Amphiphilic Molecules Exhibiting Zwitterionic Excited-State Intramolecular Proton Transfer and Near-Infrared Emission for the Detection of Amyloid ß Aggregates in Alzheimer's Disease.

Chromophores with zwitterionic excited-state intramolecular proton transfer (ESIPT) have been shown to have larger Stock shifts and red-shifted emission wavelengths compared to the conventional π-delocalized ESIPT molecules. However, there is still a dearth of design strategies to expand the current library of zwitterionic ESIPT compounds. Herein, a novel zwitterionic excited-state intramolecular proton transfer system is reported, enabled by addition of 1,4,7-triazacyclononane (TACN) fragments on a dicyanomethylene-4H-pyran (DCM) scaffold. The solvent-dependent steady-state photophysical studies, pKa measurements, and computational analysis strongly support that the ESIPT process is more efficient with two TACN groups attached to the DCM scaffold and not affected by polar protic solvents. Impressively, compound DCM-OH-2-DT exhibits a near-infrared (NIR) emission at 740 nm along with an uncommonly large Stokes shift. Moreover, DCM-OH-2-DT shows high affinity towards soluble amyloid ß (Aß) oligomers in vitro and in 5xFAD mouse brain sections, and we have successfully applied DCM-OH-2-DT for the in vivo imaging of Aß aggregates and demonstrated its potential use as an early diagnostic agent for AD. Overall, this study can provide a general molecular design strategy for developing new zwitterionic ESIPT compounds with NIR emission in vivo imaging applications.

Effects of interventions targeting the systemic inflammatory response to cardiac surgery on clinical outcomes in adults.

BACKGROUND: Organ injury is a common and severe complication of cardiac surgery that contributes to the majority of deaths. There are no effective treatment or prevention strategies. It has been suggested that innate immune system activation may have a causal role in organ injury. A wide range of organ protection interventions targeting the innate immune response have been evaluated in randomised controlled trials (RCTs) in adult cardiac surgery patients, with inconsistent results in terms of effectiveness. OBJECTIVES: The aim of the review was to summarise the results of RCTs of organ protection interventions targeting the innate immune response in adult cardiac surgery. The review considered whether the interventions had a treatment effect on inflammation, important clinical outcomes, or both. SEARCH METHODS: CENTRAL, MEDLINE, Embase, conference proceedings and two trial registers were searched on October 2022 together with reference checking to identify additional studies. SELECTION CRITERIA: RCTs comparing organ protection interventions targeting the innate immune response versus placebo or no treatment in adult patients undergoing cardiac surgery where the treatment effect on innate immune activation and on clinical outcomes of interest were reported. DATA COLLECTION AND ANALYSIS: Searches, study selection, quality assessment, and data extractions were performed independently by pairs of authors. The primary inflammation outcomes were peak IL-6 and IL-8 concentrations in blood post-surgery. The primary clinical outcome was in-hospital or 30-day mortality. Treatment effects were expressed as risk ratios (RR) and standardised mean difference (SMD) with 95% confidence intervals (CI). Meta-analyses were performed using random effects models, and heterogeneity was assessed using I2. MAIN RESULTS: A total of 40,255 participants from 328 RCTs were included in the synthesis. The effects of treatments on IL-6 (SMD -0.77, 95% CI -0.97 to -0.58, I2 = 92%) and IL-8 (SMD -0.92, 95% CI -1.20 to -0.65, I2 = 91%) were unclear due to heterogeneity. Heterogeneity for inflammation outcomes persisted across multiple sensitivity and moderator analyses. The pooled treatment effect for in-hospital or 30-day mortality was RR 0.78, 95% CI 0.68 to 0.91, I2 = 0%, suggesting a significant clinical benefit. There was little or no treatment effect on mortality when analyses were restricted to studies at low risk of bias. Post hoc analyses failed to demonstrate consistent treatment effects on inflammation and clinical outcomes. Levels of certainty for pooled treatment effects on the primary outcomes were very low. AUTHORS' CONCLUSIONS: A systematic review of RCTs of organ protection interventions targeting innate immune system activation did not resolve uncertainty as to the effectiveness of these treatments, or the role of innate immunity in organ injury following cardiac surgery.

