Germylenes Exhibiting Solid-State Emissions that Extend to NIR.

Low-valent main group compounds that fluoresce in the solid-state were previously unknown. To address this, we investigated room-temperature photoluminescence from a series of crystals of germylenes 3-8 in this article; they exhibited emissions nearly reaching the NIR. Germylene carboxylates (3-8) were synthesized by reacting dipyrromethene stabilized germylene pyrrolide (2) with carboxylic acids such as acetic acid, trifluoroacetic acid, benzoic acid, p-cyanobenzoic acid, p-nitrobenzoic acid, and acetylsalicylic acid.

Aza-Dipyrrinate Stabilized Compounds with a Low-Valent Main Group Element.

The possibility of using aza-dipyrromethene (a-DPM) ligands to stabilize compounds containing low-valent main group elements is demonstrated through the isolation of germylenes, a-DPM(p-tol)GeCl (2), a-DPM(Naph)GeCl (6), and a-DPM(Naph)GeN(TMS)2 (7) (tol=tolyl, Naph=naphthyl). Because of the presence of the a-DPM ligand, these germylenes exhibit an absorption maximum at around 640 nm, a highly red-shifted value previously unknown for germylenes.

Autophagy in acute myeloid leukemia: a paradoxical role in chemoresistance

Autophagy is a lysosomal degradation pathway that is constitutively active in almost every cell of our body at basal level. This self-eating process primarily serves to remove superfluous constituents of the cells and recycle the degraded products. Autophagy plays an essential role in cell homeostasis and can be enhanced in response to stressful conditions. Impairment in the regulation of the autophagic pathway is implicated in pathological conditions such as neurodegeneration, cardiac disorders, and cancer. However, the role of autophagy in cancer initiation and development is controversial and context-dependent. Evidence from various studies has shown that autophagy serves dual purpose and may assist in cancer progression or suppression. In the early stages of cancer initiation, autophagy acts as a quality control mechanism and prevents cancer development. When cancer is established and progresses to a later stage, autophagy helps in the survival of these cells through adaptation to stresses, including exposure to anti-cancer drugs. In this review, we highlight various studies on autophagic pathways and describe the role of autophagy in cancer, specifically acute myeloid leukemia (AML). We also discuss the prognostic significance of autophagy genes involved in AML leukemogenesis and implications in conferring resistance to chemotherapy. (AU)

LungINFseg: Segmenting COVID-19 Infected Regions in Lung CT Images Based on a Receptive-Field-Aware Deep Learning Framework.

COVID-19 is a fast-growing disease all over the world, but facilities in the hospitals are restricted. Due to unavailability of an appropriate vaccine or medicine, early identification of patients suspected to have COVID-19 plays an important role in limiting the extent of disease. Lung computed tomography (CT) imaging is an alternative to the RT-PCR test for diagnosing COVID-19. Manual segmentation of lung CT images is time consuming and has several challenges, such as the high disparities in texture, size, and location of infections. Patchy ground-glass and consolidations, along with pathological changes, limit the accuracy of the existing deep learning-based CT slices segmentation methods. To cope with these issues, in this paper we propose a fully automated and efficient deep learning-based method, called LungINFseg, to segment the COVID-19 infections in lung CT images. Specifically, we propose the receptive-field-aware (RFA) module that can enlarge the receptive field of the segmentation models and increase the learning ability of the model without information loss. RFA includes convolution layers to extract COVID-19 features, dilated convolution consolidated with learnable parallel-group convolution to enlarge the receptive field, frequency domain features obtained by discrete wavelet transform, which also enlarges the receptive field, and an attention mechanism to promote COVID-19-related features. Large receptive fields could help deep learning models to learn contextual information and COVID-19 infection-related features that yield accurate segmentation results. In our experiments, we used a total of 1800+ annotated CT slices to build and test LungINFseg. We also compared LungINFseg with 13 state-of-the-art deep learning-based segmentation methods to demonstrate its effectiveness. LungINFseg achieved a dice score of 80.34% and an intersection-over-union (IoU) score of 68.77%-higher than the ones of the other 13 segmentation methods. Specifically, the dice and IoU scores of LungINFseg were 10% better than those of the popular biomedical segmentation method U-Net.

Fully Automated Breast Density Segmentation and Classification Using Deep Learning.

Breast density estimation with visual evaluation is still challenging due to low contrast and significant fluctuations in the mammograms' fatty tissue background. The primary key to breast density classification is to detect the dense tissues in the mammographic images correctly. Many methods have been proposed for breast density estimation; nevertheless, most of them are not fully automated. Besides, they have been badly affected by low signal-to-noise ratio and variability of density in appearance and texture. This study intends to develop a fully automated and digitalized breast tissue segmentation and classification using advanced deep learning techniques. The conditional Generative Adversarial Networks (cGAN) network is applied to segment the dense tissues in mammograms. To have a complete system for breast density classification, we propose a Convolutional Neural Network (CNN) to classify mammograms based on the standardization of Breast Imaging-Reporting and Data System (BI-RADS). The classification network is fed by the segmented masks of dense tissues generated by the cGAN network. For screening mammography, 410 images of 115 patients from the INbreast dataset were used. The proposed framework can segment the dense regions with an accuracy, Dice coefficient, Jaccard index of 98%, 88%, and 78%, respectively. Furthermore, we obtained precision, sensitivity, and specificity of 97.85%, 97.85%, and 99.28%, respectively, for breast density classification. This study's findings are promising and show that the proposed deep learning-based techniques can produce a clinically useful computer-aided tool for breast density analysis by digital mammography.