Every year cervical cancer affects more than 300,000 people, and on average one woman is diagnosed with cervical cancer every minute. Early diagnosis and classification of cervical lesions greatly boosts up the chance of successful treatments of patients, and automated diagnosis and classification of cervical lesions from Papanicolaou (Pap) smear images have become highly demanded. To the authors’ best knowledge, this is the first study of fully automated cervical lesions analysis on whole slide images (WSIs) of conventional Pap smear samples. The presented deep learning-based cervical lesions diagnosis system is demonstrated to be able to detect high grade squamous intraepithelial lesions (HSILs) or higher (squamous cell carcinoma; SQCC), which usually immediately indicate patients must be referred to colposcopy, but also to rapidly process WSIs in seconds for practical clinical usage. We evaluate this framework at scale on a dataset of 143 whole slide images, and the proposed method achieves a high precision 0.93, recall 0.90, F-measure 0.88, and Jaccard index 0.84, showing that the proposed system is capable of segmenting HSILs or higher (SQCC) with high precision and reaches sensitivity comparable to the referenced standard produced by pathologists. Based on Fisher’s Least Significant Difference (LSD) test (P < 0.0001), the proposed method performs significantly better than the two state-of-the-art benchmark methods (U-Net and SegNet) in precision, F-Measure, Jaccard index. For the run time analysis, the proposed method takes only 210 seconds to process a WSI and is 20 times faster than U-Net and 19 times faster than SegNet, respectively. In summary, the proposed method is demonstrated to be able to both detect HSILs or higher (SQCC), which indicate patients for further treatments, including colposcopy and surgery to remove the lesion, and rapidly processing WSIs in seconds for practical clinical usages.

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Nitrosylation of high-spin [Fe(κ(2)-O(2)NO)(4)](2-) (1) yields {Fe(NO)}(7) mononitrosyl iron complex (MNIC) [(κ(2)-O(2)NO)(κ(1)-ONO(2))(3)Fe(NO)](2-) (2) displaying an S = 3/2 axial electron paramagnetic resonance (EPR) spectrum (g(⊥) = 3.988 and g(∥) = 2.000). The thermally unstable nitrate-containing {Fe(NO)(2)}(9) dinitrosyl iron complex (DNIC) [(κ(1)-ONO(2))(2)Fe(NO)(2)](-) (3) was exclusively obtained from reaction of HNO(3) and [(OAc)(2)Fe(NO)(2)](-) and was characterized by IR, UV-vis, EPR, superconducting quantum interference device (SQUID), X-ray absorption spectroscopy (XAS), and single-crystal X-ray diffraction (XRD). In contrast to {Fe(NO)(2)}(9) DNIC [(ONO)(2)Fe(NO)(2)](-) constructed by two monodentate O-bound nitrito ligands, the weak interaction between Fe(1) and the distal oxygens O(5)/O(7) of nitrato-coordinated ligands (Fe(1)···O(5) and Fe(1)···O(7) distances of 2.582(2) and 2.583(2) Å, respectively) may play important roles in stabilizing DNIC 3. Transformation of nitrate-containing DNIC 3 into N-bound nitro {Fe(NO)}(6) [(NO)(κ(1)-NO(2))Fe(S(2)CNEt(2))(2)] (7) triggered by bis(diethylthiocarbamoyl) disulfide ((S(2)CNEt(2))(2)) implicates that nitrate-to-nitrite conversion may occur via the intramolecular association of the coordinated nitrate and the adjacent polarized NO-coordinate ligand (nitrosonium) of the proposed {Fe(NO)(2)}(7) intermediate [(NO)(2)(κ(1)-ONO(2))Fe(S(2)CNEt(2))(2)] (A) yielding {Fe(NO)}(7) [(NO)Fe(S(2)CNEt(2))(2)] (6) along with the release of N(2)O(4) (·NO(2)) and the subsequent binding of ·NO(2) to complex 6. The N-bound nitro {Fe(NO)}(6) complex 7 undergoes Me(2)S-promoted O-atom transfer facilitated by imidazole to give {Fe(NO)}(7) complex 6 accompanied by release of nitric oxide. This result demonstrates that nitrate-containing DNIC 3 acts as an active center to modulate nitrate-to-nitrite-to-nitric oxide conversion.

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Tyrosinase, which is the crucial copper-containing enzyme involved in melanin synthesis, is strongly associated with hyperpigmentation disorders, cancer, and neurodegenerative disease; thus, it has attracted considerable interest in the fields of medicine and cosmetics. The known tyrosinase inhibitors show numerous adverse side effects, and there is a lack of safety regulations governing their use. As a result, there is a need to develop novel inhibitors with no toxicity and long-term stability. In this study, we use molecular docking and pharmacophore modeling to construct a reasonable and reliable pharmacophore model, called Hypo 1, that could be used for identifying potent natural products with crucial complementary functional groups for mushroom tyrosinase inhibition. It was observed that, out of 47,263 natural compounds, A5 structurally resembles a dipeptide (WY) and natural compound B16 is the equivalent of a tripeptide (KFY), revealing that the C-terminus tyrosine residues play a key role in tyrosinase inhibition. Tripeptides RCY and CRY, which show high tyrosinase inhibitory potency, revealed a positional and functional preference for the cysteine residue at the N-terminus of the tripeptides, essentially determining the capacity of tyrosinase inhibition. CRY and RCY used the thiol group of cysteine residues to coordinate with the Cu ions in the active site of tyrosinase and showed reduced tyrosinase activity. We discovered the novel tripeptide CRY that shows the most striking inhibitory potency against mushroom tyrosinase (IC50 = 6.16 μM); this tripeptide is more potent than the known oligopeptides and comparable with kojic acid-tripeptides. Our study provides an insight into the structural and functional roles of key amino acids of tripeptides derived from the natural compound B16, and the results are expected to be useful for the development of tyrosinase inhibitors.