Table_1_Identifying the role of vision transformer for skin cancer—A scoping review.DOCX

IntroductionDetecting and accurately diagnosing early melanocytic lesions is challenging due to extensive intra- and inter-observer variabilities. Dermoscopy images are widely used to identify and study skin cancer, but the blurred boundaries between lesions and besieging tissues can lead to incorrect identification. Artificial Intelligence (AI) models, including vision transformers, have been proposed as a solution, but variations in symptoms and underlying effects hinder their performance.ObjectiveThis scoping review synthesizes and analyzes the literature that uses vision transformers for skin lesion detection.MethodsThe review follows the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Revise) guidelines. The review searched online repositories such as IEEE Xplore, Scopus, Google Scholar, and PubMed to retrieve relevant articles. After screening and pre-processing, 28 studies that fulfilled the inclusion criteria were included.Results and discussionsThe review found that the use of vision transformers for skin cancer detection has rapidly increased from 2020 to 2022 and has shown outstanding performance for skin cancer detection using dermoscopy images. Along with highlighting intrinsic visual ambiguities, irregular skin lesion shapes, and many other unwanted challenges, the review also discusses the key problems that obfuscate the trustworthiness of vision transformers in skin cancer diagnosis. This review provides new insights for practitioners and researchers to understand the current state of knowledge in this specialized research domain and outlines the best segmentation techniques to identify accurate lesion boundaries and perform melanoma diagnosis. These findings will ultimately assist practitioners and researchers in making more authentic decisions promptly.</p

Table_2_Identifying the role of vision transformer for skin cancer—A scoping review.XLSX

IntroductionDetecting and accurately diagnosing early melanocytic lesions is challenging due to extensive intra- and inter-observer variabilities. Dermoscopy images are widely used to identify and study skin cancer, but the blurred boundaries between lesions and besieging tissues can lead to incorrect identification. Artificial Intelligence (AI) models, including vision transformers, have been proposed as a solution, but variations in symptoms and underlying effects hinder their performance.ObjectiveThis scoping review synthesizes and analyzes the literature that uses vision transformers for skin lesion detection.MethodsThe review follows the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Revise) guidelines. The review searched online repositories such as IEEE Xplore, Scopus, Google Scholar, and PubMed to retrieve relevant articles. After screening and pre-processing, 28 studies that fulfilled the inclusion criteria were included.Results and discussionsThe review found that the use of vision transformers for skin cancer detection has rapidly increased from 2020 to 2022 and has shown outstanding performance for skin cancer detection using dermoscopy images. Along with highlighting intrinsic visual ambiguities, irregular skin lesion shapes, and many other unwanted challenges, the review also discusses the key problems that obfuscate the trustworthiness of vision transformers in skin cancer diagnosis. This review provides new insights for practitioners and researchers to understand the current state of knowledge in this specialized research domain and outlines the best segmentation techniques to identify accurate lesion boundaries and perform melanoma diagnosis. These findings will ultimately assist practitioners and researchers in making more authentic decisions promptly.</p

Table_3_Identifying the role of vision transformer for skin cancer—A scoping review.XLSX

IntroductionDetecting and accurately diagnosing early melanocytic lesions is challenging due to extensive intra- and inter-observer variabilities. Dermoscopy images are widely used to identify and study skin cancer, but the blurred boundaries between lesions and besieging tissues can lead to incorrect identification. Artificial Intelligence (AI) models, including vision transformers, have been proposed as a solution, but variations in symptoms and underlying effects hinder their performance.ObjectiveThis scoping review synthesizes and analyzes the literature that uses vision transformers for skin lesion detection.MethodsThe review follows the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Revise) guidelines. The review searched online repositories such as IEEE Xplore, Scopus, Google Scholar, and PubMed to retrieve relevant articles. After screening and pre-processing, 28 studies that fulfilled the inclusion criteria were included.Results and discussionsThe review found that the use of vision transformers for skin cancer detection has rapidly increased from 2020 to 2022 and has shown outstanding performance for skin cancer detection using dermoscopy images. Along with highlighting intrinsic visual ambiguities, irregular skin lesion shapes, and many other unwanted challenges, the review also discusses the key problems that obfuscate the trustworthiness of vision transformers in skin cancer diagnosis. This review provides new insights for practitioners and researchers to understand the current state of knowledge in this specialized research domain and outlines the best segmentation techniques to identify accurate lesion boundaries and perform melanoma diagnosis. These findings will ultimately assist practitioners and researchers in making more authentic decisions promptly.</p

New furocarbazole alkaloids from <i>Lonicera quinquelocularis</i>

<div><p>Two new furocarbazole alkaloids, 3-formyl-6,7-dimethoxy-furo[1,2]carbazole (<b>1</b>) and methyl-6,7-dimethoxy-furo[1,2]carbazole-3-carboxylate (<b>2</b>), along with two known carbazole alkaloids, 3-formyl-2-hydroxy-7-methoxycarbazole (<b>3</b>) and methyl 2,7-dimethoxycarbazole-3-carboxylate (<b>4</b>) were isolated from the ethyl acetate soluble fraction of <i>Lonicera quinquelocularis</i>. Their structures were established on the basis of spectroscopic analysis.</p></div

Total ion chromatogram for culture broth of <i>P. chrysogenum</i>.

