Imaging-based deep learning in kidney diseases: recent progress and future prospects.

Kidney diseases result from various causes, which can generally be divided into neoplastic and non-neoplastic diseases. Deep learning based on medical imaging is an established methodology for further data mining and an evolving field of expertise, which provides the possibility for precise management of kidney diseases. Recently, imaging-based deep learning has been widely applied to many clinical scenarios of kidney diseases including organ segmentation, lesion detection, differential diagnosis, surgical planning, and prognosis prediction, which can provide support for disease diagnosis and management. In this review, we will introduce the basic methodology of imaging-based deep learning and its recent clinical applications in neoplastic and non-neoplastic kidney diseases. Additionally, we further discuss its current challenges and future prospects and conclude that achieving data balance, addressing heterogeneity, and managing data size remain challenges for imaging-based deep learning. Meanwhile, the interpretability of algorithms, ethical risks, and barriers of bias assessment are also issues that require consideration in future development. We hope to provide urologists, nephrologists, and radiologists with clear ideas about imaging-based deep learning and reveal its great potential in clinical practice.Critical relevance statement The wide clinical applications of imaging-based deep learning in kidney diseases can help doctors to diagnose, treat, and manage patients with neoplastic or non-neoplastic renal diseases.Key points• Imaging-based deep learning is widely applied to neoplastic and non-neoplastic renal diseases.• Imaging-based deep learning improves the accuracy of the delineation, diagnosis, and evaluation of kidney diseases.• The small dataset, various lesion sizes, and so on are still challenges for deep learning.

Clinical Applications and Recent Updates of Simultaneous Multi-slice Technique in Accelerated MRI.

Simultaneous multi-slice (SMS) is a magnetic resonance imaging (MRI) acceleration technique that utilizes multi-band radio-frequency pulses to simultaneously excite and encode multiple slices. Currently, SMS has been widely studied and applied in the MRI examination to reduce acquisition time, which can significantly improve the examination efficiency and patient throughput. Moreover, SMS technique can improve spatial resolution, which is of great value in disease diagnosis, treatment response monitoring, and prognosis prediction. This review will briefly introduce the technical principles of SMS, and summarize its current clinical applications. More importantly, we will discuss the recent technical progress and future research direction of SMS, hoping to highlight the clinical value and scientific potential of this technique.

Cu-Catalyzed C(sp3) Amination of Unactivated Secondary Alkyl Iodides Promoted by Diaryliodonium Salts.

Herein, we describe the development of a copper-catalyzed C(sp3) amination of unactivated secondary alkyl iodides mediated by diaryliodonium salts. Our protocol is enabled by the intermediacy of aryl radical species that undergo halogen atom transfer prior to interfacing with copper catalysts, thus setting the basis for a C-N bond formation at sp3-hybridized carbons. The method is characterized by its mild reaction conditions, excellent regioselectivity, and wide substrate scope.

Copper-Catalyzed C(sp3 )-Amination of Ketone-Derived Dihydroquinazolinones by Aromatization-Driven C-C Bond Scission.

Herein, we describe the development of a copper-catalyzed C(sp3 )-amination of proaromatic dihydroquinazolinones derived from ketones. The reaction is enabled by the intermediacy of open-shell species arising from homolytic C-C bond-cleavage driven by aromatization. The protocol is characterized by its operational simplicity and generality, including chemical diversification of advanced intermediates.

Dihydroquinazolinones as adaptative C(sp3) handles in arylations and alkylations via dual catalytic C-C bond-functionalization.

C-C bond forming cross-couplings are convenient technologies for the construction of functional molecules. Consequently, there is continual interest in approaches that can render traditionally inert functionality as cross-coupling partners, included in this are ketones which are widely-available commodity chemicals and easy to install synthetic handles. Herein, we describe a dual catalytic strategy that utilizes dihydroquinazolinones derived from ketone congeners as adaptative one-electron handles for forging C(sp3) architectures via α C-C cleavage with aryl and alkyl bromides. Our approach is achieved by combining the flexibility and modularity of nickel catalysis with the propensity of photoredox events for generating open-shell reaction intermediates. This method is distinguished by its wide scope and broad application profile--including chemical diversification of advanced intermediates--, providing a catalytic technique complementary to existing C(sp3) cross-coupling reactions that operates within the C-C bond-functionalization arena.

Contrasting dynamics and factor controls in leaf compared with different-diameter fine root litter decomposition in secondary forests in the Qinling Mountains after 5 years of whole-tree harvesting.

Plant litter decomposition is a crucial pathway for carbon (C) and nutrient cycling, and controls the net primary productivity in ecosystems worldwide. However, little is known about how multi-type litter (leaf and different diameter fine roots) decomposition rates and nutrient release change at the community level following whole-tree harvesting (WTH). In the present study, we followed decomposition of leaf and different diameter fine root (∅ < 0.5 mm, 0.5-1 mm, 1-2 mm) litters at plot level over 2 years in a secondary forest in the Qinling Mountains after 5 years of five different thinning treatments (0%, 15%, 30%, 45%, and 60%). Our results demonstrated that WTH had no effects on leaf and different diameter fine root litter decomposition at the plot level. Leaves had significantly higher decomposition rate than different diameter fine roots. There were significant positive correlations between decomposition rate of different diameter fine roots, but not related to leaf litter decomposition rate. WTH did not affect the nutrient release of leaf and different diameter fine root litters at the plot level. The nitrogen (N), phosphorous (P) and potassium (K) mass remaining in leaf litters were significantly higher than different diameter fine roots after 2 years decomposition, while different diameter fine roots had higher C mass remaining. Leaf and fine root litter decomposition rates were mainly influenced by stand and litter quality attributes. Nutrient release of leaf and fine root N, P and K were mainly predicted by litter quality characteristics, while there were no consistent driving factors for C release. Our results suggested that WTH had no effects on multi-type litter decomposition and nutrient release at plot level after 5 years of recovery. Moreover, leaf litters had excellent N, P and K nutrient preservation mechanisms, and C conservation in fine root litters.

Dual Catalytic Strategy for Forging sp2-sp3 and sp3-sp3 Architectures via ß-Scission of Aliphatic Alcohol Derivatives.

A dual platform for forging sp2-sp3 and sp3-sp3 carbon bonds via catalytic ß-scission of aliphatic alcohol derivatives with both aryl and alkyl halides is disclosed. This protocol is distinguished by its wide substrate scope and broad applicability, even in the context of late-stage functionalization.

Alkenyl Exchange of Allylamines via Nickel(0)-Catalyzed C-C Bond Cleavage.

A functional group exchange reaction between allylamines and alkenes via nickel-catalyzed C-C bond cleavage and formation was developed. This reaction provides a novel protocol, which does not require the use of unstable imine substrates, for the synthesis of allylamines, which are widely used in the production of fine chemicals, pharmaceuticals, and agrochemicals.