Efficacy of respiratory support therapies during pulmonary rehabilitation exercise training in chronic obstructive pulmonary disease patients: a systematic review and network meta-analysis.

BACKGROUND: Exercise training is fundamental in pulmonary rehabilitation (PR), but patients with chronic obstructive pulmonary disease (COPD) often struggle with exercise intolerance. Respiratory support during exercise in COPD patients may be a beneficial adjunct therapy. In this study, the effect of different respiratory support therapy during pulmonary rehabilitation exercise training in COPD patients was assessed through a network meta-analysis. METHODS: Five databases were searched to obtain randomized controlled trials involving different respiratory support therapies during PR exercise training in COPD patients. The Cochrane Handbook tool was employed to assess the risk bias of included studies. Network meta-analysis was performed using the STATA software. The study protocol was registered at PROSPERO (CRD42023491139). RESULTS: A total of 35 studies involving 1321 patients and 6 different interventions were included. Network meta-analysis showed that noninvasive positive pressure ventilation (NPPV) is superior in improving exercise capacity (6-Minute Walk Test distance, peak work rate, endurance time), dyspnea, and physiological change (peak VO2, tidal volume, minute ventilation and lactate level) in stable COPD patients who were at GOLD stage III or IV during PR exercise training. The final surface under the cumulative ranking curve value indicated that NPPV therapy achieved the best assistive rehabilitation effect. CONCLUSIONS: The obtained results indicate that NPPV is most powerful in assisting exercise in severe COPD patients under stable condition. Researchers should focus more on the safety, feasibility, and personalization of interventions. Furthermore, there is a need for additional high-quality trials to assess the consistency of evidence across various respiratory support approaches. TRIAL REGISTRATION: The study was registered at PROSPERO (CRD42023491139).

Sintilimab combined with bevacizumab in relapsed or persistent ovarian clear cell carcinoma (INOVA): a multicentre, single-arm, phase 2 trial.

BACKGROUND: Ovarian clear cell carcinoma rarely responds to second-line chemotherapy, the recommended treatment for relapsed epithelial ovarian cancer. Here, we report the activity and safety of sintilimab in combination with bevacizumab in patients with relapsed or persistent ovarian clear cell carcinoma. METHODS: In the prospective, multicentre, single-arm, phase 2 INOVA trial, patients aged 18-75 years with histologically confirmed relapsed or persistent ovarian clear cell carcinoma were enrolled from eight tertiary hospitals in China. Eligible patients had an Eastern Cooperative Oncology Group performance status score of 0-2 and previous exposure to at least one cycle of platinum-containing chemotherapy. Enrolled patients received sintilimab (200 mg) and bevacizumab (15 mg/kg) intravenously every 3 weeks until disease progression. The primary endpoint was objective response rate assessed by independent central review based on Response Evaluation Criteria in Solid Tumours version 1.1. Eligible enrolled patients who received at least one cycle of treatment and had at least one tumour response assessment following the baseline assessment per protocol were included in the activity analysis. Patients who received at least one dose of study drug were included in the safety analysis. The study is registered with ClinicalTrials.gov (NCT04735861) and is ongoing. FINDINGS: Between April 8, 2021, and July 3, 2023, 51 patients were screened and 41 patients received at least one dose of sintilimab in combination with bevacizumab. Response evaluation was completed in 37 patients. Objective responses were observed in 15 patients (objective response rate 40·5%; 95% CI 24·8-57·9), of which five (14%) were complete responses and ten (27%) were partial responses. At data cutoff (Jan 29, 2024), the median follow-up was 16·9 months (IQR 7·5-23·4). Three (7%) patients developed grade 3 treatment-related adverse events including one patient with proteinuria, one patient with myocarditis, and one patient with rash. No treatment-related adverse events of worse than grade 3 severity were recorded. Treatment-related serious adverse events occurred in two (5%) patients including one patient with immune-related myocarditis and another with hypertension and renal dysfunction. No treatment-related deaths occurred. INTERPRETATION: Sintilimab in combination with bevacizumab showed promising anti-tumour activity and manageable safety in patients with relapsed or persistent ovarian clear cell carcinoma. Larger, randomised trials are warranted to compare this low-toxicity, chemotherapy-free combinatorial regimen with standard chemotherapy. FUNDING: National Key Technology Research and Development Program of China, National Natural Science Foundation of China, Beijing Xisike Clinical Oncology Research Foundation, and Innovent Biologics. TRANSLATION: For the Chinese translation of the abstract see Supplementary Materials section.

