Improved predictions of total kidney volume growth rate in ADPKD using two-parameter least squares fitting.

Mayo Imaging Classification (MIC) for predicting future kidney growth in autosomal dominant polycystic kidney disease (ADPKD) patients is calculated from a single MRI/CT scan assuming exponential kidney volume growth and height-adjusted total kidney volume at birth to be 150 mL/m. However, when multiple scans are available, how this information should be combined to improve prediction accuracy is unclear. Herein, we studied ADPKD subjects ( n = 36 ) with 8+ years imaging follow-up (mean = 11 years) to establish ground truth kidney growth trajectory. MIC annual kidney growth rate predictions were compared to ground truth as well as 1- and 2-parameter least squares fitting. The annualized mean absolute error in MIC for predicting total kidney volume growth rate was 2.1 % ± 2 % compared to 1.1 % ± 1 % ( p = 0.002 ) for a 2-parameter fit to the same exponential growth curve used for MIC when 4 measurements were available or 1.4 % ± 1 % ( p = 0.01 ) with 3 measurements averaging together with MIC. On univariate analysis, male sex ( p = 0.05 ) and PKD2 mutation ( p = 0.04 ) were associated with poorer MIC performance. In ADPKD patients with 3 or more CT/MRI scans, 2-parameter least squares fitting predicted kidney volume growth rate better than MIC, especially in males and with PKD2 mutations where MIC was less accurate.

Novel biaryloxazolidinone derivatives with broad-spectrum antibacterial activity, favorable drug-like profiles and in vivo efficacy against linezolid-resistant Staphylococcusaureus.

The emergence of multidrug-resistant bacteria along with a declining pipeline of clinically useful antibiotics has led to the urgent need for the development of more effective antibacterial agents to treat drug-resistant bacteria. We previously discovered compound OB-158 with potent antibacterial activity but exhibited poor oral bioavailability. Herein, a systematic structural optimization of OB-158 to improve pharmacokinetic profiles yielded 26 novel biaryloxazolidinone analogues, and their activities against Gram-positive S. aureus, multidrug resistant S. aureus and Enterococcus faecalis were evaluated. Remarkably, compound 8b was identified with potent antibacterial activity against S. aureus (MIC = 0.06 µg/mL), MSSA (MIC = 0.125 µg/mL), MRSA (MIC = 0.06 µg/mL), LRSA (MIC = 0.125 µg/mL) and LREFa (MIC = 0.5 µg/mL). Compound 8b was demonstrated as a promising candidate through druglikeness evaluation including metabolism in microsomes and plasma, Caco-2 cell permeability, plasma protein binding, cytotoxicity, and inhibition of CYP450 and human monoamine oxidase. Notably, compound 8b displayed excellent PK profile with appropriate T1/2 of 1.49 h, high peak plasma concentration (Cmax = 2320 ng/mL), high plasma exposure (AUC0-t = 8310 h ng/mL), and superior oral bioavailability (F = 68.1 %) in Sprague-Dawley rats. Ultimately, in vivo efficacy of compound 8b in a mouse model of LRSA systemic infection was also demonstrated. Taken together, compound 8b represents a promising drug candidate for the treatment of linezolid-resistant Gram-positive bacterial strains infection.

A Primer for Utilizing Deep Learning and Abdominal MRI Imaging Features to Monitor Autosomal Dominant Polycystic Kidney Disease Progression.

Abdominal imaging of autosomal dominant polycystic kidney disease (ADPKD) has historically focused on detecting complications such as cyst rupture, cyst infection, obstructing renal calculi, and pyelonephritis; discriminating complex cysts from renal cell carcinoma; and identifying sources of abdominal pain. Many imaging features of ADPKD are incompletely evaluated or not deemed to be clinically significant, and because of this, treatment options are limited. However, total kidney volume (TKV) measurement has become important for assessing the risk of disease progression (i.e., Mayo Imaging Classification) and predicting tolvaptan treatment's efficacy. Deep learning for segmenting the kidneys has improved these measurements' speed, accuracy, and reproducibility. Deep learning models can also segment other organs and tissues, extracting additional biomarkers to characterize the extent to which extrarenal manifestations complicate ADPKD. In this concept paper, we demonstrate how deep learning may be applied to measure the TKV and how it can be extended to measure additional features of this disease.

Discovery and optimization of dihydropteridone derivatives as novel PLK1 and BRD4 dual inhibitor for the treatment of cancer.

