Transcriptional signatures of cortical structural changes in chronic insomnia disorder.

Chronic insomnia disorder (CID) is a multidimensional disease that may influence various levels of brain organization, spanning the macroscopic structural connectome to microscopic gene expression. However, the connection between genomic variations and morphological alterations in CID remains unclear. Here, we investigated brain structural changes in CID patients at the whole-brain level and whether these link to transcriptional characteristics. Brain structural data from 104 CID patients and 102 matched healthy controls (HC) were acquired to examine cortical structural alterations using morphometric similarity (MS) analysis. Partial least squares (PLS) regression and transcriptome data from the Allen Human Brain Atlas were used to extract genomes related to MS changes. Gene-category enrichment analysis (GCEA) was used to identify potential molecular mechanisms behind the observed structural changes. We found that CID patients exhibited MS reductions in the parietal and limbic regions, along with enhancements in the temporal and frontal regions compared to HCs (pFDR < .05). Subsequently, PLS and GCEA revealed that these MS alterations were spatially correlated with a set of genes, especially those significantly correlated with excitatory and inhibitory neurons and chronic neuroinflammation. This neuroimaging-transcriptomic study bridges the gap between cortical structural changes and the molecular mechanisms in CID patients, providing novel insight into the pathophysiology of insomnia and targeted treatments.

Deep drug-target binding affinity prediction with multiple attention blocks.

Drug-target interaction (DTI) prediction has drawn increasing interest due to its substantial position in the drug discovery process. Many studies have introduced computational models to treat DTI prediction as a regression task, which directly predict the binding affinity of drug-target pairs. However, existing studies (i) ignore the essential correlations between atoms when encoding drug compounds and (ii) model the interaction of drug-target pairs simply by concatenation. Based on those observations, in this study, we propose an end-to-end model with multiple attention blocks to predict the binding affinity scores of drug-target pairs. Our proposed model offers the abilities to (i) encode the correlations between atoms by a relation-aware self-attention block and (ii) model the interaction of drug representations and target representations by the multi-head attention block. Experimental results of DTI prediction on two benchmark datasets show our approach outperforms existing methods, which are benefit from the correlation information encoded by the relation-aware self-attention block and the interaction information extracted by the multi-head attention block. Moreover, we conduct the experiments on the effects of max relative position length and find out the best max relative position length value $k \in \{3, 5\}$. Furthermore, we apply our model to predict the binding affinity of Corona Virus Disease 2019 (COVID-19)-related genome sequences and $3137$ FDA-approved drugs.

Development of a knowledge-based healthcare-associated infections surveillance system in China.

BACKGROUND: In the modern era of antibiotics, healthcare-associated infections (HAIs) have emerged as a prominent and concerning health threat worldwide. Implementing an electronic surveillance system for healthcare-associated infections offers the potential to not only alleviate the manual workload of clinical physicians in surveillance and reporting but also enhance patient safety and the overall quality of medical care. Despite the widespread adoption of healthcare-associated infections surveillance systems in numerous hospitals across China, several challenges persist. These encompass incomplete coverage of all infection types in the surveillance, lack of clarity in the alerting results provided by the system, and discrepancies in sensitivity and specificity that fall short of practical expectations. METHODS: We design and develop a knowledge-based healthcare-associated infections surveillance system (KBHAIS) with the primary goal of supporting clinicians in their surveillance of HAIs. The system operates by automatically extracting infection factors from both structured and unstructured electronic health data. Each patient visit is represented as a tuple list, which is then processed by the rule engine within KBHAIS. As a result, the system generates comprehensive warning results, encompassing infection site, infection diagnoses, infection time, and infection probability. These knowledge rules utilized by the rule engine are derived from infection-related clinical guidelines and the collective expertise of domain experts. RESULTS: We develop and evaluate our KBHAIS on a dataset of 106,769 samples collected from 84,839 patients at Gansu Provincial Hospital in China. The experimental results reveal that the system achieves a sensitivity rate surpassing 0.83, offering compelling evidence of its effectiveness and reliability. CONCLUSIONS: Our healthcare-associated infections surveillance system demonstrates its effectiveness in promptly alerting patients to healthcare-associated infections. Consequently, our system holds the potential to considerably diminish the occurrence of delayed and missed reporting of such infections, thereby bolstering patient safety and elevating the overall quality of healthcare delivery.