Performance improvement of reflective phosphor film via adjusting the thickness realizing bright and efficient laser lighting.

Phosphor-in-glass-film (PiG-F) has been extensively investigated, showing great potential for use in laser lighting technique. Thickness is apparently a key parameter for PiG-F, affecting the heat dissipation, absorption, and reabsorption, thus determining the luminous efficacy and luminescence saturation threshold (LST). Conventional studies suggest that thinner films often have lower thermal load than that of the thicker ones. Unexpectedly, we found that the Lu3Al5O12:Ce (LuAG:Ce)-based PiG-F with a moderate thickness (78 µm) yielded the optimal LST of 31.9 W (14.2 W·mm-2, rather than 28.0 W (12.3 W·mm-2) for the thinnest one (56 µm). This unexpected result was further verified by thermal simulations. With the high saturation threshold together with a high luminous efficacy (∼296 lm·W-1), an ultrahigh luminous flux of 7178 lm with a luminous exitance of 2930 lm·mm-2 was thus attained. We believe the new, to the best of our knowledge, findings in this study will substantially impact the design principles of phosphors for laser lighting.

A case of alopecia areata after rabies vaccination: unreported adverse effect?

Alopecia areata (AA) is an autoimmune-related disorder characterized by non-scarring hair loss in children. We report the case of a child who had AA after the fifth dose of rabies vaccine and summarized various potential mechanisms of vaccination induced AA. This case indicates that rabies vaccine might be a predisposition of AA by causing immune dysregulation.

Full-body pose reconstruction and correction in virtual reality for rehabilitation training.

Existing statistical data indicates that an increasing number of people now require rehabilitation to restore compromised physical mobility. During the rehabilitation process, physical therapists evaluate and guide the movements of patients, aiding them in a more effective recovery of rehabilitation and preventing secondary injuries. However, the immutability of mobility and the expensive price of rehabilitation training hinder some patients from timely access to rehabilitation. Utilizing virtual reality for rehabilitation training might offer a potential alleviation to these issues. However, prevalent pose reconstruction algorithms in rehabilitation primarily rely on images, limiting their applicability to virtual reality. Furthermore, existing pose evaluation and correction methods in the field of rehabilitation focus on providing clinical metrics for doctors, and failed to offer patients efficient movement guidance. In this paper, a virtual reality-based rehabilitation training method is proposed. The sparse motion signals from virtual reality devices, specifically head-mounted displays hand controllers, is used to reconstruct full body poses. Subsequently, the reconstructed poses and the standard poses are fed into a natural language processing model, which contrasts the difference between the two poses and provides effective pose correction guidance in the form of natural language. Quantitative and qualitative results indicate that the proposed method can accurately reconstruct full body poses from sparse motion signals in real-time. By referencing standard poses, the model generates professional motion correction guidance text. This approach facilitates virtual reality-based rehabilitation training, reducing the cost of rehabilitation training and enhancing the efficiency of self-rehabilitation training.

Artificial intelligence generated content (AIGC) in medicine: A narrative review.

Recently, artificial intelligence generated content (AIGC) has been receiving increased attention and is growing exponentially. AIGC is generated based on the intentional information extracted from human-provided instructions by generative artificial intelligence (AI) models. AIGC quickly and automatically generates large amounts of high-quality content. Currently, there is a shortage of medical resources and complex medical procedures in medicine. Due to its characteristics, AIGC can help alleviate these problems. As a result, the application of AIGC in medicine has gained increased attention in recent years. Therefore, this paper provides a comprehensive review on the recent state of studies involving AIGC in medicine. First, we present an overview of AIGC. Furthermore, based on recent studies, the application of AIGC in medicine is reviewed from two aspects: medical image processing and medical text generation. The basic generative AI models, tasks, target organs, datasets and contribution of studies are considered and summarized. Finally, we also discuss the limitations and challenges faced by AIGC and propose possible solutions with relevant studies. We hope this review can help readers understand the potential of AIGC in medicine and obtain some innovative ideas in this field.

Opportunities in Assessing and Regulating Organohalogen Flame Retardants (OFRs) as a Class in Consumer Products.

