Large-scale prediction of drug–target affinity (DTA) plays an important role in drug discovery. In recent years, machine learning algorithms have made great progress in DTA prediction by utilizing sequence or structural information of both drugs and proteins. However, sequence-based algorithms ignore the structural information of molecules and proteins, while graph-based algorithms are insufficient in feature extraction and information interaction.In this article, we propose NHGNN-DTA, a node-adaptive hybrid neural network for interpretable DTA prediction. It can adaptively acquire feature representations of drugs and proteins and allow information to interact at the graph level, effectively combining the advantages of both sequence-based and graph-based approaches. Experimental results have shown that NHGNN-DTA achieved new state-of-the-art performance. It achieved the mean squared error (MSE) of 0.196 on the Davis dataset (below 0.2 for the first time) and 0.124 on the KIBA dataset (3% improvement). Meanwhile, in the case of cold start scenario, NHGNN-DTA proved to be more robust and more effective with unseen inputs than baseline methods. Furthermore, the multi-head self-attention mechanism endows the model with interpretability, providing new exploratory insights for drug discovery. The case study on Omicron variants of SARS-CoV-2 illustrates the efficient utilization of drug repurposing in COVID-19.https://github.com/hehh77/NHGNN-DTA.The source code and data are available at  However, those methods consume a lot of money and are constrained by human abilities, making it impossible to screen completely from large-scale drug databases   , which iCurrently, ML-based DTA predictions are mainly divided into two schemes: sequence-based and graph-based methods. Sequence-based methods take text embeddings of small drug molecules and proteins as output and use convolutional neural networks to predict DTA. However, sequence-based DTA prediction methods only take one-dimensional sequence information as input, ignoring the structural features of drugs and proteins. To solve this problem, graph-based methods have gradually captured scholars’ attention. GraphDTA  for interpretable DTA prediction. It includes a sequence-based adaptive feature generator and a graph-based HGNN that mixes drug and protein graphs. As shown in Through extensive comparative experiments on two well-known benchmark datasets, Davis , Pearson correlation coefficient, Spearman’s rank correlation coefficient, concordance index (CI) . However, the CI and Rm of NHGNN did not exhibit a significant improvement over the SOTA model (P > .05). In the KIBA dataset, NHGNN demonstrated superior performance over the SOTA model in both MSE and the P < .05).We first compare with previous SOTA methods for DTA prediction on Davis and KIBA datasets. Previous experiments used usually random splits to divide the training, validation, and test sets. However, a random split setting may lead to overly optimistic results, as it can cause drug and protein information to leak into the test set (P < .05). Compared with previous methods, we specifically use a feature generator to obtain high-dimensional features of drug and protein nodes instead of manually setting atomic and protein features, which may be the reason for the stronger generalization of NHGNN-DTA. The features obtained by the feature generator can better characterize drugs or proteins that were not encountered during training.In the Davis dataset, our model improved metrics by an average of 4%, 6%, and 8% for the three cold start settings. On the KIBA dataset, the average improvement was 7%, 12%, and 8%. NHGNN exhibited a statistically significant improvement over the SOTA model in all metrics under three cold start scenarios in both the Davis and KIBA datasets  database (The bottom half of To further verify the effectiveness of NHGNN-DTA, we applied this model to the repurposing of antiviral drugs of the currently circulating SARS-CoV-2 Omicron variants. Therefore, we selected the RBDs of the above five variants as targets. The crystal structures of Omicron BA.2.75 and BA.4/BA.5 were obtained from the PDB database, while the crystal structures of Omicron BA.4.6 and BF.7 have not been Publicly available, so modeled by trRosetta server (Using NHGNN-DTA, we analyzed the mechanism of AmylNitrite and targets by outputting attention weights of five target variants. As shown in in vitro assays, in vivo assays, and clinical trials.Our selected Amyl Nitrate seems to have a relatively good affinity for the recently circulating omicron variant of SARS-CoV-2, and interacts with key mutations of RBM in the RBD of the Omicron variants. However, in the article, we aim to develop an efficient computational method for drug repurposing, so the screened drugs do not represent the actual efficacy. Specific drug effects require further In this article, we propose an HGNN that can adaptively generate features. Unlike sequence-based methods, we exploit drug and protein structures by constructing hybrid graphs containing amino acid and atomic nodes. Different from graph-based methods, this model realizes the adaptive update of node features by building a feature generator to increase the information interaction between drugs and proteins at the graph level. In general, NHGNN-DTA overcomes the problems that sequence-based methods cannot obtain graph structure information and graph-based methods cannot construct suitable node features. It also combines the advantages of the above two methods. This is the first time the integration of two DTA prediction methods has been achieved according to the previous literature review. Extensive experiments show that NHGNN-DTA achieves significant improvements on two benchmark datasets compared to recent SOTA methods. In the scenario of cold start for drugs and proteins, NHGNN-DTA demonstrated its stronger generalization and robustness. Through attention visualization and case study, we demonstrate the substructure information capture ability of the model. The case study implies that Amyl Nitrite may be a potentially effective drug candidate against SARS-CoV-2 Omicron variants. Future work will focus on generating potential DP pairs to accelerate the process of drug discovery.btad355_Supplementary_DataClick here for additional data file.


