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Abstract A new iodinated BODIPY dye incorporating a thioether‐ has been synthesized and characterized. The benzimidazole unit was introduced at themeso‐pentafluorophenyl position of the BODIPY scaffold via high‐yield click chemistry. This substitution does not alter the strong absorption and emission properties of the BODIPY chromophore and provides a versatile platform for the attachment of pharmacologically important molecules. Further functionalization of the BODIPY core with iodine at the 3‐ and 5‐positions yields a derivative capable of generating reactive oxygen species when irradiated with low energy light. Experimental evidence confirms the production of both singlet oxygen and superoxide radicals, indicating this complex is capable of operating by both Type I and Type II photosensitization pathways. This dual capacity could be responsible for its effectiveness as a photosensitizer and contribute to its photobiological activity against human melanoma cells. 

Abstract Phototherapy approaches include photodynamic therapy (PDT), which utilizes chemically stable photocatalysts to sensitize the conversion of endogenous molecules such as oxygen (O₂) to form transient reactive species such as¹O₂, and photopharmacology, a complementary approach that relies on molecules that undergo self‐modifying photochemistry, such as bond cleavage reactions or isomerization, for the creation of biologically active products. While Ru(II) polypyridyl systems have demonstrated utility for both approaches, related organometallic systems are relatively less explored. Here, the photochemistry and photobiological responses were compared for five Ru(II) arene compounds containing photolabile monodentate azine ligands and the π‐expansive bidentate ligands dipyrido[3,2‐a:2′,3′‐c]phenazine (dppz), 4,5,9,16‐tetraaza‐dibenzo[a,c]naphthacene (dppn), and α‐terthienyl‐appended imidazo[4,5‐f][1,10]phenanthroline (IP‐3T). The compounds demonstrated significant light‐mediated photocytotoxicity in lung cancer and melanoma cell lines, with up to 6000‐fold increases in cytotoxicity upon irradiation. The arene systems were capable of partitioning between different excited state relaxation pathways, both releasing the monodentate ligand and generating¹O₂, but with notably low yields that did not correlate with the photocytotoxicity of the systems. The organometallic compounds exhibit less mixing of the metal‐associated and ligand‐centered excited states than analogous polypyridyl coordination compounds, providing a structurally, photochemically, and photobiologically distinct class of compounds that can support both metal‐ and ligand‐centered reactivity. 

Deep learning (DL) is of great interest in psychiatry due its potential yet largely untapped ability to utilize multidimensional datasets (such as fMRI data) to predict clinical outcomes. Typical DL methods, however, have strong assumptions, such as large datasets and underlying model opaqueness, that are suitable for natural image prediction problems but not medical imaging. Here we describe three relatively novel DL approaches that may help accelerate its incorporation into mainstream psychiatry research and ultimately bring it into the clinic as a prognostic tool. We first introduce two methods that can reduce the amount of training data required to develop accurate models. These may prove invaluable for fMRI-based DL given the time and monetary expense required to acquire neuroimaging data. These methods are (1) transfer learning − the ability of deep learners to incorporate knowledge learned from one data source (e.g., fMRI data from one site) and apply it toward learning from a second data source (e.g., data from another site), and (2) data augmentation (via Mixup) − a self-supervised learning technique in which “virtual” instances are created. We then discuss explainable artificial intelligence (XAI), i.e., tools that reveal what features (and in what combinations) deep learners use to make decisions. XAI can be used to solve the “black box” criticism common in DL and reveal mechanisms that ultimately produce clinical outcomes. We expect these techniques to greatly enhance the applicability of DL in psychiatric research and help reveal novel mechanisms and potential pathways for therapeutic intervention in mental illness.