Nomogram predicts risk of perineural invasion based on serum biomarkers for pancreatic cancer.

BACKGROUND: Pancreatic cancer is a fatal tumor, and the status of perineural invasion (PNI) of pancreatic cancer was positively related to poor prognosis including overall survival and recurrence-free survival. This study aims to develop and validate a predictive model based on serum biomarkers to accurately predict the perineural invasion. MATERIALS AND METHODS: The patients from No.924 Hospital of PLA Joint Logistic Support Force were included. The predictive model was developed in the training cohort using logistic regression analysis, and then tested in the validation cohort. The area under curve (AUC), calibration curves and decision curve analysis were used to validate the predictive accuracy and clinical benefits of nomogram. RESULTS: A nomogram was developed using preoperative total bilirubin, preoperative blood glucose, preoperative CA19-9. It achieved good AUC values of 0.753 and 0.737 in predicting PNI in training and validation cohorts, respectively. Calibration curves showed nomogram had good uniformity of the practical probability of PNI. Decision curve analyses revealed that the nomogram provided higher diagnostic accuracy and superior net benefit compared to single indicators. CONCLUSION: The present study constructed and validate a novel nomogram predicted the PNI of resectable PHAC patients with high stability and accuracy. Besides, it could better screen high-risk probability of PNI in these patients, and optimize treatment decision-making.

Glucocorticoids promote lung metastasis of pancreatic cancer cells through enhancing cell adhesion, migration and invasion.

Glucocorticoids (GCs) are the important stress hormones and widely prescribed as drugs. Although stress has been suggested as a promoter of tumor progression, the direct influence of GCs on metastasis of tumor is not fully understood. Metastasis is a major cause of death in pancreatic cancer patients. In the present study, we investigated the effect of GCs on progression of pancreatic cancer and elucidated the underlying mechanism. It was found that GCs significantly promote cell adhesion, migration, and invasion of pancreatic cancer cells in vitro and their lung metastasis in vivo. Further mechanistic studies showed that GCs notably up-regulate the expression of a trans-membrane glycoprotein, mucin 1 (MUC1) and increase the activation of AKT. Inhibiting MUC1 expression not only attenuates the activation of AKT, but also significantly reduces the promoting effects of GCs on cell adhesion, migration, invasion, and lung metastasis of pancreatic cancer cells. Moreover, GCs not only significantly up-regulate expression of Rho-associated kinase 1/2 (ROCK1/2) and matrix metalloproteinase 3 and 7 (MMP3/7), but also activate ROCK2, which are also involved in the pro-migratory and pro-invasive effects of GCs in pancreatic cancer cells. Taken together, our findings reveal that GCs promote metastasis of pancreatic cancer cells through complex mechanism. MUC1-PI3K/AKT pathway, ROCK1/2 and MMP3/7 are involved in the promoting effect of GCs on cell migration, invasion and metastasis in pancreatic cancer cells. These results suggest the importance of reducing stress and GCs administration in patients with pancreatic cancer to avoid an increased risk of cancer metastasis.

Artificial intelligence for stepwise diagnosis and monitoring of COVID-19.