Candida auris Infection in a Meningococcal Septicemia Survivor, Poland.

BACKGROUND: Candida auris is an emerging pathogen that constitutes a serious global health threat. It is difficult to identify without specific approaches, and it can be misidentified with standard laboratory methods, what may lead to inappropriate management. CASE PRESENTATION: We report, probably the first in Poland, C. auris isolation from blood cultures and wound swabs of a young male following meningococcal septicaemia, in February 2019. The patient had been previously hospitalized in the United Arab Emirates. The isolate was rapidly identified by matrix-assisted laser desorption ionization-time of flight mass spectrometry and therefore clinicians were promptly informed on the alert pathogen isolation. The targeted antifungal treatment was successful and infection control measures seemed effective. ITS-based identification and subsequent whole genome sequencing showed that the C. auris isolate belongs to South Asian lineage (clade I). CONCLUSIONS: C. auris is able to cause outbreaks in healthcare settings. Therefore, it is important to quickly identify C. auris isolates in hospital settings so that healthcare facilities can take proper precautions to limit its spread.

Malaria Detection Using Advanced Deep Learning Architecture.

Malaria is a life-threatening disease caused by parasites that are transmitted to humans through the bites of infected mosquitoes. The early diagnosis and treatment of malaria are crucial for reducing morbidity and mortality rates, particularly in developing countries where the disease is prevalent. In this article, we present a novel convolutional neural network (CNN) architecture for detecting malaria from blood samples with a 99.68% accuracy. Our method outperforms the existing approaches in terms of both accuracy and speed, making it a promising tool for malaria diagnosis in resource-limited settings. The CNN was trained on a large dataset of blood smears and was able to accurately classify infected and uninfected samples with high sensitivity and specificity. Additionally, we present an analysis of model performance on different subtypes of malaria and discuss the implications of our findings for the use of deep learning in infectious disease diagnosis.

3D Road Lane Classification with Improved Texture Patterns and Optimized Deep Classifier.

The understanding of roads and lanes incorporates identifying the level of the road, the position and count of lanes, and ending, splitting, and merging roads and lanes in highway, rural, and urban scenarios. Even though a large amount of progress has been made recently, this kind of understanding is ahead of the accomplishments of the present perceptual methods. Nowadays, 3D lane detection has become the trending research in autonomous vehicles, which shows an exact estimation of the 3D position of the drivable lanes. This work mainly aims at proposing a new technique with Phase I (road or non-road classification) and Phase II (lane or non-lane classification) with 3D images. Phase I: Initially, the features, such as the proposed local texton XOR pattern (LTXOR), local Gabor binary pattern histogram sequence (LGBPHS), and median ternary pattern (MTP), are derived. These features are subjected to the bidirectional gated recurrent unit (BI-GRU) that detects whether the object is road or non-road. Phase II: Similar features in Phase I are further classified using the optimized BI-GRU, where the weights are chosen optimally via self-improved honey badger optimization (SI-HBO). As a result, the system can be identified, and whether it is lane-related or not. Particularly, the proposed BI-GRU + SI-HBO obtained a higher precision of 0.946 for db 1. Furthermore, the best-case accuracy for the BI-GRU + SI-HBO was 0.928, which was better compared with honey badger optimization. Finally, the development of SI-HBO was proven to be better than the others.

In Vitro and In Vivo Imaging-Based Evaluation of Doxorubicin Anticancer Treatment in Combination with the Herbal Medicine Black Cohosh.

