Methylation is an important epigenetic regulation of methylation genes that plays a crucial role in regulating biological processes. While traditional methods for detecting methylation in biological experiments are constantly improving, the development of artificial intelligence has led to the emergence of deep learning and machine learning methods as a new trend. However, traditional machine learning-based methods rely heavily on manual feature extraction, and most deep learning methods for studying methylation extract fewer features due to their simple network structures. To address this, we propose a bottomneck network based on an attention mechanism and use new methods to ensure that the deep network can learn more effective features while minimizing overfitting. This approach enables the model to learn more features from nucleotide sequences and make better predictions of methylation. The model uses three coding methods to encode the original DNA sequence and then applies feature fusion based on attention mechanisms to obtain the best fusion method. Our results demonstrate that MLACNN outperforms previous methods and achieves more satisfactory performance. Epigenetic modification is a essential modification which can reversible and heritable regulate gene function without nuclear DNA sequence change. At present, the known epigenetic phenomena mainly include DNA methylation, genomic imprinting, maternal effects, gene silencing, nucleolar dominance, dormant transposon activation and RNA editing Jones . DNA metPredicting the methylation level of methylation sites is an important area of research in epigenetics. With the rapid development of sequencing technology, various methods have been developed to detect DNA methylation levels. The gold standard for genome-wide methylation determination is WGBS Habibi  whole-g single- reduced methyla. RRHP r (oxidatik-space nuclear acid pairs), DNC (dinucleotide composition), ENAC (enhanced nucleic acid composition), NAC (nucleic acid composition), RCKmer (reverse composition KMER), TNC (trinucleotide composition), EIIPs  of trinucleotides, NCP , and PseDNC (pseudo dinucleotide composition). Currently, machine learning methods are primarily combined with SVM (support vector machine) , CKSNAP . The database downloaded the m5C methylation data with the accession number of gsm432685 (whole-genome shotgun bisulfite sequencing of the H1 cell line) and the mapped GRCh37 (genome reference consor Tim human reference 37) data.We used WGBS DNA methylation data: The original data were obtained from GEO  is the primary feature extraction component in our model, as we avoid using a simple stacked convolution layer to extract methylation sequence features due to the risk of gradient disappearance, overfitting, and ineffective feature extraction. By carefully stacking MLA-BCS modules, we can avoid the gradient disappearance phenomenon and ensure that the accuracy and other indicators of each training are relatively stable. The MLA-BCS first passes the input features through an ECA block and SAM to focus on channel and spatial features that are more conducive to identifying methylation sites. It then passes through BCS with convolution residual edges of L and use one-dot coding, NCP coding, and EIIP vector coding to form an D is the dimension of a single base. Our model employs the RMSprop optimizer to adjust the learning rate of each batch with a learning rate of 0.0001 and a batch size of 128. The cross-entropy function serves as the loss function of the neural network. To prevent overfitting, we use a combination of L1 and L2 regular functions to correct the weight and variance of the filter. Additionally, we employ early stopping to prevent overfitting in the epoch dimension. To further prevent overfitting, we utilize the max pooling method, which not only speeds up the fitting of neural networks but also prevents the network from being too deep and having too many parameters. For each pooling layer, we use a max pooling of Y is the output of the current receptive field and Specifically, we begin with a single strand of DNA with a length of To prevent overfitting, we also added BN layer  for all activation functions except the final prediction result. For each output We will use the formula to represent the module in Fig. In order to quantify the performance of MLACNN and compare it with other methods, we used six common performance evaluation indicators: sensitivity (SN), specificity (SP), precision, accuracy (ACC), Matthew correlation coefficient (MCC), and AUC.ly Figs. .Fig. 4BosSamples with a methylation We compared our model with DeepCpG, DeepCpG RNN module and MRCNN. We tested the sensitivity (SN), specificity (SP), precision, accuracy (ACC), Matthews correlation coefficient (MCC) and AUC to compare the advantages and disadvantages of these three models. Among them, DeepCpG mainly uses one-hot coding and location information to input data into CNN and RNN models, and then performs simple feature fusion on the two coding methods to obtain results. The network structure used is relatively simple, so the extracted features are relatively limited. MRCNN uses a method similar to DeepCpG to one-hot code DNA data to obtain a We also evaluated the performance of MLACNN on different tissues and cell types. Specifically, we compared and predicted the performance of four tissue methylation levels, namely human liver tissue, human skin tissue, human pancreatic islet cells, and human lung tissue. The results are presented in Fig. To explore the extraction and abstraction of DNA sequence information by CNN and attention mechanism, we conducted t-SNE (t-distributed stochastic neighbor embedding) and PCA  operations on the raw sequence without any processing, the output after feature fusion, and the output after feature fusion and full connection to understand their action states. We observed that the raw data without any processing were highly mixed and challenging to cluster, whether it was t-SNE or PCA clustering. After feature fusion, the data after t-SNE clustering were mostly clustered together, but more clusters were formed after clustering, and the data after PCA clustering were clearly divided into two categories. The cluster output after full connection showed a clear cluster diagram through t-SNE and PCA clustering, with only a few misclassified samples. Therefore, our MLACNN network can effectively extract and abstract the high-level features of the original methylation data to better classify the methylation data.Although the MLACNN model performs reasonably well in predicting methylation sites, there is still significant room for improvement. With ample computing power, we can adopt strategies such as automatic parameter search or automatic kernel search to enhance the proposed network architecture, for example, using Efficientnet (Tan and Le D-dimensional convolution for feature extraction while preventing overfitting through max pooling, early stop, batch normalization, dropout, and L1 and L2 regularization. We compared the models obtained by experiments of NCP network, one-hot network and EIIP-vector network with the models after feature fusion to obtain the best fusion model. Additionally, we performed t-SNE and PCA clustering analysis on the original methylation data, the methylation data after feature fusion, and the methylation data after full connection to analyze the working principle of each layer of the neural network.In this paper, we propose a multi-latitude attention convolutional neural network for accurately predicting the methylation sites of m5C. Our approach uses an attention-based feature fusion method to fuse and predict the features extracted from the NCP network, one-hot network, and EIIP-vector network. Each network is a bottleneck with channel shuffle based on attention mechanism, and employs one-dimensional convolution and


While the majority of the human genome is made up of non-coding RNAs (ncRNAs), which do not encode proteins, they may be divided into several types: circular RNAs (circRNAs), long non-coding RNAs (lncRNAs), and microRNAs (miRNAs). By modulating the translation of coding genes, these ncRNAs have a substantial influence on tumor biology. Furthermore, since ncRNAs are produced in a variety of immune cell types and may affect both innate and adaptive immunity, they are a prospective target for increasing cancer immunotherapy effectiveness.Immune checkpoint inhibitor (ICI) treatments have limited therapeutic responses due to primary or acquired resistance, potentially driven by changes in the tumor microenvironment. Cancer cells can evade immune surveillance through the acquisition of epithelial-mesenchymal transition (EMT) phenotype and cancer stem-like cells (CSCs). MiRNAs have been studied for regulating CSC self-renewal and some miRNA-targeting therapies have entered clinical development for cancer treatment. However, recent studies suggest that lncRNAs also play critical roles in shaping the immunosuppressive tumor microenvironment (TME) and promoting tumor immune evasion, making them potential targets for enhancing anti-tumor immunity.In this Research Topic, we aim to explore the regulatory roles of lncRNAs in the TME and cancer cells, their impact on tumor immune escape, and their potential as therapeutic targets for improving the efficacy of cancer immunotherapy.Yan et al. investigated particular lncRNAs that affect important immune-related genes in non-small cell lung cancer, namely adenocarcinoma (AD) and squamous cell carcinoma (SCC). The research verified the prognostic importance of these lncRNAs in their cohorts by using risk models based on lncRNAs. The research discovered that individuals in the AD high-risk group and the SCC low-risk group had reduced immune responses, with dendritic cells playing a particularly crucial role in AD immune infiltration. Furthermore, the research predicted ICI therapy responses using T Cell Immune Dysfunction and Exclusion and immunophenoscore models, suggesting potential advantages for high-risk SCC patients. Finally, utilizing The Cancer Proteome Atlas and RNA-seq investigations of a transfected lung cancer cell line, the researchers identified the lncRNA LINC00996 as a possible therapeutic target in AD.Lv et al. explored the effect of aberrant glycosylation, a protein alteration, in the development and progression of breast cancer (BC). The researchers found glycosyltransferase-related lncRNAs (GT-lncRNAs) linked with BC prognosis and responsiveness to immune checkpoint inhibitors (ICIs) therapy using co-expression analysis. They created a risk score based on eight GT-lncRNAs to predict BC patient outcomes and stratify patients based on immune infiltration. The risk score was shown to be a stronger predictor of BC prognosis than previously published models, and patients in the high-risk category had shorter survival. Furthermore, the risk score may aid in identifying individuals who might benefit the most from ICI therapy. Finally, the researchers provided experimental evidence that the lncRNA MIR4435-2HG may have a role in BC development through a variety of processes. The research indicated that prognostic markers based on GT-lncRNA might be useful tools for predicting BC outcomes and guiding treatment choices for personalized ICI-based immunotherapies.Jiang et al. discussed the potential of ncRNAs in regulating immunological checkpoints in cancer therapy. Immune checkpoints such as PD-1, PD-L1, CD47, and BTLA are complexly regulated by ncRNAs, particularly miRNAs which bind to their 3’-untranslated region. CircRNAs also play a role in regulating immune checkpoints as “miRNA sponges.” There is still much to be learned about the specific roles of circRNAs and lncRNAs in regulating immune checkpoints, suggesting a promising avenue for further research. Nonetheless, the precise targeting ability of ncRNAs for immune checkpoints shows potential for developing innovative and effective immunotherapies for cancer.Jia et al. provided a summary of recent research on the functions and mechanisms of exosomal ncRNAs in the TME. Exosomes, which carry regulatory molecules, proteins, and nucleic acids, are important in cellular communication within the TME. The article specifically highlights the role of exosomal ncRNAs, such as lncRNAs, miRNAs, and circRNAs, in regulating TME function and contributing to cancer development and progression. This review emphasizes the central role of exosomes in remotely transporting RNAs and shaping the TME.Wu et al. summarized the mechanisms by which circRNAs affect the TME in digestive system cancers, such as immune surveillance, angiogenesis, EMT, and extracellular matrix remodeling. This review also discussed how specific regions of several circRNAs can impact three key events of the TME, namely autophagy, apoptosis and proteins involved in drug efflux control. In this view, the regulation of the TME by circRNAs is considered as a potential new therapeutic approach for cancer.The article by Zhang et al. discussed the processes by which pyroptosis-associated ncRNAs trigger pyroptosis, their impact on tumor resistance and the tumor microenvironment, and prospective biomarkers and therapeutic targets. NcRNA-regulated pyroptosis may improve tumor resistance while simultaneously inducing a significant inflammatory response and remodeling the tumor microenvironment. This study proposed that targeting pyroptosis-related ncRNAs might be a feasible therapeutic method to reduce tumor resistance, and those pyroptosis-related ncRNAs could act as biomarkers for diagnosis and immunotherapy success.Zhou et al. debated on the role of lncRNAs as key modulators of cells resident inside the melanoma tumor microenvironment. In particular, they evidence that several oncogenic and tumor suppressor lncRNAs are endowed with the ability to directly modulate the melanoma immune environment by controlling different immune cell subsets infiltrating the TME. These features can be potentially exploited to specifically target the TME via lncRNA-based immunotherapeutic strategies.In conclusion, ncRNAs regulate coding genes and modulate immunity, making them promising targets for enhancing cancer immunotherapy. Specific lncRNAs have been identified as novel targets for cancer therapy. NcRNAs also regulate immunological checkpoints, suggesting the potential for developing effective immunotherapies for cancer. Exosomal ncRNAs play a role in regulating TME function and further investigation will provide valuable insights into cancer immunity regulation. Targeting ncRNAs, including miRNAs, lncRNAs and circRNAs, is a potential strategy to overcome immunoresistance and enhance tumor immunogenicity.All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.


