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Textual language is the most natural carrier of human emotion. In natural language processing, textual emotion recognition (TER) has become an important topic due to its significant academic and commercial potential. With the advanced development of deep learning technologies, TER has attracted growing attention and has significantly been promoted in recent years. This paper provides a systematic survey of latest TER advances, focusing on approaches using deep neural networks. According to how deep learning works at each stage, TER approaches are reviewed on word embedding, architecture, and training levels, respectively. We discussed the remaining challenges and opportunities from four aspects: the shortage of large-scale and high-quality dataset, fuzzy emotional boundaries, incomplete extractable emotional information in texts, and TER in dialogue. This paper creates a systematic and in-depth overview of deep TER technologies. It provides the necessary knowledge and new insights for relevant researchers to understand better the research state, remaining challenges, and future directions in this field.

Textual emotion detection is an attractive task while previous studies mainly focused on polarity or single-emotion classification. However, human expressions are complex, and multiple emotions often occur simultaneously with non-negligible emotion correlations. In this paper, a Multi-label Emotion Detection Architecture (MEDA) is proposed to detect all associated emotions expressed in a given piece of text. MEDA is mainly composed of two modules: Multi-Channel Emotion-Specified Feature Extractor (MC-ESFE) and Emotion Correlation Learner (ECorL). MEDA captures underlying emotion-specified features through MC-ESFE module in advance. MC-ESFE is composed of multiple channel-wise ESFE networks. Each channel is devoted to the feature extraction of a specified emotion from sentence-level to context-level through a hierarchical structure. Based on obtained features, emotion correlation learning is implemented through an emotion sequence predictor in ECorL. During model training, we define a new loss function, which is called multi-label focal loss. With this loss function, the model can focus more on misclassified positive-negative emotion pairs and improve the overall performance by balancing the prediction of positive and negative emotions. The evaluation of proposed MEDA architecture is carried out on emotional corpus: RenCECps and NLPCC2018 datasets. The experimental results indicate that the proposed method can achieve better performance than state-of-the-art methods in this task.

Guidelines have recommended the use of dexmedetomidine or propofol for sedation after cardiac surgery, and propofol monotherapy for other patients. Further outcome data are required for these drugs.This systematic review and meta-analysis was prospectively registered on PROSPERO. The primary outcome was ICU length of stay. Secondary outcomes included duration of mechanical ventilation, ICU delirium, all-cause mortality, and haemodynamic effects. Intensive care patients were analysed separately as cardiac surgical, medical/noncardiac surgical, those with sepsis, and patients in neurocritical care. Subgroup analyses based on age and dosage were conducted.Forty-one trials (N=3948) were included. Dexmedetomidine did not significantly affect ICU length of stay across any ICU patient subtype when compared with propofol, but it reduced the duration of mechanical ventilation (mean difference -0.67 h; 95% confidence interval: -1.31 to -0.03 h; P=0.041; low certainty) and the risk of ICU delirium (risk ratio 0.49; 95% confidence interval: 0.29-0.87; P=0.019; high certainty) across cardiac surgical patients. Dexmedetomidine was also associated with a greater risk of bradycardia across a variety of ICU patients. Subgroup analyses revealed that age might affect the incidence of haemodynamic side-effects and mortality among cardiac surgical and medical/other surgical patients.Dexmedetomidine did not significantly impact ICU length of stay compared with propofol, but it significantly reduced the duration of mechanical ventilation and the risk of delirium in cardiac surgical patients. It also significantly increased the risk of bradycardia across ICU patient subsets.

Aim: To evaluate the efficacy and safety of corticosteroids for treating hospitalized COVID-19 patients. Materials & methods: Efficacy outcomes included time to negative SARS-CoV-2 tests, length of stay, duration and incidence of intensive unit care stay, incidence of mortality and duration and incidence of mechanical ventilation. Safety outcomes included the incidence of adverse events and severe adverse events, incidence of hyperglycemia and incidence of nosocomial infections. Results: Ninety-five randomized controlled trials (RCTs) and observational studies (n = 42,205) were included. Corticosteroids were associated with increased length of stay (based on RCT only), increased time to negative tests, decreased length of mechanical ventilation and increased odds of hyperglycemia. Conclusion: Corticosteroids should be considered in patients requiring mechanical ventilation, and glycemic monitoring may be needed when administering corticosteroids.