BACKGROUND: In 2015, the U.S. Consumer Product Safety Commission (CPSC) received and then, in 2017, granted a petition under the Federal Hazardous Substances Act to declare certain groups of consumer products as banned hazardous substances if they contain nonpolymeric, additive organohalogen flame retardants (OFRs). The petitioners asked the CPSC to regulate OFRs as a single chemical class with similar health effects. The CPSC later sponsored a National Academy of Sciences, Engineering, and Medicine (NASEM) report in 2019, which ultimately identified 161 OFRs and grouped them into 14 subclasses based on chemical structural similarity. In 2021, a follow-up discussion was held among a group of scientists from both inside and outside of the CPSC for current research on OFRs and to promote collaboration that could increase public awareness of CPSC work and support the class-based approach for the CPSC's required risk assessment of OFRs. OBJECTIVES: Given the extensive data collected to date, there is a need to synthesize what is known about OFR and how class-based regulations have previously managed this information. This commentary discusses both OFR exposure and OFR toxicity and fills some gaps for OFR exposure that were not within the scope of the NASEM report. The objective of this commentary is therefore to provide an overview of the OFR research presented at SOT 2021, explore opportunities and challenges associated with OFR risk assessment, and inform CPSC's work on an OFR class-based approach. DISCUSSION: A class-based approach for regulating OFRs can be successful. Expanding the use of read-across and the use of New Approach Methodologies (NAMs) in assessing and regulating existing chemicals was considered as a necessary part of the class-based process. Recommendations for OFR class-based risk assessment include the need to balance fire and chemical safety and to protect vulnerable populations, including children and pregnant women. The authors also suggest the CPSC should consider global, federal, and state OFR regulations. The lack of data or lack of concordance in toxicity data could present significant hurdles for some OFR subclasses. The potential for cumulative risks within or between subclasses, OFR mixtures, and metabolites common to more than one OFR all add extra complexity for class-based risk assessment. This commentary discusses scientific and regulatory challenges for a class-based approach suggested by NASEM. This commentary is offered as a resource for anyone performing class-based assessments and to provide potential collaboration opportunities for OFR stakeholders. https://doi.org/10.1289/EHP12725.

A non-contact interactive system for multimodal surgical robots based on LeapMotion and visual tags.

In recent years, the integration of robots in minimally invasive surgery has gained significant traction in clinical practice. However, conventional contact-based human-computer interaction poses the risk of bacterial infection, significantly limiting the role of robots in surgery. To address this limitation, we propose an innovative interaction method rooted in gestures and visual tags, allowing surgeons to control and fine-tune surgical robots without physical contact with the environment. By encoding the six gestures collected using LeapMotion, we can effectively control the surgical robot in a non-contact manner. Moreover, utilizing Aruco technology, we have accurately identified the 3D spatial position of the visual label, and developed 12 fine-tuning operations to refine surgical instruments. To evaluate the applicability of our proposed system in surgery, we designed a relevant experimental setup. In the experiment, we achieved enough precision. These results demonstrate that our system meets the clinical standard, providing doctors with a non-contact and flexible means of interacting with robots during surgery.

A deep weakly semi-supervised framework for endoscopic lesion segmentation.

In the field of medical image analysis, accurate lesion segmentation is beneficial for the subsequent clinical diagnosis and treatment planning. Currently, various deep learning-based methods have been proposed to deal with the segmentation task. Albeit achieving some promising performances, the fully-supervised learning approaches require pixel-level annotations for model training, which is tedious and time-consuming for experienced radiologists to collect. In this paper, we propose a weakly semi-supervised segmentation framework, called Point Segmentation Transformer (Point SEGTR). Particularly, the framework utilizes a small amount of fully-supervised data with pixel-level segmentation masks and a large amount of weakly-supervised data with point-level annotations (i.e., annotating a point inside each object) for network training, which largely reduces the demand of pixel-level annotations significantly. To fully exploit the pixel-level and point-level annotations, we propose two regularization terms, i.e., multi-point consistency and symmetric consistency, to boost the quality of pseudo labels, which are then adopted to train a student model for inference. Extensive experiments are conducted on three endoscopy datasets with different lesion structures and several body sites (e.g., colorectal and nasopharynx). Comprehensive experimental results finely substantiate the effectiveness and the generality of our proposed method, as well as its potential to loosen the requirements of pixel-level annotations, which is valuable for clinical applications.

Association between the weight-adjusted waist index and stroke: a cross-sectional study.