Patients with hepatocellular carcinoma (HCC) bear a heavy burden of disease and economic burden but have fewer treatment options. Sorafenib, a multi-kinase inhibitor, is the only approved drug that can be used to limit the progression of inoperable or distant metastatic HCC. However, enhanced autophagy and other molecular mechanisms after sorafenib exposure further induce drug resistance in HCC patients. Sorafenib-associated autophagy also generates a series of biomarkers, which may represent that autophagy is a critical section of sorafenib-resistance in HCC. Furthermore, many classic signaling pathways have been found to be involved in sorafenib-associated autophagy, including the HIF/mTOR signaling pathway, endoplasmic reticulum stress, and sphingolipid signaling, among others. In turn, autophagy also provokes autophagic activity in components of the tumor microenvironment, including tumor cells and stem cells, further impacting sorafenib-resistance in HCC through a special autophagic cell death process called ferroptosis. In this review, we summarized the latest research progress and molecular mechanisms of sorafenib-resistance-associated autophagy in detail, providing new insights and ideas for unraveling the dilemma of sorafenib-resistance in HCC. Hepatocellular carcinoma (HCC) has shown the fastest increasing mortality rate for decades , with a Autophagy is a cellular degradation and recycling process that is highly conserved in all eukaryotes. Researchers have found that cancer cells may mobilize this procedure for drug resistance to sorafenib . In mammAutophagy progression can be promoted or inhibited in different cancers, indicating that autophagy is a manipulable program that can affect cancer cell survival . HoweverSince the study of yeast identified the core autophagy-related proteins, the molecular era of autophagy research has begun . DysreguSorafenib is a novel molecular targeted therapy drug, which can target tumor cells and tumor vascular receptors. Therefore, sorafenib can significantly inhibit tumor cell proliferation on the one hand; on the other hand, it can significantly inhibit tumor angiogenesis. In other words, it can “kill two birds with one stone” and play the dual role of anti-angiogenesis and anti-tumor cell proliferation at the same time . DespiteThe complex molecular pathogenesis of HCC has also led researchers to shift their focus towards the combination therapy of sorafenib. Sorafenib has been combined with MEK/ERK pathway inhibitors, anti-angiogenic agents, mTOR pathway inhibitors, histone deacetylase inhibitors, HGF/c-Met pathway inhibitors, and EGF/EGFR pathway inhibitors . Other dTherefore, finding a solution to sorafenib resistance in the treatment of liver cancer is critical. Currently, there is no solid evidence to suggest that combining sorafenib with other drugs can solve the problem of resistance or even improve its efficacy.To address the issue of sorafenib resistance in advanced liver cancer patients, it is essential to understand the reasons behind it. Even gaining a small insight into the problem can be significant in overcoming resistance. One important aspect to consider is the relationship between sorafenib and autophagy during the treatment process. While the potential mechanism of sorafenib-induced autophagy is not fully understood, some signaling pathways have been identified . The mTOIt is well known that autophagy plays a dual role in cancer progression. In hepatocellular carcinoma (HCC), high autophagy flux induced by sorafenib treatment can result in the upregulation of autophagy-related proteins, which may subtly affect cell-to-cell communication and the entire tumor microenvironment .However, autophagy triggered by liver cancer cells resistant to sorafenib can lead to further drug resistance. For example, He et al. discovered that sorafenib-resistant hepatoma cells can upregulate miR-21, which in turn inhibits autophagy induced by sorafenib through downregulation of PTEN and altering the activation sequence of Akt pathway. This leads to drug resistance, as confirmed by transfecting miR-21 mimics into parent HCC cells, making them insensitive to sorafenib by inhibiting autophagy . SimilarLiver cancer stem cells (LCSCs) have the ability to self-renew and differentiate, which is involved in tumor progression by regulating stemness, drug resistance, and angiogenesis . In HCC,Studies have shown that hepatic stellate cells (HSCs) activation is closely related to the increase of autophagy , which iBesides, compared with normal endothelium, the sensitivity of endothelial cells of tumor vessels to some chemotherapeutic drugs is reduced . Tumor-dAutophagy can also lead to sorafenib-resistance in liver cancer by affecting immune cells. Macrophage autophagy has been found to be beneficial to hepatocarcinogenesis by inducing immunosuppressive microenvironment . Tumor-aAs described above, autophagy interacts with various cells involved in the cancer progression, impacting the effectiveness of sorafenib. Mobilizing the crosstalk between autophagy and various cells in liver cancer could provide new insights for the treatment of HCC.Tumor microenvironment (TME) has become a focal point in cancer research and drug development due to its pivotal role in cancer development and treatment . AutophaHypoxia in TME requires the regulation of hypoxia-inducible factor (HIF) to adapt to the hypoxic environment . Mitophain vitro and in vivo (Extracellular matrix (ECM) is a dynamic network composed of biophysical and biochemical factors that maintain tissue homeostasis . Recent  in vivo . Takahas in vivo .Autophagy exerts significant impacts on sorafenib efficacy during HCC treatment, not only intracellularly but also in TME, which mobilizes all available materials in tumors and adjacent non-cancerous areas to hinder sorafenib and control the whole field to promote advanced HCC progression. Therefore, identifying effective mechanisms or targetable signaling pathways that regulate autophagy and thereby reverse sorafenib-resistance is currently an important topic that urgently needs to be addressed.C-) and glutathione peroxidase 4 (GPx4)  and SLC3A2 . This amino acid antiporter maintains the intracellular redox state by importing cystine, which is then reduced to cysteine and used to synthesize the major antioxidant glutathione (GSH)  kinase activity . The hal4 (GPx4) . System ne (GSH) . GPx4 isne (GSH) .At the same time, some studies have found that autophagy can induce the occurrence of ferroptosis. Many studies have shown that autophagy is closely related to ferroptosis: Shen et al. revealed that YTHDF1 (m6A reader) triggers autophagy activation by identifying m6A binding sites to promote stability of becn1 mRNA, which eventually leads to Hepatic stellate cells (HSCs) ferroptosis . Similarin vitro and in vivo. This suggests that artesunate and sorafenib have a synergistic effect, providing a new possibility for overcoming drug resistance in hepatocellular carcinoma cells  caused by cysteine deprivation or cysteinase inhibition exacerbates sorafenib-induced ferroptosis and lipid peroxide production, leading to enhanced oxidative stress and mitochondrial ROS accumulation. Cysteine depletion may thus play a synergistic role with sorafenib by inducing iron-mediated ferroptosis . Li et ama cells . Recent ma cells . These sSeveral substances in the ferroptosis pathway can also be used to predict the prognosis of sorafenib treatment. Metallothionein (MT) is a small intracellular protein rich in cysteine that is widely expressed in eukaryotic cells. MT plays a critical role in heavy metal detoxification and antioxidation. Among them, MT1 is actively involved in resisting oxidative stress of various types of cells . StudiesLiver cancer is one of the three leading causes of cancer death in 46 countries and one of the five leading causes of cancer death in 90 countries. In 2020, the population of East Asia will represent 21.5% of the global population, but half of the world’s liver cancer deaths will occur in East Asia (54.3% and 54.1% respectively). China alone has 45.3% of the world’s liver cancer cases and 47.1% of liver cancer deaths . SorafenC− or by other mechanisms related to ferroptosis. To investigate whether sorafenib can function as a potent system XC− inhibitor or ferroptosis inducer in various cell lines, Zheng et al. conducted an experimental study on the SLC7A11 gene in two cell lines: the human fibrosarcoma cell line HT1080, widely used in ferroptosis research, and the human embryonic kidney cell line HEK293T. Their findings showed that sorafenib did not induce ferroptosis in either cell line. Furthermore, their research indicated that sorafenib failed to trigger ferroptosis in cell lines with high expression of the cystine/glutamate antiporter (xCT). Given that sorafenib is the first-line drug for patients with advanced liver cancer, Zheng et al. investigated whether sorafenib causes ferroptosis in four human hepatoma cell lines . Surprisingly, their experimental results showed that sorafenib did not induce ferroptosis in any of these cell lines. Therefore, the authors concluded that sorafenib is not a true ferroptosis inducer. Although the substrate-specific subunit SLC7A11 of the system XC− is expressed significantly, ferroptosis induced by the system XC− inhibitor can only be achieved in a small number of tumor cell lines . While most research has indicated that sorafenib can enhance the lethality of liver cancer cells through ferroptosis, some studies have found that sorafenib does not trigger ferroptosis by inhibiting the system Xll lines .There are still many unanswered questions about the relationship between sorafenib and ferroptosis (as shown in


Risk-adjustment (RA) models are used to account for severity of illness in comparing patient outcomes across hospitals. Researchers specify covariates as main effects, but they often ignore interactions or use stratification to account for effect modification, despite limitations due to rare events and sparse data. Three Agency for Healthcare Research and Quality (AHRQ) hospital-level Quality Indicators currently use stratified models, but their variable performance and limited interpretability motivated the design of better models.We analysed patient discharge de-identified data from 14 State Inpatient Databases, AHRQ Healthcare Cost and Utilization Project, California Department of Health Care Access and Information, and New York State Department of Health. We used hierarchical group lasso regularisation (HGLR) to identify first-order interactions in several AHRQ inpatient quality indicators (IQI) - IQI 09 , IQI 11 , and Patient Safety Indicator 14 (Postoperative Wound Dehiscence Rate). These models were compared with stratum-specific and composite main effects models with covariates selected by least absolute shrinkage and selection operator (LASSO).HGLR identified clinically meaningful interactions for all models. Synergistic IQI 11 interactions, such as between hypertension and respiratory failure, suggest patients who merit special attention in perioperative care. Antagonistic IQI 11 interactions, such as between shock and chronic comorbidities, illustrate that naïve main effects models overestimate risk in key subpopulations. Interactions for PSI 14 suggest key subpopulations for whom the risk of wound dehiscence is similar between open and laparoscopic approaches, whereas laparoscopic approach is safer for other groups. Model performance was similar or superior for composite models with HGLR-selected features, compared to those with LASSO-selected features.In this application to high-profile, high-stakes risk-adjustment models, HGLR selected interactions that maintained or improved model performance in populations with heterogeneous risk, while identifying clinically important interactions. The HGLR package is scalable to handle a large number of covariates and their interactions and is customisable to use multiple CPU cores to reduce analysis time. The HGLR method will allow scholars to avoid creating stratified models on sparse data, improve