Molecular graph representation learning has shown considerable strength in molecular analysis and drug discovery. Due to the difficulty of obtaining molecular property labels, pre-training models based on self-supervised learning has become increasingly popular in molecular representation learning. Notably, Graph Neural Networks (GNN) are employed as the backbones to encode implicit representations of molecules in most existing works. However, vanilla GNN encoders ignore chemical structural information and functions implied in molecular motifs, and obtaining the graph-level representation via the READOUT function hinders the interaction of graph and node representations. In this paper, we propose Hierarchical Molecular Graph Self-supervised Learning (HiMol), which introduces a pre-training framework to learn molecule representation for property prediction. First, we present a Hierarchical Molecular Graph Neural Network (HMGNN), which encodes motif structure and extracts node-motif-graph hierarchical molecular representations. Then, we introduce Multi-level Self-supervised Pre-training (MSP), in which corresponding multi-level generative and predictive tasks are designed as self-supervised signals of HiMol model. Finally, superior molecular property prediction results on both classification and regression tasks demonstrate the effectiveness of HiMol. Moreover, the visualization performance in the downstream dataset shows that the molecule representations learned by HiMol can capture chemical semantic information and properties. Graph Neural Networks are employed to encode molecular graph representations, but structural information and chemical functions are largely missing. Here, the authors develop hierarchical molecular graph self-supervised learning as a framework to learn molecule representation for property prediction. Motivated by the remarkable success of machine learning2, especially deep learning3, molecular representation learning has increasingly explored machine learning-based methods. These methods are widely used for various molecular applications, such as chemical property prediction5, drug molecule generation8, and optimization9. How to learn comprehensive and effective molecular representation remains an open challenge.In recent years, molecular representation learning has attracted growing attention in the study of chemical drug analysis and discovery12, a range of methods apply language models to handle string-based molecular representations like SMILES13 and SELFIES14. Specifically, these molecular strings are encoded by Recurrent Neural Network (like Gated Recurrent Unit (GRU)15 and Long Short-Term Memory (LSTM)16 or Transformer12, which can be trained in a supervised manner by predicting molecular properties17. Further, to handle enormous unlabeled data, many works pay attention to self-supervised learning (SSL) frameworks. The SSL decoders generally generate the original strings18 or other random SMILES of input molecules19 or recover masked tokens of molecular strings20. Due to the single dimension of strings, important topology structures of molecular graphs are ignored. Therefore, many researchers have transformed their interests from 1D molecular strings to 2D graphs22. In view of the success of SSL, graph SSL-based pre-training frameworks27 have been increasing rapidly in the past few years. They capture the topology of 2D graphs and have also shown a positive effect in molecular analysis tasks. However, their commonality for all kinds of graphs leads to the neglect of unique structural properties of chemical molecules, such as rings and functional groups.Inspired by the development of Natural Language Processing (NLP)33 consider the characteristic of molecular graphs. Zhang et al.32 model a clustering problem to learn molecular motifs, and the GNN-encoded atom representations are grouped into subgraphs for contrastive learning. Zhang et al.30 design self-supervised molecular pre-training framework, whose pretext task is predicting the motifs based on a given order (depth-first search or breadth-first search) on the graph. Wang et al.5 present three molecular graph structural augmentation patterns and aligns different augmentations of the same molecule through contrastive learning. Wang et al.33 also propose a contrastive learning framework for molecular learning, in which not only molecular pairs but also motif pairs are sampled for contrastive pre-training. Wang et al.31 takes into account the chemical reaction relationships between the molecules, i.e., the sum of molecular representations of all reactants is supposed to equal that of all products. Despite the improvement of molecule representation learning, previous works still fail to solve the following problems and challenges: (1) How to preserve and capture molecular structure adequately? Many recent molecular learning methods28 apply graph augment to construct different views and contrast multiple views for pre-training. However, some general graph data augmentations (like edge modification34 or graph diffusion35) tend to destroy the structure or attributes of molecules, so some important chemical properties are highly possible to be buried. In addition to the whole topology structure, the motif is also valuable and has an important impact on the molecular properties. Therefore, it is beneficial and challenging for molecular learning to preserve the complete molecular structure and directly incorporate the motifs. (2) How to fuse more comprehensive information into molecular graph representations? With the development of GNN40, growing molecular learning methods31 leverage GNN as the encoder backbone, in which nodes flow information among local neighbors, and the graph representation is obtained by integrating all its node representations via READOUT function. The pattern of aggregating neighborhood representations lacks a global scale. Moreover, the READOUT operation can only transfer information from low-order atoms to the high-order molecule, but can not implement the interaction between them. (3) How to design the pretext tasks of self-supervised pre-training? Self-supervised pre-training requires design pretext tasks to optimize the backbone model parameters. The pretext tasks affect the transferable ability of the pre-training model to a great extent, which determines whether the model can achieve satisfactory fine-tuning performance in downstream tasks. Therefore, it is key for self-supervised pre-training models to design reasonable pretext tasks to improve transferable ability.Many recent presentation learning worksTo resolve the aforementioned challenges, we propose a self-supervised learning framework for molecular property prediction called Hierarchical Molecular Graph Self-supervised Learning (HiMol). Figure HiMol builds a hierarchical GNN to encode molecules. It facilitates incorporating multi-order information into the molecule representation. To the best of our knowledge, HiMol is the first molecular representation learning method that encodes node-motif-graph hierarchical information.HiMol constructs motifs based on chemical rules and mine the characteristics of molecular substructures without destroying chemical structures.HiMol augments a graph-level node to simulate the molecular graph representation. It allows the molecular graph representation to participate in the training directly and realizes bidirectional transmission of local and global features.HiMol designs multi-level self-supervised pre-training tasks in accordance with hierarchical representations of molecules. It improves the transferable potential of the pre-training framework and helps learn more informative molecular representations.HiMol outperforms the state-of-the-arts (SOTA) in the downstream molecular property prediction tasks including classification and regression. It demonstrates the effectiveness of our proposed HiMol.In summary, the advantages of our HiMol are as follows:Our HiMol presents a self-supervised pre-training framework to encode hierarchical representations of the molecular graph. HiMol mainly includes the Hierarchical Molecular Graph Neural network (HMGNN) and Multi-level Self-supervised Pre-training (MSP).41, where V and E denote atom and bond sets respectively. Then we decompose G into a series of motifs k is the number of motifs. The decomposition of motifs adds a rule on the basis of BRICS42, i.e., break large ring fragments and select their minimum rings as generated motifs. All motif nodes Vm are incorporated into V, and edges between the corresponding atoms and motifs Em are linked and merged into E. In addition, we augment a graph-level node Vg, meanwhile, edges between all motifs and Vg are appended into the initial graph G. The augmented graph Given the SMILES of a molecule, we first transform it into a graph As for the fine-tuning for downstream tasks, the graph-level representations pass through a 2-layer Multi-Layer Perceptron (MLP) to predict molecular properties. The pre-trained GNN weights are transferred to the fine-tuning model and continue to be updated along with the MLP parameters under the supervision of labels. More details about HiMol method are shown in “Methods”.To evaluate the effectiveness of HiMol for molecular property prediction, we conduct classification and regression tasks on diverse datasets from MoleculeNet. Table 28 on QM9 is not given since it is expensive to calculate the similarity matrix in the process of pre-training on QM9, and no corresponding result is given in the official paper. (1) HiMol outperforms all baselines on five out of six datasets and reaches competitive performance on the remaining QM8. Notably, the MAE decreases by 55.5% over the strongest baseline on the challenging dataset QM9. (2) Similar to the results of classification tasks, the LARGE version has a small edge over the SMALL version in the regression tasks.The regression results are shown in Table 43 to project them to a two-dimensional space and different colors to distinguish molecular property labels. Note that we simply visualize the fine-tuned molecular presentations in the test set, which do not incorporate groundtruth. From Fig. To exhibit intuitively the learned representations by HiMol, we utilize t-SNETo further demonstrate the effectiveness of our pre-training framework, we visualize the molecular representations fine-tuned in downstream tasks without pre-training in Supplementary Fig. q and the candidate molecule c is calculated as follows:To further investigate the semantic information contained in the pre-trained molecular representations via HiMol, we evaluate the similarity between the query molecule and other molecules and draw the top-five most similar molecules. The cosine similarity between the query molecule 38, which are directly trained through supervised downstream tasks. Table To investigate the impact of different fine-tuning strategies on the model prediction tasks, we explore freezing mode and full fine-tuning mode. They both inherit the pre-trained weights of the hierarchical molecular graph neural network. The freezing mode freezes the parameters of HMGNN and only updates the weights of downstream classifiers, while the full fine-tuning mode trains all weights of HMGNN and classifiers in an end-to-end manner during fine-tuning. In addition, we evaluate HiMol