What Faculty and Students Value When Evaluating Human Digital Anatomy Platforms: A Mixed-Methods Study.

OBJECTIVES: There is an increasing availability of digital technologies for teaching and learning of human anatomy. Studies have shown that such applications allow for better spatial awareness than traditional methods. These digital human anatomy platforms offer users myriad features, such as the ability to manipulate 3D models, conduct prosection, investigate anatomical regions through virtual reality, or perform knowledge tests on themselves. This study examined what faculty members' value when using digital human anatomy platforms for teaching and what students value when using these platforms for learning. METHODS: Six anatomy faculty members and 21 students were selected to participate in this study. After using the three digital anatomy platforms for at least 1 week, a survey was conducted to record their feedback in 4 categories: usability, interactive features, level of detail, and learning support. Respondents' Qualitative feedback within each category was also analyzed to strengthen the study's findings. RESULTS: The study's findings showed that faculty members and students have different priorities when evaluating digital anatomy platforms. Faculty members valued platforms that provided better accuracy and detailed anatomical structures, while students prioritized usability above the rest of the features. CONCLUSION: Given that faculty and students have different preferences when selecting digital anatomy platforms, this article proposed that educators maximize the specific affordances offered by the technology by having a clear pedagogy and strategy on how the technology will be incorporated into the curriculum to help students achieve the desired learning outcomes.

Blind Super-Resolution Via Meta-Learning and Markov Chain Monte Carlo Simulation.

Learning based approaches have witnessed great successes in blind single image super-resolution (SISR) tasks, however, handcrafted kernel priors and learning based kernel priors are typically required. In this paper, we propose a Meta-learning and Markov Chain Monte Carlo based SISR approach to learn kernel priors from organized randomness. In concrete, a lightweight network is adopted as kernel generator, and is optimized via learning from the MCMC simulation on random Gaussian distributions. This procedure provides an approximation for the rational blur kernel, and introduces a network-level Langevin dynamics into SISR optimization processes, which contributes to preventing bad local optimal solutions for kernel estimation. Meanwhile, a meta-learning based alternating optimization procedure is proposed to optimize the kernel generator and image restorer, respectively. In contrast to the conventional alternating minimization strategy, a meta-learning based framework is applied to learn an adaptive optimization strategy, which is less-greedy and results in better convergence performance. These two procedures are iteratively processed in a plug-and-play fashion, for the first time, realizing a learning-based but plug-and-play blind SISR solution in unsupervised inference. Extensive simulations demonstrate the superior performance and generalization ability of the proposed approach when comparing with state-of-the-arts on synthesis and real-world datasets.

Design and analysis of a lower limb assistive exoskeleton robot.

BACKGROUND: In recent years, exoskeleton robot technology has developed rapidly. Exoskeleton robots that can be worn on a human body and provide additional strength, speed or other abilities. Exoskeleton robots have a wide range of applications, such as medical rehabilitation, logistics and disaster relief and other fields. OBJECTIVE: The study goal is to propose a lower limb assistive exoskeleton robot to provide extra power for wearers. METHODS: The mechanical structure of the exoskeleton robot was designed by using bionics principle to imitate human body shape, so as to satisfy the coordination of man-machine movement and the comfort of wearing. Then a gait prediction method based on neural network was designed. In addition, a control strategy according to iterative learning control was designed. RESULTS: The experiment results showed that the proposed exoskeleton robot can produce effective assistance and reduce the wearer's muscle force output. CONCLUSION: A lower limb assistive exoskeleton robot was introduced in this paper. The kinematics model and dynamic model of the exoskeleton robot were established. Tracking effects of joint angle displacement and velocity were analyzed to verify feasibility of the control strategy. The learning error of joint angle can be improved with increase of the number of iterations. The error of trajectory tracking is acceptable.

Design, synthesis and bioactivity evaluation of 4-hydroxycoumarin derivatives as potential anti-inflammatory agents against acute lung injury and colitis.