model calibration, and reduce bias. Future work involves testing using other combinations of risk factors, such as vital signs and laboratory values. Our study focuses on a real-world problem of considerable importance to hospitals and policy-makers who must use RA models for statutorily mandated public reporting and payment programmes. In health care, risk-adjusted (RA) outcome measures are widely used to compare performance across health care organisations. Stakeholders such as the Centers for Medicare & Medicaid Services (CMS), use these measures to rate hospitals , to infSeveral AHRQ QIs employ stratified RA models because their denominator populations are heterogenous while their numerator definitions are identical. Although stratified models represent a valid approach for estimating heterogeneous effects , the resp values [first order interactions that enforces strong hierarchy based on regression trees [Least absolute shrinkage and selection operator (LASSO) is a feature selection method for developing risk models that relies upon penalised regression, and shrinking coefficients to zero , which op values . Howeveron trees . HGLR seon trees . Both HGOur aim was to investigate whether AHRQ’s stratified QI RA models could be replaced by composite RA models, using HGLR to select and estimate clinically meaningful and interpretable interactions. These AHRQ QIs represent a useful test case for this novel approach given their widespread use by federal and state health agencies, and other stakeholders such as employer coalitions , for ranWe analysed hospital stays of adults  and adverse events (numerator) for each indicator , based oOpen Ruptured, Endo Ruptured, Open Unruptured, Endo Unruptured) in IQI 11 and two levels  in IQI 09. PSIStrata had two levels (Open And Non-Open) in PSI 14. The input features for the IQI models were DXCCSR binary categorical variables, 4 age categories, gender, hospital transfer status, and the IQIstrata. The features for PSI 14 included MS-DRGs collapsed into Modified DRGs by combining adjacent MS-DRGs with or without comorbidities or complications, 25 MDCs, age categories, sex, do-not-resuscitate (DNR) status, hospital transfer status, HCUP Elixhauser Comorbidity Software Refined [We then combined multiple population strata in IQI 09, IQI 11, and PSI 14 into a single composite model for each indicator, with the same numerator outcome, and represented the strata in a new variable named IQIStrata or PSIStrata. IQIStrata had four levels  or provide no useful clinical information  procedure unrelated to principal diagnosis"). Random undersampling of non-event cases was performed using the Random Over-Sampling Examples (ROSE package in R) method , 30. Theglinternet for HGLR, glmnet for LASSO, logistf for Firth’s logistic regression [precrec [We used the following R packages - gression , 37, pre[precrec  for precp-values indicate that the difference between the observed and predicted scores is negligible.As both AHRQ and CMS release their RA models as logistic regression models for transparency and interpretability, we reported the performance of logistic regression models using either the HGLR-selected (with interactions) or LASSO-selected (without interactions) features on the 20% test set. We report the area under the receiver operating characteristic (AUROC or C-Statistic) and the area under the precision-recall curve (AUPRC) as measures of discrimination in large imbalanced datasets. We used Firth’s logistic regression for PSI 14 due to the large standard errors for the parameters of interest, given the extremely low event rate and sparse cells. Firth’s method is specifically designed for these scenarios and is a suitable alternative to standard logistic regression for rare events , 37. ModTable Table Table p=0.067 for HGLR-selected features versus p=0.002 for LASSO-selected features).For PSI 14, the main effect of the non-open approach, relative to the open approach, was highly significant with adjusted odds ratios of 0.039  in the non-interaction model and 0.047  in the interaction model. However, the interaction model revealed that this beneficial effect of laparoscopic surgery was eliminated for patients in MDC 08 , MDC 13 , and Modified DRG 1304 . Although LASSO selected over twice as many main effects as HGLR and achieved similar model discrimination as HGLR, it could not identify the interactions shown in Figs. This research was motivated by a practical problem in the development of RA models for high-stakes, policy-relevant applications, such as hospital quality measurement and payment. Specifically, model developers often either ignore clinically important two-way interactions, leading to prediction error for subpopulations that may cluster at specific sites, or estimate fully stratified models, thereby wasting degrees of freedom and limiting the ability to select a robust feature set for every stratum. We investigated an innovative solution to this challenge, using HGLR to estimate composite models instead of stratified models while identifying clinically important interactions representing a robust set of heterogeneous effects. Given several hundred features in these applications, it is infeasible to manually specify all possible two-way combinations, nor is it advisable to specify such a model due to issues that arise during feature selection or parameter estimation such as collinearity and quasi-completeness.Analysis of IQI 11 revealed important findings with respect to the specific features selected. We were able to combine four strata and estimate a single, more robust model with interactions, which is more efficient and generates more easily interpreted estimates of effect heterogeneity. The most common pattern of the selected interactions was that comorbidities such as chronic kidney disease, pleural effusion, other lower respiratory tract disease, fluid and electrolyte disorders, heart failure and malnutrition were associated with markedly increased mortality among patients with unruptured aneurysms who were treated with endovascular repair, but had little marginal effect among patients with ruptured aneurysms for whom baseline mortality was very high. This pattern is clinically logical and consistent with prior literature on obesity , as comoUsing HGLR, we were also able to identify several previously unsuspected but clinically meaningful interactions among comorbid conditions. When these interaction effects were positive or synergistic, as for hypertension and respiratory failure, they suggest combinations of risk factors that may merit special attention as part of perioperative care. More commonly, these interaction effects were negative or antagonistic, suggesting that the combination of interacting factors was associated with little or no marginal increase in risk, compared with having one of those factors alone. For example, the presence of shock at admission is an important predictor of postoperative death. Shock was found to interact with several other clinical factors  such that the presence of shock virtually eliminated or reduced the marginal effect of the interacting factor on inpatient mortality. Failure to account for such important interactions may lead to overestimation of risk among patients with combinations of risk factors, and thus miscalibration of risk-adjustment models in portions of the risk distribution. Such miscalibration may bias risk-standardized outcome rates for specific facilities that attract patients with combinations of risk factors. Two-way interactions were also identified for IQI 09, but given the similar risk of inpatient death between patients with and without cancer undergoing pancreatic resection, these interactions were less clinically interesting than those in IQI 11 and PSI 14 models.For PSI 14, the two strata have markedly different event rates, with a very low event rate in the non-open stratum. It is widely accepted that the risk of postoperative wound dehiscence is higher with the open approach than with the laparoscopic approach, but the HGLR-derived model suggests that this difference is unexpectedly heterogeneous, and that the laparoscopic approach may not confer benefit (in terms of postoperative wound dehiscence) for patients undergoing musculoskeletal operations, such as lumbar diskectomy, or operations on the female reproductive system, such as hysterectomy. This intriguing finding requires validation in other large data sets.Since RA models are often used to evaluate healthcare entities using data beyond the reference population on which the model was estimated, it is important to evaluate the predictive and generalisation capabilities of the model. The ROC curve and the area under it show the degree of separability between the two classes . The AUPRC shows the predictive performance of the model and is helpful to understand models developed on imbalanced datasets, where the number of negative events far exceeds the number of positive events , 36, 39.Key strengths of this study include the large, diverse, and population-based sample of patients from numerous US states, and our focus on a real-world problem of considerable importance to hospitals, health care providers, researchers, and policy-makers who must use risk-adjustment models for statutorily mandated public reporting and payment programmes. HGLR has been used in various domains such as to analyse energy consumption in buildings in New York City , environIn risk-adjustment models for clinical events, it is often critical to include interactions accounting for synergistic and antagonistic effects. However, identifying such two-way interactions has been technically challenging, leading scholars to favour stratified models or to select a limited set of manually constructed interactions for evaluation. The former approach is inefficient and leads to models that are not directly comparable, as they are built on different populations and therefore do not support formal testing for heterogeneous covariate effects . The lat


Breast reconstruction is a critical component of breast cancer treatment for many women who undergo mastectomy. It aims to restore the appearance of breasts and to improve patients’ quality of life. In recent years, significant advancements in breast reconstruction techniques have been made, allowing for more natural-looking and aesthetically appealing results. It has been exciting for plastic surgeons to witness these developments firsthand and offer the latest options for breast reconstruction to patients.As a result of surgical innovation and experience throughout the years, a satisfactory outcome in free flap breast reconstruction is no longer just a simple flap survival rate. Nowadays, surgeons are focusing their efforts on giving patients the best aesthetic results while performing breast reconstruction. Autologous reconstruction is considered a standard of care in many hospitals and countries, expanding prospects when implant-based reconstruction fails in radiotherapy settings, and benefitting all patients who desire a more natural solution and who present localized excess tissue. Nowadays, advancements in microsurgical practice allow the right flap to be chosen for the right patient, transforming the potential donor site morbidity, due to flap harvest, into a clinical advantage for the patient that can improve body shape.Particular attention is reserved for donor site management. Developments in aesthetic procedures (such as abdominoplasty or thigh lift) are routinely applied when dealing with a flap donor site, resulting in improved contour as a result of pre-operative liposuction and well-hidden scars. Careful pre-operative planning using an angio-CT scan becomes mandatory in order to identify a suitable perforator, and is ideally lower than the umbilicus, forming a low abdominal scar. Some groups even advocated for the routine use of a superficial vein to supercharge the flap when the flap is harvested on a non-dominant perforator and to reduce the risk of jeopardizing the vascularization. Even if such procedures need to be attentively planned, by weighing