Non-linear materials are cornerstones of modern optics and electronics. Strong dependence on the intrinsic properties of particular materials, however, inhibits the at-will extension of demanding non-linear effects, especially those second-order ones, to widely adopted centrosymmetric materials  and technologically important burgeoning spectral domains . Here we introduce a universal route to efficient non-linear responses enabled by exciting non-linear Thomson scattering, a fundamental process in electrodynamics that was known to occur only in relativistic electrons in metamaterial composed of linear materials. Such a mechanism modulates the trajectory of charges, either intrinsically or extrinsically provided in solids, at twice the driving frequency, allowing second-harmonic generation at terahertz frequencies on crystalline silicon with extremely large non-linear susceptibility in our proof-of-concept experiments. By offering a substantially material- and frequency-independent platform, our approach opens new possibilities in the fields of on-demand non-linear optics, terahertz sources, strong field light–solid interactions and integrated photonic circuits. Metamaterials featuring locally enhanced non-linear Thomson scattering offer a practical and universal method for efficiently stimulating non-linear responses in linear solid materials. Optical non-linearities are among the most fundamental manifestations of strong light–matter interactions, lying at the core of photonics and optical information technology ,2. In paIn the optical regime, there is a long tradition of using plasmonic nanostructures to excite and control non-linear effects by locally enhancing the electric field of the optical radiation . While tω applied to a material with a bandgap, including semiconductors and insulators, the electrons in the conduction band gain a large ponderomotive energy, which despite the very low photon energy  may dramatically exceed the bandgap, thus stimulating impact ionization. As a result, bound electrons are scattered out of the valence band into the conduction band and produce an avalanche of new electron-hole pairs, featuring velocity, v, oscillating at frequency ωmalononitrile (OH1) crystal for generation and a ZnTe crystal for detection . The relThe SHG experiments were conducted at ambient conditions using the TELBE THz facility. The schematic of the set-up is plotted in nwad136_Supplemental_FileClick here for additional data file.