BACKGROUND: The weight-adjusted waist index (WWI) is a new measure of obesity, and this study aimed to determine the association between the WWI and stroke. METHODS: Using the National Health and Nutrition Examination Survey (NHANES) 2011-2020 dataset, cross-sectional data from 23,389 participants were analysed. The correlation between the WWI and stroke was investigated through multivariate logistic regression and smoothing curve fitting. Subgroup analysis and interaction tests were also carried out. RESULTS: The research involved 23,389 participants, of whom 893 (3.82%) had a stroke. The fully adjusted model revealed a positive correlation between the WWI and stroke [1.25 (1.05, 1.48)]. Individuals who were in the highest quartile of WWI exhibited a 62% higher likelihood of experiencing a stroke than those in the lowest quartile [1.62 (1.06, 2.48)]. Subgroup analysis and interaction tests revealed that this positive correlation was similar in different population settings (all P for interaction > 0.05). CONCLUSION: A higher WWI was associated with a higher prevalence of stroke. The results of this study underscore the value of the WWI in stroke prevention and management.

An uncertainty-aware self-training framework with consistency regularization for the multilabel classification of common computed tomography signs in lung nodules.

Background: Computed tomography (CT) signs of lung nodules play an important role in indicating lung nodules' malignancy, and accurate automatic classification of these signs can help doctors improve their diagnostic efficiency. However, few relevant studies targeting multilabel classification (MLC) of nodule signs have been conducted. Moreover, difficulty in obtaining labeled data also restricts this avenue of research to a large extent. To address these problems, a multilabel automatic classification system for nodule signs is proposed, which consists of a 3-dimensional (3D) convolutional neural network (CNN) and an efficient new semi-supervised learning (SSL) framework. Methods: Two datasets were used in our experiments: Lung Nodule Analysis 16 (LUNA16), a public dataset for lung nodule classification, and a private dataset of nodule signs. The private dataset contains 641 nodules, 454 of which were annotated with 6 important signs by radiologists. Our classification system consists of 2 main parts: a 3D CNN model and an SSL method called uncertainty-aware self-training framework with consistency regularization (USC). In the system, supervised training is performed with labeled data, and simultaneously, an uncertainty-and-confidence-based strategy is used to select pseudo-labeled samples for unsupervised training, thus jointly realizing the optimization of the model. Results: For the MLC of nodule signs, our proposed 3D CNN achieved satisfactory results with a mean average precision (mAP) of 0.870 and a mean area under the curve (AUC) of 0.782. In semi-supervised experiments, compared with supervised learning, our proposed SSL method could increase the mAP by 7.6% (from 0.730 to 0.806) and the mean AUC by 8.1% (from 0.631 to 0.712); it thus efficiently utilized the unlabeled data and achieved superior performance improvement compared to the recently advanced methods. Conclusions: We realized the optimal MLC of lung nodule signs with our proposed 3D CNN. Our proposed SSL method can also offer an efficient solution for the insufficiency of labeled data that may exist in the MLC tasks of 3D medical images.

A graph neural network model for the diagnosis of lung adenocarcinoma based on multimodal features and an edge-generation network.

Background: Lung cancer is a global disease with high lethality, with early screening being considerably helpful for improving the 5-year survival rate. Multimodality features in early screening imaging are an important part of the prediction for lung adenocarcinoma, and establishing a model for adenocarcinoma diagnosis based on multimodal features is an obvious clinical need. Through our practice and investigation, we found that graph neural networks (GNNs) are excellent platforms for multimodal feature fusion, and the data can be completed using the edge-generation network. Therefore, we propose a new lung adenocarcinoma multiclassification model based on multimodal features and an edge-generation network. Methods: According to a ratio of 80% to 20%, respectively, the dataset of 338 cases was divided into the training set and the test set through 5-fold cross-validation, and the distribution of the 2 sets was the same. First, the regions of interest (ROIs) cropped from computed tomography (CT) images were separately fed into convolutional neural networks (CNNs) and radiomics processing platforms. The results of the 2 parts were then input into a graph embedding representation network to obtain the fused feature vectors. Subsequently, a graph database based on the clinical and semantic features was established, and the data were supplemented by an edge-generation network, with the fused feature vectors being used as the input of the nodes. This enabled us to clearly understand where the information transmission of the GNN takes place and improves the interpretability of the model. Finally, the nodes were classified using GNNs. Results: On our dataset, the proposed method presented in this paper achieved superior results compared to traditional methods and showed some comparability with state-of-the-art methods for lung nodule classification. The results of our method are as follows: accuracy (ACC) =66.26% (±4.46%), area under the curve (AUC) =75.86% (±1.79%), F1-score =64.00% (±3.65%), and Matthews correlation coefficient (MCC) =48.40% (±5.07%). The model with the edge-generating network consistently outperformed the model without it in all aspects. Conclusions: The experiments demonstrate that with appropriate data=construction methods GNNs can outperform traditional image processing methods in the field of CT-based medical image classification. Additionally, our model has higher interpretability, as it employs subjective clinical and semantic features as the data construction approach. This will help doctors better leverage human-computer interactions.