various risks and benefits, they demonstrate a global trend towards the maximization of aesthetic outcomes .Furthermore, in abdominal-based free flaps, interest in preserving abdominal fascia and muscle tissue to improve functionality has increased. In large-volume microsurgical units with high-standard microsurgical practices, limited fascia incision, robotic harvest, and pedicle disassembly  are all smart solutions to improve functional and esthetic outcomes ,3.If a single breast following a skin sparing mastectomy can often be reconstructed with a single flap, more tissue is sometimes required to guarantee the most natural shape and volume. In this sense, stacked and conjoined flaps are gaining popularity due to microsurgical advancements. The benefit of stacked or conjoined flaps is that, when used appropriately, they nearly always provide all required aspects of breast reconstruction. A naturally aesthetically appealing breast must have a stable foundation on the chest wall, stable volume in all quadrants , and adequate skin to allow natural ptosis. The use of multiple flaps for a single breast allows for a tailored approach based on patient-centric factors and provides all aspects required for a natural breast reconstruction.Importantly, new frontiers in microsurgery are not adequately paralleled by innovations in oncologic surgery. Conservative mastectomies  are accepted as new techniques that improve aesthetic results and patient quality of life. Such mastectomies combine the oncological advantage of a complete glandular excision with the optimal cosmetic result of preserving the skin envelope and, wherever possible, the nipple–areola complex. Immediate breast reconstruction (IBR) using free flaps after nipple-sparing and skin-reducing mastectomies can provide natural-looking outcomes in a one-step surgical approach, providing several advantages for both the patient and the healthcare system, in a time- and cost-effective way. Indeed, studies show no negative impact on recurrence or patient survival, but better aesthetic outcome, less psychological distress, and lower treatment costs in IBR . FinallyImplant-based breast reconstruction (IBR) has also experienced important advancements, despite becoming less popular after the association between anaplastic large cell lymphoma (ALCL) and macro-textured implants . Round iMoreover, IBR has been traditionally performed by placing the implant in a submuscular pocket beneath the pectoralis major muscle. Lastly, pre-pectoral breast reconstruction has gained popularity due to numerous benefits in properly selected patients, as well as the pre-pectoral plane, which is also the most anatomically correct implant position to reconstruct the breast. Compared with subpectoral implant reconstruction, pre-pectoral reconstruction helps to preserve the pectoralis major muscle in its native position, resulting in decreased pain, no animation deformity, and an improved arm range of motion/strength. Although pre-pectoral reconstruction is safe and effective, the implant sits closer to the mastectomy skin flap. Acellular dermal matrices may play a critical role, allowing for the precise control of the breast envelope and providing long-term implant support . In addiIn conclusion, breast reconstruction has come a long way in recent years, as a result of advances in microsurgery, mastectomy techniques, and pre-pectoral breast reconstruction. Such improvements have raised the bar for breast reconstruction, aiming for more natural-looking and aesthetically pleasing results than ever before. Improved knowledge and skills have become essential in order to offer our patients the best possible outcomes, possibly exceeding their expectations.


Osteoporosis (OP) is one of the most common metabolic skeletal disorders and is commonly seen in the elderly population and postmenopausal women. It is mainly associated with progressive loss of bone mineral density, persistent deterioration of bone microarchitecture, and increased fracture risk. To date, drug therapy is the primary method used to prevent and treat osteoporosis. However, long-term drug therapy inevitably leads to drug resistance and specific side effects. Therefore, researchers are constantly searching for new monomer compounds from natural plants. As a candidate for the treatment of osteoporosis, curcumin (CUR) is a natural phenolic compound with various pharmacological and biological activities, including antioxidant, anti-apoptotic, and anti-inflammatory. This compound has gained research attention for maintaining bone health in various osteoporosis models. We reviewed preclinical and clinical studies of curcumin in preventing and alleviating osteoporosis. These results suggest that if subjected to rigorous pharmacological and clinical trials, naturally-derived curcumin could be used as a complementary and alternative medicine for the treatment of osteoporosis by targeting osteoporosis-related mechanistic pathways. This review summarizes the mechanisms of action and potential therapeutic applications of curcumin in the prevention and mitigation of osteoporosis and provides reference for further research and development of curcumin. Osteoporosis (OP) is a metabolic bone disease commonly occurring in the elderly and can lead to progressive loss of bone mineral density, persistent deterioration of bone microarchitecture, and increased fracture risk . OP can Natural plant active substances can be widely used as an alternative to prevent and treat OP because of their extensive biological activities . CompareCurcuma longa) is a herb known as golden spice. The root parts of turmeric are widely used not only as a dietary ingredient but also as a traditional medicine to treat various diseases; they play a pivotal role in Ayurvedic medicine and herbalism  and replaced by a new bone tissue (known as bone formation) . The dynOsteoblasts originate from mesenchymal stem cells (MSCs), which play a crucial role in bone development and formation . MSCs diCUR plays a role in bone formation by regulating the differentiation of osteoblasts. CUR promotes osteoblast proliferation and upregulates the expression of genes related to bone formation, including alkaline phosphatase (ALP), OCN, and Runx2 . Notablyin vitro. They found that long-term exposure to microgravity increased the level of oxidative stress and promoted the increase in ROS content in cells. After CUR treatment, the ROS expression decreased and osteoblast differentiation was promoted ; the process induced massive apoptosis accompanied by a decrease in ERK phosphorylation levels. CUR treatment prevented Dex-induced OP by inhibiting osteoblast apoptosis and upregulating the extracellular regulated protein kinase (ERK) phosphorylation  increased the femurBMD, improved the bone trabecular structure in rats, and significantly reversed the Dex-induced apoptosis in femoral osteoblasts.  pathway . Further pathway . CUR may pathway . Osteobl pathway . CUR act pathway . Chen etin vitro . Moreovein vitro . HoweverIn recent years, the adverse effects of diabetes on bone health have received much attention as researchers intensively studied diabetes and its complications . DOPis ain vivo results showed that CUR supplementation (100 mg/kg/d) prevented bone loss and promoted angiogenesis in DOP mice  alleviated HLS-induced oxidative stress and prevented and restored microgravity-induced bone loss. Further ogenesis . HoweverIn vivo and in vitro studies have shown that the beneficial effects of CUR on bone are primarily related to improved bone density and mechanical properties, indicating the possible use of CUR to prevent and treat bone diseases  and alendronate (5 mg/d) significantly improved the BMD status and bone turnover markers in postmenopausal patients with OP, and both were superior to monotherapy. Hence, combining CUR and alendronate may be an effective option for the prevention and treatment of postmenopausal OP . In anotIncreasing lines of evidence indicate that CUR, derived from natural plants, can prevent and treat bone-related diseases due to its biological activities, such as low toxicity and a wide range of molecular targets . Howeverin vitro nanoparticle delivery system loaded with alendronate and CUR (HA-ALN/CUR) as a novel OP treatment. HA-ALN/CUR showed enhanced ability to stimulate bone formation by upregulating bone formation markers, such as bone morphogenetic protein-2 (BMP-2), Runx2, and OCN, in MC3T3-E1 cells, ultimately promoting bone regeneration . Such compounds are believed to give false positive test results by interfering with various reactions in the assay such as chemical polymerization, fluorescence interactions, and the presence of active Michael receptors, rather than through specific target/compound interactions. Not only that, the results are also interfered with by the experimental conditions such as the purity of the phytochemicals extracted (However, despite the breakthroughs in recent years regarding the therapeutic activity of CUR in OP, many issues need to be urgently addressed. First, based on the evidence available from xtracted . Due to xtracted . In conc


Pathogen infection is one of the major causes of yield loss in the crop field. The rapid increase of antimicrobial resistance in plant pathogens has urged researchers to develop both new pesticides and management strategies for plant protection. The antimicrobial peptides (AMPs) showed potential on eliminating plant pathogenic fungi and bacteria. Here, we first summarize several overlooked advantages and merits of AMPs, which includes the steep dose-response relations, fast killing ability, broad synergism, slow resistance selection. We then discuss the possible application of AMPs for plant protection with above merits, and highlight how AMPs can be incorporated into a more efficient integrated management system that both increases the crop yield and reduce resistance evolution of pathogens. Erwinia, Pantoea, Pseudomonas, Xanthomonas, acquired high resistance to streptomycin and oxytetracycline during the past decades  defensin into potato and achieved robust resistance to fungal pathogens nd field , where tnd field , tomato nd field , rice  AMPs also play an important role in mediating plant-microbe interaction inside the legume plants. These antimicrobial peptides can both maintain and eliminate the viability of nitrogen-fixing bacteroids. At the initiation stage of root nodule, lytic AMPs kill most of the phytopathogenic fungi and bacteria, and maintain the symbiotic bacteria inside root. NCR211 expressed in the nodule interzone II-III promotes differentiation of bacteroids and the formation of nodules. Synthetic NCR211 was further proven inhibiting aggregation of free-living meliloti . In legumeliloti . Inside meliloti . Knockoumeliloti . The dir as well . Moreove as well .All together, these evidence indicates that host plant may harness host peptides to finely tune the symbiosis at different temporal and spatial scale. It is also intriguing that whether these antimicrobial host peptides can be genetically incorporated into non-legume plants to develop symbiotic associations . In addiCrop yield loss is mainly caused by plant pathogen and pests. The total loss can reaches up to 40% of crop yield at the global level according to a recent survey , which iIn design of plant protection strategy, no single component or method can accomplish all of the tasks. We argue that an optimal and cost-effective method that makes full use of AMPs’ merits can form a better solution for plant pathogen management Table 2.Antibiotic resistance of plant pathogen is always a concern for agricultural production as well as human health. The direct evidence of transfer of resistance pathogen from farm to clinics is rare . Both exLarge scale application of AMPs requires highly cost-effective production. Recently technological advancement allows one to cheaply produce AMPs either through solid-phase synthesis , prokaryAll authors listed have made a substantial, direct, and intellectual contribution to the work, and approved it for publication.