Background Rapid diagnosis of microbes in the burn wound is a big challenge in the medical field. Traditional biochemical detection techniques take hours or days to identify the species of contaminating and drug-resistant microbes. Near-infrared spectroscopy (NIRS) is evaluated to address the need for a fast and sensitive method for the detection of bacterial contamination in liquids. Methods Herin, we developed a novel technique which by using NIRS together with supporting vector machine (SVM), to identify the microbial species and drug-resistant microbes in LB medium, and to diagnose the wound colonization and wound infection models of pigs. Results The device could recognize 100% of seven kinds of microbes and 99.47% of the multi-drug resistant Staphylococcus aureus (S. aureus), with a concentration of 109 cfu ml−1 in LB medium. The accuracy of the microbial identification in colonized and infected wounds in-situ was 100%. The detection limit of NIRS with SVM for the detection of S. aureus and Escherichia coli (E. coli) was 101 cfu ml−1 in LB medium. Identification time was less than 5 s. Conclusion Our findings validate for the first time a novel technique aimed at the rapid, noncontacted, highly sensitive, and specific recognition of several microbial species including drug-resistant ones. This technique could represent a promising approach to identify diverse microbial species and a potential bedside device to rapidly diagnose infected wounds.

Objective: To assess the effectiveness of acupuncture for the treatment of Raynaud’s syndrome by conducting a systematic review and meta-analysis of randomized controlled trials (RCTs). Methods: Studies were identified from English and Chinese databases from their inception to September 2020. The outcomes of interest were remission incidence, number of daily attacks, incidence of positive cold stimulation tests and incidence of cold provocation tests. We conducted meta-analysis and network meta-analysis using meta and gemtc. Results: Six trials (n = 272 participants) were included in the meta-analysis. Pairwise meta-analyses show that acupuncture was associated with increased remission incidence (risk ratio (RR) = 1.21, 95% confidence interval (CI) = 1.10 to 1.34), decreased daily number of attacks (weighted mean difference (WMD) = −0.57, 95% CI = −1.14 to −0.01), and increased incidence of positive cold stimulation tests (RR = 1.64, 95% CI = 1.27 to 2.11). There was not enough evidence to associate acupuncture with decreased incidence of positive cold provocation tests. The network meta-analyses did not demonstrate significant results for the effectiveness of any acupuncture treatments (electroacupuncture or manual acupuncture ± moxibustion), compared with controls, in terms of remission incidence or daily number of attacks, possibly due to small sample sizes and a lack of statistical power. Conclusion: The use of acupuncture may be effective for the treatment of Raynaud’s syndrome in terms of increasing remission incidence, decreasing daily number of attacks and increasing incidences of positive cold stimulation tests. However, our findings should be interpreted with caution due to small sample sizes, very low quality of evidence and high risk of bias. Future large-scale RCTs are warranted.