BACKGROUND: Main challenges for COVID-19 include the lack of a rapid diagnostic test, a suitable tool to monitor and predict a patient's clinical course and an efficient way for data sharing among multicenters. We thus developed a novel artificial intelligence system based on deep learning (DL) and federated learning (FL) for the diagnosis, monitoring, and prediction of a patient's clinical course. METHODS: CT imaging derived from 6 different multicenter cohorts were used for stepwise diagnostic algorithm to diagnose COVID-19, with or without clinical data. Patients with more than 3 consecutive CT images were trained for the monitoring algorithm. FL has been applied for decentralized refinement of independently built DL models. RESULTS: A total of 1,552,988 CT slices from 4804 patients were used. The model can diagnose COVID-19 based on CT alone with the AUC being 0.98 (95% CI 0.97-0.99), and outperforms the radiologist's assessment. We have also successfully tested the incorporation of the DL diagnostic model with the FL framework. Its auto-segmentation analyses co-related well with those by radiologists and achieved a high Dice's coefficient of 0.77. It can produce a predictive curve of a patient's clinical course if serial CT assessments are available. INTERPRETATION: The system has high consistency in diagnosing COVID-19 based on CT, with or without clinical data. Alternatively, it can be implemented on a FL platform, which would potentially encourage the data sharing in the future. It also can produce an objective predictive curve of a patient's clinical course for visualization. KEY POINTS: • CoviDet could diagnose COVID-19 based on chest CT with high consistency; this outperformed the radiologist's assessment. Its auto-segmentation analyses co-related well with those by radiologists and could potentially monitor and predict a patient's clinical course if serial CT assessments are available. It can be integrated into the federated learning framework. • CoviDet can be used as an adjunct to aid clinicians with the CT diagnosis of COVID-19 and can potentially be used for disease monitoring; federated learning can potentially open opportunities for global collaboration.

Metal-Organic-Framework-Derived Atomically Dispersed Mn-N-C Electrocatalysts Boosting Oxygen Reduction Modulated by Anion Exchange of Permanganate.

Mn-N-C materials have received increasing interest in recent years because of their low Fenton reactivity and ORR activity comparable to those of their Fe-N-C and Co-N-C counterparts. In this contribution, an atomically dispersed Mn-N-C electrocatalyst with a prominent oxygen reduction performance was constructed by employing a cationic Cd-MOF as a precursor that can facilely and accurately introduce MnO4- anions through anion exchange. The best-performing Mn-N-C catalyst displays a 0.96 V (vs RHE) Eonset (onset potential) and a 0.87 V (vs RHE) E1/2 (half-wave potential) in an alkaline solution, which exceed those of the benchmark Pt/C catalyst. In particular, the maximal power density of the self-made zinc-air battery reaches 200 mW·cm-2, surpassing that of most reported Mn-N-C materials.

The lower bow-shaped curve as a novel reference frame to determine the lateral limit of the maxillary anterior arch for smile esthetics.

INTRODUCTION: Smile analysis in horizontally angled views is indispensable for esthetic assessment and could reveal teeth-to-lip disharmony, which might escape discovery in the frontal and profile views. However, evidence is lacking on where the anterior teeth should be positioned for esthetics in angled smiles. METHODS: Based on 3-dimensional facial image processing and geometric analysis, the lip edges were projected to the horizontal plane, and the horizontal teeth-to-lip relation was simplified and represented by the distances from dental landmarks to lower bow-shaped curves (LBSC), with the distance from facial-axis (FA) point of the canine to LBSC (FA-tangent line [TL] distance) identified as the key parameter. Using photographic modification and esthetic assessment, the effect of FA-TL distances on the attractiveness of 45° angled smiles was tested, with esthetic ranges identified. A simplified method was developed to obtain the estimative LBSC and FA-TL distances using 2-dimensional photographs and geometric analysis to facilitate clinical application. RESULTS: The FA-TL distance remarkably affected the esthetics of 45° angled smiles. Smiles were attractive when the FA-TL distance ranged from -1.0 to 1.5 mm perceived by orthodontists and from -1.5 to 1.5 mm perceived by laypersons. The 2-dimensional photograph-derived estimative FA-TL distance was not significantly different from that obtained in a 3-dimensional image, validating the simplified method. CONCLUSIONS: The LBSC could serve as a reference frame to determine the lateral limit of the maxillary anterior arch for the esthetics of 45° angled smiles. The FA-TL distance, which represented the spatial relation of the maxillary canine with the lower lip, was an esthetically essential parameter. For females aged 20-30 years, the FA point of the maxillary canines should be positioned no more than 1.5 mm labial or lingual to the LBSC.