The integration of information from multiple modalities is a highly active area of research. Previous techniques have predominantly focused on fusing shallow features or high-level representations generated by deep unimodal networks, which only capture a subset of the hierarchical relationships across modalities. However, previous methods are often limited to exploiting the fine-grained statistical features inherent in multimodal data. This paper proposes an approach that densely integrates representations by computing image features’ means and standard deviations. The global statistics of features afford a holistic perspective, capturing the overarching distribution and trends inherent in the data, thereby facilitating enhanced comprehension and characterization of multimodal data. We also leverage a Transformer-based fusion encoder to effectively capture global variations in multimodal features. To further enhance the learning process, we incorporate a contrastive loss function that encourages the discovery of shared information across different modalities. To validate the effectiveness of our approach, we conduct experiments on three widely used multimodal sentiment analysis datasets. The results demonstrate the efficacy of our proposed method, achieving significant performance improvements compared to existing approaches. Multi-modal fusion, which integrates information from multiple modalities into a compact and informative representation, poses a significant challenge as it requires effectively correlating the semantics of diverse modalities. In recent years, several approaches have been developed to learn the joint embeddings of multiple modalities ,2. HowevDeep learning techniques have demonstrated remarkable success in generating useful feature representations ,4. ConseHowever, there are two remaining issues with this method. Firstly, relying solely on high-level representations may not provide sufficient information. We employ a controlled cross-modal attention flow among the tokens within a layer to address this concern—namely, crafted features. Secondly, whether common patterns occur at the semantic level or a specific single layer of representation is unclear. In practice, fusion based on high-level representations functions similarly to traditional late fusion, which combines semantic notions from unimodal features. However, late fusion, in contrast to other fusion algorithms such as early fusion, can only capture connections at the semantic level and fails to harness other types of correlations, such as covariation at the early feature level  or hieraThe ‘Early fusion’ model permits unrestrained attention flow across an image’s various spatial and temporal regions. Although theoretically promising, comprehensive pairwise attention across all model layers may be superfluous due to the high-density, fine-grained, yet largely redundant information within visual inputs. Additionally, such a model would struggle to effectively scale to longer videos, given the quadratic complexity of pairwise attention with token sequence length. To address these concerns, we employ a controlled cross-modal attention flow among the tokens within a layer. This is achieved by allowing unrestrained attention within a modality but obliging our model to gather and ‘condense’ information from each modality before exchanging it with another. At the heart of this proposal is introducing a limited number of latent fusion units, forming an ‘attention bottleneck.’ These units serve as mandatory conduits for all cross-modal interactions within a layer.Contrastive learning has gained popularity as a paradigm for learning feature representations by solving an instance discrimination task ,8,9. RecWe propose a fusion approach called Attentive Statistics Fusion to address these issues. As shown in To address the ignorance of context statistics in the existing tensor-based fusion methods in image feature extraction, we propose statistics fusion, which correlates the features of different statistics features of images. Context statistics fusion provides a holistic perspective by integrating standard deviations and features. This enables embedding vectors to capture correlation variations efficiently and accurately.Attention bottlenecks are used to fuse statistical modal global features. Our model strategically curtails the cross-modal information flow between latent units via well-defined fusion ‘bottlenecks.’ These bottlenecks compel the model to collate and ‘condense’ the most pertinent inputs from each modality, ensuring that only the necessary information is shared with the other modalities. Multi-headed self-attention may assist in aligning and fusing token-level image and text features, which increases model abstraction capability.We aim for representation learning utilizing contrastive learning for multimodal data. The central concept is to compare multimodal anchor tuples with hard negative samples that disrupted modalities with improved positive samples acquired using an optimizable data augmentation procedure. Multiple positive samples are permitted per anchor via a supervised contrastive loss function.The contributions of this paper can be summarized as follows:Extensive research has been conducted in multimodal fusion to explore diverse approaches to integrate and fuse information from multiple sensors, including images, videos, speech, and text.One common strategy is feature-level fusion, where features from different sensors are extracted and combined to form a comprehensive representation ,14. ThisAnother prevalent fusion strategy is decision-level fusion, where decisions or predictions from different sensors are combined to make a final decision ,16. EnseMoreover, hybrid fusion techniques have also been explored, combining feature-level and decision-level fusion approaches ,18,19. TIn recent years, numerous researchers have drawn their attention to contrastive Learning ,23,24,25More recently, contrastive learning has seen increasingly wide applications in multimodality. Huang et al.  leverageIn contrast with these works, we focus on aligning and fusing token-level features and learning their common sentiment-related features to elevate model performance further.To capture the correlation across different modalities more effectively, a commonly used approach is to directly concatenate the distinct characteristics of each modality and subsequently apply multiple layers of nonlinear transformations to construct a high-level joint representation . This fuTo address the limitations of early multimodal fusion and better capture the complex correlations between modalities, a primary strategy involves reducing the impact of individual differences and emphasizing common meanings within the fused representation . This isBuilding upon previous multimodal networks, it can be deduced that their fusion strategy typically involves incorporating one common layer alongside two modality-specific layers. These multimodal units effectively capture the correlations between different layers ,38. In oTo enhance the representations, we employ contrastive learning, which aims to maximize agreement across multiple enhanced views of the same data by utilizing a contrastive loss in the latent space. Our framework is illustrated in To enhance the diversity and richness of our input samples, we apply random augmentation to each sample, resulting in a modified representation denoted as d represents the output dimension.Our objective is to train an encoder network denoted as We employ the same encoder network for the original and augmented samples to achieve this, generating two separate representation vectors. Our approach uses BERT and ViT as encoders to extract hidden representations from the text and image inputs. These models are specifically chosen to capture the intricate features within the text data.ith image element and n denotes the number of samples. These global statistics features We compute the statistical properties of the extracted feature vectors to capture important characteristics. Specifically, we calculate the standard deviation and mean for each feature.We introduce a channel attention mechanism to enhance the aggregated features’ representation power. This mechanism dynamically assigns weights to each channel in the aggregated feature vector, and the attention weight is calculated as follows:The attention weights We consider the standard deviation a significant factor in our approach, encompassing the modal features related to long-term context variability. By incorporating the standard deviation, we aim to address the limitation of neural networks in fully capturing the expansive scope of information. While the vanishing gradient issue may restrict the network’s ability to comprehend global features effectively, introducing the standard deviation can help overcome this limitation by providing a measure of contextual distance and capturing broader, more encompassing features.Human cognition seamlessly integrates high-dimensional inputs like sight and sound from multiple sources. In stark contrast, traditional machine perception models usually focus on single modalities optimized for unimodal benchmarks. Consequently, a prevalent approach for multimodal video classification is the ‘late-fusion’ technique, where each modality’s final representations or predictions are integrated later.We employ a new Transformer-based architecture, using ‘fusion bottlenecks’ at multiple layers for modality integration. Unlike traditional pairwise self-attention, our model mandates that information from various modalities navigate through a limited number of latent bottlenecks. This strategy compels the model to consolidate and compress relevant data from each modality and disseminate only what is indispensable.To mitigate the quadratic complexity inherent to pairwise attention, we incorporate a compact set of fusion bottleneck tokens, denoted as Our model is then designed to channel all cross-modal attention via these bottleneck tokens. To be precise, at layer l, token representations are calculated as follows:Here, Transformer refers to the Transformer-Encoder for multimodal data. This work uses an attentive pooling mechanism to better capture the salient features in our input vector We first compute the attention scores The output The model can focus on the most relevant elements in Supervised contrastive loss (SupCon) is utilized in scenarios where multiple samples with known labels belong to the same class. SupCon aims to enhance the discrimination of representations within the same class. The formulation of the SupCon loss is given by:In Equation , P(i) reThe supervised contrastive loss can be combined with the cross-entropy loss as a form of regularization. The overall loss function is given by:By incorporating the SupCon loss alongside the cross-entropy loss, we aim to jointly optimize the model for both classification accuracy and enhanced discrimination within the same class.To evaluate the effectiveness of our proposed strategy, we conduct experiments on three publicly available datasets: MVSA-Single, MVSA-Multiple2 , and HFMWe preprocess the original MVSA datasets using the same procedure as  to ensurFor the MVSA datasets, we randomly split the data into training, validation, and test sets using an 8:1:1 ratio. This division allows us to effectively