Automated Neutrophil Quantification and Histological Score Estimation in Ulcerative Colitis.

BACKGROUND: In the management of ulcerative colitis (UC), histological remission is increasingly recognized as the ultimate goal. The absence of neutrophil infiltration is crucial for assessing remission. This study aimed to develop an artificial intelligence (AI) system capable of accurately quantifying and localizing neutrophils in UC biopsy specimens to facilitate histological assessment. METHODS: Our AI system, which incorporates semantic segmentation and object detection models, was developed to identify neutrophils in hematoxylin and eosin-stained whole slide images. The system assessed the presence and location of neutrophils within either the epithelium or lamina propria and predicted components of the Nancy Histological Index and the PICaSSO Histologic Remission Index. We evaluated the system's performance against that of experienced pathologists and validated its ability to predict future clinical relapse risk in patients with clinically remitted UC. The primary outcome measure was the clinical relapse rate, defined as a partial Mayo score of ≥3. RESULTS: The model accurately identified neutrophils, achieving a performance of 0.77, 0.81, and 0.79 for precision, recall, and F-score, respectively. The system's histological score predictions showed a positive correlation with the pathologists' diagnoses (Spearman's ρ = 0.68-0.80; P < .05). Among patients who relapsed, the mean number of neutrophils in the rectum was higher than in those who did not relapse. Furthermore, the study highlighted that higher AI-based PICaSSO Histologic Remission Index and Nancy Histological Index scores were associated with hazard ratios increasing from 3.2 to 5.0 for evaluating the risk of UC relapse. CONCLUSIONS: The AI system's precise localization and quantification of neutrophils proved valuable for histological assessment and clinical prognosis stratification.

Phototunable magnetism in copper octacyanomolybdate.

We introduce copper molybdenum cyanides of general formula Cu2[Mo(CN)8] · nH2O, which can serve as optofunctional magnetic devices. Their ground states generally stay paramagnetic down to temperatures of the K order but exhibit a spontaneous magnetization upon photoirradiation usually below a few tens of K. To interest us still further, such a ferromagnetic stateinduced by blue-laser irradiation is demagnetized step by step through further application of red or near-infrared laser pulses. We solve this intriguing photomagnetism. The ground-state properties are fully revealed by means of a group-theoretical technique. Taking account of experimental observations, we simulate applying pump laser pulses to a likely ground state and successfully reproduce both the magnetization and demagnetization dynamics. We monitor the photorelaxation process through angle-resolved photoemission spectroscopy. Electrons are fully itinerant in any of the photoinduced steady states, forming a striking contrast to the initial equilibrium state of atomic aspect. The fully demagnetized final steady state looks completely different from the initial paramagnetism but bears good analogy to one of the possible ground states available with the Coulomb repulsion on Cu sites suppressed.

Bottom-up realization of a porous metal-organic nanotubular assembly.

Nanotubes are generally prepared from their constituent elements at high temperatures, and thus it is difficult to control their size, shape and electronic states. One useful approach for synthesizing well-defined nanostructures involves the use of building blocks such as metal ions and organic molecules. Here, we show the successful creation of an assembly of infinite square prism-shaped metal-organic nanotubes obtained from the simple polymerization of a square-shaped metal-organic frame. The constituent nanotube has a one-dimensional (1D) channel with a window size of 5.9×5.9 Å(2), and can adsorb water (H(2)O) and alcohol vapours, whereas N(2) and CO(2) do not adhere. It consists of four 1D covalent chains that constitute a unique electronic structure of 'charge-density wave (CDW) quartets' on crystallization. Moreover, exchanging structural components and guest molecules enables us to control its semiconductive bandgap. These findings demonstrate the possibility of bottom-up construction of new porous nanotubes, where their degrees of freedom in both pore space and framework can be used.

Versatile transformation system that is applicable to both multiple transgene expression and gene targeting for Thraustochytrids.

A versatile transformation system for thraustochytrids, a promising producer for polyunsaturated fatty acids and fatty acid-derived fuels, was established. G418, hygromycin B, blasticidin, and zeocin inhibited the growth of thraustochytrids, indicating that multiple selectable marker genes could be used in the transformation system. A neomycin resistance gene (neo(r)), driven with an ubiquitin or an EF-1α promoter-terminator from Thraustochytrium aureum ATCC 34304, was introduced into representatives of two thraustochytrid genera, Aurantiochytrium and Thraustochytrium. The neo(r) marker was integrated into the chromosomal DNA by random recombination and then functionally translated into neo(r) mRNA. Additionally, we confirmed that another two genera, Parietichytrium and Schizochytrium, could be transformed by the same method. By this method, the enhanced green fluorescent protein was functionally expressed in thraustochytrids. Meanwhile, T. aureum ATCC 34304 could be transformed by two 18S ribosomal DNA-targeting vectors, designed to cause single- or double-crossover homologous recombination. Finally, the fatty acid Δ5 desaturase gene was disrupted by double-crossover homologous recombination in T. aureum ATCC 34304, resulting in an increase of dihomo-γ-linolenic acid (C(20:3n-6)) and eicosatetraenoic acid (C(20:4n-3)), substrates for Δ5 desaturase, and a decrease of arachidonic acid (C(20:4n-6)) and eicosapentaenoic acid (C(20:5n-3)), products for the enzyme. These results clearly indicate that a versatile transformation system which could be applicable to both multiple transgene expression and gene targeting was established for thraustochytrids.

Deep learning-based automated quantification of goblet cell mucus using histological images as a predictor of clinical relapse of ulcerative colitis with endoscopic remission.

BACKGROUND: Mucin depletion is one of the histological indicators of clinical relapse among patients with ulcerative colitis (UC). Mucin depletion is evaluated semiquantitatively by pathologists using histological images. Therefore, the interobserver concordance is not extremely high, and an objective evaluation method is needed. This study was conducted to demonstrate that our automated quantitative method using a deep learning-based model is useful in predicting the prognosis of patients with UC. METHODS: Deep learning-based models were trained to detect goblet cell mucus area from whole slide images of biopsy specimens. This study involved 114 patients with UC in endoscopic remission with a partial Mayo score of ≤ 1. Biopsy specimens were collected during colonoscopy, and the ratio of goblet cell mucus area to the epithelial cell and goblet cell mucus area was calculated as goblet cell ratio (GCR). The follow-up time was 12 months, and the primary outcome was the relapse rate. Clinical relapse was defined as partial Mayo score of ≥ 3. RESULTS: Sixteen patients (14%) experienced clinical relapse. In the relapsed group, the GCRs of specimens obtained from the cecum, ascending colon, and rectum were significantly lower than those of specimens in the relapse-free group (p = 0.010, p = 0.027, p < 0.01). In the rectum, patients with a GCR of ≤ 12% had a significantly higher relapse rate than those with a GCR of > 12% (45% [10/22] vs. 6.5% [6/92]; p < 0.01). CONCLUSIONS: Quantifying goblet cell mucus areas using a deep learning-based model is useful in predicting the clinical relapse in patients with UC in clinical and endoscopic remission.