Objective: Granulomatosis with polyangiitis (GPA) is an autoimmune disorder characterized by necrotizing granulomatous inflammation of small and medium-sized vessels. This systematic review aimed to highlight the most common ophthalmic manifestations and to uncover their associations with antineutrophil cytoplasmic antibody (ANCA) positivity and the presence of granulomas. Methods: A literature search of PubMed, Web of Science, and Scopus electronic databases was performed from journal inception to March 21, 2021, for case reports and a series of ophthalmic GPAs. Cytoplasmic-ANCA (c-ANCA), perinuclear-ANCA (p-ANCA), and granulomas were analyzed against many ophthalmic signs and symptoms. 306 patients with GPA were retrospectively studied.Results: Granulomas were present in 47.7% of our sample, c-ANCA in 59.2%, and p-ANCA in 10.8%. Scleritis was significantly associated with higher odds for c-ANCA positivity. Eye discharge, episcleritis, proptosis, and central nervous system (CNS) involvement were each significantly associated with lower odds for c-ANCA positivity. Orbital mass was significantly associated with lower odds for p-ANCA positivity. CNS involvement was significantly associated with higher odds for p-ANCA positivity  and orbital mass was significantly associated with lower odds for p-ANCA positivity.Conclusions: We recommend that clinicians should consider ocular or orbital GPA in patients presenting with non-specific eye complaints, such as vision impairment, orbital mass, or proptosis, and obtain further assessments to determine the possible presence of granuloma, c-ANCA, or p-ANCA.Abbreviations: GPA = Granulomatosis with Polyangiitis, ANCA = antineutrophil cytoplasmic antibody, c-ANCA = cytoplasmic-ANCA, p-ANCA = perinuclear-ANCA, CNS = central nervous system, AAVs = ANCA-associated vasculitides, SD = standard deviation, GU = genitourinary, ENT = ear nose and throat, OR = odds ratio, CI = confidence interval AAVs are rare with incidence reported between 10 to 20 cases per million [2]. GPA is the most common of the AAVs, with an incidence of 5 to 10 cases per million, and a peak incidence at approximately 55 years of age [2]. GPA is characterized by necrotizing granulomatous inflammation and usually presents clinically with upper and lower respiratory tract granulomas, small-to-medium vessel vasculitis, and necrotizing crescentic glomerulonephritis [3]. However, ocular and orbital manifestations may arise in 16-78% of GPA patients, with up to 27% of GPA undiagnosed cases noting ophthalmic manifestations as the primary symptom during disease onset [4].GPA is a small-to-medium vessel vasculitis and represents a subgroup within the disorders known as ANCA-associated vasculitides (AAVs) [5]. Although GPA is most commonly associated with c-ANCA, some studies report up to a third of patients positive for p-ANCA, and a small number of patients are negative for both [5]. There are different approaches to understanding the development of ANCAs; some propose associations with environmental causes or infections, supporting a cause of molecular mimicry, while others present associations with human leukocyte antigen DQ polymorphisms [5].ANCAs are commonly found in patients with any of the three AAVs: GPA, eosinophilic granulomatosis with polyangiitis, and microscopic polyangiitis. These conditions commonly damage small and medium-sized arteries, arterioles, and venules via autoimmune dysfunction against leukocyte proteinase-3 for c-ANCA and myeloperoxidase for p-ANCA [6]. Recurrent or persistent ANCA titers are associated with a greater risk of worsened AAV disease progression and renal failure [7]. Patients with p-ANCA positivity generally have an overall worse prognosis and a higher risk of renal disease progression, such as ANCA-associated nephritis and end-stage renal disease .ANCA positivity can be of prognostic value for measuring GPA disease severity [10-12]. We systematically reviewed and analyzed 306 patients from published case reports and case series to evaluate ophthalmic GPA. We compared the presence of granuloma, c-ANCA, and p-ANCA for ocular, orbital, and systemic signs and symptoms. The published research regarding ophthalmic GPA consists of retrospective reviews and cohort studies with sample sizes ranging from 17 to 113 patients . We suggested that the previous diagnosis of GPA was associated with lower odds for granuloma, due to successful treatment suppression of granulomatous inflammation. Also, ocular masses were associated with more severe disease course, and systemic diseases, and were found in patients with concurrent necrotizing scleritis, peripheral ulcerative keratitis, and orbital wall destruction among those with ophthalmic GPA [12]. We speculated that the significant association of orbital masses and necrotizing scleritis with increased odds for granuloma in our analysis may be due to their presence in the clinical course for severe GPA, and the increased inflammatory destruction led to granuloma formation. In our multivariate analysis for granuloma (Table 4), scleritis was significantly associated with higher odds of c-ANCA positivity. We were not aware of any research