train and evaluate our models while reasonably balancing the datasets’ subsets.For our experiments, we utilize PyTorch and HuggingFace Transformers  librarieWe employ the BERT-base as the text encoder within the fusion module and ViT  as the iTo specify the batch sizes for the experiments, we set them to 32 for MVSA-Single, 64 for MVSA-Multiple2, and 128 for HFM datasets. These batch sizes are selected to ensure efficient training while considering each dataset’s specific characteristics and computational requirements.We use the AdamW optimizer with a learning rate of All experiments are conducted on an A6000 GPU, which provides computational power for efficient model training and evaluation. The GPU accelerates the training process and enables faster experimentation.Our model is benchmarked against both unimodal sentiment models and multimodal baseline models.Unimodal Baselines: We consider well-established models for text classification tasks like CNN, Bi-LSTM, and TGNN [and TGNN , a text-and TGNN , a pre-tand TGNN , an imagand TGNN , which iMultimodal Baselines: We compare our approach with several multimodal sentiment analysis models. These include MultiSentiNet [SentiNet , a deep SentiNet , a hieraSentiNet  is a co-SentiNet  integratSentiNet  propose SentiNet  offers aSentiNet  introducOur observations from the results are as follows: (1) Our model demonstrates comparable performance to other robust baseline models across all three datasets. (2) Our model outperforms the other baseline models on all three datasets. This could be attributed to the sparsity and noise present in the emotive characteristics of images, making it challenging for the models to extract meaningful features for sentiment analysis. In contrast, our approach incorporates statistical features of the images, while the multi-head attention method may capture the global features of the images. (3) The performance gain of the multimodal models is more limited for simpler tasks. For example, on the HFM dataset, the improvement in our model compared to BERT is less pronounced than on the MVSA-Single dataset. This can be attributed to HFM being a binary classification task, while MVSA-Single involves three-class classification.Overall, our proposed model exhibits strong performance compared to the baseline models, indicating its effectiveness in capturing the sentiment information from both text and image modalities.We conducted further evaluations to assess the impact of the statistical fusion modules, Transformer-based multi-layer fusion modules, and supervised contrastive learning. The results of these experiments are presented in Moreover, incorporating supervised contrastive learning enhances the model’s performance even further. This suggests that contrastive learning enables the model to learn common sentiment aspects while effectively differentiating between various sentiment data. By maximizing agreement and separation within the latent space, the contrastive learning mechanism aids in capturing essential patterns and improving the model’s ability to understand the underlying sentiment dynamics.Overall, these results highlight the effectiveness of our proposed model, showcasing the benefits of the statistical fusion modules, Transformer-based multi-layer fusion modules, and supervised contrastive learning in capturing and leveraging multimodal sentiment information.The funnel Transformer  is an inTo investigate the impact of different layers within the Transformer Encoder on the model’s performance, we conducted experiments by varying the number of layers for both the text-image Transformer bottleneck fusion and the image Transformer layers. This is illustrated in For our experiments, we considered different combinations of layers for the text-image Transformer fusion and image Transformer layers, such as 3-2, 5-2, and 6-1, respectively, for the three datasets. These combinations allowed us to analyze the contributions of text and images separately and understand their impact on the model’s performance.These experiments highlight the importance of considering the distinct contributions of text and images to the dataset. This allows us to optimize the model by appropriately allocating the Transformer layers within the MLF module to leverage each modality’s specific characteristics and relevance for sentiment analysis tasks.To demonstrate the effectiveness of our proposed contrastive learning approach in facilitating the model’s understanding of sentiment-related common features from multimodal inputs, we conducted a visualization experiment using the MVSA-Single dataset. We employed dimensionality reduction techniques to visualize the data feature vectors from the final layer of our model. In this experiment, we utilized the t-SNE (t-distributed stochastic neighbor embedding) dimensionality reduction method to generate a two-dimensional feature vector, which we then visualized.Furthermore, our visualization results exhibit a grouping pattern for the neutral sentiment data instead of scattering them across the vector space, as is observed with BERT. This is due to the relatively smaller amount of neutral sentiment data available. The grouping of neutral data indicates that our model captures common features associated with neutral sentiment, contributing to its improved performance.Overall, these visualization results highlight the efficacy of incorporating contrastive learning in enabling the model to acquire and leverage common sentiment-related traits, thereby enhancing its overall performance.To provide a more intuitive understanding of our model’s validity, we present a set of illustrative examples and a case study demonstrating the model’s efficacy. In particular, we compare sentiment labels derived from our model and those predicted by the BERT model.The case study is structured as follows: the leftmost column showcases the example image, the second column features the corresponding textual information, the third column displays the sentiment prediction determined by the BERT pre-trained model, and the final column exhibits our model’s performance. This layout is intended to facilitate a direct comparison between our model and the BERT pre-training model, thereby highlighting the relative merits of our approach.As demonstrated in In conclusion, this paper introduces novel multimodal fusion methods for multimodal analysis tasks, specifically focusing on the association of individual statistical features across multiple modalities. Furthermore, we incorporate contrastive learning to aid the model in learning sentiment-related features from multimodal data and improve its ability to extract and fuse multimodal data features. Our proposed approaches have demonstrated superior performance through extensive experiments compared to baseline methods. These findings highlight the effectiveness of our methods in capturing and leveraging the synergies between different modalities, ultimately leading to improved performance in sentiment analysis tasks.


The risk-benefit profile of therapeutic hypothermia is controversial with several randomized controlled trials providing conflicting results.The purpose of this systematic review and meta-analysis was to determine if therapeutic hypothermia provides beneficial neurologic outcomes relative to adverse effects.~33 degrees Celsius) to normothermia or the standard of care (36 - 38 degrees Celsius). Data were collected using the Covidence systematic review software. Statistical analysis was performed by Review Manager software. Risk of bias, sensitivity, and heterogeneity were analyzed using the Cochran’s Collaboration tool, trial sequential analysis (TSA) software, and I2 statistic respectively.MEDLINE and EMBASE databases were searched for randomized controlled trials of post-cardiac arrest patients comparing therapeutic hypothermia  were included. No significant differences existed between the hypothermia group and normothermia for favorable neurologic outcome  or all-cause mortality . When compared to normothermia, the hypothermia group had greater risk of adverse effects , which was driven by the onset of arrhythmias. Subgroup analyses revealed that therapeutic hypothermia provided greater neurologic benefit in trials with a higher percentage of subjects with shockable rhythms . Trial sequential analysis revealed statistical futility for therapeutic hypothermia and favorable neurologic outcome, mortality, and adverse effects.Therapeutic hypothermia does not provide consistent benefit in neurologic outcome or mortality in the general cardiac arrest population. Patients with shockable rhythms may show favorable neurologic outcome with therapeutic hypothermia and further investigation in this population is warranted. Any potential benefit associated with therapeutic hypothermia must be weighed against the increased risk of adverse effects, particularly the onset of arrhythmias. Cardiopulmonary arrest is the result of cessation of adequate cardiac and pulmonary perfusion and is associated with a high degree of morbidity and mortality. Out-of-hospital and in-hospital arrest survival ranges from 10% to 20%, respectively . The resTherapeutic hypothermia, generally targeting mild hypothermia (32 to 34 degrees Celsius), is a strategy aimed at reducing complications following a potential anoxic injury after cardiopulmonary arrest . NeuroprThe current literature regarding therapeutic hypothermia is heterogeneous, including a variety of different populations and interventions with varying results. Studies have included patients with cardiopulmonary arrest from shockable rhythms and non-shockable rhythms; protocols with varying target temperature; the utilization of several cooling methods; and differing durations of hypothermia. The 2017 Neurocritical Care Society guidelines for therapeutic hypothermia define targeted temperature management (TTM) as encompassing therapeutic hypothermia, controlled normothermia, and treatment of fever ,11. HoweThe goal of our systematic review and meta-analysis was to analyze the highest quality available literature and determine if mild hypothermia provides benefit in neurologic outcomes or mortality, evaluate if further randomized controlled trials are futile to conduct, and to assess whether this intervention results in increased adverse events. Additionally, we aim to determine if any subgroups in particular benefit from therapeutic hypothermia.This systematic review and meta-analysis followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses Guidelines and the Cochrane Handbook for Systematic Reviews –15. The The studies we included encompassed randomized controlled trials of patients following cardiopulmonary arrest, which compared mild therapeutic hypothermia (temperatures consisting of 32 to 34 degrees Celsius) to normothermia or the standard of care (36 to 38 degrees Celsius). Exclusion criteria consisted of studies that had no comparator group, retrospective analyses, studies examining therapeutic mild hypothermia for indications other than post-cardiac arrest, and studies involving pediatric patients (age < 18 years).a priori and aimed to determine factors that may be associated with a favorable neurologic outcome. These subgroups included the following: shockable arrhythmia, bystander-performed cardiopulmonary resuscitation (CPR), and duration of cooling. As individual patient