without pre-training and vanilla GINFigure To analyze the effectiveness of each part of our proposed HiMol framework, we conduct ablation experiments on HMGNN and MSP during the pre-training process. Figure In Fig. α simply sums all the loss values, removing the α weight parameter to balance. It can be observed that the combination of multi-level self-supervised tasks promotes the comprehensiveness transfer ability of pre-training compared with a single pretext task. In addition, the learnable weight α adjusts the importance of the loss of different parts, which can better coordinate the relationship between multi-level tasks to improve the performance of pre-training.The ablation versions for MSP are shown in Fig. 30 considering motif structure. They generally decompose molecules in a large-scale database into motifs and build a motif dictionary, then motif prediction serves as self-supervised pretext tasks in the pre-training process of molecular representation learning. However, these methods have some disadvantages: (1) When the constructed motif dictionary is large, the computation of motif prediction is expensive. (2) Motifs as the self-supervised signal only, are difficult to be incorporated by hidden representations directly. We encode motifs contained in each molecule in the encoder part, which directly integrates the motif structure into the hidden representations. Moreover, the augmentation of motif-level nodes better captures the affiliation relationship between atoms and motifs.First of all, we propose to encode the constructed motifs while learning the atom representations. HiMol mines the substructure of molecules through motifs. There are many existing molecular representation learning methods44 proposes to attach a token to the sequence, whose representation is regarded as the sequence-level feature. Referring to BERT, Graphormer45 adds a virtual node into the graph, which is linked and updated with all nodes, and the feature of the virtual node is the graph representation. Inspired by the success of the aforementioned works, we augment a graph-level node to extract the molecular graph representation via participating in aggregation and updating with all atoms. Nevertheless, related methods only connect the graph-level node with normal nodes, they are not conducive to capturing subgraph structure. To integrate motif nodes, we connect graph-level nodes with motif nodes to achieve hierarchical aggregation. Compared with the READOUT, our HiMol implements the training and updating of graph nodes and realizes the interaction with multi-order nodes after incorporating molecular motif structure.In addition to motif-level nodes, we also augment a graph-level node into the molecular graph, which participates in the aggregation process to extract molecule representation. Most molecular learning methods use vanilla GNNs as the backbones and squeeze node representations into graph representation through the READOUT function, i.e., graph pooling methods like MAX, SUM, and MEAN. The process of GNNs is introduced in Supplementary Note In this paper, we propose Hierarchical Molecular Graph Self-supervised Learning (HiMol) to learn molecular presentations. Specifically, we design Hierarchical Molecular Graph Neural Network (HMGNN) backbone to encode node-motif-graph hierarchical representations, which mines chemical semantic information implied at motifs and realizes the interaction of multi-order features. Furthermore, we build multi-level molecular-dependent tasks as self-supervised signals, capturing more comprehensive information from multiple perspectives. Extensive molecular property prediction experiments demonstrate that HiMol shows great improvements over SOTA methods.The superior performance of molecular representations reflects the potential of our HiMol framework, and there are interesting and promising future works on the basis of HiMol. (1) Combining molecular features with different dimensions, in addition to 2D graphs. (2) Simulating the generative process of molecules and extending our model to molecule generation and optimization tasks.We propose a Hierarchical Molecular Graph Neural network (HMGNN) to encode and represent molecular graphs, which mainly contains three parts: (1) motif construction; (2) augmented graph construction; (3) hierarchical representation encoder.First, we decompose each input molecule graph into a group of motifs. BRICS algorithm is a traditional approach to fragment molecule graphs, which designs chemical rules to break bonds. Specifically, BRICS structurally decomposes molecules into multiple functional fragments according to whether bonds can be combined. However, BRICS splits molecules based on limited chemical rules, resulting in coarse granularity of decomposition. After BRICS decomposition, some large motifs still contain a few more general and functional substructures to be partitioned. Therefore, we add a decomposition rule on the basis of BRICS, i.e., decompose more than one ring substructures connected by common chemical bonds into minimum rings. Figure Vm to the molecular graph. Naturally, node-motif edges Em are added between each motif node and all the atom nodes it covers. Since the final output is the molecular graph representation, we construct a graph-level node Vg into the molecular graph and link it to all motif nodes to form motif-graph edges Eg. The final augmented graph is represented as:All obtained motifs are added as nodes 38 is the most widely used to encode molecular graph representation. Inspired by this, we apply GIN as the backbone model of our HiMol, the aggregation pattern of lth layer is given as follows:l-layer, respectively, uv, and v. The detailed input features of atoms and bonds are shown in Supplementary Table On the constructed graph mentclass2pt{minimAfter encoding the hierarchical representations through HMGNN, we design generative and predictive pretext tasks to perform Multi-level Self-supervised Pre-training (MSP). The types of self-supervised learning corresponding to different pretext tasks are described in Supplementary Note yij represents the link between vi and vj, yv,k = 1 represents the atom type of node v is k, and ye,k = 1 represents the bond type of edge e is k. Their corresponding Katom and Kbond are the number of atom types and bond types.For atom-level representations, we design three generative tasks including bond links, atom types, and bond types, aiming at reconstructing graph structure and attributes. Specifically, three 2-layer MLPs with ReLU activation function project atom-level representations to predict three generative tasks, respectively. Cross entropy loss is employed to optimize the atom-level representations as follows:ya and yb are the number of atoms and bonds, respectively; H to the predicted values y. Finally, we add a learnable vector weight For graph-level representations, two predictive tasks on the overall properties of molecules are performed, i.e., the number of atoms and bonds. Similarly, two respective 2-layer MLPs with softplus activation function are applied to predict the graph-level tasks. Based on SmoothL1 loss, the loss of predictive tasks are given in Equations  and 8)..8).7\doc46 dataset. To evaluate the effectiveness of HiMol, we conduct molecular property prediction experiments on 12 datasets from MoleculeNet47, including six classification task datasets and six regression task datasets. All downstream datasets are split into 80/10/10% for train/validation/test through scaffold-split, which is split in accordance with the molecular structures and provides more challenge for prediction tasks than random-split. The statistical data of all datasets are summarized in Supplementary Table For the pre-training of HiMol, we utilize sampled 250K unlabeled molecules from the ZINC1539, GPT_GNN48, AttributeMask49, ContextPred49, InfoGraph50, GraphLoG26, GraphCL25, and JOAO51. Furthermore, we compare several methods designed specifically for molecular graphs, i.e., MoCL28, MolCLR5, G_Motif29, and MGSSL30. The detailed description of baselines is demonstrated in Supplementary Note To evaluate the effectiveness of our proposed HiMol, different types of self-supervised learning SOTA are conducted as benchmarks. We compare diverse universal graph learning methods, including GraphSAGEAll experiments are conducted on the Linux server with Nvidia GeForce GTX 1080 Ti GPU and Intel(R) Xeon(R) CPU E5-2620 v4 @ 2.10GHz. In the pre-training phase, the HiMol run 100 epochs using the Adam optimizer with a learning rate of 0.001. We train the LARGE and SMALL versions, with 3-layer and 5-layer GIN as the HMGNN backbone. The dropout rate for the GIN backbone is set to 0.5. The batch size is set as 32 and the number of workers for dataloader is 8. The hidden representations of all levels are 512-dimension.In the fine-tuning phrase, we implement a 2-layer MLP with ELU activation function as the classifier. The weights of both pre-trained HMGNN and the classifier are optimized by the Adam optimizer with respective learning rates ranging from 1e-4 to 1e-3. For each downstream task, we fine-tune for 100 epochs and report the average test results of five runs. The batch size is 32 and the number of workers is 4. The embedding dimension is 512, the same as that in the pre-training phase. The dropout rate is adjusted in the range . The parameter settings for each dataset during the fine-tuning process are reported in Supplementary Table 28. MoCL does not implement transfer learning, and it is computationally expensive to calculate the similarity matrix of ZINC15 dataset during pre-training. Therefore, we adopt the same manner as the official work: pre-training on each downstream dataset and then fine-tuning on the same dataset for molecular property prediction tasks. All baselines maintain the same split proportion and manner on the downstream datasets.For the purpose of fairness, we pre-train all the baselines on the same dataset ZINC15 according to their official codes, except MoCLPeer Review FileSupplementary Information