<p>Total ion chromatogram (TIC, black) and normalized extracted ion chromatograms (EIC, colored) of secondary metabolites from the roquefortine/meleagrin pathway in the culture broth of <i>P. chrysogenum</i> DS54555. HTD (<b>1</b>, 8.8 min), DHTD (<b>2</b>, 9.4 min), glandicoline A (<b>5</b>, 16.8 min), roquefortine D (<b>3</b>, 18.3 min), glandicoline B (<b>6</b>, 18.4 min), meleagrin (<b>7</b>, 19.6 min), roquefortine C (<b>4</b>, 21.4 min).</p

Internal standard corrected concentrations (RR = response ratio) of secondary metabolites from roquefortine/meleagrin pathway.

<p>The metabolite concentrations in the culture broth of the Δ<i>roqR</i> (A), Δ<i>roqD</i> (B), Δ<i>roqM</i> (C), Δ<i>roqO</i> (D), Δ<i>roqN</i> (E) and Δ<i>roqT</i> (F) strains was compared to the host strain <i>P. chrysogenum</i> DS54555.</p

Southern blot analysis for deletion of the genes in the roquefortine/meleagrin pathway.

<p>Southern blot hybridization was performed with total DNA extracted from <i>P. chrysogenum</i> DS54555 strains with a deletion of the following genes: <i>roqA</i> (A), <i>roqR</i> (B), <i>roqD</i> (C), <i>roqM</i> (D), <i>roqO</i> (E), <i>roqN</i> (F) and <i>roqT</i> (G). The DNA was digested with the restriction enzymes as indicated in the schemes.</p

Change in production of roquefortine/meleagrin metabolites after precursor addition compared to production in cultures without addition.

<p>Colored cells show mean levels that are significant (P<0.05) different.</p

Födoval av torsk (Gadus morrhua L.) i Skagerrak och Kattegatt under februari 1981 /

<div><p>Profiling and structural elucidation of secondary metabolites produced by the filamentous fungus <i>Penicillium chrysogenum</i> and derived deletion strains were used to identify the various metabolites and enzymatic steps belonging to the roquefortine/meleagrin pathway. Major abundant metabolites of this pathway were identified as histidyltryptophanyldiketopiperazine (HTD), dehydrohistidyltryptophanyldi-ketopiperazine (DHTD), roquefortine D, roquefortine C, glandicoline A, glandicoline B and meleagrin. Specific genes could be assigned to each enzymatic reaction step. The nonribosomal peptide synthetase RoqA accepts L-histidine and L-tryptophan as substrates leading to the production of the diketopiperazine HTD. DHTD, previously suggested to be a degradation product of roquefortine C, was found to be derived from HTD involving the cytochrome P450 oxidoreductase RoqR. The dimethylallyltryptophan synthetase RoqD prenylates both HTD and DHTD yielding directly the products roquefortine D and roquefortine C without the synthesis of a previously suggested intermediate and the involvement of RoqM. This leads to a branch in the otherwise linear pathway. Roquefortine C is subsequently converted into glandicoline B with glandicoline A as intermediates, involving two monooxygenases (RoqM and RoqO) which were mixed up in an earlier attempt to elucidate the biosynthetic pathway. Eventually, meleagrin is produced from glandicoline B involving a methyltransferase (RoqN). It is concluded that roquefortine C and meleagrin are derived from a branched biosynthetic pathway.</p></div

Organization of the roquefortine/meleagrin biosynthetic gene cluster and transcriptomic analysis.

<p>(A) Roquefortine/meleagrin biosynthetic gene cluster and their orthologs in phylogenetically relative species. Homologous proteins are indicated with the same color. (B) Microarray analysis of the roquefortine biosynthetic genes in <i>P. chrysogenum</i> DS54555 using shake flask culture conditions in the absence (−) or presence (+) phenylacetic acid (PAA). (C) Correlation between the expression level of <i>roqA</i> and the concentration of the product HTD (<b>1)</b> present in the growth media. The concentration of <b>1</b> was determined by HPLC-UV-MS.</p