data were not available for the subgroup analyses, the median time or frequency across all included studies was calculated, and the outcome was assessed dichotomously as either greater than the median time or frequency or less than or equal to the median time or frequency for the aforementioned subgroups.The primary outcome of this study was to evaluate the degree of favorable neurologic outcome following TTM at study follow up. A favorable neurologic outcome was defined as a modified Rankin Scale (mRS) score of 0 to 2, a Cerebral Performance Category (CPC) score of 1 to 2, or the closest relevant score on an any alternate system. Secondary outcomes included all-cause mortality at longest study reported follow-up period and serious adverse events including arrhythmias, major bleeding, documented infection, and seizure defined by each respective study. A subgroup analysis was planned Both MEDLINE and EMBASE were searched using a comprehensive population, intervention, comparator, and outcome (PICO) question and MeSH terms. We also searched references listed in retrieved trials, guidelines, systematic reviews, and other meta-analysis to identify potential studies to include. Two investigators (SCT and PMR) independently screened the abstract, title, or both to determine which studies had possibility for inclusion. Covidence software  was utilized to upload search results and conduct bias risk assessments. Potential articles for inclusion were further examined by each investigator (SCT and PMR) to review for inclusion and exclusion criteria in the analysis. Discrepancies between reviewers were discussed further, and if unable to be resolved between the two investigators, a third reviewer (RM) was involved to determine if the study met inclusion or exclusion criteria.Information was manually extracted into a standardized data extraction form. Demographics, such as age and sex, were recorded, in addition to other information, including the number of patients with a shockable rhythm, a bystander-performed CPR, and an out-of-hospital cardiac arrest. TTM information, including time to ROSC, time from ROSC to TTM initiation, time to goal temperature, cooling and rewarming methods, and duration of cooling were recorded. Outcomes, including neurologic outcomes and mortality, were collected as reported in each study and recorded within the extraction form. Adverse effects were subject to the definitions used per each trial protocol and were collected if they were reported in a per-participant methodology. If outcomes were unavailable in the published data, attempts were made to contact corresponding authors for the included studies.The Cochran’s Collaboration tool was utilized to assess for risk of bias in a blinded manner. This tool assesses bias of each study according to the following: random sequence generation, allocation concealment, blinding, incomplete outcome data, selective reporting, and other potential sources of bias. To assess the risk of publication and reporting bias, an Egger’s test was performed to evaluate funnel plot asymmetry.2 statistic. Trial sequential analysis (TSA) software was used to test model robustness using an O’Brien-Fleming alpha-spending approach. The TSA takes into account heterogeneity of studies in addition to estimation of information size to better control for type 1 and type 2 errors.Review Manager 5.4  was used for statistical analysis, in which all outcomes were dichotomous in nature. The Cochran-Mantel-Haenszel Method was used to calculate risk ratios for outcomes using a significance level of 0.05. Due to differing methodology and study interventions, a random-effects model was pursued in analyzing outcomes. Heterogeneity between trials were assessed by IA total of 1825 studies were identified via search on MEDLINE and EMBASE with five being included in the analyses  . The maiAll of the included studies compared some degree of mild hypothermia to normothermia or the reference standard of care. The target hypothermia for majority of studies ,4–6 was No trial met criteria for low risk of bias due to the infeasibility to adequately blind participants and treating physicians. Risk of bias assessments are displayed in the Supplementary Appendix . Furthermore, the Egger’s test was negative for publication bias .The primary outcome of neurologic outcome was available for 3611 subjects. Of the included trials, four trials used the Cerebral Performance Category (CPC) or similar scale for determining neurologic outcomes ,3,5,6. TA total of 3613 subjects from the five trials had follow up data available for all-cause mortality. Longest follow up for all-cause mortality mirrored that of the longest follow up for neurologic outcomes, with majority of studies reporting the outcome at six months. Overall, mortality was 54.2% (1009/1862) and 54.7% (1017/1860) in the hypothermia and normothermia groups, respectively (Table S1). There was no significant difference in all-cause mortality between groups  with a heterogeneity of 54% .Definitions for each adverse effect were extracted from each study (Table S2). All except one trial  presentea priori had meaningful data to perform subgroup analyses. Subgroups were divided into the following: initial shockable rhythm , bystander CPR (studies with > 70% vs. < 70% bystander CPR), and duration of cooling (studies with > 24 hours vs. ≤ 24 hours of cooling). Of the subgroup analyses performed, studies with a higher percentage of patients with initial shockable rhythm  were more likely to have a favorable neurologic outcome . The remaining subgroup analyses on neurologic outcome revealed no significant differences between groups and are reported in the supplementary appendices . Additionally, with the signal of favorable neurologic outcome in trials with larger percentages of patients with shockable rhythms, we conducted a subgroup analysis for the outcome of mortality. Four trials, representing 1685 subjects, reported individual patient data for this analysis [When comparing the available data of the five trials, three specified subgroups TSA revealed the z-curve for outcomes of favorable neurologic outcome and mortality crossed the lines of futility, signifying randomized controlled trials with these populations would not demonstrate a benefit and would be considered futile . Furthermore, the z-curve for the outcome of adverse effects crossed the line of harm, suggesting that if the systematic review was a randomized trial that it may have been stopped early by an independent data safely monitoring committee due to risk of harm .The results of this systematic review suggest there is no benefit of mild therapeutic hypothermia with respect to favorable neurologic outcome and mortality. Our subgroup analysis also demonstrated an increased risk of adverse effects with the hypothermia group in comparison to the group receiving normothermia, particularly driven by incidence of arrhythmias. Despite the theoretical coagulopathic and immunosuppressant effects, we did not find an association between hypothermia and bleeding or infection. To our knowledge, our findings present the most comprehensive analysis of adverse effects from therapeutic hypothermia following cardiac arrest. Subsequently, our meta-analysis revealed new findings for futility of further randomized controlled trials across all of the aforementioned outcomes through TSA.Favorable neurologic recovery is arguably one of the most important outcomes with regard to post-cardiopulmonary arrest. Clinically, favorable neurologic outcome is defined by the mRS or CPC scores, which describe the functionality of a patient after suffering a neurologic injury ,17. SimiLimitations of this outcome in our meta-analysis are important to note. One of the included trials in the analysis utilized their own neurologic outcome scale, which was not validated in previous studies . HoweverWhether therapeutic hypothermia improves survival is controversial, especially if neurologic outcome is not favorable. Our results are similar to previous meta-analyses that demonstrated no survival benefit with hypothermia –23,27. IThis outcome was also associated with limitations necessary to note. Each of these trials reported mortality at variable follow up durations, which ranged from hospital discharge to six months. Although unlikely to confound our findings with the use of a random effects model, it is important to note these outcomes were measured at differing time points. Additionally, another important limitation was for our subgroup analysis on shockable rhythms. The recent randomized control trial by Dankiewicz and colleagues did not report individual patient data in regards to mortality and shockable rhythms . AlthougAdverse effects are important when considering any therapeutic strategy to determine the risk to benefit ratio for the patient. Therapeutic hypothermia is not a benign therapy and has been associated with various adverse effects. Few of the previous meta-analyses have reported on adverse effect findings; however, of those referenced, patients randomized to therapeutic hypothermia were at an increased risk for arrhythmias ,19,21,22Important limitations exists in regards to adverse effect reporting and our analysis. First, each trial utilized their own method for reporting, with some trials using an independent data safety monitoring committee, while others were subjected to physician reporting. Due to each trial using their own definitions for adverse effects, we were unable to delineate the predominant arrhythmia reported. Similarly, the standard of care for adverse effect reporting has changed over time and become more objective according to the requirements of independent data safety monitoring committees; however, our analysis with TSA and alpha spending is aimed to adjust for this change, as mentioned previously. Additionally, one trial reported adverse effects over a total of reported events, rather than per participant, hindering our ability to include those adverse effects in our initial subgroup analysis . AdminisIn regards to future directions of randomized controlled trials, TSA revealed futility of further trials in the comparison of hypothermia to normothermia with the outcomes of mortality, neurologic outcomes, and adverse effects. A potential direction for further analyses is prevention of pyrexia in post-cardiac arrest management and re-focusing TTM efforts on patients with shockable rhythms. Pyrexia increases metabolic demand and can lead to consequential cerebral inflammation, increased cerebral blood flow and subsequent elevated intracranial pressure, and neurotransmitter excitotoxicity ,30. FollTherapeutic hypothermia does not appear to result in a favorable neurologic outcome or survival benefit in comparison to normothermia in the general population presenting with cardiac arrest. Our subgroup analyses showed a signal towards favorable neurologic outcome with therapeutic hypothermia in shockable rhythm. Additionally, an increased risk of adverse effects, particularly driven by arrhythmias, was observed in the therapeutic hypothermia group. Our results are consistent with previous meta-analyses and present new findings of futility for further randomized controlled trials assessing therapeutic hypothermia compared to normothermia with the outcomes of favorable neurologic outcome, mortality, and adverse effects.