As a substitution for hormone replacement therapy, many breast cancer patients use black cohosh (BC) extracts in combination with doxorubicin (DOX)-based chemotherapy. In this study, we evaluated the viability and survival of BC- and DOX-treated MCF-7 cells. A preclinical model of MCF-7 xenografts was used to determine the influence of BC and DOX administration on tumor growth and metabolism. The number of apoptotic cells after incubation with both DOX and BC was significantly increased (~100%) compared to the control. Treatment with DOX altered the potential of MCF-7 cells to form colonies; however, coincubation with BC did not affect this process. In vivo, PET-CT imaging showed that combined treatment of DOX and BC induced a significant reduction in both metabolic activity (29%) and angiogenesis (32%). Both DOX and BC treatments inhibited tumor growth by 20% and 12%, respectively, and combined by 57%, vs. control. We successfully demonstrated that BC increases cytotoxic effects of DOX, resulting in a significant reduction in tumor size. Further studies regarding drug transport and tumor growth biomarkers are necessary to establish the underlying mechanism and potential clinical use of BC in breast cancer patients.

A Machine-Learning-Based Approach to Prediction of Biogeographic Ancestry within Europe.

Data obtained with the use of massive parallel sequencing (MPS) can be valuable in population genetics studies. In particular, such data harbor the potential for distinguishing samples from different populations, especially from those coming from adjacent populations of common origin. Machine learning (ML) techniques seem to be especially well suited for analyzing large datasets obtained using MPS. The Slavic populations constitute about a third of the population of Europe and inhabit a large area of the continent, while being relatively closely related in population genetics terms. In this proof-of-concept study, various ML techniques were used to classify DNA samples from Slavic and non-Slavic individuals. The primary objective of this study was to empirically evaluate the feasibility of discerning the genetic provenance of individuals of Slavic descent who exhibit genetic similarity, with the overarching goal of categorizing DNA specimens derived from diverse Slavic population representatives. Raw sequencing data were pre-processed, to obtain a 1200 character-long binary vector. A total of three classifiers were used-Random Forest, Support Vector Machine (SVM), and XGBoost. The most-promising results were obtained using SVM with a linear kernel, with 99.9% accuracy and F1-scores of 0.9846-1.000 for all classes.

Exploiting Dynamic Vector-Level Operations and a 2D-Enhanced Logistic Modular Map for Efficient Chaotic Image Encryption.

Over the past few years, chaotic image encryption has gained extensive attention. Nevertheless, the current studies on chaotic image encryption still possess certain constraints. To break these constraints, we initially created a two-dimensional enhanced logistic modular map (2D-ELMM) and subsequently devised a chaotic image encryption scheme based on vector-level operations and 2D-ELMM (CIES-DVEM). In contrast to some recent schemes, CIES-DVEM features remarkable advantages in several aspects. Firstly, 2D-ELMM is not only simpler in structure, but its chaotic performance is also significantly better than that of some newly reported chaotic maps. Secondly, the key stream generation process of CIES-DVEM is more practical, and there is no need to replace the secret key or recreate the chaotic sequence when handling different images. Thirdly, the encryption process of CIES-DVEM is dynamic and closely related to plaintext images, enabling it to withstand various attacks more effectively. Finally, CIES-DVEM incorporates lots of vector-level operations, resulting in a highly efficient encryption process. Numerous experiments and analyses indicate that CIES-DVEM not only boasts highly significant advantages in terms of encryption efficiency, but it also surpasses many recent encryption schemes in practicality and security.

Influence of augmentation on the performance of the double ResNet-based model for chest X-ray classification.