Multi-scaled self-attention for drug-target interaction prediction based on multi-granularity representation.

BACKGROUND: Drug-target interaction (DTI) prediction plays a crucial role in drug discovery. Although the advanced deep learning has shown promising results in predicting DTIs, it still needs improvements in two aspects: (1) encoding method, in which the existing encoding method, character encoding, overlooks chemical textual information of atoms with multiple characters and chemical functional groups; as well as (2) the architecture of deep model, which should focus on multiple chemical patterns in drug and target representations. RESULTS: In this paper, we propose a multi-granularity multi-scaled self-attention (SAN) model by alleviating the above problems. Specifically, in process of encoding, we investigate a segmentation method for drug and protein sequences and then label the segmented groups as the multi-granularity representations. Moreover, in order to enhance the various local patterns in these multi-granularity representations, a multi-scaled SAN is built and exploited to generate deep representations of drugs and targets. Finally, our proposed model predicts DTIs based on the fusion of these deep representations. Our proposed model is evaluated on two benchmark datasets, KIBA and Davis. The experimental results reveal that our proposed model yields better prediction accuracy than strong baseline models. CONCLUSION: Our proposed multi-granularity encoding method and multi-scaled SAN model improve DTI prediction by encoding the chemical textual information of drugs and targets and extracting their various local patterns, respectively.

Improving patient self-description in Chinese online consultation using contextual prompts.

BACKGROUND: Online health care consultation has been widely adopted to supplement traditional face-to-face patient-doctor interactions. Patients benefit from this new modality of consultation because it allows for time flexibility by eliminating the distance barrier. However, unlike the traditional face-to-face approach, the success of online consultation heavily relies on the accuracy of patient-reported conditions and symptoms. The asynchronous interaction pattern further requires clear and effective patient self-description to avoid lengthy conversation, facilitating timely support for patients. METHOD: Inspired by the observation that doctors talk to patients with the goal of eliciting information to reduce uncertainty about patients' conditions, we proposed and evaluated a machine learning-based computational model towards this goal. Key components of the model include (1) how a doctor diagnoses (predicts) a disease given natural language description of a patient's conditions, (2) how to measure if the patient's description is incomplete or more information is needed from the patient; and (3) given the patient's current description, what further information is needed to help a doctor reach a diagnosis decision. This model makes it possible for an online consultation system to immediately prompt a patient to provide more information if it senses that the current description is insufficient. RESULTS: We evaluated the proposed method by using classification-based metrics (accuracy, macro-averaged F-score, area under the receiver operating characteristics curve, and Matthews correlation coefficient) and an uncertainty-based metric (entropy) on three Chinese online consultation corpora. When there was one consultation round, our method delivered better disease prediction performance than the baseline method (No Prompts) and two heuristic methods (Uncertainty-based Prompts and Certainty-based Prompts). CONCLUSION: The disease prediction performance correlated with uncertainty of patients' self-described symptoms and conditions. However, heuristic solutions ignored the context to decrease large amounts of uncertainty, which did not improve the prediction performance. By elaborate design, a machine-learning algorithm can learn the inner connection between a patient's self-description and the specific information doctors need from doctor-patient conversations to provide prompts, which can enrich the information in patient self-description for a better performance in disease prediction, thereby achieving online consultation with fewer rounds of doctor-patient conversation.

When BERT meets Bilbo: a learning curve analysis of pretrained language model on disease classification.