Acute lung injury (ALI) and inflammatory bowel disease (IBD) are common inflammatory illnesses that seriously affect people's health. Herein, a series of 4-hydroxylcoumarin (4-HC) derivatives were designed and synthesized. The inhibitory effects of these compounds on LPS-induced interleukin-6 (IL-6) release from J774A.1 cells were then screened via ELISA assay, compound B8 showed 3 times more active than the lead compound 4-HC. The most active compound B8 had the IC50 values of 4.57 µM and 6.51 µM for IL-6 release on mouse cells J774A.1 and human cells THP-1, respectively. Furthermore, we also found that B8 could act on the MAPK pathway. Based on the target prediction results of computer virtual docking, kinase inhibitory assay was carried out, and it revealed that targeting IRAK1 was a key mechanism for B8 to exert anti-inflammatory activity. Moreover, B8 exerted a good therapeutic effect on the dextran sulfate sodium (DSS)-induced colitis model and liposaccharide (LPS)-induced ALI mouse models. The acute toxicity experiments indicated that high-dose B8 caused no adverse reactions in mice, confirming its safety in vivo. Additionally, the preliminary pharmacokinetic (PK) parameters of B8 in SD rats were also examined, revealing a bioavailability (F) of 28.72 %. In conclusion, B8 is a potential candidate of drug for the treatment of ALI and colitis.

P3N-PIPO but not P3 is the avirulence determinant in melon carrying the Wmr resistance against watermelon mosaic virus, although they contain a common genetic determinant.

Viruses employ a series of diverse translational strategies to expand their coding capacity, which produces viral proteins with common domains and entangles virus-host interactions. P3N-PIPO, which is a transcriptional slippage product from the P3 cistron, is a potyviral protein dedicated to intercellular movement. Here, we show that P3N-PIPO from watermelon mosaic virus (WMV) triggers cell death when transiently expressed in Cucumis melo accession PI 414723 carrying the Wmr resistance gene. Surprisingly, expression of the P3N domain, shared by both P3N-PIPO and P3, can alone induce cell death, whereas expression of P3 fails to activate cell death in PI 414723. Confocal microscopy analysis revealed that P3N-PIPO targets plasmodesmata (PD) and P3N associates with PD, while P3 localizes in endoplasmic reticulum in melon cells. We also found that mutations in residues L35, L38, P41, and I43 of the P3N domain individually disrupt the cell death induced by P3N-PIPO, but do not affect the PD localization of P3N-PIPO. Furthermore, WMV mutants with L35A or I43A can systemically infect PI 414723 plants. These key residues guide us to discover some WMV isolates potentially breaking the Wmr resistance. Through searching the NCBI database, we discovered some WMV isolates with variations in these key sites, and one naturally occurring I43V variation enables WMV to systemically infect PI 414723 plants. Taken together, these results demonstrate that P3N-PIPO, but not P3, is the avirulence determinant recognized by Wmr, although the shared N terminal P3N domain can alone trigger cell death.IMPORTANCEThis work reveals a novel viral avirulence (Avr) gene recognized by a resistance (R) gene. This novel viral Avr gene is special because it is a transcriptional slippage product from another virus gene, which means that their encoding proteins share the common N-terminal domain but have distinct C-terminal domains. Amazingly, we found that it is the common N-terminal domain that determines the Avr-R recognition, but only one of the viral proteins can be recognized by the R protein to induce cell death. Next, we found that these two viral proteins target different subcellular compartments. In addition, we discovered some virus isolates with variations in the common N-terminal domain and one naturally occurring variation that enables the virus to overcome the resistance. These results show how viral proteins with common domains interact with a host resistance protein and provide new evidence for the arms race between plants and viruses.

Development and validation of a nomogram for predicting in-hospital death in cirrhotic patients with acute kidney injury.