Purpose: A pandemic disease elicited by the SARS-CoV-2 virus has become a serious health issue due to infecting millions of people all over the world. Recent publications prove that artificial intelligence (AI) can be used for medical diagnosis purposes, including interpretation of X-ray images. X-ray scanning is relatively cheap, and scan processing is not computationally demanding. Material and methods: In our experiment a baseline transfer learning schema of processing of lung X-ray images, including augmentation, in order to detect COVID-19 symptoms was implemented. Seven different scenarios of augmentation were proposed. The model was trained on a dataset consisting of more than 30,000 X-ray images. Results: The obtained model was evaluated using real images from a Polish hospital, with the use of standard metrics, and it achieved accuracy = 0.9839, precision = 0.9697, recall = 1.0000, and F1-score = 0.9846. Conclusions: Our experiment proved that augmentations and masking could be important steps of data pre-processing and could contribute to improvement of the evaluation metrics. Because medical professionals often tend to lack confidence in AI-based tools, we have designed the proposed model so that its results would be explainable and could play a supporting role for radiology specialists in their work.

Minimization of Energy Losses in the BLDC Motor for Improved Control and Power Supply of the System under Static Load.

In this article we present the optimal method of controlling and supplying a BLDC motor under static load, proposed and implemented as a result of the research. A research infrastructure was developed to measure and analyze variants of the motor control. In the research we determine possible losses of electric energy released in the form of heat in the tested engine elements. The test results showed that the lowest energy losses are provided by the variant where the control signals are obtained from an external magnetic disc and the motor is powered by an additional DC/DC converter. The conclusions from the analyses allowed for the selection of the best variant of motor control and power supply, which minimizes energy losses during the BLDC motor operation.

Energy Efficient Consensus Approach of Blockchain for IoT Networks with Edge Computing.

Blockchain technology is gaining a lot of attention in various fields, such as intellectual property, finance, smart agriculture, etc. The security features of blockchain have been widely used, integrated with artificial intelligence, Internet of Things (IoT), software defined networks (SDN), etc. The consensus mechanism of blockchain is its core and ultimately affects the performance of the blockchain. In the past few years, many consensus algorithms, such as proof of work (PoW), ripple, proof of stake (PoS), practical byzantine fault tolerance (PBFT), etc., have been designed to improve the performance of the blockchain. However, the high energy requirement, memory utilization, and processing time do not match with our actual desires. This paper proposes the consensus approach on the basis of PoW, where a single miner is selected for mining the task. The mining task is offloaded to the edge networking. The miner is selected on the basis of the digitization of the specifications of the respective machines. The proposed model makes the consensus approach more energy efficient, utilizes less memory, and less processing time. The improvement in energy consumption is approximately 21% and memory utilization is 24%. Efficiency in the block generation rate at the fixed time intervals of 20 min, 40 min, and 60 min was observed.

Hybrid Technique for Cyber-Physical Security in Cloud-Based Smart Industries.

New technologies and trends in industries have opened up ways for distributed establishment of Cyber-Physical Systems (CPSs) for smart industries. CPSs are largely based upon Internet of Things (IoT) because of data storage on cloud servers which poses many constraints due to the heterogeneous nature of devices involved in communication. Among other challenges, security is the most daunting challenge that contributes, at least in part, to the impeded momentum of the CPS realization. Designers assume that CPSs are themselves protected as they cannot be accessed from external networks. However, these days, CPSs have combined parts of the cyber world and also the physical layer. Therefore, cyber security problems are large for commercial CPSs because the systems move with one another and conjointly with physical surroundings, i.e., Complex Industrial Applications (CIA). Therefore, in this paper, a novel data security algorithm Dynamic Hybrid Secured Encryption Technique (DHSE) is proposed based on the hybrid encryption scheme of Advanced Encryption Standard (AES), Identity-Based Encryption (IBE) and Attribute-Based Encryption (ABE). The proposed algorithm divides the data into three categories, i.e., less sensitive, mid-sensitive and high sensitive. The data is distributed by forming the named-data packets (NDPs) via labelling the names. One can choose the number of rounds depending on the actual size of a key; it is necessary to perform a minimum of 10 rounds for 128-bit keys in DHSE. The average encryption time taken by AES (Advanced Encryption Standard), IBE (Identity-based encryption) and ABE (Attribute-Based Encryption) is 3.25 ms, 2.18 ms and 2.39 ms, respectively. Whereas the average time taken by the DHSE encryption algorithm is 2.07 ms which is very much less when compared to other algorithms. Similarly, the average decryption times taken by AES, IBE and ABE are 1.77 ms, 1.09 ms and 1.20 ms and the average times taken by the DHSE decryption algorithms are 1.07 ms, which is very much less when compared to other algorithms. The analysis shows that the framework is well designed and provides confidentiality of data with minimum encryption and decryption time. Therefore, the proposed approach is well suited for CPS-IoT.