The sun is the most sustainable source of photons onthe earthbut is rarely used in photochemical transformations due its relativelylow and variable intensity, broad wavelength range, and lack of focus.Luminescent solar concentrator-based photomicroreactors (LSC-PMs)can be an answer to all these issues, but widespread adoption is plaguedby challenges associated with their complicated manufacturing. Herein,we developed a new strategy to accelerate and ease the productionof LSC-PMs by depositing a thin luminescent film on commercially andwidely available silicon-based microreactors. The protocol is fastand operationally simple, and the luminescent coating can be easilyremoved and replaced. This enables rapid tuning of the luminescentcoating to fit the requirements of the photocatalytic system and toincrease the photon flux inside the microreactor channels. Implementation of a novel method forfast screening of luminescentcoatings enables energy-efficient solar photochemistry using conventionalsilicon-based microreactors. In addition, the photon intensity reaching thereaction channels can be augmented through a wave-guiding effect insidethe refractive reactor material.14 In combinationwith self-optimization protocols16 and solarpanels, we were able to develop an autonomous and off-grid solar mini-plant,which enables the production of complex organic molecules using solarlight as the sole energy source.17However, the true transformative potential of photocatalysisresidesin the possibility of using the sun as a renewable, free, and perennialsource of energy.19 or a combination of PMMA and perfluorinated capillaries. Both fabricationprocesses are labor- and time-intensive, preventing mass productionand adoption. Recently, a 3D-printing technique has been developedby Kim and co-workers,21 but this fabrication method alsohas its limitations with regard to mass production. As the forerunnerin microreactor technology, silicon-based microreactors have beenfrequently used due to their chemical resistance, transparency, andexcellent heat-transfer characteristics.23 Consequently, silicon-based microreactors have been commercializedand are available in a wide variety of designs and sizes. The materialitself is compatible with a number of applications, including flowchemistry,24 photochemistry,25 microfluidics,26 cellbiology,27 and lab-on-a-chip uses.28 In our aim to further increase the utility ofthe LSC-PM concept, we wondered if we could use commercially availablesilicon-based microreactors as a foundation to create a silicon-basedLSC-PM. As such, standardized flow reactor technology, with knownmass and heat transfer characteristics, can be exploited for varioussolar-driven photochemical applications. Herein, we show that we canindeed coat silicon-based microreactors with a luminescent layer usingan operationally simple spin-coating procedure and subsequently turnthem into solar-harvesting reactors, which are comparable in efficiencyto our previous designs in terms of photon conversion and photon flux.Moreover, as a unique feature of this strategy, the luminescent layercan be easily removed and exchanged, allowing to tune the reactorto the photocatalytic needs and multiple reusing of the base reactor.To date, LSC-PM reactors have been mainly fabricated from PDMS29 However, in our design, this coating methodunlocks flexibility in the design process: instead of designing amicroreactor within an LSC matrix, which requires expert knowledgein microchannel design, this approach makes use of commercially availablesilicon-based microreactors and converts these widely available reactordesigns into solar-harvesting ones. Since the coating has a similarrefractive index to PMMA (∼1.49), the incident light will beguided to the surface of the glass and enter the silicon-based microreactor.Indeed, with a refractive index of ∼1.47 for Borofloat 33 glass,the light will not be trapped at the glass–coating interfaceand photon losses can be minimized significantly. In contrast, whenthe light reaches the coating–air interface, the large differencein refractive index favors internal reflection. Consequently, thephotons are waveguided within the reactor material until they reachthe reaction channels  with the luminescent coatings deposited on the top surfaces.The edge emission measurement quantifies the light escaping from theedge of the substrate while irradiating its top surface with simulatedsunlight (AM 1.5G) and can be correlated with the photon flux receivedinside the reaction channels.14 The spectrumcaptured from the side of the sample is a result of both the downconversionand the waveguiding effect inside the reactor material.31To compare the performancesof individual coatings, the edge emissionswere determined for standard glass substrates (30 × 30 mmSupporting Information). This result differsfrom previous LSC-PM designs, where increased concentrations of luminescentdye resulted in higher reabsorption losses. However, in a coated,silicon-based LSC-PM, reabsorption by the dye can only occur insidethe thin coating layer, but not in the 6 mm-thick silicon-based microreactoritself, thus effectively minimizing reabsorption losses. The maximumdye loading of the coating is governed by the solubility in the monomersolution. Increasing the dye loading above this solubility limit willnot result in a higher luminescence and will even result in inhomogeneities,causing undesired light scattering and absorption losses.32 After testing the solubility and homogeneityof the different dyes and thicknesses, optimal conditions were determined(see the Supporting Information). A thicknessof 18 μm resulted in the greatest edge emission, while retaininga homogeneously spread coating layer, and maximum loadings of 1.0,0.5, and 0.25 wt % were determined for the red dye Lumogen F Red 305(LR305), the green dye DFSB-K160, and the violet dye Lumogen F Violet570 (LV570), respectively.First, the luminescent dye loading insidethe deposited coatingwas investigated. The edge emission measurements demonstrated thata higher dye loading led to greater edge emission because more incidentlight can be absorbed and released inside the waveguide (see the Supporting Information). Unfortunately, the overall measured edge emission was not significantlyenhanced by this addition. This observation can be rationalized bythe fact that most unabsorbed photons are situated in a wavelengthrange where neither the device nor the reaction mixture absorbs.14 Finally, the optimal result was obtained whenthe luminescent coating was applied on both sides of the silicon-basedmicroreactor (denoted as two-sided coating). Having two luminescentlayers increases the odds of absorption and re-emission inside thelight guide, and edge emissions are comparable in profile and intensityrelative to the PMMA reference.After this coating optimization,the LR305-based coating was comparedto a previously designed PMMA sample doped with LR305. As depictedin Supporting Information) can be screened in atime- and resource-efficient fashion, allowing selection of the bestcoating for any photochemical process.It is known that different photocatalystsabsorb optimally in differentwavelength ranges, which is mostly situated around their absorptionmaxima. This implies that for every photocatalyst, a different reactormust be fabricated to match the emission of the reactor with the absorptioncharacteristics of the photocatalyst. In doing so, the energy efficiencyof the solar-driven photocatalytic reaction can be increased significantly.However, it is important to point out that using the method describedherein, a suitable luminescent dye can easily be deposited withoutmodifying the standard reactor design 2A–D. FurTo demonstrate the efficiency ofthe luminescent coatings for light harvesting, we examined the performanceof the silicon-based LSC-PMs in a series of benchmark photocatalyticreactions. From these data, the overall apparent reaction rate constantswere extracted and compared.34 Singlet oxygen subsequentlyreacts with furfural to yield 5-hydroxy-2(5H)-furanone.This useful photochemical transformation is the first step in theproduction of maleic acid,35 polymers,and coatings.36 Since this transformationis generally photon-limited, increasing the photon flux leads to animproved reaction yield, as depicted in The red LSC-PM, with a coatingcontaining the dye LR305, was evaluated in the oxidation of the biobasedplatform molecule furfural using methylene blue as the photosensitizerto convert triplet oxygen into singlet oxygen 3A.33,34 37 The kinetic curve of thistransformation is shown in The green LSC-PM, with a coating containing theluminescent greendye (DFSB-K160), was benchmarked in the [4 + 2] cycloaddition betweenα-terpinene and singlet oxygen using Rose Bengal as the photosensitizer3B. This 3Cl2.39 As a benchmarkreaction, we selected the C–N cross-coupling between 1-bromo-3-(trifluoromethyl)benzeneand morpholine using ligand-free NiBr2 and Ru(bpy)3Cl2 as the photocatalyst .15While thegreen and red region in the visible light spectrum canbe used for photocatalysis, blue light has been more widely employeddue to the higher energy content present in blue photons and its abilityto excite various metal-based photocatalysts, such as the common photocatalystRu(bpy)catalyst 3C.38 As  of ∼1.13F. This 40 Theimplementation of the coating builds upon our previous work on simulatingPDMS- and PMMA-based LSC-PMs.17 The commercial silicon-basedreactor was 3D-modeled using the open-source software Onshape andthe information provided by the reactor manufacturer . The coating was then represented as a homogeneousthin layer (18 μm) both on the top and bottom surfaces of thereactor  of the custom-madeLightBox used in the experimental setup was implemented into the codeas well. Using this ray-tracing model, the Monte Carlo simulationswere performed to calculate the reacted ray fraction of differentreactor configurations. The reacted ray fraction is defined here asthe number of rays effectively absorbed by the reaction medium dividedby the number of rays incident on the reactor surface, effectivelyfiltering out the generated rays that would not reach the reactor. Supporting Information). The reacted ray fractions determined are representative for theefficiency of the reactor devices, and their relative performancecan be investigated by comparing these efficiencies. As was foundexperimentally by looking into the relative reaction rate constants,the LR305-coated reactor performed better by a factor of ∼2.4.The simulations showed a performance enhancement by a factor of ∼2.3between the noncoated and two-side-coated reactor (3.5 vs 7.8%). The experimental and simulation results showed good agreement,where one cause for worse performance in the simulations is expectedto be the exclusion of the effects originating from interactions withthe LightBox, such as additional reflections from the walls and bottomplate. In reality, even though these are black and primarily absorbing,some reflection is observed, adding light intensity that the modeldoes not account for. To further validate the model, thedifference between the noncoated and clear-coated (coating withoutdye-doping) reactor was investigated and matched the experimentalresults . This also showed that the enhanced performance was due tothe inclusion of the luminescent dye, both experimentally as for thesimulations. With this model, the relative performance of the deviceis also simulated for the two one-side-coated configurations (top-coatedand bottom-coated). The performance of these configurations (5.7%top-coated and 6.2% bottom-coated) was lower than the two-side-coatedreactor (7.8%), as expected from the measured relative edge emission,shown in Supporting Information), geometrical setups,and orientations as long as the properties are representative forthe system.The validation of the model was carried out byrunning additionalsimulations until the computed reacted ray fractions converged toa stable value  and specializedmanufacturing techniques, this novel approach is more practical andversatile and separates the reactor design aspects from its solar-harvestingapplications. We believe that this fabrication method can be advantageousregarding both the access to bespoke reactors and the mass productionof solar-harvesting reactor technology.We have developed a new and operationallysimple strategy to convertcommercially and widely available silicon-based microreactors intolight-harvesting photochemical reactors. The strategy exploits a conventionalspin-coating technique to coat silicon-based microreactors with alayer of a luminescent material. Essentially, this method allows oneto convert a conventional, transparent microreactor into an efficientluminescent solar concentrator-based photomicroreactor, as demonstratedby both experimental and simulation data. Notably, the coatings canbe easily removed and exchanged to match the needs of the differentphotocatalytic reactions, enabling applications spanning the entirevisible light spectrum. While previous LSC-PM designs required somechemical engineering knowledge, such as microreactor design (