Authors: Ye-Jean Park, Abhinav Pillai, Jiawen Deng, Mehul Gupta, Edward Guo, Mike Paget, and Christopher Naugler Large language models (LLMs) – artificial intelligence (AI) systems based on deep learning algorithms– are capable of processing and summarizing vast volumes of text and subsequently generating human-like responses when prompted (1). Through 1) language model pretraining, where the model learns a large corpus of raw, unlabeled text in an unsupervised manner, and 2) fine-tuning, where the model learns to apply knowledge from one task to another particular task using labelled training data, LLMs are ultimately able to understand and create a countless number of text styles and syntaxes (1). Techniques such as transfer learning have enabled LLMs to expand their capabilities beyond pretraining and fine-tuning, and now include techniques such as zero-shot and few-shot learning, allowing them to perform new tasks with little to no additional training data (2, 3). Therefore, LLMs have broad applicability to systems ranging from natural language processors (NLPs) to language translators to virtual assistant chatbots. Several types of LLMs exist, including transformer models, such as Bidirectional Encoder Representations from Transformers (BERT) or Generative Pre-trained Transformer (GPT) (4). Such models are based on what is known as a transformer architecture, a type of neural network model considered the state-of-the-art system used for natural language processing tasks given its self-attention mechanism (used to process input sequences in parallel form and therefore learn context and relationships between words in the given sequences) (4). ChatGPT, an AI model created by Open AI lab in San Francisco that launched in November of 2022, is a variant of GPT and build with the GPT-3.5 large language model (5). As an LLM, ChatGPT is trained on an approximately 570 GB-sized dataset consisting of billions of words and text from multiple languages, including from CommonCrawl and WebText, internet-based book corpora, Wikipedia (8). ChatGPT also possesses a language model head along with a dialogue generation head, used for Reinforcement Learning from Human Feedback (RLHF) to understand and generate human-like text (8). Furthermore, ChatGPT performs “continual/incremental learning”, wherein the model can maintain a memory of previous input and prompts to subsequently improve the accuracy and relevance of its future responses with each iteration of text by strengthening its neural network (5). ChatGPT is also “auto-aggressive”, meaning it can predict and generate future words based on previous sequences presented to it in a uni-directional manner, whereas the comparator BERT has bi-directional architecture and therefore analyzes a word based on the word’s left and right context (4, 7-9). With all these characteristics, ChatGPT has recently emerged as a more promising candidate for original text and dialogue generation, and is a prevalent subject of ongoing discourse and research. Given its potential impact in the real-world context, GPT has been increasingly explored across many contexts and disciplines. For instance, researchers internationally are assessing the model’s performance on business, law, and medical school board exams, its ability to code in Python and JavaScript, and its capabilities of creating original literature and poems (10-13). One particular area of interest is the broader applicability of LLMs like GPT in the realm of biomedicine and healthcare. In fact, BioGPT was launched in late 2022 and was trained on millions of PubMed abstracts to be one of the first biomedical-specific versions of GPT and thereby address limitations of GPT variants in providing evidence-based, tailored responses in biomedical sciences (14). In a similar vein, GPT-4 was launched on March 15, 2023, and said to address several limitations of GPT-3 models (as evidenced by its ranking in some of the highest percentiles on standardized exams rather than in the lower percentiles, possessing greater accuracy across multiple languages, and GPT-4’s capacity to accept prompts consisting of images and diagrams, unlike GPT-3 (15)). Overall, LLMs – particularly GPT and its variants – appear to possess both promising but also concerning features if integrated into the healthcare setting. For instance, several authors have described LLMs’ potential in clinical decision support to provide further information for healthcare teams to make more informed decisions for patient care (16, 18). Others have highlighted the improved speed and efficiency of clinical practice by using programs like ChatGPT in composing patient charts or in creating efficient, standardized discharge notes (18). Furthermore, all of these could be further vetted by the healthcare team to avoid errors while saving time for healthcare providers to focus on delivering optimized patient care (19). Likewise, outside of the immediate clinical setting, individuals may use widely accessible LLMs like ChatGPT in asking their health-related questions. Thus, LLMs may not only have the potential to elevate patient care and optimize hospital administrative tasks, but also in empowering patients with healthcare knowledge that, ideally, is always verified with their healthcare teams. Despite this, concerning elements of using LLMs in healthcare may include the presence of bias and lack of transparency in the training dataset, along with ethical/legal