A Hybrid Mayfly-Aquila Optimization Algorithm Based Energy-Efficient Clustering Routing Protocol for Wireless Sensor Networks.

In recent times, Wireless Sensor Networks (WSNs) are becoming more and more popular and are making significant advances in wireless communication thanks to low-cost and low-power sensors. However, since WSN nodes are battery-powered, they lose all of their autonomy after a certain time. This energy restriction impacts the network's lifetime. Clustering can increase the lifetime of a network while also lowering energy use. Clustering will bring several similar sensors to one location for data collection and delivery to the Base Station (BS). The Cluster Head (CH) uses more energy when collecting and transferring data. The life of the WSNs can be extended, and efficient identification of CH can minimize energy consumption. Creating a routing algorithm that considers the key challenges of lowering energy usage and maximizing network lifetime is still challenging. This paper presents an energy-efficient clustering routing protocol based on a hybrid Mayfly-Aquila optimization (MFA-AOA) algorithm for solving these critical issues in WSNs. The Mayfly algorithm is employed to choose an optimal CH from a collection of nodes. The Aquila optimization algorithm identifies and selects the optimum route between CH and BS. The simulation results showed that the proposed methodology achieved better energy consumption by 10.22%, 11.26%, and 14.28%, and normalized energy by 9.56%, 11.78%, and 13.76% than the existing state-of-art approaches.

Optimal Cluster Head Selection in WSN with Convolutional Neural Network-Based Energy Level Prediction.

Currently, analysts in a variety of nations have developed various WSN clustering protocols. The major characteristic is the Low Energy Adaptive Clustering Hierarchy (LEACH), which attained the objective of energy balance by sporadically varying the Cluster Heads (CHs) in the region. Nevertheless, because it implements an arbitrary number system, the appropriateness of CH is complete with suspicions. In this paper, an optimal cluster head selection (CHS) model is developed regarding secure and energy-aware routing in the Wireless Sensor Network (WSN). Here, optimal CH is preferred based on distance, energy, security (risk probability), delay, trust evaluation (direct and indirect trust), and Received Signal Strength Indicator (RSSI). Here, the energy level is predicted using an improved Deep Convolutional Neural Network (DCNN). To choose the finest CH in WSN, Bald Eagle Assisted SSA (BEA-SSA) is employed in this work. Finally, the results authenticate the effectiveness of BEA-SSA linked to trust, RSSI, security, etc. The Packet Delivery Ratio (PDR) for 100 nodes is 0.98 at 500 rounds, which is high when compared to Grey Wolf Optimization (GWO), Multi-Objective Fractional Particle Lion Algorithm (MOFPL), Sparrow Search Algorithm (SSA), Bald Eagle Search optimization (BES), Rider Optimization (ROA), Hunger Games Search (HGS), Shark Smell Optimization (SSO), Rider-Cat Swarm Optimization (RCSO), and Firefly Cyclic Randomization (FCR) methods.

Efficient Middleware for the Portability of PaaS Services Consuming Applications among Heterogeneous Clouds.