BACKGROUND: The purpose of this study was to develop a nomogram for predicting in-hospital mortality in cirrhotic patients with acute kidney injury (AKI) in order to identify patients with a high risk of in-hospital death early. METHODS: This study collected data on cirrhotic patients with AKI from 2008 to 2019 using the Medical Information Mart for Intensive Care IV. Multivariate logistic regression was used to identify confounding factors related to in-hospital mortality, which were then integrated into the nomogram. The concordance index (C-Index) was used to evaluate the accuracy of the model predictions. The area under the curve (AUC) and decision curve analysis (DCA) was used to assess the predictive performance and clinical utility of the nomogram. RESULTS: The final study population included 886 cirrhotic patients with AKI, and 264 (29.8%) died in the hospital. After multivariate logistic regression, age, gender, cerebrovascular disease, heart rate, respiration rate, temperature, oxygen saturation, hemoglobin, blood urea nitrogen, serum creatinine, international normalized ratio, bilirubin, urine volume, and sequential organ failure assessment score were predictive factors of in-hospital mortality. In addition, the nomogram showed good accuracy in estimating the in-hospital mortality of patients. The calibration plots showed the best agreement with the actual presence of in-hospital mortality in patients. In addition, the AUC and DCA curves showed that the nomogram has good prediction accuracy and clinical value. CONCLUSIONS: We have created a prognostic nomogram for predicting in-hospital death in cirrhotic patients with AKI, which may facilitate timely intervention to improve prognosis in these patients.

Integrated machine learning reveals aquatic biological integrity patterns in semi-arid watersheds.

Semi-arid regions present unique challenges for maintaining aquatic biological integrity due to their complex evolutionary mechanisms. Uncovering the spatial patterns of aquatic biological integrity in these areas is a challenging research task, especially under the compound environmental stress. Our goal is to address this issue with a scientifically rigorous approach. This study aims to explore the spatial analysis and diagnosis method of aquatic biological based on the combination of machine learning and statistical analysis, so as to reveal the spatial differentiation patterns and causes of changes of aquatic biological integrity in semi-arid regions. To this end, we have introduced an innovative approach that combines XGBoost-SHAP and Fuzzy C-means clustering (FCM), we successfully identified and diagnosed the spatial variations of aquatic biological integrity in the Wei River Basin (WRB). The study reveals significant spatial variations in species number, diversity, and aquatic biological integrity of phytoplankton, serving as a testament to the multifaceted responses of biological communities under the intricate tapestry of environmental gradients. Delving into the depths of the XGBoost-SHAP algorithm, we discerned that Annual average Temperature (AT) stands as the pivotal driver steering the spatial divergence of the Phytoplankton Integrity Index (P-IBI), casting a positive influence on P-IBI when AT is below 11.8 °C. The intricate interactions between hydrological variables (VF and RW) and AT, as well as between water quality parameters (WT, NO3-N, TP, COD) and AT, collectively sculpt the spatial distribution of P-IBI. The fusion of XGBoost-SHAP with FCM unveils pronounced north-south gradient disparities in aquatic biological integrity across the watershed, segmenting the region into four distinct zones. This establishes scientific boundary conditions for the conservation strategies and management practices of aquatic ecosystems in the region, and its flexibility is applicable to the analysis of spatial heterogeneity in other complex environmental contexts.

Traffic trajectory data analysis technology based on HMM model map matching algorithm.