RhIII -Catalyzed C6-Selective Oxidative C-H/C-H Crosscoupling of 2-Pyridones with Thiophenes.

A rhodium(III)-catalyzed C6-selective dehydrogenative cross-coupling of 2-pyridones with thiophenes was developed for the synthesis of 6-thiophenyl pyridin-2(1H)-one derivatives. In this reaction, the excellent site selectivity was controlled by the 2-pyridyl directing group on the nitrogen of the pyridone ring. Control experiments indicated that the N-pyridyl was essential for the transformation. To the best of our knowledge, this procedure is the first successful example of the direct C6 heteroarylation of 2-pyridones with electron-rich thiophene derivatives. 4-Pyridone was also used as substrate to generate the corresponding C2 heteroarylated product. Moreover, this pyridyl directing group was readily removable to generate the biheteroaryl structures with a free N-H group.

Rhodium-catalyzed C-H activation/cyclization of aryl sulfoximines with iodonium ylides towards polycyclic 1,2-benzothiazines.

The synthesis of 1,2-benzothiazine derivatives through rhodium-catalyzed C-H activation/cyclization of S-aryl sulfoximines with iodonium ylides was developed for the first time. In this report, C-H and N-H bond functionalization was realized towards a series of tricyclic and tetracyclic sulfoximine derivatives with moderate to excellent yields under simple reaction conditions.

Rh(iii)-Catalyzed regioselective C4 alkylation of indoles with allylic alcohols: direct access to ß-indolyl ketones.

A Rh(iii)-catalyzed and weak coordination carbonyl guided direct C4 alkylation of indoles with allylic alcohols was developed with excellent regioselectivity. This reaction was conducted under mild conditions, leading to a variety of ß-indolyl ketones with good functional group tolerance in moderate to good yields.

circRNA.33186 Contributes to the Pathogenesis of Osteoarthritis by Sponging miR-127-5p.

Osteoarthritis (OA), the most prevalent age-related joint disorder, is characterized by chronic inflammation, progressive articular cartilage destruction, and subchondral bone sclerosis. Accumulating evidences indicate that circular RNAs (circRNAs) play a critical role in various diseases, but the function of circRNAs in OA remains largely unknown. Here we showed that circRNA.33186 was significantly upregulated in IL-1ß)-treated chondrocytes and in cartilage tissues of a destabilized medial meniscus (DMM)-induced OA mouse model. Knockdown of circRNA.33186 increased anabolic factor (type II collagen) expression and decreased catabolic factor (MMP-13) expression. Knockdown of circRNA.33186 also promoted proliferation and inhibited apoptosis in IL-1ß-treated chondrocytes. Silencing of circRNA.33186 in vivo markedly alleviated DMM-induced OA. Mechanistic study showed that circRNA.33186 directly binds to and inhibits miR-127-5p, thereby increasing MMP-13 expression, and contributes to OA pathogenesis. Taken together, our findings demonstrated a fundamental role of circRNA.33186 in OA progression and provide a potential drug target in OA therapy.

Characterization of the Aroma of an Instant White Tea Dried by Freeze Drying.

The aroma of an instant white tea (IWT) was extracted through simultaneous distillation-extraction (SDE) and analyzed by sensory evaluation, gas chromatography-mass spectrometry-olfactometry (GC-MS-O), aroma reconstruction, omission test and synergistic interaction analysis. Sensory evaluation showed the IWT was dominated with floral and sweet notes. The SDE extract had the aroma similar to the IWT. The main volatile components in the SDE extract were benzyl alcohol, linalool, hotrienol, geraniol, α-terpineol, coumarin, camphene, benzeneacetaldehyde, 2-hexanone, cis-jasmin lactone and phenylethyl alcohol. GC-MS-O and aroma reconstruction experiments showed 16 aroma-active compounds. Linalool, trans-ß-damascenone and camphene were the major contributors to floral, sweet and green notes based on flavor dilution analysis and omission test. Linalool and trans-ß-damascenone had synergistic effect to promote floral and sweet notes. Camphene and trans-ß-damascenone had synergistic effect to reduce green and sweet notes. The study helps to understand the aroma of IWT and antagonism interactions among aroma-active volatiles.