BACKGROUND: Natural language processing (NLP) tasks in the health domain often deal with limited amount of labeled data due to high annotation costs and naturally rare observations. To compensate for the lack of training data, health NLP researchers often have to leverage knowledge and resources external to a task at hand. Recently, pretrained large-scale language models such as the Bidirectional Encoder Representations from Transformers (BERT) have been proven to be a powerful way of learning rich linguistic knowledge from massive unlabeled text and transferring that knowledge to downstream tasks. However, previous downstream tasks often used training data at such a large scale that is unlikely to obtain in the health domain. In this work, we aim to study whether BERT can still benefit downstream tasks when training data are relatively small in the context of health NLP. METHOD: We conducted a learning curve analysis to study the behavior of BERT and baseline models as training data size increases. We observed the classification performance of these models on two disease diagnosis data sets, where some diseases are naturally rare and have very limited observations (fewer than 2 out of 10,000). The baselines included commonly used text classification models such as sparse and dense bag-of-words models, long short-term memory networks, and their variants that leveraged external knowledge. To obtain learning curves, we incremented the amount of training examples per disease from small to large, and measured the classification performance in macro-averaged [Formula: see text] score. RESULTS: On the task of classifying all diseases, the learning curves of BERT were consistently above all baselines, significantly outperforming them across the spectrum of training data sizes. But under extreme situations where only one or two training documents per disease were available, BERT was outperformed by linear classifiers with carefully engineered bag-of-words features. CONCLUSION: As long as the amount of training documents is not extremely few, fine-tuning a pretrained BERT model is a highly effective approach to health NLP tasks like disease classification. However, in extreme cases where each class has only one or two training documents and no more will be available, simple linear models using bag-of-words features shall be considered.

Improving rare disease classification using imperfect knowledge graph.

BACKGROUND: Accurately recognizing rare diseases based on symptom description is an important task in patient triage, early risk stratification, and target therapies. However, due to the very nature of rare diseases, the lack of historical data poses a great challenge to machine learning-based approaches. On the other hand, medical knowledge in automatically constructed knowledge graphs (KGs) has the potential to compensate the lack of labeled training examples. This work aims to develop a rare disease classification algorithm that makes effective use of a knowledge graph, even when the graph is imperfect. METHOD: We develop a text classification algorithm that represents a document as a combination of a "bag of words" and a "bag of knowledge terms," where a "knowledge term" is a term shared between the document and the subgraph of KG relevant to the disease classification task. We use two Chinese disease diagnosis corpora to evaluate the algorithm. The first one, HaoDaiFu, contains 51,374 chief complaints categorized into 805 diseases. The second data set, ChinaRe, contains 86,663 patient descriptions categorized into 44 disease categories. RESULTS: On the two evaluation data sets, the proposed algorithm delivers robust performance and outperforms a wide range of baselines, including resampling, deep learning, and feature selection approaches. Both classification-based metric (macro-averaged F1 score) and ranking-based metric (mean reciprocal rank) are used in evaluation. CONCLUSION: Medical knowledge in large-scale knowledge graphs can be effectively leveraged to improve rare diseases classification models, even when the knowledge graph is incomplete.

Analysis and Improvement of a Mutual Authentication Scheme for Wireless Body Area Networks.

An increase in aging population and the consequent chronic diseases pose not only serious effects to the economy but also a heavy burden to the medical system. Wireless body area networks (WBANs) provide a simple and low-cost strategy for health monitoring and telemedicine of the elderly. Many authentication schemes based on WBAN have been presented to address the sensitivity and privacy of collected data and the open characteristic of wireless networks. Wu et al. recently presented an efficient anonymous authentication scheme for WBANs, in which a one-side bilinear pairing methodology was applied to reduce the burden on the WBAN client side. However, we demonstrate that their scheme suffers from client impersonation attacks and that the adversary can easily forge a legal client to access the network service. In this paper, we analyze the limitations of Wu et al.'s scheme and design a novel mutual authentication scheme for WBANs that adopt asymmetric bilinear pairing to enhance security. Results of security and performance analyses reveal that the new scheme offers more effective security, better performance, and higher efficiency than Wu et al.'s scheme. We also provide a formal security proof of the protocol by using BAN authentication logic.

RoMo: Robust Unsupervised Multimodal Learning with Noisy Pseudo Labels.

The rise of the metaverse and the increasing volume of heterogeneous 2D and 3D data have led to a growing demand for cross-modal retrieval, which allows users to query semantically relevant data across different modalities. Existing methods heavily rely on class labels to bridge semantic correlations, but it is expensive or even impossible to collect large-scale welll-abeled data in practice, thus making unsupervised learning more attractive and practical. However, unsupervised cross-modal learning is challenging to bridge semantic correlations across different modalities due to the lack of label information, which inevitably leads to unreliable discrimination. Based on the observations, we reveal and study a novel problem in this paper, namely unsupervised cross-modal learning with noisy pseudo labels. To address this problem, we propose a 2D-3D unsupervised multimodal learning framework that harnesses multimodal data. Our framework consists of three key components: 1) Self-matching Supervision Mechanism (SSM) warms up the model to encapsulate discrimination into the representations in a self-supervised learning manner. 2) Robust Discriminative Learning (RDL) further mines the discrimination from the learned imperfect predictions after warming up. To tackle the noise in the predicted pseudo labels, RDL leverages a novel Robust Concentrating Learning Loss (RCLL) to alleviate the influence of the uncertain samples, thus embracing robustness against noisy pseudo labels. 3) Modality-invariance Learning Mechanism (MLM) minimizes the cross-modal discrepancy to enforce SSM and RDL to produce common representations. We perform comprehensive experiments on four 2D-3D multimodal datasets, comparing our method against 14 state-of-the-art approaches, thereby demonstrating its effectiveness and superiority.

