Postinterventional Petechial Hemorrhage Associated With Poor Functional Outcome After Successful Recanalization Following Endovascular Therapy.

BACKGROUND AND OBJECTIVES: Endovascular therapy (EVT) has emerged as the standard for treating patients with acute ischemic stroke due to large vessel occlusion. The aim of this study was to investigate the relationship between early petechial hemorrhage and patient outcomes after successful EVT of anterior circulation. METHODS: We retrospectively analyzed multicenter data from 316 patients who underwent EVT for acute occlusion of anterior circulation. Patients were divided into petechial hemorrhage group and without hemorrhage group based on post-EVT head imaging. Logistical regression analysis was performed to determine independent predictors for petechial hemorrhage, and for petechial hemorrhage as a predictor of early neurological improvement, favorable outcome at 90 days (modified Rankin Scale 0-2), and 90-day mortality, with adjustment for all factors significantly associated with these endpoints in univariate regression to P < .10. RESULTS: Of 316 included patients with successful EVT, 49 (15.50%) had petechial hemorrhage. The petechial hemorrhage group showed less early neurological improvement (36.73% compared with 53.56%, P = .030), less favorable outcomes at 90 days (32.65% compared with 61.80%, P < .001, absolute risk difference 29.15%), and higher mortality at 90 days (28.57% compared with 10.49%, P = .001) then the group without hemorrhage. Petechial hemorrhage was inversely associated with favorable 90-day outcome (odds ratio = 0.415, 95% CI 0.206-0.835) and higher mortality rate at 90 days (odds ratio = 2.537, 95% CI 1.142-5.635) in multivariable regression but was not independently associated with early neurological improvement. CONCLUSION: In patients with anterior large vessel occlusion who underwent successful EVT, petechial hemorrhage was associated with poor functional outcome and 90-day mortality when adjusted for complete recanalization, pre-EVT National Institute of Health Stroke Scale/Score, and Alberta Stroke Program Early Computed Tomography Score. Despite the relatively lower rate of a favorable 90-day outcome with petechial hemorrhage compared with no petechial hemorrhage, the absolute rate of a favorable outcome exceeds the natural history of medical management for this condition.

Mpox-AISM: AI-mediated super monitoring for mpox and like-mpox.

Swift and accurate diagnosis for earlier-stage monkeypox (mpox) patients is crucial to avoiding its spread. However, the similarities between common skin disorders and mpox and the need for professional diagnosis unavoidably impaired the diagnosis of earlier-stage mpox patients and contributed to mpox outbreak. To address the challenge, we proposed "Super Monitoring", a real-time visualization technique employing artificial intelligence (AI) and Internet technology to diagnose earlier-stage mpox cheaply, conveniently, and quickly. Concretely, AI-mediated "Super Monitoring" (mpox-AISM) integrates deep learning models, data augmentation, self-supervised learning, and cloud services. According to publicly accessible datasets, mpox-AISM's Precision, Recall, Specificity, and F1-score in diagnosing mpox reach 99.3%, 94.1%, 99.9%, and 96.6%, respectively, and it achieves 94.51% accuracy in diagnosing mpox, six like-mpox skin disorders, and normal skin. With the Internet and communication terminal, mpox-AISM has the potential to perform real-time and accurate diagnosis for earlier-stage mpox in real-world scenarios, thereby preventing mpox outbreak.

A high-performance metal halide perovskite-based laser-driven display.

Laser-driven liquid crystal displays (LCDs) comprising metal halide perovskites (MHPs) as the blue-to-green/red color converters are at the forefront of ongoing intense research on the development and improvement of display devices. However, the inferior high photoluminescence quantum yield (PLQY) of MHPs under the excitation of high-power blue light and photoluminescence deterioration at high temperatures remain major concerns. Herein, we design a kind of octylamine-modified MHP via binding energy engineering, and the synthesized materials show PLQY of 97.6% under the excitation of a blue laser at 450 nm. Meanwhile, this design endows a structural self-healing ability to achieve a high PLQY and luminescence stability under high temperature (90 °C) and high flux excitation (386 mW cm-2). The blue light-excitable MHPs with a near unity PLQY, strong stability, and low PLQY deterioration are further encapsulated into a laser-driven LCD device. This prototype demonstrates excellent color gamut (132% NTSC, 98% Rec. 2020), illuminance intensity (>10 000 lux), and energy consumption (47.5% of commercial consumption), and hence is expected to be beneficial for the reduction of energy consumption in backlight display devices, particularly in large-screen outdoor displays.

A practical framework RNMF for exploring the association between mutational signatures and genes using gene cumulative contribution abundance.