Design and Feasibility Study of a Leg-exoskeleton Assistive Wheelchair Robot with Tests on Gluteus Medius Muscles.

The muscles of the lower limbs directly influence leg motion, therefore, lower limb muscle exercise is important for persons living with lower limb disabilities. This paper presents a medical assistive robot with leg exoskeletons for locomotion and leg muscle exercises. It also presents a novel pedal-cycling actuation method with a crank-rocker mechanism. The mechanism is driven by a single motor with a mechanical structure that ensures user safety. A control system is designed based on a master-slave control with sensor fusion method. Here, the intended motion of the user is detected by pedal-based force sensors and is then used in combination with joystick movements as control signals for leg-exoskeleton and wheelchair motions. Experimental data is presented and then analyzed to determine robotic motion characteristics as well as the assistance efficiency with attached electromyogram (EMG) sensors. A typical muscle EMG signal analysis shows that the exercise efficiency for EMG activated amplitudes of the gluteus medius muscles approximates a walking at speed of 3 m/s when cycling at different speeds (i.e., from 16 to 80 r/min) in a wheelchair. As such, the present wheelchair robot is a good candidate for enabling effective gluteus medius muscle exercises for persons living with gluteus medius muscle disabilities.

The mechanisms and significance of up-regulation of RhoB expression by hypoxia and glucocorticoid in rat lung and A549 cells.

Small guanosine triphosphate (GTP)-binding protein RhoB is an important stress sensor and contributes to the regulation of cytoskeletal organization, cell proliferation and survival. However, whether RhoB is involved in the hypoxic response and action of glucocorticoid (GC) is largely unknown. In this study, we investigated the effects of hypoxia or/and GC on the expression and activition of RhoB in the lung of rats and human A549 lung carcinoma cells, and further studied its mechanism and significance. We found that hypoxia and dexamethasone (Dex), a synethic GC, not only significantly increased the expression and activation of RhoB independently but also coregulated the expresion of RhoB in vitro and in vivo. Up-regulation of RhoB by hypoxia was in part through stabilizing the RhoB mRNA and protein. Inhibiting hypoxia-activated hypoxia-inducible transcription factor-1α (HIF-1α), c-Jun N-terminal kinase (JNK) or extracellular signal-regulated kinase (ERK) with their specific inhibitors significantly decreased hypoxia-induced RhoB expression, indicating that HIF-1α, JNK and ERK are involved in the up-regulation of RhoB in hypoxia. Furthermore, we found that knockdown of RhoB expression by RhoB siRNA not only significantly reduced hypoxia-enhanced cell migration and cell survival in hypoxia but also increased the sensitivity of cell to paclitaxel (PTX), a chemotherapeutic agent, and reduced Dex-enhanced resistance to PTX-chemotherapy in A549 cells. Taken together, the novel data revealed that hypoxia and Dex increased the expression and activation of RhoB, which is important for hypoxic adaptation and hypoxia-accelerated progression of lung cancer cells. RhoB also enhanced the resistance of cell to PTX-chemotherapy and mediated the pro-survival effect of Dex.

Neutrophil CD64 as a Marker of Bacterial Infection in Acute Exacerbations of Chronic Obstructive Pulmonary Disease.

Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are responsible for most mortality in patients with chronic obstructive pulmonary disease (COPD) and are caused mainly by bacterial infection. We analyzed and compared neutrophil CD64 expression (using the ratio of CD64 level in neutrophils to that in lymphocytes as an index), serum C-reactive protein (CRP), procalcitonin (PCT) levels, white blood cell (WBC) count, and neutrophil percentage among healthy subjects and patients with stable COPD or AECOPD. Compared with patients with COPD and healthy subjects, patients with AECOPD demonstrated significantly increased CD64 index, CRP, PCT, WBC count, and neutrophil percentage. Interestingly, CD64 index and PCT were both significantly higher in patients with AECOPD with positive bacterial sputum culture than those with negative culture. Furthermore, CD64 index and PCT were positively correlated in AECOPD, and there was also correlation between CD64 index and CRP, WBC, and neutrophil percentage. These data suggest that CD64 index is a relevant marker of bacterial infection in AECOPD. We divided patients with AECOPD into CD64-guided group and conventional treatment group. In CD64-guided group, clinicians prescribed antibiotics based on CD64 index; while in the conventional treatment group, clinicians relied on experience and clinical symptoms to determine the necessity for antibiotics. We found that the efficacy of antibiotic treatment in CD64-guided group was significantly improved compared with the conventional treatment group, including reduction of hospital stays and cost and shortened antibiotic treatment duration. Thus, the CD64 index has important diagnostic and therapeutic implications for antibiotic treatment of patients with AECOPD.

Role of linker histone H1c during the reprogramming of Chinese swamp buffalo (Bubalus Bubalis) embryos produced by somatic cell nuclear transfer.

During reprogramming, there is exchange of histone H1c and the oocyte-specific linker histone, and H1c may play a critically important role in the reprogramming process of somatic cell nuclear transfer (SCNT). The aim of the present study was to investigate the role of the H1c gene in SCNT reprogramming in Chinese swamp buffalo (Bubalus bubalis) using RNA interference (RNAi). Chinese swamp buffalo H1c gene sequences were obtained and H1c-RNAi vectors were designed, synthesised and then transfected into a buffalo fetal skin fibroblast cell line. Expression of H1c was determined by real-time polymerase chain reaction to examine the efficiency of vector interference. These cells were then used as a nuclear donor for SCNT so as to observe the further development of SCNT embryos. Inhibition of H1c gene expression in donor cells significantly improved the developmental speed of embryos from the 1-cell to 8-cell stage. Furthermore, compared with the control group, inhibition of H1c gene expression significantly reduced the blastocyst formation rate. It is concluded that linker histone H1c is very important in SCNT reprogramming in Chinese swamp buffalo. Correct expression of the H1c gene plays a significant role in preimplantation embryonic development in B. bubalis.

Procalcitonin guidance for reduction of antibiotic use in patients hospitalized with severe acute exacerbations of asthma: a randomized controlled study with 12-month follow-up.

INTRODUCTION: Patients with severe acute exacerbations of asthma often receive inappropriate antibiotic treatment. We aimed to determine whether serum procalcitonin (PCT) levels can effectively and safely reduce antibiotic exposure in patients experiencing exacerbations of asthma. METHODS: In this randomized controlled trial, a total of 216 patients requiring hospitalization for severe acute exacerbations of asthma were screened for eligibility to participate and 169 completed the 12-month follow-up visit. Patients were randomized to either PCT-guided (PCT group) or standard (control group) antimicrobial therapy. In the control group, patients received antibiotics according to the attending physician's discretion; in the PCT group, patients received antibiotics according to an algorithm based on serum PCT levels. The primary end point was antibiotic exposure; secondary end points were clinical recovery, length of hospital stay, clinical and laboratory parameters, spirometry, number of asthma exacerbations, emergency room visits, hospitalizations and need for corticosteroid use due to asthma. RESULTS: PCT guidance reduced antibiotic prescription (48.9% versus 87.8%, respectively; P < 0.001) and antibiotic exposure (relative risk, 0.56; 95% confidence interval, 0.44 to 0.70; P < 0.001) compared to standard therapy. There were no significant differences in clinical recovery, length of hospital stay or clinical, laboratory and spirometry outcomes in both groups. Number of asthma exacerbations, emergency room visits, hospitalizations and need for corticosteroid use due to asthma were similar during the 12-month follow-up period. CONCLUSION: A PCT-guided strategy allows antibiotic exposure to be reduced in patients with severe acute exacerbation of asthma without apparent harm. TRIAL REGISTRATION: Chinese Clinical Trial Register ChiCTR-TRC-12002534 (registered 26 September 2012).