The rapid growth of traffic trajectory data and the development of positioning technology have driven the demand for its analysis. However, in the current application scenarios, there are some problems such as the deviation between positioning data and real roads and low accuracy of existing trajectory data traffic prediction models. Therefore, a map matching algorithm based on hidden Markov models is proposed in this study. The algorithm starts from the global route, selects K nearest candidate paths, and identifies candidate points through the candidate paths. It uses changes in speed, angle, and other information to generate a state transition matrix that match trajectory points to the actual route. When processing trajectory data in the experiment, K = 5 is selected as the optimal value, the algorithm takes 51 ms and the accuracy is 95.3%. The algorithm performed well in a variety of road conditions, especially in parallel and mixed road sections, with an accuracy rate of more than 96%. Although the time loss of this algorithm is slightly increased compared with the traditional algorithm, its accuracy is stable. Under different road conditions, the accuracy of the algorithm is 98.3%, 97.5%, 94.8% and 96%, respectively. The accuracy of the algorithm based on traditional hidden Markov models is 95.9%, 95.7%, 95.4% and 94.6%, respectively. It can be seen that the accuracy of the algorithm designed has higher precision. The experiment proves that the map matching algorithms based on hidden Markov models is superior to other algorithms in terms of matching accuracy. This study makes the processing of traffic trajectory data more accurate.

Stromal lenticule addition keratoplasty with corneal crosslinking for corneal ectasia secondary to FS-LASIK: a case series.

AIM: To explore the clinical efficacy and safety of stromal lenticule addition keratoplasty (SLAK) with corneal crosslinking (CXL) on patients with corneal ectasia secondary to femtosecond laser-assisted in situ keratomileusis (FS-LASIK). METHODS: A series of 5 patients undertaking SLAK with CXL for the treatment of corneal ectasia secondary to FS-LASIK were followed for 4-9mo. The lenticules were collected from patients undertaking small incision lenticule extraction (SMILE) for the correction of myopia. Adding a stromal lenticule was aimed at improving the corneal thickness for the safe application of crosslinking and compensating for the thin cornea to improve its mechanical strength. RESULTS: All surgeries were conducted successfully with no significant complications. Their best corrected visual acuity (BCVA) ranged from 0.05 to 0.8-2 before surgery. The pre-operational total corneal thickness ranged from 345-404 µm and maximum keratometry (Kmax) ranged from 50.8 to 86.3. After the combination surgery, both the corneal keratometry (range 55.9 to 92.8) and total corneal thickness (range 413-482 µm) significantly increased. Four out of 5 patients had improvement of corneal biomechanical parameters (reflected by stiffness parameter A1 in Corvis ST). However, 3 patients showed decreased BCVA after surgery due to the development of irregular astigmatism and transient haze. Despite the onset of corneal edema right after SLAK, the corneal topography and thickness generally stabilized after 3mo. CONCLUSION: SLAK with CXL is a potentially beneficial and safe therapy for advanced corneal ectasia. Future work needs to address the poor predictability of corneal refractometry and compare the outcomes of different surgical modes.

Superfast Zincophilic Ion Conductor Enables Rapid Interfacial Desolvation Kinetics for Low-Temperature Zinc Metal Batteries.

Low-temperature rechargeable aqueous zinc metal batteries (AZMBs) as highly promising candidates for energy storage are largely hindered by huge desolvation energy barriers and depressive Zn2+ migration kinetics. In this work, a superfast zincophilic ion conductor of layered zinc silicate nanosheet (LZS) is constructed on a metallic Zn surface, as an artificial layer and ion diffusion accelerator. The experimental and simulation results reveal the zincophilic ability and layer structure of LZS not only promote the desolvation kinetics of [Zn(H2O)6]2+ but also accelerate the Zn2+ transport kinetics across the anode/electrolyte interface, guiding uniform Zn deposition. Benefiting from these features, the LZS-modified Zn anodes showcase long-time stability (over 3300 h) and high Coulombic efficiency with ≈99.8% at 2 mA cm-2, respectively. Even reducing the environment temperature down to 0 °C, ultralong cycling stability up to 3600 h and a distinguished rate performance are realized. Consequently, the assembled Zn@LZS//V2O5-x full cells deliver superior cyclic stability (344.5 mAh g-1 after 200 cycles at 1 A g-1) and rate capability (285.3 mAh g-1 at 10 A g-1) together with a low self-discharge rate, highlighting the bright future of low-temperature AZMBs.

H6N2 reassortant avian influenza virus isolate in wild birds in Jiangxi Province, China.

