results (11/35, 31%) were found for chronic obstructive pulmonary disease. The others were distributed among the diagnoses of asthma or unspecified obstructive airway disease, various infectious diseases, bronchogenic carcinoma, dyspepsia or gastroesophageal reflux disease, and adverse effects of angiotensin-converting enzyme inhibitors. Positive odds ratios were found for cough as a predictor of chronic obstructive pulmonary disease, influenza, COVID-19 infections, and bronchial carcinoma, whereas the results for cough as a predictor of asthma and other nonspecified obstructive airway diseases were inconsistent. CONCLUSIONS: Reliable data on cough as a predictor of various diagnoses encountered in general practice are scarce. The example of cough does not provide a sufficient database to contribute odds to a machine learning-based diagnostic algorithm in a meaningful way.

[Corrigendum] Effects of green tea, matcha tea and their components epigallocatechin gallate and quercetin on MCF­7 and MDA­MB­231 breast carcinoma cells.

Subsequently to the publication of this paper, the authors have realized that the name of the fifth listed author, Theresa Vilsmaier, was spelt incorrectly (it appeared as "Vilsmeier" in print). The corrected author list, as it shown have appeared in the paper, is shown above. The authors regret that the name of the fifth author on the paper was spelt incorrectly, and apologize to the readers for any inconvenience caused. [The original article was published in Oncology Reports 41: 387-396, 2019; DOI: 10.3892/or.2018.6789].

Effects of green tea, matcha tea and their components epigallocatechin gallate and quercetin on MCF­7 and MDA-MB-231 breast carcinoma cells.

We investigated the anticarcinogenic potential of green tea and its components epigallocatechin gallate (EGCG) and quercetin, as well as tamoxifen, on MCF-7 and MDA-MB-23 breast cancer cells. Using high-performance liquid chromatography, the quantity of EGCG and quercetin in green tea was analyzed. The receptor status of the cells was confirmed immunohistochemically. Various viability and cytotoxicity tests were later performed to investigate the effects of the substances. After incubating the cells with green tea extract, EGCG, quercetin and tamoxifen, a decrease in viability (MTT test) or proliferation (BrdU assay) was found in all cell tests with varying effects, depending on the assay used. The effects were similar in both cell lines. This work confirmed that EGCG and quercetin are contained in green tea and that both substances in pure form and as green tea have an anticarcinogenic effect on both estrogen receptor-positive and -negative breast cancer cells. This effect could also be demonstrated with tamoxifen in both cell lines (MTT and BrdU assays). These results suggest that the effects observed in these experiments are not generated only via estrogen receptor-mediated pathways.