Dynamic neural networks: advantages and challenges.

This perspective article delves into the transformative realm of dynamic neural networks, which is reshaping AI with adaptable structures and improved efficiency, bridging the gap between artificial and human intelligence.

Probabilistic Contrastive Learning for Long-Tailed Visual Recognition.

Long-tailed distributions frequently emerge in real-world data, where a large number of minority categories contain a limited number of samples. Such imbalance issue considerably impairs the performance of standard supervised learning algorithms, which are mainly designed for balanced training sets. Recent investigations have revealed that supervised contrastive learning exhibits promising potential in alleviating the data imbalance. However, the performance of supervised contrastive learning is plagued by an inherent challenge: it necessitates sufficiently large batches of training data to construct contrastive pairs that cover all categories, yet this requirement is difficult to meet in the context of class-imbalanced data. To overcome this obstacle, we propose a novel probabilistic contrastive (ProCo) learning algorithm that estimates the data distribution of the samples from each class in the feature space, and samples contrastive pairs accordingly. In fact, estimating the distributions of all classes using features in a small batch, particularly for imbalanced data, is not feasible. Our key idea is to introduce a reasonable and simple assumption that the normalized features in contrastive learning follow a mixture of von Mises-Fisher (vMF) distributions on unit space, which brings two-fold benefits. First, the distribution parameters can be estimated using only the first sample moment, which can be efficiently computed in an online manner across different batches. Second, based on the estimated distribution, the vMF distribution allows us to sample an infinite number of contrastive pairs and derive a closed form of the expected contrastive loss for efficient optimization. Other than long-tailed problems, ProCo can be directly applied to semi-supervised learning by generating pseudo-labels for unlabeled data, which can subsequently be utilized to estimate the distribution of the samples inversely. Theoretically, we analyze the error bound of ProCo. Empirically, extensive experimental results on supervised/semi-supervised visual recognition and object detection tasks demonstrate that ProCo consistently outperforms existing methods across various datasets.

Fine-Grained Recognition With Learnable Semantic Data Augmentation.

Fine-grained image recognition is a longstanding computer vision challenge that focuses on differentiating objects belonging to multiple subordinate categories within the same meta-category. Since images belonging to the same meta-category usually share similar visual appearances, mining discriminative visual cues is the key to distinguishing fine-grained categories. Although commonly used image-level data augmentation techniques have achieved great success in generic image classification problems, they are rarely applied in fine-grained scenarios, because their random editing-region behavior is prone to destroy the discriminative visual cues residing in the subtle regions. In this paper, we propose diversifying the training data at the feature-level to alleviate the discriminative region loss problem. Specifically, we produce diversified augmented samples by translating image features along semantically meaningful directions. The semantic directions are estimated with a covariance prediction network, which predicts a sample-wise covariance matrix to adapt to the large intra-class variation inherent in fine-grained images. Furthermore, the covariance prediction network is jointly optimized with the classification network in a meta-learning manner to alleviate the degenerate solution problem. Experiments on four competitive fine-grained recognition benchmarks (CUB-200-2011, Stanford Cars, FGVC Aircrafts, NABirds) demonstrate that our method significantly improves the generalization performance on several popular classification networks (e.g., ResNets, DenseNets, EfficientNets, RegNets and ViT). Combined with a recently proposed method, our semantic data augmentation approach achieves state-of-the-art performance on the CUB-200-2011 dataset. Source code is available at https://github.com/LeapLabTHU/LearnableISDA.