H6 avian influenza virus is widely prevalent in wild birds and poultry and has caused human infection in 2013 in Taiwan, China. During our active influenza surveillance program in wild waterfowl at Poyang Lake, Jiangxi Province, an H6N2 AIV was isolated and named A/bean goose/JiangXi/452-4/2013(H6N2). The isolate was characterized as a typical low pathogenic avian influenza virus (LPAIV) due to the presence of the amino acid sequence PQIETR↓GLFGAI at the cleavage site of the hemagglutinin (HA) protein. The genetic evolution analysis revealed that the NA gene of the isolate originated from North America and exhibited the highest nucleotide identity (99.29%) with a virus recovered from wild bird samples in North America, specifically A/bufflehead/California/4935/2012(H11N2). Additionally, while the HA and PB1 genes belonged to the Eurasian lineage, they displayed frequent genetic interactions with the North American lineage. The remaining genes showed close genetic relationships with Eurasian viruses. The H6N2 isolate possessed a complex genome, indicating it is a multi-gene recombinant virus with genetic material from both Eurasian and North American lineages.

Identification of rare and novel PHEX variants in X-linked hypophosphatemia.

CONTEXT: X-linked hypophosphatemia (XLH) is a rare metabolic bone disease caused by inactivation mutations in the PHEX gene. Despite the extensive number of reported PHEX variants, only a few cases of chromosomal abnormalities have been documented. OBJECTIVE: We aimed to identify the pathogenic variants in six unrelated families with a clinical diagnosis of XLH and to propose a genetic workflow for hypophosphatemia patients suspected of XLH. METHODS: Multiple genetic testing assays were used to analyze the six families' genetic profiles, including whole exome sequencing, multiplex ligation-dependent probe amplification, whole genome sequencing, reverse transcript polymerase chain reaction, Sanger sequencing, and karyotyping. RESULTS: The study identified six novel pathogenic variants, including one mosaic variant (exon 16-22 deletion), three chromosomal abnormalities (46, XN, inv[X][pter→p22.11::q21.31→p22.11::q21.31 →qter], 46, XN, inv[X][p22.11p22.11], and XXY), a nonclassical intron variant (NM_000444.6, c.1701_31A > G), and a deletion variant (NM_000444.6, c.64_5464-186 del5215) of PHEX. Additionally, a genetic testing workflow was proposed to aid in diagnosing patients suspected of XLH. CONCLUSION: Our research expands the mutation spectrum of PHEX and highlights the significance of utilizing multiple genetic testing methods to diagnose XLH.

Feasibility of Using Intraoperative Neurophysiological Monitoring for Detecting Bone Layer of Cervical Spine Surgery.

STUDY DESIGN: Intraoperative neurophysiological monitoring (IONM) as a guide to bone layer estimation was examined during posterior cervical spine lamina grinding. OBJECTIVE: To explore the feasibility of IONM to estimate bone layer thickness. SUMMARY OF BACKGROUND DATA: Cervical laminoplasty is a classic operation for cervical spondylosis. To increase safety and accuracy, surgery-assistant robots are currently being studied. It combines the advantages of various program awareness methods to form a feasible security strategy. In the field of spinal surgery, robots have been successfully used to help place pedicle screws. IONM is used to monitor intraoperative nerve conditions in spinal surgery. This study was designed to explore the feasibility of adding IONM to robot safety strategies. METHODS: Chinese miniature pig model was used. Electrodes were placed on the lamina, and the minimum stimulation threshold of DNEP for each lamina was measured (Intact lamina, IL). The laminae were ground to measure the DNEP threshold after incomplete grinding (Inner cortical bone preserved, ICP) and complete grinding (Inner cortical bone grinded, ICG). Subsequently, the lateral cervical mass screw canal drilling was performed, and the t-EMG threshold of the intact and perforated screw canals was measured and compared. RESULT: The threshold was significantly lower than that of the recommended threshold of DENP via percutaneous cervical laminae measurement. The DNEP threshold decreases with the process of laminae grinding. The DNEP threshold of the IL group was significantly higher than ICP and ICG group, while there was no significant difference between the ICP group and the ICG group. There was no significant relationship between the integrity of the cervical spine lateral mass screw path and t-EMG threshold. CONCLUSIONS: It is feasible to use DENP threshold to estimate lamina thickness. Cervical lateral mass screw canals by t-EMG showed no help to evaluate the integrity.