Our study compared split-sample and entire-sample methods for estimating and validating a suicide prediction model.There is increasing interest in clinical prediction models for rare outcomes such as suicide, psychiatric hospitalizations, and opioid overdose. Accurate model validation is needed to guide model selection and decisions about whether and how prediction models should be used. Split-sample estimation and validation of clinical prediction models, in which data are divided into training and testing sets, may reduce predictive accuracy and precision of validation. Using all data for estimation and validation increases sample size for both procedures, but validation must account for overfitting, or We compared performance of random forest models estimated in a sample of 9,610,318 mental health visits (“entire-sample”) and in a 50% subset (“split-sample”) as evaluated in a prospective validation sample of 3,754,137 visits. We assessed optimism of three internal validation approaches: for the split-sample prediction model, validation in the held-out testing set and, for the entire-sample model, cross-validation and bootstrap optimism correction.th, 95th, 90th, and 75th percentiles of the risk score distribution, indicated similar conclusions: bootstrap optimism correction overestimated classification accuracy in the prospective validation set.The split-sample and entire-sample prediction models showed similar prospective performance; the area under the curve, AUC, and 95% confidence interval was 0.81 (0.77–0.85) for both. Performance estimates evaluated in the testing set for the split-sample model (AUC = 0.85 [0.82–0.87]) and via cross-validation for the entire-sample model (AUC = 0.83 [0.81–0.85]) accurately reflected prospective performance. Validation of the entire-sample model with bootstrap optimism correction overestimated prospective performance (AUC = 0.88 [0.86–0.89]). Measures of classification accuracy, including sensitivity and positive predictive value at the 99While previous literature demonstrated the validity of bootstrap optimism correction for parametric models in small samples, this approach did not accurately validate performance of a rare-event prediction model estimated with random forests in a large clinical dataset. Cross-validation of prediction models estimated with all available data provides accurate independent validation while maximizing sample size.The online version contains supplementary material available at 10.1186/s12874-023-01844-5. Internal validation refers specifically to methods that use the same data available for model estimation to anticipate performance in new observations from the same underlying sample [There is growing interest in using prediction models to guide clinical care . A key ag sample . AccuratWe considered the use-case of a prediction model to estimate suicide risk in the 90 days following an outpatient mental health visit. Mental health providers are particularly interested in identifying patients at high risk of suicide at clinical encounters so that suicide prevention interventions can be made. In our dataset of over 13 million visits, suicide is a rare event—there are 23 events per 100,000 visits. The data also contain over a hundred predictors characterizing patient’s clinical history, including mental health diagnoses, medications, and encounters. In this empirical evaluation, our goal was two-fold: first, to estimate the best possible suicide prediction model to optimize identification of high-risk patients and, second, to accurately estimate how well the prediction model will perform in future use.Split-sample estimation and validation, in which the entire available sample is randomly divided into two subsets, one used exclusively for model estimation (“training”) and another used for validation (“testing”), is common . Using aoptimism, or over-estimation of performance, when performance is assessed in the same data used for estimation. Optimism is likely to be larger when the number of events is small relative to the number of predictors and when using more flexible prediction models [Methods have been proposed that use the entire available dataset for both model estimation and validation. Using all available data for estimation requires validation methods that account for the potential for overfitting, that is, when a prediction model is accurate in the sample used for estimation but not accurate for new observations. Overfitting causes n models , 11.In this study, we considered two validation methods to adjust for optimism error when using the same sample for both prediction model estimation and validation: cross-validation and bootstrap optimism correction. Cross-validation  is typicAlternatively, some authors have advocated that applying an optimism correction estimated via bootstrap sampling is the preferred method to obtain unbiased and efficient estimates of the predictive performance of a model estimated in the entire sample , 15–17. Using the entire sample for both model estimation and validation is particularly appealing for predicting rare events to address concerns about missing important predictors, uncertainty in model estimation, and high variability in performance estimates that arise even in samples with millions of observations. While estimating a prediction model with the entire sample may result in better clinical performance, accurate internal validation that accounts for overfitting remains a concern; it is unknown whether optimism estimates obtained via bootstrap accurately correct for optimism when prediction models are constructed with data-driven (or “data-hungry”)  machine We empirically evaluated spit-sample and entire-sample methods for estimating and validating a random forest prediction model for risk of suicide following an outpatient mental health visit. Our analysis investigated two questions. First, we examined whether a suicide prediction model estimated in the entire sample, instead of a subset of data, improved risk prediction as evaluated in a prospective validation set. Second, we assessed whether three internal validation approaches adequately adjusted for overfitting to produce accurate performance estimates by comparing validation estimates from each approach—(1) validation in an independent testing set of the prediction model estimated with the split-sample approach and, for the prediction model estimated in the entire sample, (2) cross-validation and (3) bootstrap optimism correction —to prospective performance.Our sample included all outpatient mental health specialty visits for patients age 13 years or older at seven health systems  between January 1, 2009 and September 30, 2017 ; 3-month, 1-year, and 5-year history of mental health and substance use diagnoses, prescriptions, and encounters ; comorbidities (measured by Charlson Comorbidity Index categories);  prior sudevelopment dataset containing all visits from January 1, 2009-September 30, 2014 and a prospective validation dataset containing visits from January 1, 2015- September 30, 2017. The development dataset was used for prediction model estimation and internal validation. The prospective validation dataset was used to evaluate future performance of prediction models estimated in the development dataset; we used this set-up to reflect predictive performance if models were applied in the same population and setting later in time [The sample was divided into a First, we describe the general methodology we used to estimate random forest models with our dataset. We then describe the different approaches we evaluated for estimating prediction models and validating performance.We used random forest models to estimate the probability of 90-day suicide . Random We selected measures of prediction model performance that reflected how our health system partners plan to use suicide prediction models to inform mental health care. From our experience, health system leaders choose a threshold of the continuous risk score to classify patients as “high-risk” . Suicide prevention interventions are then offered at visits classified as high-risk. To evaluate possible thresholds, health system stakeholders need to know whether each binary categorization of risk scores identifies people in need of suicide prevention (sensitivity) while limiting unnecessary interventions (specificity). At each threshold, the intensity and cost of suicide prevention interventions must also be balanced against the event rate in those flagged as high-risk . Thresholds are selected based on classification accuracy as well as health system capacity and intervention effectiveness.th, 95th, 90th, and 75th percentiles of the suicide risk score distribution. . Prediction model performance was also evaluated using area under the curve (AUC), as this measure summarizes sensitivity and specificity across the range of all possible thresholds.To inform health system decisions about whether and how to implement a suicide prediction model, we focus prediction model evaluation on measures of classification accuracy  at thresholds under consideration—the 99training set containing all visits from a random sample of half of the people in the dataset. All visits from the remaining 50% comprised the testing set used for internal validation. 5-fold cross-validation was done in the training set to identify the tuning parameter combination that optimized out-of-fold AUC. After tuning parameters were selected, the final split-sample prediction model was estimated with all visits in the training set.For the split-sample approach, we divided the development dataset into a The split-sample prediction model was then applied to obtain suicide risk predictions for all visits in the testing set. AUC and classification accuracy of the split-sample prediction model was evaluated in the testing set Classification accuracy was calculated for thresholds defined at percentiles of the suicide risk prediction distribution in the training set . We constructed 95% confidence intervals (CIs) of performance measures in the testing set via 1,000 bootstrap samples . BootstrAll visits in the development dataset were used for the entire-sample prediction model estimation and internal validation. 5-fold cross-validation within the entire development dataset was used to identify the tuning parameter combination that optimized out-of-fold AUC. The final entire-sample prediction model was estimated using all visits in the development sample and the selected tuning parameters.Cross-validation was used to estimate entire-sample model performance. Following cross-fold validation to select the optimal tuning parameters for the entire-sample prediction model, out-of-fold predictions for chosen hyperparameters were saved. Performance metrics were calculated in the out-of-fold predictions. For measures of classification accuracy, prediction thresholds were defined in the distribution of in-sample predictions in the entire development sample. Quantile-based 95% CIs around performance measures were estimated in 500 event-stratified person-level bootstrap samples.  