In this study, we have designed, synthesized and tested three series of novel dihydropteridone derivatives possessing isoindolin-1-one or isoindoline moieties as potent inhibitors of PLK1/BRD4. Remarkably, most of the compounds showed preferable inhibitory activity against PLK1 and BRD4. Compound SC10 exhibited excellent inhibitory activity with IC50 values of 0.3 nM and 60.8 nM against PLK1 and BRD4, respectively. Meanwhile, it demonstrated significant anti-proliferative activities against three tumor-derived cell lines (MDA-MB-231 IC50 = 17.3 nM, MDA-MB-361 IC50 = 8.4 nM, and MV4-11 IC50 = 5.4 nM). Moreover, SC10 exhibited moderate rat liver microsomal stability (CLint = 21.3 µL·min-1·mg-1), acceptable pharmacokinetic profile (AUC0-t = 657 ng·h·mL-1, oral bioavailability of 21.4 %) in Sprague-Dawley rats, reduced hERG toxicity, acceptable PPB and CYP450 inhibition. Further research indicated that SC10 could induce MV4-11 cell arrest at the S phase and apoptosis in a dose-dependent manner. This investigation provided us with an initial point for developing novel anticancer agents as dual inhibitors of PLK1 and BRD4.

Test Retest Reproducibility of Organ Volume Measurements in ADPKD Using 3D Multimodality Deep Learning.

RATIONALE AND OBJECTIVES: Following autosomal dominant polycystic kidney disease (ADPKD) progression by measuring organ volumes requires low measurement variability. The objective of this study is to reduce organ volume measurement variability on MRI of ADPKD patients by utilizing all pulse sequences to obtain multiple measurements which allows outlier analysis to find errors and averaging to reduce variability. MATERIALS AND METHODS: In order to make measurements on multiple pulse sequences practical, a 3D multi-modality multi-class segmentation model based on nnU-net was trained/validated using T1, T2, SSFP, DWI and CT from 413 subjects. Reproducibility was assessed with test-re-test methodology on ADPKD subjects (n = 19) scanned twice within a 3-week interval correcting outliers and averaging the measurements across all sequences. Absolute percent differences in organ volumes were compared to paired students t-test. RESULTS: Dice similarlity coefficient > 97%, Jaccard Index > 0.94, mean surface distance < 1 mm and mean Hausdorff Distance < 2 cm for all three organs and all five sequences were found on internal (n = 25), external (n = 37) and test-re-test reproducibility assessment (38 scans in 19 subjects). When averaging volumes measured from five MRI sequences, the model automatically segmented kidneys with test-re-test reproducibility (percent absolute difference between exam 1 and exam 2) of 1.3% which was better than all five expert observers. It reliably stratified ADPKD into Mayo Imaging Classification (area under the curve=100%) compared to radiologist. CONCLUSION: 3D deep learning measures organ volumes on five MRI sequences leveraging the power of outlier analysis and averaging to achieve 1.3% total kidney test-re-test reproducibility.

Interface design for all-solid-state lithium batteries.

The operation of high-energy all-solid-state lithium-metal batteries at low stack pressure is challenging owing to the Li dendrite growth at the Li anodes and the high interfacial resistance at the cathodes1-4. Here we design a Mg16Bi84 interlayer at the Li/Li6PS5Cl interface to suppress the Li dendrite growth, and a F-rich interlayer on LiNi0.8Mn0.1Co0.1O2 (NMC811) cathodes to reduce the interfacial resistance. During Li plating-stripping cycles, Mg migrates from the Mg16Bi84 interlayer to the Li anode converting Mg16Bi84 into a multifunctional LiMgSx-Li3Bi-LiMg structure with the layers functioning as a solid electrolyte interphase, a porous Li3Bi sublayer and a solid binder (welding porous Li3Bi onto the Li anode), respectively. The Li3Bi sublayer with its high ionic/electronic conductivity ratio allows Li to deposit only on the Li anode surface and grow into the porous Li3Bi sublayer, which ameliorates pressure (stress) changes. The NMC811 with the F-rich interlayer converts into F-doped NMC811 cathodes owing to the electrochemical migration of the F anion into the NMC811 at a high potential of 4.3 V stabilizing the cathodes. The anode and cathode interlayer designs enable the NMC811/Li6PS5Cl/Li cell to achieve a capacity of 7.2 mAh cm-2 at 2.55 mA cm-2, and the LiNiO2/Li6PS5Cl/Li cell to achieve a capacity of 11.1 mAh cm-2 with a cell-level energy density of 310 Wh kg-1 at a low stack pressure of 2.5 MPa. The Mg16Bi84 anode interlayer and F-rich cathode interlayer provide a general solution for all-solid-state lithium-metal batteries to achieve high energy and fast charging capability at low stack pressure.