Cloud providers create a vendor-locked-in environment by offering proprietary and non-standard APIs, resulting in a lack of interoperability and portability among clouds. To overcome this deterrent, solutions must be developed to exploit multiple clouds efficaciously. This paper proposes a middleware platform to mitigate the application portability issue among clouds. A literature review is also conducted to analyze the solutions for application portability. The middleware allows an application to be ported on various platform-as-a-service (PaaS) clouds and supports deploying different services of an application on disparate clouds. The efficiency of the abstraction layer is validated by experimentation on an application that uses the message queue, Binary Large Objects (BLOB), email, and short message service (SMS) services of various clouds via the proposed middleware against the same application using these services via their native code. The experimental results show that adding this middleware mildly affects the latency, but it dramatically reduces the developer's overhead of implementing each service for different clouds to make it portable.

A Novel Blockchain-Based Healthcare System Design and Performance Benchmarking on a Multi-Hosted Testbed.

As a result of the proliferation of digital and network technologies in all facets of modern society, including the healthcare systems, the widespread adoption of Electronic Healthcare Records (EHRs) has become the norm. At the same time, Blockchain has been widely accepted as a potent solution for addressing security issues in any untrusted, distributed, decentralized application and has thus seen a slew of works on Blockchain-enabled EHRs. However, most such prototypes ignore the performance aspects of proposed designs. In this paper, a prototype for a Blockchain-based EHR has been presented that employs smart contracts with Hyperledger Fabric 2.0, which also provides a unified performance analysis with Hyperledger Caliper 0.4.2. The additional contribution of this paper lies in the use of a multi-hosted testbed for the performance analysis in addition to far more realistic Gossip-based traffic scenario analysis with Tcpdump tools. Moreover, the prototype is tested for performance with superior transaction ordering schemes such as Kafka and RAFT, unlike other literature that mostly uses SOLO for the purpose, which accounts for superior fault tolerance. All of these additional unique features make the performance evaluation presented herein much more realistic and hence adds hugely to the credibility of the results obtained. The proposed framework within the multi-host instances continues to behave more successfully with high throughput, low latency, and low utilization of resources for opening, querying, and transferring transactions into a healthcare Blockchain network. The results obtained in various rounds of evaluation demonstrate the superiority of the proposed framework.

BBNSF: Blockchain-Based Novel Secure Framework Using RP2-RSA and ASR-ANN Technique for IoT Enabled Healthcare Systems.

The wearable healthcare equipment is primarily designed to alert patients of any specific health conditions or to act as a useful tool for treatment or follow-up. With the growth of technologies and connectivity, the security of these devices has become a growing concern. The lack of security awareness amongst novice users and the risk of several intermediary attacks for accessing health information severely endangers the use of IoT-enabled healthcare systems. In this paper, a blockchain-based secure data storage system is proposed along with a user authentication and health status prediction system. Firstly, this work utilizes reversed public-private keys combined Rivest-Shamir-Adleman (RP2-RSA) algorithm for providing security. Secondly, feature selection is completed by employing the correlation factor-induced salp swarm optimization algorithm (CF-SSOA). Finally, health status classification is performed using advanced weight initialization adapted SignReLU activation function-based artificial neural network (ASR-ANN) which classifies the status as normal and abnormal. Meanwhile, the abnormal measures are stored in the corresponding patient blockchain. Here, blockchain technology is used to store medical data securely for further analysis. The proposed model has achieved an accuracy of 95.893% and is validated by comparing it with other baseline techniques. On the security front, the proposed RP2-RSA attains a 96.123% security level.

Molecular Imaging and Nanotechnology-Emerging Tools in Diagnostics and Therapy.

Personalized medicine is emerging as a new goal in the diagnosis and treatment of diseases. This approach aims to establish differences between patients suffering from the same disease, which allows to choose the most effective treatment. Molecular imaging (MI) enables advanced insight into molecule interactions and disease pathology, improving the process of diagnosis and therapy and, for that reason, plays a crucial role in personalized medicine. Nanoparticles are widely used in MI techniques due to their size, high surface area to volume ratio, and multifunctional properties. After conjugation to specific ligands and drugs, nanoparticles can transport therapeutic compounds directly to their area of action and therefore may be used in theranostics-the simultaneous implementation of treatment and diagnostics. This review summarizes different MI techniques, including optical imaging, ultrasound imaging, magnetic resonance imaging, nuclear imaging, and computed tomography imaging with theranostics nanoparticles. Furthermore, it explores the potential use of constructs that enables multimodal imaging and track diseases in real time.