and 3,754,137 visits in the prospective validation dataset (2015–2017). Suicides were observed following 2,318 visits in the development data  and following 710 visits in the prospective validation data   results in a strong prediction model, as demonstrated with good performance on the prospective validation set, as well as b) gives an accurate internal estimate of the prospective validation performance. We first report performance of the split-sample and entire-sample suicide prediction models in the prospective dataset; we then present optimism of internal validation estimates compared to prospective performance.th percentile, prospective sensitivity was 52.0% (41.7–61.1%) for the split-sample prediction model and 53.0% (43.0–62.3%) for the entire-sample prediction model. At the same threshold, PPV was 7.9 events per 100,000 visits  for the split-sample model and 9.1 events per 100,000 visits  for the entire-sample model. Prospective performance of the two models was also similar for specificity  in the prospective validation set, and the entire-sample model had an AUC of 0.811 (0.768–0.849) Table . SensitiInternal validation estimates obtained in the split-sample testing set and cross-validation in the entire-sample better reflected prospective performance than using bootstrap optimism-correction. The cross-validation estimate of AUC in the entire-sample model  was closest to the prospective AUC  are similar to the prospective sensitivities  while the bootstrap optimism correction estimate (63.2%) over-estimated sensitivity of the entire-sample model.Interval validation estimates of sensitivity Table  and specBecause the overall event rate was lower in the prospective validation set, all internal validation methods overestimated PPV. As seen in other performance metrics, overestimation was greatest for the bootstrap optimism corrected estimate of PPV for the entire-sample model and more modest for the split-sample testing and entire-sample cross-validation estimates of PPV Table .In this case study, we empirically evaluated approaches for prediction model estimation and internal validation in the context of predicting risk of suicide following an outpatient mental health visit. Our first aim was to compare performance of a prediction model estimated with random forests in a 50% randomly sampled training subset of a development dataset containing all visits from January 20,019- September 2014 from seven health systems (split-sample prediction model) to that of a random forest model estimated in the entire development dataset (entire-sample model). We found that performance of the entire-sample and split-sample prediction models were comparable in the prospective validation dataset containing all visits from 2015–2017; using all visits in the development dataset for estimation conferred no advantage with respect to risk discrimination or classification accuracy. In these analyses, the different performance metrics examined agreed on the relative performance of the models being compared. In practice, this is not always the case, thus we recommend study teams select a primary performance metric that reflects both how the prediction model will be used in a clinical setting as well as the priorities of stakeholders before conducting analyses so that conclusions may focus on that measure of performance.Our second aim was to evaluate whether three validation approaches adequately adjusted for overfitting. Internal estimates of prediction model performance were compared to prospective performance to assess optimism. While we expect performance in future data may degrade somewhat due to non-random differences in patterns of care  is the reason that bootstrap optimism correction failed in this setting. We expect that bootstrap optimism correction did not adequately quantify prediction model optimism because each bootstrap sample contains, on average, 63.2% of the observations in the original sample. So, comparing the in-sample performance of a prediction model estimated with the bootstrap sample to its performance in the original sample does not provide an out-of-sample assessment. Other bootstrap-based optimism correction approaches, including the 0.632 method  and the Data-driven machine learning methods designed to balance bias and variance can fit very complex models (incorporating interactions and non-linear relations). As datasets grow in size, these methodologies learn increasingly complex models. In contrast, the complexity of simple parametric models, like linear logistic regression does not change (as there is no bias/variance tradeoff). The example explored here suggests that this difference is key: while bootstrap optimism correction is adequate for simpler non-adaptive models, internal validation with out-of-sample observations  may be needed for more complex models that are trained to balance bias and variance. While considering only one example dataset and a single machine learning method are limitations of our study, the undesirable properties of bootstrap optimism correction in this case study point to limitations of the method not previously explored in the clinical prediction modeling literature. There is an opportunity for future study of internal validation methods for other non-parametric modeling approaches, such as boosting and artificial neural networks.This case study illustrates an example where bootstrap optimism correction did not provide accurate internal validation. While we cannot conclude this method is inaccurate for a broad class of problems , we suggest caution if using bootstrap optimism correction in a similar setting and recommend comparing optimism-corrected performance estimates to those obtained from an out-of-sample method, such as cross-validation.Additional file 1:Table S1. Data availability dates for participating sites. Table S2. List of all predictors for random forest models. Table S3. Cross-validated AUC estimates for tuning parameter selection for (a) split-sample prediction model and (b) entire-sample prediction model. Table S4. Description of development and prospective sample. Table S5. Specificity (95% CI) of prediction models from split-sample and entire sample estimation approaches in the development dataset and prospective validation dataset. Figure S1. Moving estimate of (a)cross-validated and (b) bootstrap optimism-corrected AUC (95% CI), 1-500 bootstrap samples. Figure S2. Moving estimate of (a) cross-validated and (b) bootstrap optimism-corrected sensitivity (95% CI) above 99th percentile threshold, 1-500 bootstrap samples.


To evaluate the effectiveness of bamlanivimab at reducing return emergency department (ED) visits in primarily Latinx/Hispanic patients with mild or moderate coronavirus disease 2019 (COVID-19). Secondary aims were to evaluate the prevention of subsequent hospitalizations and deaths in a resource-limited United States (U.S.)-Mexico border hospital.We conducted a retrospective, open-label interventional study on 270 eligible adult patients diagnosed with mild-moderate severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection who met criteria for receiving bamlanivimab from November 1, 2020 to January 31, 2021. The main outcomes of 14-day return visits to the ED and hospitalizations due to COVID-19 were compared between two groups – those who received bamlanivimab (exposed group) and those who did not receive bamlanivimab (unexposed group). Outcomes were analyzed through chi-square tests followed by multivariate regression modeling to adjust for patient demographics, characteristics, and comorbidities.2, 91.5% identified as Latinx/Hispanic, 51.9% male, and 80.7% reported at least one comorbidity. Most commonly reported comorbidities were obesity (22.6%), hypertension (59.6%), and diabetes (41.1%). The bamlanivimab group had a 22.8%  risk reduction or 84.4%  absolute reduction of ED return visits within 14 days compared to controls after adjusting for chronic kidney disease. The bamlanivimab group had 19.0%  risk reduction or 96.2%  absolute reduction of subsequent hospitalizations compared to unexposed patients after adjusting for diabetes status.There were 136 COVID-19 patients who received bamlanivimab in the ED prior to discharge and an unexposed group of 134 COVID-19 patients who were evaluated and discharged from the ED without receiving bamlanivimab. Overall, mean age was 61.7 (S.D. +/−13.9) years, mean body mass index (BMI) 31.0 (S.D. +/−6.6) kg/mBamlanivimab infusions for high-risk COVID-19 patients in the ED substantially reduced the risk of return visits to the ED and hospitalizations in our primarily Latinx/Hispanic population. Monoclonal antibody infusions may help reduce hospital utilization during COVID-19 surges at U.S.-Mexico border hospitals. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is responsible for the current coronavirus disease 2019 (COVID-19) pandemic, has burdened healthcare systems across the United States to their breaking point because of rapid influxes of critically ill patients who require weeks of hospitalization, intensive care resources, and healthcare personnel.3Resource challenges are further compounded by cross-border traffic that makes populations at the US-Mexico border more vulnerable to surges of communicable diseases such as COVID-19.7Our study was performed at the El Centro Regional Medical Center (ECRMC), which is located 12 miles north of the US-Mexico border in Imperial County, California, and serves a predominantly Latinx/Hispanic patient population. Imperial County has the second-highest number of COVID-19 cases and the highest COVID-19 death rate per population  in the entire state of California by the summer of 2020.What do we already know about this issue?Latinx patients have the worst COVID-19 outcomes. Bamlanivimab, an outpatient monoclonal antibody treatment, can help prevent COVID hospitalizations and improve outcomes.What was the research question?Could bamlanivimab prevent ED return visits and hospitalizations in high-risk Latinx COVID-19 patients at a border hospital?What was the major finding of the study?Bamlanivimab given to high-risk, Latinx COVID-19 patients at a border hospital ED decreased ED return visits and hospitalizations.How does this improve population health?Monoclonal antibodies administered in a low-resource ED may help decrease ED return visits in high-risk Latinx patients and hospitalizations during a surge.The response to the COVID-19 pandemic in limited-resource border hospitals such as ECRMC has highlighted major challenges. With the possibility of new surges from more variants there is still a need for innovative, rapidly operationalized solutions. In conjunction with the state and local public health authorities, ECRMC has been using monoclonal antibody treatment for high-risk, non-hospitalized patients with mild-to-moderate COVID-19 since November 2020. The primary goal was to reduce subsequent hospitalizations in high-risk patients and alleviate further pressure on a resource-scarce healthcare system. Its use has been predicated on the Emergency Use Authorization (EUA) from the US Food and Drug