Design and Synthesis of 1,3,5-Triazines or Pyrimidines Containing Dithiocarbamate Moiety as PI3Kα Selective Inhibitors.

Recent studies have shown that phosphoinositide 3-kinase (PI3K) plays a vital role in cell division, and it has become a therapeutic target for many cancers. In this paper, some new 1,3,5-triazine or pyrimidine skeleton derivatives containing dithiocarbamate were designed and synthesized based on the reasonable drug design strategy from the previously effective compound 2-(difluoromethyl)-1-[4,6-di(4-morpholinyl)-1,3,5-triazin-2-yl]-1H-benzimidazole (ZSTK-474), in order to get effective selective PI3Kα inhibitors that have not been reported in the literature. In addition, the inhibitory activities of these compounds on PI3Kα and two tumor cell lines in vitro (HCT-116, U87-MG) were evaluated. The representative compound 13 showed a half-maximal inhibitory concentration (IC50) value of 1.2 nM for PI3Kα and an exciting kinase selectivity. Compound 13 displayed strong efficacy in HCT-116 and U87-MG cell lines with IC50 values of 0.83 and 1.25 µM, respectively. In addition, compound 13 induced obvious tumor regression in the U87-MG cell line xenografts mouse model, with no obvious signs of toxicity after intraperitoneal injection at a dose of 40 mg/kg. Compound 13 can be an effective selective inhibitor of PI3Kα, and it provides patients with an opportunity to avoid the side effects related to the wider inhibition of the class I PI3K family.

Medicinal chemistry strategies targeting NLRP3 inflammasome pathway: A recent update from 2019 to mid-2023.

Nod-like receptor protein 3 (NLRP3), a therapeutic target that has a close relationship with inflammatory diseases, has drawn significant attention from researchers in the field. An increasing number of NLRP3 inhibitors have been reported since NLRP3 was identified as a biomarker and inflammatory therapeutic target. Inhibiting NLRP3 has been widely studied as therapeutics for the treatment of cryopyrin associated periodic syndrome (CAPS), inflammatory bowel disease (IBD), nonalcoholic steatohepatitis (NASH), arthrolithiasis, Alzheimer's disease (AD) and Parkinson's disease (PD). This review updates the recently reported (2019 to mid-2023) molecule inhibitors targeting the NLRP3 inflammasome pathway, summarizes their structure-activity relationships (SARs), and discusses the therapeutic effects on inflammatory diseases. I hope this review will contribute to the development of novel inhibitors targeting NLRP3 inflammasome pathway as potential drugs.

Clinical Quality Control of MRI Total Kidney Volume Measurements in Autosomal Dominant Polycystic Kidney Disease.

Total kidney volume measured on MRI is an important biomarker for assessing the progression of autosomal dominant polycystic kidney disease and response to treatment. However, we have noticed that there can be substantial differences in the kidney volume measurements obtained from the various pulse sequences commonly included in an MRI exam. Here we examine kidney volume measurement variability among five commonly acquired MRI pulse sequences in abdominal MRI exams in 105 patients with ADPKD. Right and left kidney volumes were independently measured by three expert observers using model-assisted segmentation for axial T2, coronal T2, axial single-shot fast spin echo (SSFP), coronal SSFP, and axial 3D T1 images obtained on a single MRI from ADPKD patients. Outlier measurements were analyzed for data acquisition errors. Most of the outlier values (88%) were due to breathing during scanning causing slice misregistration with gaps or duplication of imaging slices (n = 35), slice misregistration from using multiple breath holds during acquisition (n = 25), composing of two overlapping acquisitions (n = 17), or kidneys not entirely within the field of view (n = 4). After excluding outlier measurements, the coefficient of variation among the five measurements decreased from 4.6% pre to 3.2%. Compared to the average of all sequences without errors, TKV measured on axial and coronal T2 weighted imaging were 1.2% and 1.8% greater, axial SSFP was 0.4% greater, coronal SSFP was 1.7% lower and axial T1 was 1.5% lower than the mean, indicating intrinsic measurement biases related to the different MRI contrast mechanisms. In conclusion, MRI data acquisition errors are common but can be identified using outlier analysis and excluded to improve organ volume measurement consistency. Bias toward larger volume measurements on T2 sequences and smaller volumes on axial T1 sequences can also be mitigated by averaging data from all error-free sequences acquired.

