Visual Question Answering (VQA) is a significant cross-disciplinary issue in the fields of computer vision and natural language processing that requires a computer to output a natural language answer based on pictures and questions posed based on the pictures. This requires simultaneous processing of multimodal fusion of text features and visual features, and the key task that can ensure its success is the attention mechanism. Bringing in attention mechanisms makes it better to integrate text features and image features into a compact multi-modal representation. Therefore, it is necessary to clarify the development status of attention mechanism, understand the most advanced attention mechanism methods, and look forward to its future development direction. In this article, we first conduct a bibliometric analysis of the correlation through CiteSpace, then we find and reasonably speculate that the attention mechanism has great development potential in cross-modal retrieval. Secondly, we discuss the classification and application of existing attention mechanisms in VQA tasks, analysis their shortcomings, and summarize current improvement methods. Finally, through the continuous exploration of attention mechanisms, we believe that VQA will evolve in a smarter and more human direction.

The etiology of neuropsychiatric disorders involves complex biological processes at different omics layers, such as genomics, transcriptomics, epigenetics, proteomics, and metabolomics. The advent of high-throughput technology, as well as the availability of large open-source datasets, has ushered in a new era in system biology, necessitating the integration of various types of omics data. The complexity of biological mechanisms, the limitations of integrative strategies, and the heterogeneity of multi-omics data have all presented significant challenges to computational scientists. In comparison to early and late integration, intermediate integration may transform each data type into appropriate intermediate representations using various data transformation techniques, allowing it to capture more complementary information contained in each omics and highlight new interactions across omics layers. Here, we reviewed multi-modal intermediate integrative techniques based on component analysis, matrix factorization, similarity network, multiple kernel learning, Bayesian network, artificial neural networks, and graph transformation, as well as their applications in neuropsychiatric domains. We depicted advancements in these approaches and compared the strengths and weaknesses of each method examined. We believe that our findings will aid researchers in their understanding of the transformation and integration of multi-omics data in neuropsychiatric disorders.

This systematic review and meta-analysis aimed to identify the effect of multi-modal therapies that combined physical and psychological therapies for kinesiophobia caused by musculoskeletal disorders compared with uni-modal therapy of only phycological therapy or psychological therapy. The search terms and their logical connector were as following: (1) \"kinesiophobia\" at the title or abstract; and (2) \"randomized\" OR \"randomized\" at title or abstract; not (3) \"design\" OR \"protocol\" at the title. They were typed into the databases of Medline (EBSCO), PubMed, and Ovid, following the different input rules of these databases. The eligibility criteria were: (1) Adults with musculoskeletal disorders or illness as patients; (2) Multi-modal therapies combined physical and psychological therapy as interventions; (3) Uni-modal therapy of only physical or psychological therapy as a comparison; (4) The scores of the 17-items version of the Tampa Scale of Kinesiophobia as the outcome; (5) Randomized controlled trials as study design. As a result, 12 studies were included with a statistically significant polled effect of 6.99 (95% CI 4.59 to 9.38). Despite a large heterogeneity within studies, multi-modal therapies might be more effective in reducing kinesiophobia than the unimodal of only physical or psychological therapy both in the total and subdivision analysis. The effect might decrease with age. What\'s more, this review\'s mathematical methods were feasible by taking test-retest reliability of the Tampa Scale of Kinesiophobia into consideration.

Adjuvants are medications that work synergistically with local anesthetics to help enhance the duration and quality of analgesia in regional techniques. Regional anesthesia has become more prevalent as evidence continues to show efficacy, enhancement of patient care, increased patient satisfaction, and improved patient safety. Practitioners in the perioperative setting need to not only be familiar with regional techniques but also the medications used for them. Some examples of adjuvant medications for regional techniques include dexamethasone, alpha 2 agonists such as clonidine and dexmedetomidine, midazolam, buprenorphine, NMDA antagonists, including ketamine and magnesium, neostigmine, sodium bicarbonate, epinephrine, and non-steroidal anti-inflammatory drugs. The aim of the present investigation, therefore, is to provide a comprehensive review of the most commonly used non-opioid adjuvants in clinical practice today. Regional adjuvants can improve patient safety, increase patient satisfaction, and enhance clinical efficacy. Future studies and best practice techniques can facilitate standardization of regional anesthesia adjuvant dosing when providing nerve blocks in clinical practice.

Hepatocellular carcinoma (HCC) arises from a number of cirrhosis-related and non-cirrhosis-related exposures and is one of the leading causes of cancer-related deaths worldwide. Achieving a durable cure currently relies on either resection or transplantation, but since most patients will be diagnosed with inoperable disease, there is great interest in achieving more effective systemic therapies. At a molecular level, HCC is heterogeneous, but initial treatment strategies, including the use of multi-targeted tyrosine kinase inhibitors and checkpoint inhibitors, have been fairly homogenous, depending on general host factors and overall tumor burden rather than specific molecular signatures. Over the past 2 decades, however, there has been significant success in identifying key molecular targets, including driver mutations involving the telomerase reverse transcriptase, p53, and beta-catenin genes, and significant work is now being devoted to translating these discoveries into the development of robust and well-tolerated targeted therapies. Furthermore, multi-modal therapies have also begun to emerge, harnessing possible synergism amongst a variety of different treatment classes. As the findings of these landmark trials become available over the next several years, the landscape of the systemic management of advanced HCC will change significantly.