drawbacks when LLMs provide inaccurate recommendations and potentially lead to harm to patients (10, 19, 20). Furthermore, there are also logistical concerns with integrating LLMs into the current system of EMRs or paper charts worldwide, given data privacy issues and security breaches within or between institutions (19, 20). The creation and dissemination of robust, evidence-based research, preferably conducted with a clear and standardized method, should help the scientific community further explore these concerns alongside the potential benefits of LLMs in a clinical context. There is currently a paucity of research around the specific research criteria, recommendations, and methods of evaluating the clinical utility and limitations of GPT models. Thus, the objectives of our scoping review are: Objectives: To elucidate the research landscape regarding the clinical utility of LLMs – with a particular focus on ChatGPT/GPT-3 and GPT-4 and provide a summary of its effectiveness, accuracy, and efficacy (i.e., in the realms of clinical decision-making support, operational efficiency, and patient communication) To explore the ethical, legal, socioeconomic, and other implications of integrating LLMs in the clinical setting To scope practical barriers to the implementation of LLMs in the clinical context (i.e. costs, EMRs integration, data sharing across institutions) and key factors of successful implementation of LLMs in healthcare settings To propose a standardized, more rigorous methodology of investigating and evaluating LLMs’ clinical utility for future research studies To recommend specific clinical contexts that LLMs should be further investigated and may have the highest potential for impacting patient care. References: Yang X, Chen A, PourNejatian N, Shin HC, Smith KE, Parisien C, Compas C, Martin C, Costa AB, Flores MG, Zhang Y, Magoc T, Harle CA, Lipori G, Mitchell DA, Hogan WR, Shenkman EA, Bian J, Wu Y. A large language model for electronic health records. NPJ Digit Med. 2022 Dec 26;5(1):194. doi: 10.1038/s41746-022-00742-2. Weiss, K., Khoshgoftaar, T.M. & Wang, D. A survey of transfer learning. J Big Data 3, 9 (2016). https://doi.org/10.1186/s40537-016-0043-6 Kojima T, Shane SG, Reid M, Matsuo Y, Iwasaw Y. Large Language Models are Zero-Shot Reasoners. arXiv. 2022 May 24: v4. https://doi.org/10.48550/arXiv.2205.11916 Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez A, Kaiser L, Polosukhin I. Attention Is All You Need. arXiv. 2017 June 12;v1. https://doi.org/10.48550/arXiv.1706.03762 Open AI. https://openai.com/blog/chatgpt. Accessed March 17, 2023. Liu S, Wright AP, Patterson BL, Wanderer JP, Turer RW, Nelson SD, McCoy AB, Sittig DF, Wright A. Assessing the Value of ChatGPT for Clinical Decision Support Optimization. medRxiv [Preprint]. 2023 Feb 23:2023.02.21.23286254. doi: 10.1101/2023.02.21.23286254. Invgate. https://blog.invgate.com/gpt-3-vs-bert. Accessed March 17, 2023. Devlin J, Chang MW, Lee K, Toutanova K. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv [Preprint]. 2018 Oct; v1. https://doi.org/10.48550/arXiv.1810.04805 Ji Z, Wei Q, Xu H. BERT-based Ranking for Biomedical Entity Normalization. AMIA Jt Summits Transl Sci Proc. 2020 May 30;2020:269-277. Sallam M. The Utility of ChatGPT as an Example of Large Language Models in Healthcare Education, Research and Practice: Systematic Review on the Future Perspectives and Potential Limitations. medRxiv [Pre-print]. 2023 Feb 21: doi: https://doi.org/10.1101/2023.02.19.23286155 Gilson A, Safranek CW, Huang T, Socrates V, Chi L, Taylor RA, Chartash D. How Does ChatGPT Perform on the United States Medical Licensing Examination? The Implications of Large Language Models for Medical Education and Knowledge Assessment. JMIR Med Educ. 2023 Feb 8;9:e45312. doi: 10.2196/45312. PMID: 36753318 Kelly, S. ChatGPT passes exams from law and business schools. https://www.cnn.com/2023/01/26/tech/chatgpt-passes-exams/index.html. Accessed March 17, 2023. Chaarani, J. ChatGPT wrote a poem about winter, but is it truly art? This Waterloo AI ethicist weighs in. https://www.cbc.ca/news/canada/kitchener-waterloo/chatgpt-ai-text-university-waterloo-maura-grossman-1.6703819#:~:text=ChatGPT%20is%20artificial%20intelligence%20chatbot,still%20spotlighting%20concerns%20around%20accuracy.. Accessed March 17, 2023. Luo R, Sun L, Xia Y, Qin T, Zhang S, Poon H, Liu T. BioGPT: Generative Pre-trained Transformer for Biomedical text Generation and Mining. arXiv [Pre-print]. 2023 Jan;v2. https://doi.org/10.48550/arXiv.2210.10341 Open AI. https://openai.com/research/gpt-4. Accessed March 17, 2023. Levine DM, Tuwani R, Kompa B, Varma A, Finlayson SG, Mehrotra A, Beam A. The Diagnostic and Triage Accuracy of the GPT-3 Artificial Intelligence Model. medRxiv [Preprint]. 2023 Feb 1:2023.01.30.23285067. doi: 10.1101/2023.01.30.23285067. Patel SB, Lam K. ChatGPT: the future of discharge summaries? Lancet Digit Health. 2023 Mar;5(3):e107-e108. doi: 10.1016/S2589-7500(23)00021-3. Stewart, Jonathon et al "Applications of Natural Language Processing at Emergency Department Triage: A Systematic Review." medRxiv (2022): 2022.12.20.22283735. Web. 21 Feb. 2023. Stokel-Walker C, Van Noorden R. What ChatGPT and generative AI mean for science. Nature. 2023 Feb;614(7947):214-216. doi: 10.1038/d41586-023-00340-6. Nov, Oded, Nina Singh, and Devin M. Mann. "Putting ChatGPT’s Medical Advice to the (Turing) Test." medRxiv (2023): 2023.01.23.23284735. Web. 22 Feb. 2023.