Design, synthesis, and biological evaluation of novel dihydropteridone derivatives possessing oxadiazoles moiety as potent inhibitors of PLK1.

Polo like kinase 1 (PLK1) is a serine/threonine kinase that is widely distributed in eukaryotic cells and plays an important role in multiple phases of the cell cycle. Its importance in tumorigenesis has been increasingly recognized in recent years. Herein, we describe the optimization of a series of novel dihydropteridone derivatives (13a-13v and 21g-21l) possessing oxadiazoles moiety as potent inhibitors of PLK1. Compound 21g exhibited improved PLK1 inhibitory capability with an IC50 value of 0.45 nM and significant anti-proliferative activities against four tumor-derived cell lines (MCF-7 IC50 = 8.64 nM, HCT-116 IC50 = 26.0 nM, MDA-MB-231 IC50 = 14.8 nM and MV4-11 IC50 = 47.4 nM) with better pharmacokinetic characteristics than BI2536 in mice (AUC0-t = 11 227 ng h mL-1vs 556 ng h mL-1). Moreover, 21g exhibited moderate liver microsomal stability and excellent pharmacokinetic profile (AUC0-t = 11227 ng h mL-1, oral bioavailability of 77.4%) in Balb/c mice, acceptable PPB, improved PLK1 inhibitory selectivity, and no apparent toxicity was observed in the acute toxicity assay (20 mg/kg). Further investigation showed that 21 g could arrest HCT-116 cells in G2 phase and induce apoptosis in a dose-dependent manner. These results indicate that 21g is a promising PLK1 inhibitor.

Controlling Intermolecular Interaction and Interphase Chemistry Enabled Sustainable Water-tolerance LiMn2 O4 ||Li4 Ti5 O12 Batteries.

Solid electrolyte interphase (SEI) formation and H2 O activity reduction in Water-in-Salt electrolytes (WiSE) with an enlarged stability window of 3.0 V have provided the feasibility of the high-energy-density aqueous Li-ion batteries. Here, we extend the cathodic potential of WiSE by rationally controlling intermolecular interaction and interphase chemistry with the introduction of trimethyl phosphate (TMP) into WiSE. The TMP not merely limits the H2 O activity via the strong interaction between TMP and H2 O but also contributes to the formation of reinforced SEI involving phosphate and LiF by manipulating the Li+ solvation structure. Thus, water-tolerance LiMn2 O4 (LMO)||Li4 Ti5 O12 (LTO) full cell with a P/N ratio of 1.14 can be assembled and achieve a long cycling life of 1000 times with high coulombic efficiency of >99.9 %. This work provides a promising insight into the cost-effective practical manufacture of LMO||LTO cells without rigorous moisture-free requirements.

Enhancing Li+ Transport in NMC811||Graphite Lithium-Ion Batteries at Low Temperatures by Using Low-Polarity-Solvent Electrolytes.

LiNix Coy Mnz O2 (x+y+z=1)||graphite lithium-ion battery (LIB) chemistry promises practical applications. However, its low-temperature (≤ -20 °C) performance is poor because the increased resistance encountered by Li+ transport in and across the bulk electrolytes and the electrolyte/electrode interphases induces capacity loss and battery failures. Though tremendous efforts have been made, there is still no effective way to reduce the charge transfer resistance (Rct ) which dominates low-temperature LIBs performance. Herein, we propose a strategy of using low-polarity-solvent electrolytes which have weak interactions between the solvents and the Li+ to reduce Rct , achieving facile Li+ transport at sub-zero temperatures. The exemplary electrolyte enables LiNi0.8 Mn0.1 Co0.1 O2 ||graphite cells to deliver a capacity of ≈113 mAh g-1 (98 % full-cell capacity) at 25 °C and to remain 82 % of their room-temperature capacity at -20 °C without lithium plating at 1/3C. They also retain 84 % of their capacity at -30 °C and 78 % of their capacity at -40 °C and show stable cycling at 50 °C.