on the association of scleritis with c-ANCA positivity. The sclera is an avascular structure that receives nutrients mainly from the aqueous humor [14]. The aqueous humor contains neutrophilic enzymes and inflammatory cytokines that reach the sclera . We suggested that c-ANCA-positive patients may be more likely to present with scleritis due to activated neutrophil enzymes reaching the sclera via the aqueous humor. In our multivariate analysis of c-ANCA positivity , we found that orbital mass had lower odds of p-ANCA positivity. Another study found that 86% of patients with orbital mass were positive for c-ANCA, but only 25% of those with p-ANCA [20]. We suggested that p-ANCA-positive patients may have a less severe form of ocular and orbital GPA, and therefore have lower odds of orbital mass. In our multivariate analysis (21]. We suggested that the higher odds of CNS involvement in p-ANCA positive patients found in our study supports a growing body of evidence associating p-ANCA positivity with neuronal, and therefore CNS damage. Our data also demonstrated that CNS involvement was significantly associated with higher odds for p-ANCA. P-ANCA positivity is associated with a wide variety of AAVs – Microscopic polyangiitis, eosinophilic granulomatosis with polyangiitis, and polyarteritis nodosa. These AAVs are associated with a variety of constitutional CNS symptoms such as mononeuritis multiplex caused by damage to the vasa vasorum of the neuronal axon [One strength of this study was that this was the largest systematic review of c-ANCA positivity, p-ANCA positivity, and granulomatous formation, combining over 300 patients with ocular and/or orbital manifestations of GPA. This study had some limitations, including the exclusion of non-English language studies and observational studies. Variables such as follow-up time, time of symptom onset to diagnosis, and treatment/outcome were not included. Our reporting of “no” for variables included either not present or not mentioned in the included articles. Future studies could explore potential differences for race/ethnicity as many of our retrieved cases did not report such information. In conclusion, ocular or orbital GPA most commonly presents with vision impairment, orbital mass, or proptosis. About half of the patients have positive c-ANCA and have granulomas on biopsy, while positive p-ANCA only occurred in approximately 10% of patients. We recommend that clinicians should consider ocular or orbital GPA in patients presenting with non-specific eye complaints such as vision impairment, orbital mass, or proptosis, and obtain further assessments to determine the possible presence of granuloma, c-ANCA, or p-ANCA. Conflict of Interest statementThe authors declare no conflict of interest.AcknowledgmentsWe would like to acknowledge Joshua Nowitz from the American University of the Caribbean School of Medicine for his contribution to the preliminary statistical analysis of the data for this manuscript.Sources of FundingNone were provided.DisclosuresNone.


Micro- and nanopatterns perform unique functions and have attracted attention in various industrial fields, such as electronic devices, microfluidics, biotechnology, optics, sensors, and smart and anti-adhesion surfaces. To put fine-patterned products to practical use, low-cost patterning technology is necessary. Nanoimprint lithography (NIL) is a promising technique for high-throughput nanopattern fabrication. In particular, thermal nanoimprint lithography (T-NIL) has the advantage of employing flexible materials and eliminating chemicals and solvents. Moreover, T-NIL is particularly suitable for compostable and recyclable materials, especially when applying biobased materials for use in optics and electronics. These attributes make T-NIL an eco-friendly process. However, the processing time of normal T-NIL is longer than that of ultraviolet (UV) NIL using a UV-curable resin because the T-NIL process requires heating and cooling time. Therefore, many studies focus on improving the throughput of T-NIL. Specifically, a T-NIL process based on a roll-to-roll web system shows promise for next-generation nanopatterning techniques because it enables large-area applications with the capability to process webs several meters in width. In this review, the T-NIL process, roll mold fabrication techniques, and various materials are introduced. Moreover, metal pattern transfer techniques using a combination of nanotransfer printing, T-NIL, and a reverse offset are introduced. Micro- and nanopatterned surfaces are highly useful as functional surfaces, as well as in the fabrication of electronic devices. For example, to realize artificial intelligence systems, improving the performance of the central and graphic processing units is essential. Based on Moore’s law for semiconductor devices, the limit to the miniaturization of the transistor size will soon be reached. To achieve advancements beyond Moore’s law, monolithic three-dimensional (3D) integrated circuits are widely investigated . DevelopMicro-total analysis systems (µ-TAS)  and biotOptical applications widely utilize micro- and nanopatterns. For example, micro-lens