Objective Structured Clinical Examinations (OSCEs) and written tests are commonly used to assess health professional students, but it remains unclear whether the additional human resources and expenses required for OSCEs, both in-person and online, are worthwhile for assessing competencies. This scoping review summarized literature identified by searching MEDLINE and EMBASE comparing 1) OSCEs and written tests and 2) in-person and online OSCEs, for assessing health professional trainees’ competencies. For Q1, 21 studies satisfied inclusion criteria. The most examined health profession was medical trainees (19, 90.5%), the comparison was most frequently OSCEs versus multiple-choice questions (MCQs) (18, 85.7%), and 18 (87.5%) examined the same competency domain. Most (77.5%) total score correlation coefficients between testing methods were weak ( r < 0.40). For Q2, 13 articles were included. In-person and online OSCEs were most used for medical trainees (9, 69.2%), checklists were the most prevalent evaluation scheme (7, 63.6%), and 14/17 overall score comparisons were not statistically significantly different. Generally low correlations exist between MCQ and OSCE scores, providing insufficient evidence as to whether OSCEs provide sufficient value to be worth their additional cost. Online OSCEs may be a viable alternative to in-person OSCEs for certain competencies where technical challenges can be met.

Nitrogen is one of the most important nutrients for wheat growth and has a critical influence on yield and quality. This study aims to examine how medium nitrogen level (240 kg/hm2) affects the starch granule development, starch accumulation, and structural characteristics of wheat starch. The results showed that nitrogen treatment could reduce the biosynthesis of starch and amylose, delay the degradation of starch in pericarp, and promote the proportion of B-type small starch granule in endosperm compared with those in the N0. In addition, the composition and distribution of starch granules were changed, the crystal structure in the inner lamellae and ordered structure in the external region of starch granules were affected, and the swelling power and solubility of starch during wheat development were increased. The effect of nitrogen treatment on the mRNA expression of enzymes related to starch biosynthesis or degradation varied in different developmental stages. During middle and later grain filling stages, AGPase, GBSSI, and GBSSII were lower, and SSS, SBE, and DBE were higher in N240 than in N0. This study indicated that nitrogen application at booting stage significantly affected the structural characteristics of starch, and ultimately determines its quality.