The role of diet in diabetes gastroparesis treatment: a systematic review and meta-analysis.

Background: Diabetic gastroparesis is a common complication in patient with diabetes. Dietary intervention has been widely used in the treatment of diabetic gastroparesis. The aim of this study is to evaluate the role of diet in the treatment of diabetic gastroparesis. Methods: This systematic review was conducted a comprehensive search of randomized controlled trials using dietary interventions for the treatment of diabetic gastroparesis up to 9 November 2023. The primary outcomes were gastric emptying time and clinical effect, while fasting blood glucose, 2-hour postprandial blood glucose and glycosylated hemoglobin were secondary outcomes. Data analysis was performed using RevMan 5.4 software, and publication bias test was performed using Stata 15.1 software. Results: A total of 15 randomized controlled trials involving 1106 participants were included in this review. The results showed that patients with diabetic gastroparesis benefit from dietary interventions (whether personalized dietary care alone or personalized dietary care+routine dietary care). Compared with routine dietary care, personalized dietary care and personalized dietary care+routine dietary care can shorten the gastric emptying time, improve clinical efficacy, and reduce the level of fasting blood glucose, 2-hour postprandial blood glucose and glycosylated hemoglobin. Conclusions: Limited evidence suggests that dietary intervention can promote gastric emptying and stabilize blood glucose control in patients with diabetic gastroparesis. Dietary intervention has unique potential in the treatment of diabetic gastroparesis, and more high-quality randomized controlled trials are needed to further validate our research results. Systematic review registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42023481621.

Deep Supervised Multi-View Learning With Graph Priors.

This paper presents a novel method for supervised multi-view representation learning, which projects multiple views into a latent common space while preserving the discrimination and intrinsic structure of each view. Specifically, an apriori discriminant similarity graph is first constructed based on labels and pairwise relationships of multi-view inputs. Then, view-specific networks progressively map inputs to common representations whose affinity approximates the constructed graph. To achieve graph consistency, discrimination, and cross-view invariance, the similarity graph is enforced to meet the following constraints: 1) pairwise relationship should be consistent between the input space and common space for each view; 2) within-class similarity is larger than any between-class similarity for each view; 3) the inter-view samples from the same (or different) classes are mutually similar (or dissimilar). Consequently, the intrinsic structure and discrimination are preserved in the latent common space using an apriori approximation schema. Moreover, we present a sampling strategy to approach a sub-graph sampled from the whole similarity structure instead of approximating the graph of the whole dataset explicitly, thus benefiting lower space complexity and the capability of handling large-scale multi-view datasets. Extensive experiments show the promising performance of our method on five datasets by comparing it with 18 state-of-the-art methods.

Cross-Modal Retrieval With Partially Mismatched Pairs.

In this paper, we study a challenging but less-touched problem in cross-modal retrieval, i.e., partially mismatched pairs (PMPs). Specifically, in real-world scenarios, a huge number of multimedia data (e.g., the Conceptual Captions dataset) are collected from the Internet, and thus it is inevitable to wrongly treat some irrelevant cross-modal pairs as matched. Undoubtedly, such a PMP problem will remarkably degrade the cross-modal retrieval performance. To tackle this problem, we derive a unified theoretical Robust Cross-modal Learning framework (RCL) with an unbiased estimator of the cross-modal retrieval risk, which aims to endow the cross-modal retrieval methods with robustness against PMPs. In detail, our RCL adopts a novel complementary contrastive learning paradigm to address the following two challenges, i.e., the overfitting and underfitting issues. On the one hand, our method only utilizes the negative information which is much less likely false compared with the positive information, thus avoiding the overfitting issue to PMPs. However, these robust strategies could induce underfitting issues, thus making training models more difficult. On the other hand, to address the underfitting issue brought by weak supervision, we present to leverage of all available negative pairs to enhance the supervision contained in the negative information. Moreover, to further improve the performance, we propose to minimize the upper bounds of the risk to pay more attention to hard samples. To verify the effectiveness and robustness of the proposed method, we carry out comprehensive experiments on five widely-used benchmark datasets compared with nine state-of-the-art approaches w.r.t. the image-text and video-text retrieval tasks. The code is available at https://github.com/penghu-cs/RCL.