Medium-and heavy-duty vehicles (MHDVs) comprise only a small fraction of vehicles on the road but disproportionately contribute to greenhouse gas emissions and air pollution from the transportation sector. Given the large variety of vehicle types—ranging from heavy-duty pickup trucks and box trucks to full-size buses and Class 8 tractor semi-trailers—and applications, multiple technologies offer opportunities to decarbonize MHDVs including battery-electric vehicles, hydrogen fuel cell vehicles, and sustainable liquid fuels. Here we provide an overview of the status, opportunities, challenges, and uncertainties for these competing—and potentially complementary—technologies, including consideration of supporting infrastructure and prospects for future success. We find a bright outlook for zero-emission vehicles and discuss remaining barriers and uncertainties around fleet decisions and changes to vehicle operation, infrastructure, manufacturing, and future fuel and technology trends that can be informed through analysis.  Energy transportation; Civil engineering; Transportation engineering; Energy Systems Both BEVs and FCEVs are zero-emission vehicles (ZEVs), as they eliminate tailpipe emissions and associated air pollution.,,,,,,BEVs, once considered impractical for MHDVs, have experienced significant cost reductions and technology improvements over the last decade, largely driven by reductions in battery costs.,Hydrogen FCEVs offer driving ranges and refueling times comparable to today’s diesel trucks. However, the costs to purchase these vehicles and produce clean hydrogen are currently much higher,Finally, “drop-in” sustainable fuels such as advanced sustainable biofuels  or e-fuels (liquid synthetic fuels produced using captured carbon and hydrogen produced by electrolysis of water with renewable electricity) can provide significant greenhouse gas emissions savings and do not require major changes to existing vehicles or fuel dispensing infrastructure. However, sustainable fuels will not reduce criteria air pollutant emissions , and several constraints could limit their ability to scale up to fully replace petroleum in MHDV applications. Specifically, limited feedstock supply, unintended environmental consequences, and high costs could hinder the growth of sustainable biofuels, while significant electricity requirements, uncertain technology timelines, and high production costs could limit the role of e-fuels.This article offers a research and industry perspective on the status, opportunities, and challenges for various medium- and heavy-duty ZEV technologies in the US. We start by reviewing the current segmentation of the MHDV market (vehicle types and applications), including an overview of the current status and future outlook for ZEVs. Next, we review several key economic drivers and infrastructure barriers for transitioning to zero-emission MHDVs. We conclude by summarizing major takeaways and outlining several key uncertainties to be addressed in future analyses. Although the same technologies are used to decarbonize MHDV applications in other regions of the world, the freight and energy systems vary significantly across regions. Insights provided can inform other regions but might vary for places with different energy prices and availability, denser road networks that could more easily accommodate solutions like overhead catenary charging, or other differences that impact vehicle use, charging and fueling availability, and other key aspects required to transition to clean transportation solutions.e.g., firefighting, tree trimming), each with their own unique driving and power requirements. The vast majority (approximately 90%) of MHDVs are used in applications with typical shipment distances  of less than 250 miles,,,The US MHDV market is heterogeneous, reflecting a variety of needs and applications. Thus, multiple decarbonization solutions may be required to fully displace petroleum fuels. MHDV stock and emissions reflect the significant variation in vehicle characteristics and use across market segments. Vehicle stock  and substantially higher VMT. Variations in vehicle stock, activity, and fuel economy suggest that different applications may require divergent technology solutions to decarbonize.Energy use and tailpipe emissions  with depot-based charging at power levels commonly used for light-duty applications . It is uncertain which ZEV technology will be most widely adopted for long-haul applications (9% of vehicles but 48% of energy use and emissions). Although BEVs carry an advantage over FCEVs in terms of overall efficiency and technology readiness, recent studies show that further improvements to battery range, charging speeds, and gravimetric energy densities will be needed to enable long-haul BEVs to operate similarly to diesel trucks today.,,The US market for medium- and heavy-duty ZEVs is at an early stage, trailing the light-duty market by several years. Most heavy-duty (Class 7–8) ZEV deployments in the US to date have been pilot demonstrations to familiarize fleet operators with the new technologies.,,,e.g., California Energy Commission’s Energy Infrastructure Incentives for Zero-Emission Commercial Vehiclese.g., Low Carbon Fuel StandardDespite today’s low adoption levels, traditional truck manufacturers and new technology providers are increasingly announcing plans to bring new ZEVs to market, whereas customers continue to lock in large preorders years before their expected delivery dates.Many analysts now project significant growth in future medium- and heavy-duty ZEV adoption, though the extent and timing vary. For example, in the International Energy Agency’s recent “Announced Policies” scenario, 12% of heavy-duty truck sales, 37% of medium-duty van sales, and 43% of bus sales are projected to be electric in the US by 2030.Excluding buses, three-fourths of zero-emission MHDVs deployed in the US from 2010 to 2021 were medium-duty BEVs.Although many factors influence technology adoption, economics will drive ZEV adoption for commercial applications to a greater extent than personal vehicles. Multiple studies use total cost of driving or ownership to project ZEV adoption, considering factors such as the upfront vehicle purchase price, fuel and maintenance costs, vehicle lifetime and discount rates, vehicle use, and occasionally additional factors like infrastructure availability and penalties for reduced payload capacity or increased refueling times (for BEVs in particular). Many of these assumptions are highly uncertain, including future technology cost and performance projections and the financial considerations that drive fleet decision-making. For example, the financial horizon over which fleet operators make their purchase decisions heavily impacts comparative cost calculations. Cost savings for ZEVs increase over time as the vehicles are driven more and operating costs become a more dominant factor in the total cost of ownership. That said, adoption models sometimes take a first-owner perspective and only consider the first few years of a vehicle’s life, which could under-represent the value of ZEVs over their entire life. In addition, assumed range requirements are a significant driver of modeled ZEV purchase prices, but these requirements  for individual fleets are uncertain and could change over time. The lower operating costs of ZEVs could induce changes to fleet logistics and vehicle use that are challenging to predict but might lead to reduced vehicle and operational costs.,,Fuel costs are a key determinant of operating costs yet are also highly uncertain. Fossil fuel prices are highly volatile because of complex global geopolitical and market forces. There is also uncertainty around the price of clean hydrogen, given the present lack of markets and infrastructure. While less volatile, electricity prices will be influenced by location and time of day, affecting the cost of charging .A major barrier to large-scale ZEV adoption today is the lack of fueling solutions and networks. ZEVs will not be adopted at scale if they cannot be reliably fueled, and building the required infrastructure will take multiple years and likely decades to achieve ubiquitous convenient refueling access for all vehicles and applications. Despite sharing some early market similarities, the refueling infrastructure to support BEVs and FCEVs will look quite different.e.g., loading/unloading) to charge at moderate power levels, usually referred to as “opportunity charging.” This could take place at public or semipublic locations where vehicles routinely dwell. For certain applications, BEVs may be able to perform all their charging while parked at their depot, with privately installed kilowatt-level charging equipment and sufficient temporal flexibility to further reduce electricity costs through managed charging.BEV charging for MHDVs is expected to be heterogeneous, matching specific operating behaviors and charging needs with a combination of slow private or semiprivate charging and fast public charging. BEVs can take advantage of extended downtimes . A key barrier to zero-carbon FCEVs is the cost to produce clean hydrogen, currently about an order of magnitude higher than the $0.50–$1.70/kg for producing hydrogen from natural gas, by far the most popular method today.Both BEVs and FCEVs face a “chicken-and-egg” problem with respect to the deployment of charging/fueling infrastructure, but with electric truck production ramping up it is becoming more pressing to ensure that the required charging infrastructure is timely deployed and operated reliably to enable adoption and use of ZEVs. For example, the Port of Long Beach estimates that about 60% of drayage trucks could require public charging.Decarbonizing MHDVs is a critical step for achieving climate and air quality goals and will require a rapid transition to ZEVs—possibly complemented by sustainable liquid fuels for certain applications and legacy vehicles. Effective collaboration across multiple stakeholders in the public and private space is critical to enable a full transition to ZEVs, supported by a multi-pronged approach that includes: (1) R&D investments to further improve technologies; (2) investments in manufacturing, supply chains, and supporting infrastructure; and (3) policies and regulation aimed at supporting clean technologies and reducing GHG and pollutant emissions.•First, the trade-offs between various technology solutions within different applications remains unclear and will be largely dependent on uncertain technology and fuel cost trajectories and ever-changing fleet logistics and vehicle operations. This uncertainty can slow down investments and adoption of clean technologies. Further research into the decision-making criteria for various fleet operators could help to better understand and match fleet priorities with best-suited technologies and accelerate investments•Second, significant infrastructure investments will be required to provide charging/refueling and to transport and deliver clean fuels to stations. Without reforms, the lack of streamlined and harmonized permitting processes, and very long lead times for energy infrastructure build out  will complicate and delay the deployment of ZEV infrastructure, thereby slowing down the ZEV market transition. Moreover, the ability to scale up manufacturing and establish reliable supply chains for raw materials and critical rare-earth metals will require major investments•Third, although MHDVs can leverage economies of scale from other technologies sharing similar components and materials , competing demand for limited materials could lead to shortages that delay and/or increase the short-term costs for ZEVs. Research into solutions that enable the development of robust supply chains for minerals and materials critical to ZEVs, including end-of-life recycling processes, is highly warranted•Fourth, large increases in clean electricity demand for BEVs, as well as hydrogen and e-fuel production, could prove challenging for the power system and will require careful planning. For biofuels, effective and fully sustainable production pathways will need to be demonstrated at scaleAlthough the outlook for commercial medium- and heavy-duty ZEVs is bright, and ZEV technologies are steadily improving, several barriers and uncertainties remain to be explored.Finally, this paper summarizes our perspectives on the current status and expectations for transitioning to zero-emission commercial vehicles, but difficult-to-foresee trends and disruptions could impact future mobility needs and the potential for various decarbonization technologies. For example, automation could enable increased vehicle utilization and shifting economics in favor of BEVs for their lower operating costs while also prioritizing faster recharge speeds to maximize asset utilization. The introduction of new advanced technologies and trends could disrupt freight demands, logistics, and vehicle usage patterns for MHDVs in a way that fundamentally reshapes the outlook for different decarbonization solutions. In general, zero-emission technologies use for commercial vehicles is still in its infancy, and data-driven analyses like the ones used to inform our perspectives will need to be updated to provide robust and timely insights to inform decision-making, especially in areas of high uncertainty.