Administration (FDA).9There have been several strategies using passive immunity to enable the humoral immune response against SARS-CoV-2, some of which include convalescent plasma, immune globulin, and monoclonal antibodies.13There is limited evidence from ongoing randomized control trials that these monoclonal antibodies may decrease viral load and the progression of COVID-19 disease in high-risk, non-hospitalized patients with mild or moderate symptoms.The purpose of our study was to evaluate the effectiveness of bamlanivimab in preventing return ED visits, hospitalizations, and mortality within the Latinx/Hispanic population in a border community hospital. Emergency departments can rapidly and easily operationalize systems for early distribution of monoclonal antibodies to SARS-CoV-2 patients who are at high risk for developing severe COVID-19 disease early in the course of their illness. Aside from the costs associated with monoclonal antibodies themselves, infusing monoclonal antibodies early in COVID-19 disease is not personnel or resource intensive. If even modest reductions in subsequent return ED visits and hospitalizations could be demonstrated, targeted, large-scale monoclonal antibody infusions may significantly reduce the burden on these EDs and healthcare systems.We performed a retrospective, cohort study of the monoclonal antibody bamlanivimab  in non-hospitalized, adult ED patients diagnosed with COVID-19 at a border hospital. The primary outcomes of interest were return visit to the ED within 14 days and subsequent hospitalization in patients who did not receive bamlanivimab (unexposed) vs patients who received bamlanivimab (exposed). Mortality outcomes were also described. We selected the 14-day outcome based on observational reports demonstrating that on average, patients were hospitalized for dyspnea 7–10 days after initial symptoms.Per Bledsoe and Worster, we accessed the electronic health records (EHR) database to identify ED visits and hospitalizations. Abstractors were trained in obtaining the necessary data from these various EHR, and cases were selected by criteria that had defined inclusion and exclusion criteria. Quality control of the data was done throughout the data collection by three of the investigators from this study.Included within this study were adult patients (≥18 years) with a laboratory-confirmed diagnosis of COVID-19 detected in our ED or patients who had tested positive at an outside healthcare facility and presented to our ED requesting treatment with monoclonal antibodies. All patients from outside facilities were required to have documentation of a newly positive COVID-19 test within seven days. Screening for inclusion criteria were required prior to treatment for all patients. Eligible ED patients who were diagnosed with COVID-19 in the ED were offered treatment immediately if monoclonal antibodies were available. Patients who had been discharged prior to receiving COVID-19 test results were called back the following day with results. If monoclonal antibodies were available at that time and the patient met eligibility criteria they were invited to return to the ED for infusion.To meet eligibility requirements for bamlanivimab infusion, patients must have had fewer than 10 days of symptoms, mild or moderate disease with no oxygen requirement, and were considered high risk for progression to severe disease based on the following criteria:≥65 years of age or≥55 years of age AND with one of the following:Cardiovascular disease, hypertension, or chronic obstructive pulmonary disease/other chronic respiratory disease such as asthma.Body mass index (BMI) ≥35Chronic kidney diseaseDiabetes mellitus type 2Immunosuppressive disease or taking immunosuppressive medication.Excluded from the study were patients who did not consent for treatment, pregnant women, patients who did not meet the above inclusion criteria, and patients who upon initial presentation already had an oxygen requirement or required immediate admission to the hospital.As part of the response to the large influx of COVID-19 positive patients, ECRMC erected an emergent, tent-based COVID-19 hospital and monoclonal antibody infusion center in the ED parking lot where patients were treated from November 2020–January 2021. Eligible patients diagnosed with SARS-CoV-2 infection were either referred by their primary care physician within the 10-day symptom onset for monoclonal antibody infusions or were diagnosed at our COVID-19 tent hospital within the same time frame. During this surge, the county health department initially allocated a very limited supply of bamlanivimab for the hospital. Bamlanivimab was at times randomly distributed due to demand exceeding supply on a given day. The ECRMC pharmacists were also required to verify medications on site and prepare the monoclonal antibody infusions during business hours only. Therefore, bamlanivimab was available on a first-come first-served basis during business hours.Emergency department patients were consented and given information regarding bamlanivimab and the risks and benefits of treatment in their preferred language, which was primarily Spanish. The treatment was unblinded to the patient and the associated healthcare workers. Patients in the treatment group received bamlanivimab (700 milligrams per 20 milliliters [mL]) mixed with 250 mL normal saline and infused over one hour. After approval by ECRMC hospital pharmacist, an infusion would be sent to the ED. An observation time of one hour was performed by the patient’s nurse and physician following infusion to observe for any serious hypersensitivity reaction and anaphylaxis. If patients tolerated medication and had no adverse response after one hour they were discharged from the ED.International Classification of Diseases, 10th revision, billing codes in the ED EHR using MedHost information management software , and we reviewed documentation by the treating emergency clinician and the pharmacy infusion records. Patient characteristics from both exposed group and unexposed group were recorded on a password-protected, patient-deidentified Microsoft Excel spreadsheet . These specific characteristics included age, gender, BMI, ethnicity, and comorbidities including cardiovascular disease/hyperlipidemia, hypertension, chronic obstructive pulmonary disease (COPD) or other chronic respiratory disease such as asthma, chronic kidney disease, diabetes mellitus, or current disease state of immunosuppression or currently taking immunosuppressive medication  and reported COVID-19 symptom onset (days).We performed a retrospective chart review from November 1, 2020–January 31, 2021 of patients diagnosed with mild to moderate SARS-CoV-2 infection who presented to ECRMC ED or were referred from community primary care physicians. A convenience sample consisted of eligible patients who received treatment with bamlanivimab (exposed patients) and eligible unexposed patients who tested positive for COVID-19 in the ED during this time same period. We collected data directly through a retrospective review of the hospital’s EHR. We then performed a search of the patient’s chief complaint and the Using Cerner healthcare technology services , we assessed patient outcomes at 14 days post treatment through health records for inpatient care and for return visits. We additionally reviewed severe COVID-19 complications including repeat ED visits, hospitalization, transfer to outside hospitals, and complications such as intubation and death. We performed follow up on these patients with San Diego Health Connect  to ensure that the patients had not beem seen or admitted at a different county hospital.We used frequencies and percentages to express categorical data such as ethnicity, gender, age ≥ 55 years or older, BMI ≥ 35, and the presence of at least one comorbidity . Additionally, these factors (variables) along with the exposure to bamlanivimab or absence of exposure were examined to see whether there were associations with the following outcomes within 14 days: ED visit, hospitalization, and mortality.Means and standard deviations were used to express continuous data such as age, BMI, and time since onset of COVID-19 symptoms. We used t-tests to examine whether there was a statistically significant difference between the unexposed groups and the bamlanivimab group. Chi-square tests and bivariate analyses were used to find an association between the exposure to bamlanivimab and patients’ characteristics and outcomes variables. Using bivariate analysis, factors that were significant to α level of 0.10 were then entered in the full multivariate regression models for ED return visits in 14 days and hospitalizations. The factors that were significant to α level of 0.05 or less were then kept and entered into the final, reduced multivariate regression models. We used the variables that reached to α level of 0.05 in the final, reduced models to calculate reduction of the outcome – ED return visits in 14 days and hospitalizations. All analyses were performed using SAS© Studio Release 3.8 .P<.0001), age ≥ 55 years old (P <.0001), and BMI ≥ 35 (P = 0.0032). Overall, 80.7% patients reported at least one of the comorbidities. The frequencies of each of the comorbidities in both the unexposed and exposed groups are summarized in P = 0.0094) and cancer (P = 0.0422) within the exposed group being higher.The ECRMC ED with an annual volume of 46,000 patients had a total of 7,735 patients within the three-month period, November 1, 2020–January 31, 2021. To detect a 15% different in treatment effect and using a confidence level of 95%, α of 0.05, and 80% power, we determined that a total sample size would need to be 256; at least 57 patients per treatment arm were required for the likelihood ratio chi-square for the outcome ED return visits. For the same parameters, 87 hospitalized patients were required.Outcomes of both unexposed patients, exposed bamlanivimab patients, and the differences in their proportions between groups are reported in P < .0001), hospitalization (P = 0.0011), and death (P = 0.0235). In building the multivariate regression models of outcomes of ED visits within 14 days  increased risk of an ED return visit in 14 days and a 2.27  increased risk of hospitalization compared to those who received bamlanivimab (exposed group). There were no deaths in the exposed group and five deaths overall in the unexposed group. We placed the variables into a bivariate analysis to build various regression models of ED return visits in 14 days and hospitalizations . The bivP =.0001) absolute reduction of ED return visits within 14 days compared to the unexposed groups after adjusting for chronic kidney disease (P = 0.0008) absolute reduction of subsequent hospitalizations compared to unexposed patients after adjusting for diabetes status  had a 22.8%  risk reduction or 84.4% , two patients with chest pain, and no patients with allergic/anaphylaxis reaction. Although all patients were initially discharged from the ED after their infusion of bamlanivimab, four of the nine patients with adverse reactions were later hospitalized due to worsening of COVID-19 pneumonia. There were no subsequent deaths reported in the nine patients who reported an adverse drug reaction.The number needed to treat for ED visits within 14 days was 4.73 and for