Multiview Clustering by Consensus Spectral Rotation Fusion.

Multiview clustering (MVC) aims to partition data into different groups by taking full advantage of the complementary information from multiple views. Most existing MVC methods fuse information of multiple views at the raw data level. They may suffer from performance degradation due to the redundant information contained in the raw data. Graph learning-based methods often heavily depend on one specific graph construction, which limits their practical applications. Moreover, they often require a computational complexity of O(n3 ) because of matrix inversion or eigenvalue decomposition for each iterative computation. In this paper, we propose a consensus spectral rotation fusion (CSRF) method to learn a fused affinity matrix for MVC at the spectral embedding feature level. Specifically, we first introduce a CSRF model to learn a consensus low-dimensional embedding, which explores the complementary and consistent information across multiple views. We develop an alternating iterative optimization algorithm to solve the CSRF optimization problem, where a computational complexity of O(n2 ) is required during each iterative computation. Then, the sparsity policy is introduced to design two different graph construction schemes, which are effectively integrated with the CSRF model. Finally, a multiview fused affinity matrix is constructed from the consensus low-dimensional embedding in spectral embedding space. We analyze the convergence of the alternating iterative optimization algorithm and provide an extension of CSRF for incomplete MVC. Extensive experiments on multiview datasets demonstrate the effectiveness and efficiency of the proposed CSRF method.

Multi-gate Mixture of Multi-view Graph Contrastive Learning on Electronic Health Record.

Electronic Health Record (EHR) is the digital form of patient visits containing various medical data, including diagnosis, treatment, and lab events. Representation learning of EHR with deep learning methods has been beneficial for patient-related prediction tasks. Recently, studies have focused on revealing the inherent graph structure between medical events in EHR. Graph neural network (GNN) methods are prevalent and perform well in various prediction tasks. However, the inherent relationships between various medical events must be marked, which is complicated and time-consuming. Most research works adopt the straightforward structure of GNN models on a single prediction task which could not fully exploit the potential of EHR representations. Compared with previous work, the multi-task prediction could utilize the latent information of concealed correlations between different prediction tasks. In addition, self-contrastive learning on graphs could improve the representation learned by GNN. We propose a multi-gate mixture of multi-view graph contrastive learning (MMMGCL) method, aiming to get a more reasonable EHR representation and improve the performances of downstream tasks. First, each patient visit is represented as a graph with a well-designed hierarchically fully-connected pattern. Second, node features in the manually constructed graph are pre-trained via the Glove method with hierarchical ontology knowledge. Finally, MMMGCL processes the pre-trained graph and adopts a joint learning strategy to simultaneously optimize task and contrastive losses. We verify our method on two large open-source medical datasets, Medical Information Mart for Intensive Care (MIMIC-III) and the eICU Collaborative Research Database (eICU). Experiment results show that our method could improve performance compared to straightforward graph-based methods on prediction tasks of patient readmission, mortality, and length of stay.

Causal relationship between rheumatoid arthritis and hypothyroidism or hyperthyroidism: a bidirectional two-sample univariable and multivariable Mendelian randomization study.