Pharmacological interventions for the prevention of renal injury in surgical patients: a systematic literature review and meta-analysis.

BACKGROUND: The aim of this systematic review was to summarise the results of randomised controlled trials (RCTs) that have evaluated pharmacological interventions for renoprotection in people undergoing surgery. METHODS: Searches were conducted to update a previous review using the Cochrane Central Register of Controlled Trials, MEDLINE, and EMBASE to August 23, 2019. RCTs evaluating the use of pharmacological interventions for renal protection in the perioperative period were included. The co-primary outcome measures were 30-day mortality and acute kidney injury (AKI). Pooled effect estimates were expressed as risk ratios (RRs) (95% confidence intervals). RESULTS: We included 228 trials enrolling 56 047 patients. Twenty-three trials were considered to be at low risk of bias across all domains. Atrial natriuretic peptides (14 trials; n=2207) reduced 30-day mortality (RR: 0.63 [0.41, 0.97]) and AKI events (RR: 0.43 [0.33, 0.56]) without heterogeneity. These effects were consistent across cardiac surgery and vascular surgery subgroups, and in sensitivity analyses restricted to studies at low risk of bias. Inodilators (13 trials; n=2941) reduced mortality (RR: 0.71 [0.53, 0.94]) and AKI events (RR: 0.65 [0.50, 0.85]) in the primary analysis and in cardiac surgery cohorts. Vasopressors (4 trials; n=1047) reduced AKI (RR: 0.56 [0.36, 0.86]). Nitric oxide donors, alpha-2-agonists, and calcium channel blockers reduced AKI in primary analyses, but not after exclusion of studies at risk of bias. Overall, assessment of the certainty of the effect estimates was low. CONCLUSIONS: There are multiple effective pharmacological renoprotective interventions for people undergoing surgery.

Patient blood management interventions do not lead to important clinical benefits or cost-effectiveness for major surgery: a network meta-analysis.

BACKGROUND: Patient blood management (PBM) interventions aim to improve clinical outcomes by reducing bleeding and transfusion. We assessed whether existing evidence supports the routine use of combinations of these interventions during and after major surgery. METHODS: Five systematic reviews and a National Institute of Health and Care Excellence health economic review of trials of common PBM interventions enrolling participants of any age undergoing surgery were updated. The last search was on June 1, 2019. Studies in trauma, burns, gastrointestinal haemorrhage, gynaecology, dentistry, or critical care were excluded. The co-primary outcomes were: risk of receiving red cell transfusion and 30-day or hospital all-cause mortality. Treatment effects were estimated using random-effects models and risk ratios (RR) with 95% confidence intervals (CIs). Heterogeneity assessments used I2. Network meta-analyses used a frequentist approach. The protocol was registered prospectively (PROSPERO CRD42018085730). RESULTS: Searches identified 393 eligible randomised controlled trials enrolling 54 917 participants. PBM interventions resulted in a reduction in exposure to red cell transfusion (RR=0.60; 95% CI 0.57, 0.63; I2=77%), but had no statistically significant treatment effect on 30-day or hospital mortality (RR=0.93; 95% CI 0.81, 1.07; I2=0%). Treatment effects were consistent across multiple secondary outcomes, sub-groups and sensitivity analyses that considered clinical setting, type of intervention, and trial quality. Network meta-analysis did not demonstrate additive benefits from the use of multiple interventions. No trial demonstrated that PBM was cost-effective. CONCLUSIONS: In randomised trials, PBM interventions do not have important clinical benefits beyond reducing bleeding and transfusion in people undergoing major surgery.