Differentiation and immunosuppressive function of CD19+CD24hiCD27+ regulatory B cells are regulated through the miR-29a-3p/NFAT5 pathway.

BACKGROUND: Regulatory B cells (Bregs) is an indispensable element in inducing immune tolerance after liver transplantation. As one of the microRNAs (miRNAs), miR-29a-3p also inhibits translation by degrading the target mRNA, and yet the relationship between Bregs and miR-29a-3p has not yet been fully explored. This study aimed to investigate the impact of miR-29a-3p on the regulation of differentiation and immunosuppressive functions of memory Bregs (mBregs) and ultimately provide potentially effective therapies in inducing immune tolerance after liver transplantation. METHODS: Flow cytometry was employed to determine the levels of Bregs in peripheral blood mononuclear cells. TaqMan low-density array miRNA assays were used to identify the expression of different miRNAs, electroporation transfection was used to induce miR-29a-3p overexpression and knockdown, and dual luciferase reporter assay was used to verify the target gene of miR-29a-3p. RESULTS: In patients experiencing acute rejection after liver transplantation, the proportions and immunosuppressive function of mBregs in the circulating blood were significantly impaired. miR-29a-3p was found to be a regulator of mBregs differentiation. Inhibition of miR-29a-3p, which targeted nuclear factor of activated T cells 5 (NFAT5), resulted in a conspicuous boost in the differentiation and immunosuppressive function of mBregs. The inhibition of miR-29a-3p in CD19+ B cells was capable of raising the expression levels of NFAT5, thereby promoting B cells to differentiate into mBregs. In addition, the observed enhancement of differentiation and immunosuppressive function of mBregs upon miR-29a-3p inhibition was abolished by the knockdown of NFAT5 in B cells. CONCLUSIONS: miR-29a-3p was found to be a crucial regulator for mBregs differentiation and immunosuppressive function. Silencing miR-29a-3p could be a potentially effective therapeutic strategy for inducing immune tolerance after liver transplantation.

Effects of Steel Slag on the Hydration Process of Solid Waste-Based Cementitious Materials.

Aiming to enhance the comprehensive utilization of steel slag (SS), a solid waste-based binder consisting of SS, granulated blast furnace slag (BFS), and desulfurization gypsum (DG) was designed and prepared. This study investigated the reaction kinetics, phase assemblages, and microstructures of the prepared solid waste-based cementitious materials with various contents of SS through hydration heat, XRD, FT-IR, SEM, TG-DSC, and MIP methods. The synergistic reaction mechanism between SS and the other two wastes (BFS and DG) is revealed. The results show that increasing SS content in the solid waste-based binder raises the pH value of the freshly prepared pastes, advances the main hydration reaction, and shortens the setting time. With the optimal SS content of 20%, the best mechanical properties are achieved, with compressive strengths of 19.2 MPa at 3 d and 58.4 MPa at 28 d, respectively. However, as the SS content continues to increase beyond 20%, the hydration process of the prepared binder is delayed. The synergistic activation effects between SS and BFS with DG enable a large amount of ettringite (AFt) formation, guaranteeing early strength development. As the reaction progresses, more reaction products CSH and Aft are precipitated. They are interlacing and overlapping, jointly refining and densifying the material's microstructure and contributing to the long-term strength gain. This study provides a reference for designing and developing solid waste-based binders and deepens the insightful understanding of the hydration mechanism of the solid waste-based binder.