Objective: The causal relationship between Rheumatoid arthritis (RA) and hypothyroidism/hyperthyroidism remains controversial due to the limitations of conventional observational research, such as confounding variables and reverse causality. We aimed to examine the potential causal relationship between RA and hypothyroidism/hyperthyroidism using Mendelian randomization (MR). Method: We conducted a bidirectional two-sample univariable analysis to investigate the potential causal relationship between hypothyroidism/hyperthyroidism and RA. Furthermore, we performed a multivariate analysis to account for the impact of body mass index (BMI), smoking quantity, and alcohol intake frequency. Results: The univariable analysis indicated that RA has a causative influence on hypothyroidism (odds ratio [OR]=1.07, 95% confidence interval [CI]=1.01-1.14, P=0.02) and hyperthyroidism (OR=1.32, 95% CI=1.15-1.52, P<0.001). When hypothyroidism/hyperthyroidism was considered as an exposure variable, we only observed a causal relationship between hypothyroidism (OR=1.21, 95% CI=1.05-1.40, P=0.01) and RA, whereas no such connection was found between hyperthyroidism (OR=0.91, 95% CI=0.83-1.01, P=0.07) and RA. In the multivariate MR analyses, after separately and jointly adjusting for the effects of daily smoking quantity, alcohol intake frequency, and BMI, the causal impact of RA on hypothyroidism/hyperthyroidism and hypothyroidism on RA remained robust. However, there is no evidence to suggest a causal effect of hyperthyroidism on the risk of RA (P >0.05). Conclusion: Univariate and multivariate MR analyses have validated the causal association between RA and hypothyroidism/hyperthyroidism. Hypothyroidism confirmed a causal relationship with RA when employed as an exposure variable, whereas no such relationship was found between hyperthyroidism and RA.

Hierarchical Hashing Learning for Image Set Classification.

With the development of video network, image set classification (ISC) has received a lot of attention and can be used for various practical applications, such as video based recognition, action recognition, and so on. Although the existing ISC methods have obtained promising performance, they often have extreme high complexity. Due to the superiority in storage space and complexity cost, learning to hash becomes a powerful solution scheme. However, existing hashing methods often ignore complex structural information and hierarchical semantics of the original features. They usually adopt a single-layer hashing strategy to transform high-dimensional data into short-length binary codes in one step. This sudden drop of dimension could result in the loss of advantageous discriminative information. In addition, they do not take full advantage of intrinsic semantic knowledge from whole gallery sets. To tackle these problems, in this paper, we propose a novel Hierarchical Hashing Learning (HHL) for ISC. Specifically, a coarse-to-fine hierarchical hashing scheme is proposed that utilizes a two-layer hash function to gradually refine the beneficial discriminative information in a layer-wise fashion. Besides, to alleviate the effects of redundant and corrupted features, we impose the ℓ2,1 norm on the layer-wise hash function. Moreover, we adopt a bidirectional semantic representation with the orthogonal constraint to keep intrinsic semantic information of all samples in whole image sets adequately. Comprehensive experiments demonstrate HHL acquires significant improvements in accuracy and running time. We will release the demo code on https://github.com/sunyuan-cs.

Single-Cell RNA-Seq Debiased Clustering via Batch Effect Disentanglement.

A variety of single-cell RNA-seq (scRNA-seq) clustering methods has achieved great success in discovering cellular phenotypes. However, it remains challenging when the data confounds with batch effects brought by different experimental conditions or technologies. Namely, the data partitions would be biased toward these nonbiological factors. Meanwhile, the batch differences are not always much smaller than true biological variations, hindering the cooperation of batch integration and clustering methods. To overcome this challenge, we propose single-cell RNA-seq debiased clustering (SCDC), an end-to-end clustering method that is debiased toward batch effects by disentangling the biological and nonbiological information from scRNA-seq data during data partitioning. In six analyses, SCDC qualitatively and quantitatively outperforms both the state-of-the-art clustering and batch integration methods in handling scRNA-seq data with batch effects. Furthermore, SCDC clusters data with a linearly increasing running time with respect to cell numbers and a fixed graphics processing unit (GPU) memory consumption, making it scalable to large datasets. The code will be released on Github.

Electroacupuncture for acute gouty arthritis: a systematic review and meta-analysis of randomized controlled trials.