Objective To perform a network meta-analysis (NMA) on the efficacy of antiosteoporotic interventions in the prevention of vertebral and non-vertebral fractures in adult patients taking glucocorticoids (GCs). Methods We performed NMAs based on a prospectively developed protocol. A librarian-assisted database search of MEDLINE, EMBASE, Web of Science, Cumulative Index of Nursing and Allied Health Literature (CINAHL), the Cochrane Central Register of Controlled Trials (CENTRAL) and Chinese databases was conducted for randomized controlled trials (RCTs) comparing antiosteoporotic interventions in adult patients taking GCs. Outcomes were vertebral and non-vertebral fracture incidences. Results We included 56 RCTs containing 6479 eligible patients in our analysis. We found that alendronate and teriparatide were associated with decreased odds of both vertebral and non-vertebral fractures. Denosumab and risedronate were associated with decreased odds of vertebral fractures, while etidronate, ibandronate and alfacalcidol were associated with decreased odds of non-vertebral fractures. We observed low network heterogeneity as indicated by the I2 statistic, and we did not detect evidence of publication bias. All outcomes were based on a moderate quality of evidence according to GRADE. Conclusion Bisphosphonates, teriparatide and denosumab are associated with decreased odds of fracture in patients undergoing GC therapy. Vitamin D metabolites and analogues (e.g. alfacalcidol) may have greater anti-fracture efficacy compared with plain vitamin D. Systematic Review Registration The International Prospective Register of Systematic Reviews (PROSPERO)—CRD42019127073

The present study aimed to study the preparation and quality evaluation of potato steamed bread by using potato flour, wheat flour, and gluten at the presence of yeast and inorganic additives. As the rheological properties of the potato–wheat formulated flour negatively related to the potato flour, the potato–wheat formulated flour with 35% potato flour was set as the basic flour (100%). The effects of wheat gluten on the rheological properties of the dough were also evaluated, and gluten addition amount was set at 6.5%. The effects of yeast, sodium bicarbonate, citric acid, and monocalcium phosphate addition on steamed bread properties have been studied and optimized by orthogonal test. The obtained potato steamed bread formula was 100% basic flour (potato/wheat mass ratio of 35:65), 6.5% wheat gluten, 1.1% yeast, 1.4% NaHCO3, 0.75% citric acid, and 0.50% Ca(H2PO4)2. The prepared potato steamed bread has good sensory and texture properties, with natural potato flavor., Potato steamed bread.

The nutritional composition and chemical properties of the Chinese highland barley bran oil were characterized in this study. The barley bran oil extracted with solvent possessed relatively high acid value and peroxide value, indicating that the oil should be further refined before using. The fatty acid composition of the oil showed that the content of unsaturated fatty acids was 80.12 g/100 g, in which the content of polyunsaturated fatty acids was as high as 60.41 g/100 g. The overall triacylglycerol profile showed that the oil contained 27 TAGs including 21 regioisomers. Major TAGs included LLL (21.08 g/100 g), PLL (19.27 g/100 g), LLO (12.24 g/100 g), and LLLn (12.17 g/100 g). The total unsaponifiable matter of the oil reached up to 10.74 g/100 g oil. The total phytosterol content reached 7.90 g/100 g oil, in which β-sitosterol was the most predominant, with the content of 5.69 g/100 g oil. Other important sterols included campesterol (1.32 g/100 g oil), lanosterol (0.70 g/100 g oil) and stigmasterol (0.19 g/100 g oil).