EfficientTrain++: Generalized Curriculum Learning for Efficient Visual Backbone Training.

The superior performance of modern computer vision backbones (e.g., vision Transformers learned on ImageNet-1K/22K) usually comes with a costly training procedure. This study contributes to this issue by generalizing the idea of curriculum learning beyond its original formulation, i.e., training models using easier-to-harder data. Specifically, we reformulate the training curriculum as a soft-selection function, which uncovers progressively more difficult patterns within each example during training, instead of performing easier-to-harder sample selection. Our work is inspired by an intriguing observation on the learning dynamics of visual backbones: during the earlier stages of training, the model predominantly learns to recognize some 'easier-to-learn' discriminative patterns in the data. These patterns, when observed through frequency and spatial domains, incorporate lower-frequency components, and the natural image contents without distortion or data augmentation. Motivated by these findings, we propose a curriculum where the model always leverages all the training data at every learning stage, yet the exposure to the 'easier-to-learn' patterns of each example is initiated first, with harder patterns gradually introduced as training progresses. To implement this idea in a computationally efficient way, we introduce a cropping operation in the Fourier spectrum of the inputs, enabling the model to learn from only the lower-frequency components. Then we show that exposing the contents of natural images can be readily achieved by modulating the intensity of data augmentation. Finally, we integrate these two aspects and design curriculum learning schedules by proposing tailored searching algorithms. Moreover, we present useful techniques for deploying our approach efficiently in challenging practical scenarios, such as large-scale parallel training, and limited input/output or data pre-processing speed. The resulting method, EfficientTrain++, is simple, general, yet surprisingly effective. As an off-the-shelf approach, it reduces the training time of various popular models (e.g., ResNet, ConvNeXt, DeiT, PVT, Swin, CSWin, and CAFormer) by [Formula: see text] on ImageNet-1K/22K without sacrificing accuracy. It also demonstrates efficacy in self-supervised learning (e.g., MAE). Code is available at: https://github.com/LeapLabTHU/EfficientTrain.

Latency-aware Unified Dynamic Networks for Efficient Image Recognition.

Dynamic computation has emerged as a promising strategy to improve the inference efficiency of deep networks. It allows selective activation of various computing units, such as layers or convolution channels, or adaptive allocation of computation to highly informative spatial regions in image features, thus significantly reducing unnecessary computations conditioned on each input sample. However, the practical efficiency of dynamic models does not always correspond to theoretical outcomes. This discrepancy stems from three key challenges: 1) The absence of a unified formulation for various dynamic inference paradigms, owing to the fragmented research landscape; 2) The undue emphasis on algorithm design while neglecting scheduling strategies, which are critical for optimizing computational performance and resource utilization in CUDA-enabled GPU settings; and 3) The cumbersome process of evaluating practical latency, as most existing libraries are tailored for static operators. To address these issues, we introduce Latency-Aware Unified Dynamic Networks (LAUDNet), a comprehensive framework that amalgamates three cornerstone dynamic paradigms-spatially-adaptive computation, dynamic layer skipping, and dynamic channel skipping-under a unified formulation. To reconcile theoretical and practical efficiency, LAUDNet integrates algorithmic design with scheduling optimization, assisted by a latency predictor that accurately and efficiently gauges the inference latency of dynamic operators. This latency predictor harmonizes considerations of algorithms, scheduling strategies, and hardware attributes. We empirically validate various dynamic paradigms within the LAUDNet framework across a range of vision tasks, including image classification, object detection, and instance segmentation. Our experiments confirm that LAUDNet effectively narrows the gap between theoretical and real-world efficiency. For example, LAUDNet can reduce the practical latency of its static counterpart, ResNet-101, by over 50% on hardware platforms such as V100, RTX3090, and TX2 GPUs. Furthermore, LAUDNet surpasses competing methods in the trade-off between accuracy and efficiency. Code is available at: https://www.github.com/LeapLabTHU/LAUDNet.