arrays are essential for improving the performance of image sensors . Anti-reMoreover, many sensor applications utilize micro- and nanopatterns, and fine metal patterning is indispensable for these applications. Touch sensor panels, which can transmit light emitted from a display device, consist of a micro-scale metal wire on a transparent substrate . To obtaPrintable electronics have important applications in electricity generation, particularly in organic solar cells  and therSmart surfaces and anti-adhesion surfaces in facilities help reduce the cost of maintenance such as cleaning, defrosting, and disinfection. For example, bacterial anti-adhesion surfaces are needed to prevent biofilm formation . A surfaAs noted above, micro- and nanopatterns are necessary for next-generation devices in various fields. A high throughput patterning technique with design flexibility, including 3D patterns, is highly desired. Fine patterns can be obtained by cutting , laser bTo meet the demand for next-generation fine patterning techniques, NIL was developed in 1995 by Chou et al. . Here, aThe original NIL process is commonly referred to as thermal nanoimprint (T-NIL) because it uses a thermoplastic resin. In principle, the throughput of T-NIL is lower than that of UV-NIL because T-NIL requires time for heating to reach a low viscosity and cooling. However, T-NIL offers the advantages of lower material cost compared with UV-NIL, and an eco-friendly process that does not require VOC chemicals. Therefore, in this review, recent progress regarding T-NIL in the development of the process , mold fagT), and the thermoplastic layer was deformed according to the mold pattern. Then, the mold and substrate were cooled to less than gT. Finally, the mold was released from the substrate. Typically, there is a residual layer under the transferred pattern, which must be removed by a plasma etching process to perform the lift-off and an etching process for the substrate. For example, 25 nm diameter and 120 nm period metal dots were fabricated by planar T-NIL and a lift-off process.gT. Heating the entire film above or close to gT would cause deformation or stretching. The double-sided patterned film is particularly valuable for optical applications. This process can also be applied to thermosetting plastics.In the double-sided process, the substrate is replaced by another mold, as shown in To enhance the throughput of T-NIL, a roll-to-roll web system  can be eObtaining planar NIL molds using a conventional lithography process with EBL or photolithography is easy. However, these molds are typically made of silicon wafers or quartz, which are hard materials. A roller T-NIL process using a roll mold  is suitaTo further improve the throughput and transferred pattern quality, belt-type T-NIL processes have been proposed ,30. ThesgT, the pattern collapses immediately. Thus, cooling rollers or additional cooling equipment are essential for high throughput. In Ogino, et al., dots with a diameter of 200 nm and a height of 240 nm were formed on a 15-meter-long polystyrene sheet within 27 min [n 27 min .The above T-NIL processes focus on the fabrication of plastic or resin patterns. However, some sensors, such as SERS sensors, require metal nanopatterns on a plastic substrate. Nanotransfer printing (nTP)  is a proTwo-tone nTP has been proposed as an alternative nTP method  Figure f–h. HerenTP typically uses a metal layer deposited by physical vapor deposition, which requires a long time to prepare a vacuum condition. As a result, obtaining the metal layer on a large area is difficult. By contrast, reverse offset (RO) technology  providesIn principle, the size of the transferred patterns obtained by T-NIL processes depends on the features of the mold. Therefore, the fabrication technique for NIL molds is crucial. Planar NIL molds are typically obtained by photolithography or EBL. However, increasing the throughput of planar T-NIL processes is challenging. Consequently, roll mold fabrication techniques are essential for next-generation T-NIL processes. In particular, seamless roll molds increase the product yield by eliminating wasted space on the transferred substrate caused by seams. In this section, various fabrication techniques for the seamless roll mold are described.Machining processes have a long history and are the most common technique for micro- and nanopatterning. Single-point diamond turning is a promising method for obtaining a desired pattern on a roll. The diameter of the diamond tip determines the obtained pattern size. Sun et al. applied a focused ion beam to sharpen diamond tips . The mouDirect laser beam writing (DLW) techniques have attracted much attention for obtaining a fine roll mold in air . The difThe minimum pattern width of normal DLW is determined by the diffraction limit. Electron beam (EB) lithography is a promising technique that can potentially obtain a sub-10 nm pattern. Taniguchi et al. presented a rotating stage to write a designed pattern onto a roll substrate . SimilarSelf-assembled patterning using anodic aluminum oxide (AAO)  and blocgT. This is because higher temperatures cause the web film to