PTNet3D: A 3D High-Resolution Longitudinal Infant Brain MRI Synthesizer Based on Transformers.

An increased interest in longitudinal neurodevelopment during the first few years after birth has emerged in recent years. Noninvasive magnetic resonance imaging (MRI) can provide crucial information about the development of brain structures in the early months of life. Despite the success of MRI collections and analysis for adults, it remains a challenge for researchers to collect high-quality multimodal MRIs from developing infant brains because of their irregular sleep pattern, limited attention, inability to follow instructions to stay still during scanning. In addition, there are limited analytic approaches available. These challenges often lead to a significant reduction of usable MRI scans and pose a problem for modeling neurodevelopmental trajectories. Researchers have explored solving this problem by synthesizing realistic MRIs to replace corrupted ones. Among synthesis methods, the convolutional neural network-based (CNN-based) generative adversarial networks (GANs) have demonstrated promising performance. In this study, we introduced a novel 3D MRI synthesis framework- pyramid transformer network (PTNet3D)- which relies on attention mechanisms through transformer and performer layers. We conducted extensive experiments on high-resolution Developing Human Connectome Project (dHCP) and longitudinal Baby Connectome Project (BCP) datasets. Compared with CNN-based GANs, PTNet3D consistently shows superior synthesis accuracy and superior generalization on two independent, large-scale infant brain MRI datasets. Notably, we demonstrate that PTNet3D synthesized more realistic scans than CNN-based models when the input is from multi-age subjects. Potential applications of PTNet3D include synthesizing corrupted or missing images. By replacing corrupted scans with synthesized ones, we observed significant improvement in infant whole brain segmentation.

Formation of LiF-rich Cathode-Electrolyte Interphase by Electrolyte Reduction.

The capacity of transition metal oxide cathode for Li-ion batteries can be further enhanced by increasing the charging potential. However, these high voltage cathodes suffer from fast capacity decay because the large volume change of cathode breaks the active materials and cathode-electrolyte interphase (CEI), resulting in electrolyte penetration into broken active materials and continuous side reactions between cathode and electrolytes. Herein, a robust LiF-rich CEI was formed by potentiostatic reduction of fluorinated electrolyte at a low potential of 1.7 V. By taking LiCoO2 as a model cathode, we demonstrate that the LiF-rich CEI maintains the structural integrity and suppresses electrolyte penetration at a high cut-off potential of 4.6 V. The LiCoO2 with LiF-rich CEI exhibited a capacity of 198 mAh g-1 at 0.5C and an enhanced capacity retention of 63.5 % over 400 cycles as compared to the LiF-free LiCoO2 with only 17.4 % of capacity retention.

Fully transformer network for skin lesion analysis.

Automatic skin lesion analysis in terms of skin lesion segmentation and disease classification is of great importance. However, these two tasks are challenging as skin lesion images of multi-ethnic population are collected using various scanners in multiple international medical institutes. To address them, most recent works adopt convolutional neural networks (CNNs) for skin lesion analysis. However, due to the intrinsic locality of the convolution operator, CNNs lack the ability to capture contextual information and long-range dependency. To improve the baseline performance established by CNNs, we propose a Fully Transformer Network (FTN) to learn long-range contextual information for skin lesion analysis. FTN is a hierarchical Transformer computing features using Spatial Pyramid Transformer (SPT). SPT has linear computational complexity as it introduces a spatial pyramid pooling (SPP) module into multi-head attention (MHA)to largely reduce the computation and memory usage. We conduct extensive skin lesion analysis experiments to verify the effectiveness and efficiency of FTN using ISIC 2018 dataset. Our experimental results show that FTN consistently outperforms other state-of-the-art CNNs in terms of computational efficiency and the number of tunable parameters due to our efficient SPT and hierarchical network structure. The code and models will be public available at: https://github.com/Novestars/Fully-Transformer-Network.

Solid-State Electrolyte Design for Lithium Dendrite Suppression.