Acute gouty arthritis (AGA) is a metabolic disorder in which recurrent pain episodes can severely affect the quality of life of gout sufferers. Electroacupuncture (EA) is a non-pharmacologic therapy. This systematic review aimed to assess the efficacy and safety of electroacupuncture in treating acute gouty arthritis. We searched eight Chinese and English databases from inception to July 30, 2023, and 242 studies were retrieved. Finally, 15 randomized controlled trials (n=1076) were included in a meta-analysis using Review Manager V.5.4.1. meta-analysis results included efficacy rate, visual rating scale (VAS) for pain, serum uric acid level (SUA), immediate analgesic effect, and incidence of adverse events. Electroacupuncture (or combined non-pharmacologic) treatment of AGA was significantly different from treatment with conventional medications (RR = 1.14, 95% confidence interval CI = 1.10 to 1.19, P < 0.00001). The analgesic effect of the electroacupuncture group was superior to that of conventional Western drug treatment (MD = -2.26, 95% CI = -2.71 to -1.81, P < 0.00001). The electroacupuncture group was better at lowering serum uric acid than the conventional western drug group (MD =-31.60, CI -44.24 to -18.96], P < 0.00001). In addition, electroacupuncture combined with Western drugs had better immediate analgesic effects than conventional Western drug treatment (MD = -1.85, CI -2.65 to -1.05, P < 0.00001). Five studies reported adverse events in the electroacupuncture group versus the drug group, including 19 cases of gastrointestinal symptoms and 6 cases of neurological symptoms (RR = 0.20, 95% CI = 0.04 to 0.88, P = 0.03). Systematic review registration: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=450037, identifier CRD42023450037.

Unsupervised Contrastive Cross-Modal Hashing.

In this paper, we study how to make unsupervised cross-modal hashing (CMH) benefit from contrastive learning (CL) by overcoming two challenges. To be exact, i) to address the performance degradation issue caused by binary optimization for hashing, we propose a novel momentum optimizer that performs hashing operation learnable in CL, thus making on-the-shelf deep cross-modal hashing possible. In other words, our method does not involve binary-continuous relaxation like most existing methods, thus enjoying better retrieval performance; ii) to alleviate the influence brought by false-negative pairs (FNPs), we propose a Cross-modal Ranking Learning loss (CRL) which utilizes the discrimination from all instead of only the hard negative pairs, where FNP refers to the within-class pairs that were wrongly treated as negative pairs. Thanks to such a global strategy, CRL endows our method with better performance because CRL will not overuse the FNPs while ignoring the true-negative pairs. To the best of our knowledge, the proposed method could be one of the first successful contrastive hashing methods. To demonstrate the effectiveness of the proposed method, we carry out experiments on five widely-used datasets compared with 13 state-of-the-art methods. The code is available at https://github.com/penghu-cs/UCCH.

Acupuncture for postoperative gastrointestinal dysfunction in cancer: a systematic review and meta-analysis.

Background: Postoperative gastrointestinal dysfunction (PGD) in cancer is the commonest and most severe postoperative complication in patients with cancer. Acupuncture has been widely used for PGD in cancer. This study aimed to evaluate the efficacy and safety of acupuncture for PGD in cancer. Methods: We comprehensively searched eight randomised controlled trials (RCTs) of acupuncture for PGD in cancer published until November 2022. Time to first flatus (TFF) and time to first defecation (TFD) were the primary outcomes, and time to bowel sound recovery (TBSR) and the length of hospital stay (LOS) were the secondary outcomes. The Cochrane Collaboration Risk of Bias Tool was used to assess the quality of the RCTs, and the Grading of Recommendations Assessment, Development, and Evaluations (GRADE) system was used to evaluate the certainty of the evidence. The meta-analysis was performed using RevMan 5.4, and a publication bias test was performed using Stata 15.1. Results: Sixteen RCTs involving 877 participants were included in this study. The meta-analysis indicated that acupuncture could effectively reduce the TFF, TFD, and TBSR compared with routine treatment (RT), sham acupuncture, and enhanced recovery after surgery (ERAS). However, acupuncture did not shorten the LOS compared with RT and ERAS. The subgroup analysis revealed that acupuncture could significantly reduce the TFF and TFD. Acupuncture effectively reduced the TFF and TFD in all cancer types included in this review. Besides, local acupoints in combination with distal acupoints could reduce the TFF and TFD, and distal-proximal acupoints could significantly reduce the TFD. No trial reported adverse events of acupuncture. Conclusions: Acupuncture is an effective and relatively safe modality for treating PGD in cancer. We anticipate that there will be more high-quality RCTs involving more acupuncture techniques and cancer types, focusing on combining acupoints for PGD in cancer, further determining the effectiveness and safety of acupuncture for PGD in patients with cancer outside China. Systematic review registration: https://www.crd.york.ac.uk/prospero, identifier CRD42022371219.