stretch after the nip rollers, resulting in the breakage of the thermoplastic film. Therefore, the transferred pattern is obtained by plastic deformation. Notably, self-relaxation of the transferred pattern is observed when the temperature is significantly lower than gT [gT, the viscoelastic property of the thermoplastic film is dominant, causing failure of the mold filling [Roll-to-roll T-NIL with roll molds requires the surface temperature of the mold to be less than  than Tg . Moreove filling . This phThe heating and cooling times limit the throughput in the T-NIL process. In particular, the heating method is critical to improving the throughput. Hence, a fast T-NIL employing an integrated heater , with a As described above, nickel electroplating is widely used to obtain replica molds for roll-to-roll T-NIL. However, electroplating requires a significant amount of time, resulting in a high cost. In addition, treatment of the waste solutions of nickel plating is troublesome. Consequently, replica molds with high-temperature durability and release properties suitable for the T-NIL process are required. PDMS is typically used for replica molds in T-NIL. However, it is a thermosetting resin and thus requires a long time to cure. Hence, organic-inorganic hybrid resins  using a In roll-to-roll T-NIL processing, film quality plays a crucial role. Common thermoplastic polymers are used, and various parameters such as softening temperature, melting temperature, glass transition temperature, the viscosity of the melted polymer, stringing coefficient, surface energy, bendability, and film thickness affect the replication quality. gT if the processing time is kept sufficiently short. The thermoplastic properties of the film determine the maximum temperature that can be used; however, increasing the film thickness makes it possible to achieve temperatures close to In some cases, the surface energy of the thermoplastic material may be insufficient because of the “flow in capillarity” forces caused by the film itself. From a successful process perspective, the film must be flexible enough to be guided from one roll to another. High thermal expansion of the film may potentially impact the desired size of the pattern; however, this can be accounted for in the design of the mold.One interesting category of future materials is recyclable or even compostable films. For example, novel films made of nanocellulose have unique features such as high transparency, high-temperature tolerance, and, surprisingly, nanoimprintability. These wood fiber-based materials consist of selected nanometer-scale fibers whose film properties can be tuned to improve foldability or printability. In the case of films made of cellulose nanofiber (CNF), the printed structure remains intact even when the moisture content of the film increases, unlike conventional paper. CNF films can be thermally nanoimprinted  or impriAs previously mentioned, web tension and aligning methods are essential in high-speed roll-to-roll T-NIL. In the printing industry, many feedback methods are used. These methods consider web tension and optimize it in situ. Tension is critical for avoiding web stretching or misalignment and ensuring accurate and reliable patterning.The most significant advantage of T-NIL is the flexibility of the transferred material and its environmental properties in practical use: temperature durability, transparency, chemical durability, and mechanical strength . In thisgT is relatively low, typically below 100 °C. Polystyrene is known for its radiation resistance, making it suitable for applications such as Petri dishes and food containers. On one hand, polyethylene terephthalate (PET) is transparent and offers excellent gas barrier properties but has limited resistance to acids, alkalis, and chemicals. On the other hand, polyethylene naphthalate shares similar properties with PET but exhibits higher thermal durability and hydrolysis resistance. Polyethylene (PE) is known for its high chemical durability and low density compared with other plastics. PE also has a low brittle temperature (around −40 °C). Polypropylene (PP) also exhibits high chemical durability and has a higher hardness than PE. However, PP is prone to turning white when exposed to sunlight, indicating poor weather resistance. Cyclic olefin copolymer is valued for its transparency and high refractive index (>1.5), making it suitable for optical lenses. Polycarbonate is known for its excellent impact resistance and self-extinguishing properties. Polyether ether ketone is well known for its exceptional thermal durability (>250 °C) and mechanical properties. It is also suitable for use in water and steam conditions. Polyimide (PI) and polyetherimide (PEI) exhibit high thermal resistance (>300 °C for PI and >170 °C for PEI). However, PEI is more cost-effective compared with PI. Both PI and PEI are transparent materials, although they tend to have a yellowish color. Fluorinated ethylene propylene (FEP) and ethylene tetrafluoroethylene (ETFE) are thermoplastic resins used in various applications. Compared with polytetrafluoroethylene (PTFE), which has excellent chemical durability