Comparative Effects of Different Nutritional Supplements on Inflammation, Nutritional Status, and Clinical Outcomes in Colorectal Cancer Patients: A Systematic Review and Network Meta-Analysis.

Nutritional supplements have been widely used in colorectal cancer (CRC) patients. The aim of this network meta-analysis (NMA) was to compare the effects of different nutritional supplements on inflammation, nutritional status, and clinical outcomes in CRC patients. Four electronic databases were searched until December 2022. Randomized controlled trials (RCTs) comparing nutritional supplements of omega-3 fatty acids (omega-3), arginine, vitamin D, glutamine, probiotics, or their combinations with placebo or standard treatment were selected. The outcomes were inflammatory indicators, nutritional indicators, and clinical outcomes. A random-effects Bayesian NMA was performed to rank the effect of each supplement. In total, 34 studies involving 2841 participants were included. Glutamine was superior in decreasing tumor necrosis factor-α (MD -25.2; 95% CrI [-32.62, -17.95]), whereas combined omega-3 and arginine supplementation was more effective in decreasing interleukin-6 (MD -61.41; 95% CrI [-97.85, -24.85]). No nutritional supplements significantly maintained nutritional indicators in CRC patients. Regarding clinical outcomes, glutamine ranked highest in reducing the length of hospital stay (MD -3.71; 95% CrI [-5.89, -1.72]) and the incidence of wound infections (RR 0.12; 95% CrI [0, 0.85]), and probiotics were rated as best in reducing the incidence of pneumonia (RR 0.38; 95% CrI [0.15, 0.81]). Future well-designed RCTs are needed to further confirm these findings.

A touch-free human-robot collaborative surgical navigation robotic system based on hand gesture recognition.

Robot-assisted minimally invasive surgery (RAMIS) has gained significant traction in clinical practice in recent years. However, most surgical robots rely on touch-based human-robot interaction (HRI), which increases the risk of bacterial diffusion. This risk is particularly concerning when surgeons must operate various equipment with their bare hands, necessitating repeated sterilization. Thus, achieving touch-free and precise manipulation with a surgical robot is challenging. To address this challenge, we propose a novel HRI interface based on gesture recognition, leveraging hand-keypoint regression and hand-shape reconstruction methods. By encoding the 21 keypoints from the recognized hand gesture, the robot can successfully perform the corresponding action according to predefined rules, which enables the robot to perform fine-tuning of surgical instruments without the need for physical contact with the surgeon. We evaluated the surgical applicability of the proposed system through both phantom and cadaver studies. In the phantom experiment, the average needle tip location error was 0.51 mm, and the mean angle error was 0.34 degrees. In the simulated nasopharyngeal carcinoma biopsy experiment, the needle insertion error was 0.16 mm, and the angle error was 0.10 degrees. These results indicate that the proposed system achieves clinically acceptable accuracy and can assist surgeons in performing contactless surgery with hand gesture interaction.

Issues in patients' experiences of enhanced recovery after surgery (ERAS) : a systematic review of qualitative evidence.

OBJECTIVE: To explore patients' experiences of enhanced recovery after surgery (ERAS) and to identify issues in the implementation of ERAS from the patient's perspective. DESIGN: The systematic review and qualitative analysis were based on the Joanna Briggs Institute's methodology for conducting synthesis. DATA SOURCES: Relevant studies published in four databases, that is, Web of Science, PubMed, Ovid Embase and the Cochrane Library, were systematically searched, and some studies were supplemented by key authors and reference lists. STUDY SELECTION: Thirty-one studies were identified, involving 1069 surgical patients enrolled in the ERAS programme. The inclusion and exclusion criteria were formulated based on the Population, Interest of phenomena, Context, Study design criteria recommended by the Joanna Briggs Institute to determine the scope of article retrieval. The inclusion criteria were as follows: ERAS patients' experiences; qualitative data; English language and published from January 1990 to August 2021. DATA EXTRACTION: Data were extracted from relevant studies using the standardised data extraction tool from Joanna Briggs Institute Qualitative Assessment and Review Instrument for qualitative research. DATA SYNTHESIS: The themes in the structure dimension are as follows: (1) patients cared about the timeliness of healthcare professionals' help; (2) patients cared about the professionalism of family care; and (3) patients misunderstood and worried about the safety of ERAS. The themes in the process dimension are as follows: (1) patients needed adequate and accurate information from healthcare professionals; (2) patients needed to communicate adequately with healthcare professionals; (3) patients hoped to develop a personalised treatment plan and (4) patients required ongoing follow-up services. The theme in the outcome dimension is as follows: patients wanted to effectively improve severe postoperative symptoms. CONCLUSIONS: Evaluating ERAS from the patient's perspective can reveal the omissions and deficiencies of healthcare professionals in clinical care so that problems in patients' recovery process can be solved in a timely manner, reducing potential barriers to the implementation of ERAS. PROSPERO REGISTRATION NUMBER: CRD42021278631.