FM-FCN: A Neural Network with Filtering Modules for Accurate Vital Signs Extraction.

Neural networks excel at capturing local spatial patterns through convolutional modules, but they may struggle to identify and effectively utilize the morphological and amplitude periodic nature of physiological signals. In this work, we propose a novel network named filtering module fully convolutional network (FM-FCN), which fuses traditional filtering techniques with neural networks to amplify physiological signals and suppress noise. First, instead of using a fully connected layer, we use an FCN to preserve the time-dimensional correlation information of physiological signals, enabling multiple cycles of signals in the network and providing a basis for signal processing. Second, we introduce the FM as a network module that adapts to eliminate unwanted interference, leveraging the structure of the filter. This approach builds a bridge between deep learning and signal processing methodologies. Finally, we evaluate the performance of FM-FCN using remote photoplethysmography. Experimental results demonstrate that FM-FCN outperforms the second-ranked method in terms of both blood volume pulse (BVP) signal and heart rate (HR) accuracy. It substantially improves the quality of BVP waveform reconstruction, with a decrease of 20.23% in mean absolute error (MAE) and an increase of 79.95% in signal-to-noise ratio (SNR). Regarding HR estimation accuracy, FM-FCN achieves a decrease of 35.85% in MAE, 29.65% in error standard deviation, and 32.88% decrease in 95% limits of agreement width, meeting clinical standards for HR accuracy requirements. The results highlight its potential in improving the accuracy and reliability of vital sign measurement through high-quality BVP signal extraction. The codes and datasets are available online at https://github.com/zhaoqi106/FM-FCN.

Deep PIM kinase substrate profiling reveals new rational co-therapeutic strategies for acute myeloid leukemia.

Provirus integration site for Moloney murine leukemia virus (PIM) family serine/threonine kinases perform pro-tumorigenic functions in hematologic malignancies and solid tumors by phosphorylating substrates involved in tumor metabolism, cell survival, metastasis, inflammation, and immune cell invasion. However, a comprehensive understanding of PIM kinase functions is currently lacking. Multiple small molecule PIM kinase inhibitors are currently being evaluated as co-therapeutics in cancer patients. To further illuminate PIM kinase functions in cancer, we deeply profiled PIM1 substrates using the reverse in-gel kinase assay to identify downstream cellular processes targetable with small molecules. Pathway analyses of putative PIM substrates nominated RNA splicing and rRNA processing as PIM-regulated cellular processes. PIM inhibition elicited reproducible splicing changes in PIM-inhibitor-responsive acute myeloid leukemia (AML) cell lines. PIM inhibitors synergized with splicing modulators targeting splicing factor 3b subunit 1 (SF3B1) and serine-arginine protein kinase 1 (SRPK1) to kill AML cells. PIM inhibition also altered rRNA processing, and PIM inhibitors synergized with an RNA polymerase I inhibitor to kill AML cells and block AML tumor growth. These data demonstrate that deep kinase substrate knowledge can illuminate unappreciated kinase functions, nominating synergistic co-therapeutic strategies. This approach may expand the co-therapeutic armamentarium to overcome kinase-inhibitor resistant disease that limits durable responses in malignant disease.

Inulin Reduces Kidney Damage in Type 2 Diabetic Mice by Decreasing Inflammation and Serum Metabolomics.