and low surface tension, the viscosity of melted FEP and ETFE is low. Therefore, FEP and ETFE are available for molding, while PTFE requires a cutting process. Cellulose acetate (CA) is a transparent and biodegradable material. In addition, the UV light resistance of CA is high. Polysulfone and polyethersulfone are commonly employed in medical applications such as dialysis membranes because of their excellent mechanical properties and thermal durability, allowing for autoclave sterilization. Polyvinyl chloride is known for its good weather resistance but has relatively low impact resistance. Furthermore, eco-friendly transparent materials such as CA and CNF are essential for sustainable development.Poly(methyl) methacrylate (PMMA) is commonly used in optical applications because of its high transparency. However, its Various T-NIL processes are introduced, and seamless roll mold fabrication techniques are reviewed. Planar T-NIL is suitable for silicon- or quartz-based processes because of the rigid nature of these materials. By contrast, roller or roll-to-roll type T-NIL is very compatible with fabricating nanopatterns of plastic substrates. In particular, using a T-die extrusion system facilitates continuous nanopatterned production. Because the belt-type roll-to-roll system contains many rolls, it enables easier film temperature control. However, more rolls increase the equipment cost. To date, various fabrication techniques for seamless roll molds have been investigated (as shown in this review). Seamless roll molds are readily available. Flexible replica molds are a suitable choice when the presence of a seam is not an issue in the final products, such as when using a µ-TAS sheet as an individual package. These molds can be easily attached to a roll substrate, offering the advantage of lower mold costs. Consequently, roll-to-roll T-NIL with roll molds is suitable for mass production because of its low equipment cost.Although the N-curve of the transferred pattern is of concern, precision temperature control of the roll mold can address this issue. To achieve a stable temperature on the curved surface of a roll mold, it is crucial to develop a reliable temperature measurement method. As noted above, flexible nickel or resin replica mold is typically used; however, forming an emissivity coating layer on the replica mold for radiation thermometers is challenging. Therefore, calibration techniques for the measured temperature on a roll mold are required to ensure accurate temperature monitoring.In the future, a higher throughput roll-to-roll T-NIL process will be needed. Not only local heating methods but also a local active cooling method should be developed. In particular, for non-uniform patterns in large mold areas , a new local temperature control method is required to regulate temperature distribution effectively.In the roll-to-roll T-NIL process, the significant difference in the thermal conductivity between metal roll molds and thermoplastic films helps to generate a temperature gradient in the film, thus preventing deformation. However, polymer replica molds have similar thermal properties to thermoplastic films. Consequently, a new replica mold made of a polymer with a high thermal conductivity should be investigated to reduce mold cost and increase the throughput in roll-to-roll T-NIL. A high-temperature release agent is required for applications involving increased aspect ratios of transferred nanopatterns.Moreover, to facilitate future sustainable development, such as smart-built environments and wearable systems , the T-N


Air pollution is a leading cause of human diseases. Accurate air quality predictions are critical to human health. However, it is difficult to extract spatiotemporal features among complex spatiotemporal dependencies effectively. Most existing methods focus on constructing multiple spatial dependencies and ignore the systematic analysis of spatial dependencies. We found that besides spatial proximity stations, functional similarity stations, and temporal pattern similarity stations, the shared spatial dependencies also exist in the complete spatial dependencies. In this paper, we propose a novel deep learning model, the spatiotemporal adaptive attention graph convolution model, for city-level air quality prediction, in which the prediction of future short-term series of PM2.5 readings is preferred. Specifically, we encode multiple spatiotemporal dependencies and construct complete spatiotemporal interactions between stations using station-level attention. Among them, we design a Bi-level sharing strategy to extract shared inter-station relationship features between certain stations efficiently. Then we extract multiple spatiotemporal features with multiple decoders, which it is extracted from the complete spatial dependencies between stations. Finally, we fuse multiple spatiotemporal features with a gating mechanism for multi-step predictions. Our model achieves state-of-the-art experimental results in several real-world datasets. With an estimated nearly 12% of global disease deaths in 2019 being directly or indirectly caused by air pollution1, the negative