hospitalizations was 7.61. The number needed to harm for ED visits within 14 days was 0.047 and for subsequent hospitalizations was 0.076.The data on monoclonal antibody therapies (casirivimab/imdevimab and bamlanivimab) that were given an EUA by the FDA in November 2020 is constantly evolving. While the majority of the studies to date show a reduction of viral load, there are some studies on patient-centered outcomes and the impact of initiating therapeutic regimens on hospital systems.19Due to variants, the FDA in April 2021 issued a statement that bamlanivimab cannot be used as a single-therapy regimen for mild-moderate COVID-19 in high-risk patients. Therefore, bamlanivimab was being combined with etesevimab until June 25, 2021, when the EUA was then discontinued for the combination.24Even prior to the COVID-19 pandemic, ED crowding has been a major issue faced by EDs throughout the US. In the ECRMC ED, which already had the highest patient-to-bed ratio of any ED in San Diego or Imperial counties prior to the pandemic, crowding reached critical levels.Our study is one of the first to date to examine the efficacy of administering a monoclonal antibody to a primarily Latinx/Hispanic population in an ED at a border hospital, specifically to mitigate COVID-19 hospitalizations. Our regression modeling demonstrated significant reduction of return visits to the ED and in hospitalizations after controlling for chronic kidney disease and diabetes, respectively. These significant reductions may impact mortality due to the decrease in ED return visits and hospitalizations.The original bamlanivimab study used for the FDA EUA showed that the overall incidence of ED return visit, hospitalization, and/or death within 30 days was only 5.8% in the placebo group.Financially, the costs of monoclonal antibody infusion are substantially less compared to hospitalization for COVID-19. According to the Centers for Medicare and Medicaid Services, the cost of hospitalization for COVID-19 has been estimated to be from $21,936 to $74,310 depending on severity, complications, and insurance coverage.28This was an observational study conducted in a single-center ED at a US-Mexico border hospital; thus, it may not be generalizable to the larger population. Additionally, selection bias may have been introduced through the non-randomized convenience sampling of patients with multiple comorbidities and mild symptoms of SARS-CoV-2 infection. Because the researchers were aware of the treatment allocation, this could have also biased their assessment.The distribution of bamlanivimab was unpredictable due to the surging prevalence of COVID-19 in the community. This created a lack of predictability in who ultimately received treatment with bamlanivimab, and eligible patients may or may not have received treatment in a timely manner. However, this may have been minimized by an ongoing active campaign to inform patients and physicians within the community regarding bamlanivimab infusions in the ED such that patients did come during designated hours of infusion, which would have enabled us to include as many potential eligible patients as possible. Although some may question the reliability of the ED return visit outcome, the patients in our study were followed in detail and the data appears to support a substantial decrease in preventing subsequent hospitalizations for the exposed group. Additionally, the endotracheal intubation/ventilator and mortality outcomes were too small to allow for comparisons. These outcomes were likely confounded because some patients who expired had been placed on comfort care prior to being intubated.Given concerns that COVID-19 surges are imminent, we decided that these results should be submitted for publication in hopes that the data could be used to quickly operationalize in areas most vulnerable to overwhelming COVID-19 surges. It is noteworthy that the November and December surge was prior to the discovery of the several COVID-19 variants now known to be spreading across the country. There is evidence to suggest that bamlanivimab alone may not have similar effectiveness against variants.23Future studies may focus on how monoclonal antibody infusions could efficiently be operationalized as these ED visits could potentially increase the number of patients seeking monoclonal antibody treatment, which could be a potential confounder in surges. Additionally of value would be a multicenter, randomized, and placebo-controlled study with a large proportion of patients who belong to vulnerable populations such as Latinx/Hispanics in order to observe whether or not monoclonal antibodies do have a similar effect, especially with the advent of variants of SARS-CoV-2.Our study found a 22.8% risk reduction in return ED visits and 19.0% risk reduction of subsequent hospitalizations in high-risk COVID-19 patients who received bamlanivimab compared to those who did not receive bamlanivimab. We believe that operationalizing monoclonal antibody infusions in high-risk COVID-19 patients could be made into an effective strategy for mitigating the COVID-19 surges at lower-resourced US-Mexico border hospitals.


Enthesitis is a common clinical feature of spondyloarthritis (SpA). For reliable assessment of enthesitis the Heel Enthesitis Magnetic Resonance Imaging Scoring System (HEMRIS) was developed. The aims of this study were to evaluate changes in HEMRIS over time and to evaluate whether these changes correlated with changes in clinical parameters. This single-center observational study followed patients with SpA and psoriasis, regardless of presence of clinical heel enthesitis, for two years. Clinical evaluation and ankle MRIs were performed annually. Changes in HEMRIS were compared at one-year intervals using the Wilcoxon signed-rank test. The association between changes in the HEMRIS with changes in clinical parameters was evaluated using Spearman’s correlation coefficient. In total, 38 patients were included. An increase in the inflammatory and structural HEMRIS was identified in, respectively, 12 (17.9%) and 4 (6.0%) patients in one-year intervals. We found non-significant changes in the HEMRIS during longitudinal follow-up. Changes in the HEMRIS did not correlate with changes in local or general disease activity. Our results show that MRI-findings of enthesitis assessed with HEMRIS changed in a small number of patients in a one-year interval in an observational setting. Changes in HEMRIS were not associated with changes in clinical disease activity. Enthesitis, defined as inflammation at the anatomical location where tendons, ligaments and joint capsules insert to bone, is a key clinical feature of spondylarthropathies, including psoriatic arthritis (PsA) and ankylosing spondylitis (AS) ,2. RecomTo monitor enthesitis at an early stage and more accurately, imaging techniques, such as ultrasound and MRI could be employed . An advaBesides its potential for evaluation of treatment effects, HEMRIS could provide insights in the pathogenesis of PsA. It has been hypothesized that SpA originates at the enthesis . ImagingThe aims of this study are to evaluate the sensitivity of HEMRIS for change during longitudinal follow-up and to assess whether e changes in HEMRIS are associated with changesin clinical disease activity in psoriasis (Pso), PsA and AS patients. As a secondary outcome, we compared HEMRIS in Pso patients with and without progression to PsA to assess if HEMRIS was predictive of later development of PsA.In this observational longitudinal study, carried out in an academic hospital, we included patients aged 18–65 years who were diagnosed with cutaneous Pso , PsA (fulfilling Classification Criteria for Psoriatic Arthritis (CASPAR) ) and AS Both ankles were evaluated separately on a 3-Tesla MRI scanner  using a head coil. The MRI protocol included the following sagittal sequences: T2-weighted, T2-weighted Spectral Attenuated Inversion Recovery, T1-weighted SPIR  before contrast, and T1-weighted SPIR after contrast. Baseline ankle MRIs were assessed by two independent musculoskeletal radiologists for inflammatory and structural features of heel enthesitis using the HEMRIS. The HEMRIS is a scoring system containing the following inflammatory and structural pathologies: intratendon hypersignal, peritendon hypersignal, bone marrow edema, bursitis (Achilles tendon only), tendon/aponeurosis thickening, enthesophyte, bone erosion and intra-tendon hypersignal on T1-Weighted images. All pathologies are scored on a scale of 0–3 (none/mild/moderate/severe). Examples of all grades of different HEMRIS pathologies are provided in an imaging reference atlas .The maximum HEMRIS of 2 ankles is 24 for entheseal inflammation at the Achilles tendon, structural damage at the Achilles tendon and structural damage at the plantar fascia; 18 for entheseal inflammation at the plantar fascia; 42 for total entheseal inflammation; and 48 for total structural damage.All HEMRIS subscores were averaged as suggested in the original HEMRIS publication . AveragePsoriasis activity was measured using the Psoriasis Area and Severity Index(PASI) . PASI > Non-normally distributed continuous data were reported using medians and interquartile ranges (IQRs). Categorical variables were reported in frequencies and percentages. The initial HEMRIS of Pso patients with and without clinical progression to PsA were compared with the Mann–Whitney U Test.Patients were followed during two years. We performed pairwise comparisons of the HEMRIS and clinical outcomes in one-year intervals. Overall change in HEMRIS in one-year intervals was assessed using the Wilcoxon signed-rank test. The potential association of changes in HEMRIS with changes clinical disease activity was assessed using Spearman’s correlation coefficient. Differences in HEMRIS at baseline in Pso patients with and without progression to PsA were compared using the Mann-Whitney U Test. Due to the small sample size, we did not perform corrections for multiple measurements.p < 0.05. Statistical analysis was performed using SPSS version 26 .Patients that were lost to follow-up before the first follow-up visit were excluded from all analyses. Patients that were lost to follow-up after year 1 were only excluded from pairwise analyses comparing year 1 and 2. The predetermined significance level was set at At baseline, 38 patients (76 ankles) were included. Baseline patient characteristics and MRI-results are described in detail in our previous paper . For theSerial MRI observations were available for 36 participants at 2 time points (baseline and 52 weeks) and for 34 patients at 3 time points . At baseline, one ankle-MRI was excluded from analysis because of insufficient image quality due to failure of fat suppression. At 104 weeks, the Achilles tendon could not be examined on one MRI-scan due to an artifact. In total, 137 serial MRI observations of heel enthesitis were available for change in HEMRIS; 137 at the location of the Achilles tendon and 138 at the location of the plantar fascia. At patient level (summated left and right HEMRIS) the total number of serial MRI observations for change in HEMRIS is 68 at the location of the Achilles tendon and 67 at the location of the plantar fascia.In most patients, the overall mild psoriasis severity remained stable throughout the study. When compared at one-year intervals, a