This study is aimed at assessing the impact of soluble dietary fiber inulin on the treatment of diabetes-related chronic inflammation and kidney injury in mice with type 2 diabetes (T2DM). The T2DM model was created by feeding the Institute of Cancer Research (ICR) mice a high-fat diet and intraperitoneally injecting them with streptozotocin (50 mg/kg for 5 consecutive days). The thirty-six ICR mice were divided into three dietary groups: the normal control (NC) group, the T2DM (DM) group, and the DM + inulin diet (INU) group. The INU group mice were given inulin at the dose of 500 mg/kg gavage daily until the end of the 12th week. After 12 weeks, the administration of inulin resulted in decreased serum levels of fasting blood glucose (FBG), low-density lipoprotein cholesterol (LDL-C), blood urea nitrogen (BUN), and creatinine (CRE). The administration of inulin not only ameliorated renal injury but also resulted in a reduction in the mRNA expressions of inflammatory factors in the spleen and serum oxidative stress levels, when compared to the DM group. Additionally, inulin treatment in mice with a T2DM model led to a significant increase in the concentrations of three primary short-chain fatty acids (SCFAs) (acetic acid, propionic acid, and butyric acid), while the concentration of advanced glycation end products (AGEs), a prominent inflammatory factor in diabetes, exhibited a significant decrease. The results of untargeted metabolomics indicate that inulin has the potential to alleviate inflammatory response and kidney damage in diabetic mice. This beneficial effect is attributed to its impact on various metabolic pathways, including glycerophospholipid metabolism, taurine and hypotaurine metabolism, arginine biosynthesis, and tryptophan metabolism. Consequently, oral inulin emerges as a promising treatment option for diabetes and kidney injury.

Development and external validation of a multidimensional deep learning model to dynamically predict kidney outcomes in IgA nephropathy.

BACKGROUND: Accurately predicting kidney outcomes in IgA nephropathy is crucial for clinical decision making. Insufficient use of longitudinal data in previous studies has limited the accuracy and interpretability of prediction models for failing to reflect the chronic nature of IgA nephropathy. This study aimed at establishing a multivariable dynamic deep learning model using comprehensive longitudinal data for the prediction of kidney outcomes in IgA nephropathy. METHODS: In this retrospective cohort study of 2,056 IgA nephropathy patients at 18 kidney centers, a total of 28,317 data points were collected by the sliding window method. Among them, 15,462 windows in a single center were randomly assigned to training (80%) and validation (20%) sets while 8797 windows in 18 kidney centers were assigned to an independently test set. Interpretable Multi-Variable Long Short-Term Memory (IMV-LSTM), a deep learning model, was implemented to predict kidney outcomes (kidney failure or 50% decline in kidney function) based on time-invariant variables measured at biopsy and time-variant variables measured during follow-up. Risk performance was evaluated using Kaplan-Meier analysis and the C statistic. Trajectory analysis was performed to assess the various trends of clinical variables during follow-up. RESULTS: The model achieved a higher C statistic (0.93; 95% CI, 0.92-0.95) on the test set than the XGBoost prediction model that we developed in a previous study using only baseline information (C statistic, 0.84; 95% CI, 0.80-0.88). Kaplan-Meier analysis showed that groups with lower predicted risks from the full model survived longer than groups with higher risks. Time-variant variables demonstrated higher importance scores than time-invariant variables. Within time-variant variables, more recent measurements showed higher importance scores. Further interpretation showed that certain trajectory groups of time-variant variables such as serum creatinine and urine protein were associated with elevated risks of adverse outcomes. CONCLUSIONS: In IgA nephropathy, a deep learning model can be used to accurately and dynamically predict kidney prognosis based on longitudinal data, and time-variant variables show strong ability to predict kidney outcome.

Electric Polarization from a Many-Body Neural Network Ansatz.

Ab initio calculation of dielectric response with high-accuracy electronic structure methods is a long-standing problem, for which mean-field approaches are widely used and electron correlations are mostly treated via approximated functionals. Here we employ a neural network wave function ansatz combined with quantum Monte Carlo method to incorporate correlations into polarization calculations. On a variety of systems, including isolated atoms, one-dimensional chains, two-dimensional slabs, and three-dimensional cubes, the calculated results outperform conventional density functional theory and are consistent with the most accurate calculations and experimental data. Furthermore, we have studied the out-of-plane dielectric constant of bilayer graphene using our method and reestablished its thickness dependence. Overall, this approach provides a powerful tool to accurately describe electron correlation in the modern theory of polarization.