BACKGROUND: Mutational signatures are somatic mutation patterns enriching operational mutational processes, which can provide abundant information about the mechanism of cancer. However, understanding of the pathogenic biological processes is still limited, such as the association between mutational signatures and genes. METHODS: We developed a simple and practical R package called RNMF (https://github.com/zhenzhang-li/RNMF) for mutational signature analysis, including a key model of cumulative contribution abundance (CCA), which was designed to highlight the association between mutational signatures and genes and then applying it to a meta-analysis of 1073 individuals with esophageal squamous cell carcinoma (ESCC). RESULTS: We revealed a number of known and previously undescribed SBS or ID signatures, and we found that APOBEC signatures (SBS2* and SBS13*) were closely associated with PIK3CA mutation, especially the E545k mutation. Furthermore, we found that age signature is closely related to the frequent mutation of TP53, of which R342* is highlighted due to strongly linked to age signature. In addition, the CCA matrix image data of genes in the signatures New, SBS3*, and SBS17b* were helpful for the preliminary evaluation of shortened survival outcome. These results can be extended to estimate the distribution of mutations or features, and study the potential impact of clinical factors. CONCLUSIONS: In a word, RNMF can successfully achieve the correlation analysis of mutational signatures and genes, proving a strong theoretical basis for the study of mutational processes during tumor development.

Efficient and accurate identification of ear diseases using an ensemble deep learning model.

Early detection and appropriate medical treatment are of great use for ear disease. However, a new diagnostic strategy is necessary for the absence of experts and relatively low diagnostic accuracy, in which deep learning plays an important role. This paper puts forward a mechanic learning model which uses abundant otoscope image data gained in clinical cases to achieve an automatic diagnosis of ear diseases in real time. A total of 20,542 endoscopic images were employed to train nine common deep convolution neural networks. According to the characteristics of the eardrum and external auditory canal, eight kinds of ear diseases were classified, involving the majority of ear diseases, such as normal, Cholestestoma of the middle ear, Chronic suppurative otitis media, External auditory cana bleeding, Impacted cerumen, Otomycosis external, Secretory otitis media, Tympanic membrane calcification. After we evaluate these optimization schemes, two best performance models are selected to combine the ensemble classifiers with real-time automatic classification. Based on accuracy and training time, we choose a transferring learning model based on DensNet-BC169 and DensNet-BC1615, getting a result that each model has obvious improvement by using these two ensemble classifiers, and has an average accuracy of 95.59%. Considering the dependence of classifier performance on data size in transfer learning, we evaluate the high accuracy of the current model that can be attributed to large databases. Current studies are unparalleled regarding disease diversity and diagnostic precision. The real-time classifier trains the data under different acquisition conditions, which is suitable for real cases. According to this study, in the clinical case, the deep learning model is of great use in the early detection and remedy of ear diseases.

La2O2CO3:Tb3+ one-dimensional nanorod with green persistent luminescence.

Trivalent terbium-doped oxycarbonate (La2O2CO3:1%Tb3+) one-dimensional nanorods are synthesized via a facile precipitation method. The average length of La2O2CO3:1%Tb3+ nanorods is 184.5 nm. Doping Tb3+ ions led to several visible emission peaks at 486 nm, 542 nm, and 587 nm under excitation of 258 nm wavelength light. The green afterglow at 542 nm can be detected almost 600 s after ceasing the UV-light irradiation. It can be calculated that the La2O2CO3:1%Tb3+ sample has one shallow trap depth (E = 0.848 eV) by measuring the thermoluminescence. All the results indicate that a simple precipitation method can synthesize a one-dimensional nanorod with green persistent luminescence.

Study of the structural and the spectral characteristics of [C3N3(NH2)3]n (n = 1-4) clusters.

Previous research has shown that interactions between melamine molecules within a cluster can give rise to the molecular self-assembly and that the spectral characteristic of melamine can be used to inspect melamine in a carrier. Although the structural and spectral characteristics of an isolated single melamine molecule and the molecular arrays on metal or semiconductor surfaces have been studied extensively, little is known about that of isolated multimolecular melamine clusters. In this work, density functional theory (DFT) calculations at the ω-B97XD/6-311++G(d,p) level were performed to study the structural and spectral characteristics of isolated melamine clusters [C3N3(NH2)3]n (n = 1-4) in the ground state. The calculation shows that a ground-state single melamine molecule takes a quasi-planar structure. The C and N atoms of the molecule are in one plane, which we call the molecular plane, while the H atoms deviate slightly from the molecular plane. When melamine molecules gather to form a cluster, the intermolecular hydrogen bonds (IHBs) N-H···N will arise, with the lengths of H···N in the range from 1.960 to 1.970 Å; the length of N-H will be elongated to 1.022 Å from its original of 1.004 Å, the N-H···N bond angles will be about 176°, and the average single-bond binding energy will be approximately -0.285 eV. In a multimolecular cluster, each melamine molecule still takes the quasi-planar structure. Each molecular plane in the cluster retains a planar structure, and some H atoms diverge more from their molecular planes. The molecular planes in a cluster are not coplanar, and the dihedral angle between the molecular planes of two neighboring melamine molecules ranges from 38 to 40°. In addition, the theoretical study of the infrared (IR) spectrum and nuclear magnetic resonance (NMR) spectrum of [C3N3(NH2)3]n (n = 1-4) was conducted. The results confirm the existence of IHBs in a multimolecular melamine cluster and reveal the symmetry of the electron cloud distribution in the melamine clusters. Experimental study of the IR for solid-state melamine and (13)C NMR spectra for both solid- and liquid-state melamine samples were also carried out, in which the corresponding spectral characteristics of [C3N3(NH2)3]n (n = 2-4) clusters deduced from theoretical study were observed. Findings of this study may serve as theoretical references for future identification and utilization of melamine clusters.