decrease in the PASI (disease activity no longer classified as ‘moderate-severe’) occurred in three patients (13.0%). No increases in PASI from mild to moderate-high disease activity were observed. Two (18.2%) patients with Pso developed psoriatic arthritis during the two years follow-up. Two Pso patients were lost to follow-up .Activity of PsA increased in n = 3 (13.0%), decreased in n = 6 (26.0%) and remained stable in n = 9 (39.1%) patients in one-year observation intervals. One PsA patient was lost to follow-up, another was excluded from the analysis at the last time point since no MRI-scan was obtained .Activity of ankylosing spondylitis, increased in 2 (8.3%) patients, decreased in 2 patients (8.3%) and remained stable in 19 patients (79.2%) during one-year observation intervals.During follow-up, newly diagnosed clinical enthesitis was observed in four (3.2%) Achilles tendon entheses and six (3.2%) plantar fascia. Treatment with a DMARD was initiated in 6 (8.6%) patients during longitudinal follow-up .Total HEMRIS for both ankles were low at the start of one-year intervals and after follow-up  patients with limited clinical disease activity at the enthesis, we evaluated whether ankle enthesitis on MRI, assessed using the HEMRIS, was sensitive to change during longitudinal follow-up. No significant differences were observed in the HEMRIS after a one-year follow-up intervals . The secIn the original publication of HEMRIS by the OMERACT group, change in inflammatory pathologies before and after anti-TNF therapy was evaluated in a group of SpA patients . The staA possible explanation for the minimal changes in the HEMRIS in our study, is the low initial HEMRIS results , allowinThe finding that minimal changes in HEMRIS are not associated with changes in general disease activity, could be considered unexpected, since enthesitis has been attributed a major role in SpA disease pathogenesis in the proposed ‘enthesitis model’ . With thThis analysis included patients with cutaneous Pso to assess if subclinical MRI changes at the enthesis could predict future progression to PsA. We did not find differences between HEMRIS at inclusion in Pso subjects with and without later development of PsA . The resStrengths of the current study include its longitudinal design, assessment of MRI-scans by experienced radiologists and the comparison of MRI-results with clinical assessments of both general disease activity and local disease activity at the enthesis. One limitation of the study is its relatively small sample size. Since this is an exploratory study, no sample size calculations were made, however this could increase the chance of a type II error. To increase power, we analyzed the data over one-year intervals. With a total follow-up duration of two years, each subject occurred in the dataset twice , or four times . Analyses were not adjusted for multiple measurements in patients because of limited power, however no significant changes in HEMRIS were observed in the unadjusted analyses. Another limitation is the observational study design. Further research in larger populations is required to further establish the responsiveness of HEMRIS to change and its association with change in clinical disease activity. In the ongoing TOFA-PREDICT study (EudraCT Number 2017-003900-28), we aim to investigate the effect of three different DMARDs  on HEMRIS results in a PsA population with more active disease.MRI findings of heel enthesitis assessed with HEMRIS changed in a small number of patients in an observational setting. Although changes of enthesitis evaluated on MRI were minimal in this study, quantitative MRI assessment of enthesitis could potentially visualize change in these structures in great detail.


Acute normovolaemic haemodilution (ANH), as a blood-conservation technique, avoids the need for allogeneic blood transfusions. The historic practice of cold-storing type-O whole blood (WB) in military fields popularised the transfusion of refrigerated WB to treat acute bleeding. In this study, we compared the effects of room temperature (RT) and refrigeration up to 24 hours on the coagulation properties of WB for ANH.Each WB sample, collected from 12 male volunteers, was divided into two parts, one stored at RT and the other refrigerated for 24 hours. Complete blood counts (CBC), blood gas levels, and coagulation profiles were measured, and rotational thromboelastometry (ROTEM) measurements were performed at the initial collection time point (baseline) and at 6, 12, and 24 hours after initial collection.The preservation of platelet aggregation response induced by arachidonic acid and adenosine diphosphate was better in cold-stored WB compared to that in RT-stored WB. The platelet aggregation response induced by thrombin receptor-activating peptide 6 was significantly decreased in all samples after 24 hours of storage when compared with that at baseline. The lactate levels in WB stored at RT increased significantly after 6 hours of storage compared to that of cold-stored samples. There were no significant differences in CBC, coagulation parameters, and ROTEM variables between the cold-stored and RT-stored WB samples.WB for ANH stored in the refrigerator showed better metabolic characteristics after 6 hours of storage and better aggregation response after 12 hours of storage than WB stored at RT. Rocking of WB is not recommended due to chances of inducing red blood cell sublethal injury and haemolysis ), clot formation time (s), alpha angle (angle of tangent at a 2-mm amplitude [°]), maximum clot firmness (MCF [mm]), and maximum lysis (%); MCF was also measured for the FIBTEM assays. The platelet component of the clot strength was assessed from the clot elasticity , which rP < 0.05. All statistical analyses, except statistical power analyses determined using G*Power 3.1, were performed using SPSS software .Data were tested for normal distributions using the Shapiro-Wilk’s test. Changes in platelet aggregation, CBC, coagulation profiles, blood gas levels, and ROTEM values for each stored condition between baseline and 6, 12, and 24 hours after phlebotomy were compared using two-way analysis of variance (ANOVA) for repeated measures. Differences in platelet aggregation, CBC, coagulation profiles, blood gas levels, and ROTEM values between RT and cold storage at 6, 12, and 24 hours after initial collection were analysed by post-hoc comparisons using a paired Student’s t-test. The level of significance was adjusted according to the Bonferroni correction. The criterion for rejection of the null hypothesis was (f) was set at 0.25.We conducted a power analysis based on a pilot study, that assessed a significant difference among groups according to elapsed time using repeated-measures ANOVA in response to stimulation with AA (ARATEM), ADP (ADPTEM), and TRAP (TRAPTEM). The analysis showed that 12 participants would be required in each group to achieve 80% power for a study design with four measurements between two groups. The α-level was set at 0.05, and the effect size The effects of storage conditions on platelet aggregation parameters such as amplitude at 6 minutes, maximum slope of aggregation curve, and AUC induced by AA, TRAP, and ADP are summarised in The WB coagulation parameters assessed by ROTEM are presented in In this study, citrate-phosphate-dextrose adenine anticoagulated WB refrigerated for 24 hours showed better AA and ADP-induced platelet aggregation and metabolism than WB stored at RT. The ROTEM results, platelet counts, and fibrinogen levels were stable for cold-stored WB and were comparable to the changes observed in WB stored at RT.The markedly reduced ability of platelets stored at RT to aggregate upon stimulation with AA and ADP may be attributed to the progressive deterioration of the energy generating mechanism . The traIn contrast, platelets stored at 4°C exhibit better aggregation when stimulated by AA and ADP than those stored at RT after 12 hours of storage, which is consistent with previous reports , 10, andGlucose consumption was slower for cold-stored WB than that for the RT counterpart, which concurs with the lower glycolytic metabolism of cold-stored WB . LactateThe metabolic assay data of this study showed signs of storage lesions at as early as 6 hours of storage at RT, which may have caused the changes in platelet aggregation. When the supply and demand of energy are imbalanced, platelets rapidly lose their capacity to respond to aggregation- and secretion-inducing agents . Stable WB aggregometry is particularly dependent on platelet count even within the normal range . AlthougMean platelet volume and platelet distribution width remained unchanged in both RT and cold-stored samples, suggesting no significant changes in the size of single platelets and platelet size distribution, a finding in contrast with those of previous studies , 24, wheOur data showed that PT, APTT, fibrinogen level, and antithrombin activity in WB stored at both temperatures for ANH were maintained within normal reference limits over the 24-hour storage period. This observation is consistent with that of a previous study  where moIn our study, the average EXTEM CTs of RT and cold-stored WB samples over time were slightly higher than the reference range; however, the differences would not be clinically significant as the ROTEM-guided transfusion algorithm recommends the administration of coagulation factors when the EXTEM CT exceeds 100 s. The platelet component of clot strength, measured by clot elasticity in both stored samples remained constant, showing that the clotting ability of cold-stored platelets was comparable to that of platelets stored at RT, despite better metabolic indices and aggregation response. The discrepancy between progressively reduced platelet aggregation response and constant platelet components as a result of ROTEM could be attributed to the sensitivity of viscoelastic tests to platelet function. Clot firmness is directly proportional to platelet counts and is associated with platelet function depending on the transmission of platelet contractile force to fibrin , which iThis study had some limitations. First, it was based solely on in-vitro experiments conducted under static conditions, which could result in different platelet functions and kinetics when administered in vivo. Thus, our observations should be complemented with clinical trials to compare the in-vivo properties of RT and cold-stored WB for haemostasis during surgery. Second, cold storage induces many changes in platelets not corresponding with aging or storage lesions of platelets stored at RT –30. ThesThis study demonstrated that RT-stored WB showed the development of storage lesions after 6 hours and lost platelet aggregation response after 12 hours of initial collection compared with cold-stored WB, as determined by blood gas levels, coagulation profiles, and aggregation responses. These data suggest that cold-stored WB has better metabolic and functional competence than RT-stored WB, although a more comprehensive analysis of coagulation profiles and haemostatic effects in clinical settings is required to confirm our findings.S1 Data(XLS)Click here for additional data file.S2 Data(XLS)Click here for additional data file.S3 Data(XLS)Click here for additional data file.S4 Data(XLSX)Click here for additional data file.