Efficacy and safety of preoperative chewing gum for undergoing elective surgery: A meta-analysis of randomised controlled trials.

AIMS AND OBJECTIVES: This meta-analysis aimed to investigate the safety and feasibility of preoperative chewing gum in adult patients undergoing elective surgery. BACKGROUND: Postoperative chewing gum has been shown to be safe and effective for most surgeries, while the safety and efficacy of preoperative chewing gum are still controversial. DESIGN: A meta-analysis of randomised controlled trials was performed. NO PATIENT OR PUBLIC CONTRIBUTION: This was a meta-analysis involving no people or animals. METHODS: The literature search was performed in 9 databases from inception to July 2022. Randomised controlled trials that compared the safety and efficacy of preoperative chewing gum and preoperative chewing no gum in adult patients undergoing elective surgery were included. The study was reported in compliance with PRISMA statement. TRIAL REGISTRATION: PROSPERO CRD42022330223. RESULTS: Fourteen trials involving 1433 adult patients who undergo elective surgery were pooled in this meta-analysis. The results showed that preoperative chewing gum group resulted in no significant difference in gastric pH (p = .13) and gastric fluid volume (p = .25) compared with non-gum-chewing group. In comparison with the non-gum-chewing group, the gum-chewing group was associated with shorter preoperative thirst score (p = .02), lower incidence of postoperative nausea (p = .0004), lower incidence of postoperative sore throat, lower incidence of postoperative hoarseness, lower postoperative pain score, shorter first postoperative anal exhaust time (p < .00001), shorter first postoperative defecation time (p < .00001) and shorter hospital days (p = .02). CONCLUSIONS: Preoperative chewing gum was associated with lower discomforts and complication rates, without increasing gastric pH and gastric fluid volume. This strategy may be an innovative, feasible and safe choice for elective surgery in adults. RELEVANCE TO CLINICAL PRACTICE: This study's results could be used as an evidence for the implementation of preoperative chewing gum in perioperative care for adult patients undergoing elective surgery.

Red/processed meat consumption and non-cancer-related outcomes in humans: umbrella review.

The associations of red/processed meat consumption and cancer-related health outcomes have been well discussed. The umbrella review aimed to summarise the associations of red/processed meat consumption and various non-cancer-related outcomes in humans. We systematically searched the systematic reviews and meta-analyses of associations between red/processed meat intake and health outcomes from PubMed, Embase, Web of Science and the Cochrane Library databases. The umbrella review has been registered in PROSPERO (CRD 42021218568). A total of 40 meta-analyses were included. High consumption of red meat, particularly processed meat, was associated with a higher risk of all-cause mortality, CVD and metabolic outcomes. Dose-response analysis revealed that an additional 100 g/d red meat intake was positively associated with a 17 % increased risk of type 2 diabetes mellitus (T2DM), 15 % increased risk of CHD, 14 % of hypertension and 12 % of stroke. The highest dose-response/50 g increase in processed meat consumption at 95 % confident levels was 1·37, 95 % CI (1·22, 1·55) for T2DM, 1·27, 95 % CI (1·09, 1·49) for CHD, 1·17, 95 % CI (1·02, 1·34) for stroke, 1·15, 95 % CI (1·11, 1·19) for all-cause mortality and 1·08, 95 % CI (1·02, 1·14) for heart failure. In addition, red/processed meat intake was associated with several other health-related outcomes. Red and processed meat consumption seems to be more harmful than beneficial to human health in this umbrella review. It is necessary to take the impacts of red/processed meat consumption on non-cancer-related outcomes into consideration when developing new dietary guidelines, which will be of great public health importance. However, more additional randomised controlled trials are warranted to clarify the causality.