All-solid-state Li metal batteries have attracted extensive attention due to their high safety and high energy density. However, Li dendrite growth in solid-state electrolytes (SSEs) still hinders their application. Current efforts mainly aim to reduce the interfacial resistance, neglecting the intrinsic dendrite-suppression capability of SSEs. Herein, the mechanism for the formation of Li dendrites is investigated, and Li-dendrite-free SSE criteria are reported. To achieve a high dendrite-suppression capability, SSEs should be thermodynamically stable with a high interface energy against Li, and they should have a low electronic conductivity and a high ionic conductivity. A cold-pressed Li3 N-LiF composite is used to validate the Li-dendrite-free design criteria, where the highly ionic conductive Li3 N reduces the Li plating/stripping overpotential, and LiF with high interface energy suppresses dendrites by enhancing the nucleation energy and suppressing the Li penetration into the SSEs. The Li3 N-LiF layer coating on Li3 PS4 SSE achieves a record-high critical current of >6 mA cm-2 even at a high capacity of 6.0 mAh cm-2 . The Coulombic efficiency also reaches a record 99% in 150 cycles. The Li3 N-LiF/Li3 PS4 SSE enables LiCoO2 cathodes to achieve 101.6 mAh g-1 for 50 cycles. The design principle opens a new opportunity to develop high-energy all-solid-state Li metal batteries.

Dense Deconvolutional Network for Skin Lesion Segmentation.

Automatic delineation of skin lesion contours from dermoscopy images is a basic step in the process of diagnosis and treatment of skin lesions. However, it is a challenging task due to the high variation of appearances and sizes of skin lesions. In order to deal with such challenges, we propose a new dense deconvolutional network (DDN) for skin lesion segmentation based on residual learning. Specifically, the proposed network consists of dense deconvolutional layers (DDLs), chained residual pooling (CRP), and hierarchical supervision (HS). First, unlike traditional deconvolutional layers, DDLs are adopted to maintain the dimensions of the input and output images unchanged. The DDNs are trained in an end-to-end manner without the need of prior knowledge or complicated postprocessing procedures. Second, the CRP aims to capture rich contextual background information and to fuse multilevel features. By combining the local and global contextual information via multilevel feature fusion, the high-resolution prediction output is obtained. Third, HS is added to serve as an auxiliary loss and to refine the prediction mask. Extensive experiments based on the public ISBI 2016 and 2017 skin lesion challenge datasets demonstrate the superior segmentation results of our proposed method over the state-of-the-art methods.

Solid-State Electrolyte Anchored with a Carboxylated Azo Compound for All-Solid-State Lithium Batteries.

Organic electrode materials are promising for green and sustainable lithium-ion batteries. However, the high solubility of organic materials in the liquid electrolyte results in the shuttle reaction and fast capacity decay. Herein, azo compounds are firstly applied in all-solid-state lithium batteries (ASSLB) to suppress the dissolution challenge. Due to the high compatibility of azobenzene (AB) based compounds to Li3 PS4 (LPS) solid electrolyte, the LPS solid electrolyte is used to prevent the dissolution and shuttle reaction of AB. To maintain the low interface resistance during the large volume change upon cycling, a carboxylate group is added into AB to provide 4-(phenylazo) benzoic acid lithium salt (PBALS), which could bond with LPS solid electrolyte via the ionic bonding between oxygen in PBALS and lithium ion in LPS. The ionic bonding between the active material and solid electrolyte stabilizes the contact interface and enables the stable cycle life of PBALS in ASSLB.

Dense deconvolution net: Multi path fusion and dense deconvolution for high resolution skin lesion segmentation.

BACKGROUND: Dermoscopy imaging has been a routine examination approach for skin lesion diagnosis. Accurate segmentation is the first step for automatic dermoscopy image assessment. OBJECTIVE: The main challenges for skin lesion segmentation are numerous variations in viewpoint and scale of skin lesion region. METHODS: To handle these challenges, we propose a novel skin lesion segmentation network via a very deep dense deconvolution network based on dermoscopic images. Specifically, the deep dense layer and generic multi-path Deep RefineNet are combined to improve the segmentation performance. The deep representation of all available layers is aggregated to form the global feature maps using skip connection. Also, the dense deconvolution layer is leveraged to capture diverse appearance features via the contextual information. Finally, we apply the dense deconvolution layer to smooth segmentation maps and obtain final high-resolution output. RESULTS: Our proposed method shows the superiority over the state-of-the-art approaches based on the public available 2016 and 2017 skin lesion challenge dataset and achieves the accuracy of 96.0% and 93.9%, which obtained a 6.0% and 1.2% increase over the traditional method, respectively. CONCLUSIONS: By utilizing Dense Deconvolution Net, the average time for processing one testing images with our proposed framework was 0.253 s.