To assess and compare the relative efficacy and safety of anti-SARS-CoV-2 antibody regimens for COVID-19.This systematic review and random-effects network meta-analysis was conducted according to PRISMA-NMA. Literature searches were conducted across MEDLINE, EMBASE, PubMed, Web of Science, CENTRAL, and CNKI up to February 20th, 2022. Interventions were ranked using P scores.Fifty-five RCTs (N = 45,005) were included in the review. Bamlanivimab + etesevimab (OR 0.13, 95% CI 0.02-0.77) was associated with a significant reduction in mortality compared to standard of care/placebo. Casirivimab + imdevimab reduced mortality (OR 0.67, 95% CI 0.50-0.91) in baseline seronegative patients only. Four different regimens led to a significant decrease in the incidence of hospitalization compared to standard of care/placebo with sotrovimab ranking first in terms of efficacy (OR 0.20, 95% CI 0.08-0.48). No treatment improved incidence of mechanical ventilation, duration of hospital/ICU stay, and time to viral clearance. Convalescent plasma and anti-COVID IVIg both led to a significant increase in adverse events compared to standard of care/placebo, but no treatment increased the odds of serious adverse events.Anti-SARS-CoV-2 mAbs are safe, and could be effective in improving mortality and incidence of hospitalization. Convalescent plasma and anti-COVID IVIg were not efficacious and could increase odds of adverse events. Future trials should further examine the effect of baseline seronegativity, disease severity, patient risk factors, and SARS-CoV-2 strain variation on the efficacy of these regimes.PROSPERO-CRD42021289903.

Background Promoting wound healing is crucial to restore the vital barrier function of injured skin. Growth factor products including epidermal growth factor (EGF), fibroblast growth factor (FGF) and granulocyte-macrophage colony stimulating factor (GM-CSF) have been used for decades although no systematic evaluation exists regarding their effectiveness and safety issues in treating acute skin wounds. This has resulted in a lack of guidelines and standards for proper application regimes. Therefore, this systematic review and meta-analysis was performed to critically evaluate the effectiveness and safety of these growth factors on skin acute wounds and provide guidelines for application regimes. Methods We searched PubMed/Medline (1980–2020), Cochrane Library (1980–2020), Cochrane CENTRAL (from establishment to 2020), ClinicalTrials.gov (from establishment to 2020), Chinese Journal Full-text Database (CNKI, 1994–2020), China Biology Medicine disc (CBM, 1978–2019), Chinese Scientific Journal Database (VIP, 1989–2020) and Wanfang Database (WFDATA, 1980–2019). Randomized controlled trials (RCTs), quasi-RCTs and controlled clinical trials treating patients with acute skin wounds from various causes and with those available growth factors were included. Results A total of 7573 papers were identified through database searching; 229 papers including 281 studies were kept after final screening. Administering growth factors significantly shortened the healing time of acute skin wounds, including superficial burn injuries [mean difference (MD) = −3.02; 95% confidence interval (CI):−3.31 ~ −2.74; p < 0.00001], deep burn injuries (MD = −5.63; 95% CI:−7.10 ~ −4.17; p < 0.00001), traumata and surgical wounds (MD = −4.50; 95% CI:−5.55 ~ −3.44; p < 0.00001). Growth factors increased the healing rate of acute skin wounds and decreased scar scores. The incidence of adverse reactions was lower in the growth factor treatment group than in the non-growth factor group. Conclusions The studied growth factors not only are effective and safe for managing acute skin wounds, but also accelerate their healing with no severe adverse reactions.

Rich in phenolic compounds, Moringa oleifera leaf extract (ME) exhibits significant antioxidant activity both in vitro and in vivo. ME has already been widely used in fields of medicine, functional food, and cosmetics. Ultrasonic extraction (UE) method has been improved to be one of the most effective ways to extract phenols from M. oleifera leaves. The purpose of this study was to optimize ultrasonic extraction of phenols by response surface methodology (RSM). Four parameters were discussed, such as ethanol concentration, solvent-sample ratio, extraction temperature, and extraction time. Also, purification methods of the crude ME by organic solvent extraction and column chromatography were examined. Antioxidant activities of ME and each fraction were evaluated by DPPH, ABTS, and hydroxy radical-scavenging activities and reducing power. The phenol content of the purified ME reached up to 962.6 mg RE/g, extremely higher than the crude extract 107.22 ± 1.93 mg RE/g. The antioxidant activity of the purified ME was also significantly improved. Furthermore, phenols were identified by using the HPLC-MS method, and the results showed that there were 6 phenolic acids and derivatives and 7 flavonoids in ME. Quercetin-3-O-β-D-glucoside isolated from ME showed excellent DPPH and ABTS radical-scavenging abilities, which were comparable to VC.