impact of air pollution on public health and the environment has made it increasingly become the focus of science2. Policymakers monitor pollution concentrations in real-time by establishing air monitoring stations to keep abreast of regional pollution. In addition, air quality prediction is vital to reducing human disease. Accurate air quality prediction can assist policymakers in scientifically regulating corporate pollutant emissions, thereby reducing the concentration of pollutants in the air.Air pollution is a harmful substance mixed in the air in various gaseous  to model the temporal trends of linear, or nonlinear relationships between sequences. The prediction accuracy of these methods is limited as they do not consider the interactions between stations. Some efforts10 have been made to leverage potential correlations between different stations, which reveal the importance of spatial correlation for air quality prediction. Due to the high construction costs of air quality monitoring stations, the number of monitoring stations is relatively small and scattered in various locations in the city. Some recent studies13 attempt to use graph convolutional networks (GCN) to model the non-Euclidean between stations. In the methods based on GCN, all monitoring stations of the whole city are regarded as nodes in the graph, and correlations correspond to the graph's edges. Multiple specific types of inter-station relationships are used to model complex spatiotemporal dependencies in air quality prediction. Each specific inter-station relationship represents a spatial dependence, such as spatial proximity, functional similarity, or temporal pattern similarity.Differently from long-term predictions, which focus on capturing long-term dependencies in the temporal domain, we prefer to focus on accurate short-term air quality predictions. However, it is difficult to extract spatiotemporal features effectively, due to the complex spatiotemporal dependencies among air quality monitoring stations. Many researchers have worked on accurate air quality prediction and have made significant progress. Earlier studies focus on the temporal evolution of individual stationsAlthough modeling multiple specific types of inter-station relationships contributes to the prediction accuracy, there are combinations of shared inter-station in the complete spatial dependencies. For stations, Fig. spatiotemporal adaptive attention graph convolution network model (STAA-GCN), for city-level air quality prediction. STAA-GCN captures multiple spatiotemporal features efficiently from the complex spatiotemporal dependencies and uses the encoder-decoder architecture to generate multi-step predictions. First, seven parallel encoder sets were used to encode all inter-station relationships for the complete spatiotemporal dependency construction. We design spatiotemporal relationship extraction units for specific and shared relationships, respectively. Considering complete station interaction can be correlated with either seven inter-station relationships or their combinations. We use a station-level attention mechanism to learn the interaction process. In addition, we consider the dynamic, diffusible nature of air pollution, which can lead to unequal dynamic dependencies between stations. For this reason, we design multi-angle soft attention to adaptively capture the dynamic influence relationships between stations from multiple perspectives. For multi-step predictions, Multiple inter-station relationships are decoded from the complete spatiotemporal dependencies between stations and fused using a gating mechanism at each time step. The main contributions of this paper are as follows:We propose a novel deep learning model. We constructed complete spatiotemporal dependency using seven parallel encoder sets and station-level attention mechanisms, from which we effectively extracted multiple spatiotemporal features.We design a multi-angle soft attention mechanism, which captures unequal dependencies between stations from multiple perspectives.STAA-GCN automatically learned shared inter-station relationships between certain stations via a Bi-level sharing strategy.We test our model on three publicly available datasets, and the experimental results show the superiority of our model.To address the above challenges, we propose a novel deep learning model, the 15, traditional methods4, and deep learning methods7. The classical physical models are based on the principle of atmospheric dispersion and use many relevant factor data to evolve the pollutant dispersion process. It is well known that there are complex correlations between relevant factors, which make it difficult to establish their numerical functions and lead to suboptimal prediction results. The traditional models, often with multi-source heterogeneous data, are employed to model temporal trends of linear between sequences. However, these methods are built from the temporal evolution of individual stations, in which spatial dependencies between stations are ignored.Air quality forecasting is receiving increasing attention due to the deteriorating air environment. Existing works can be broadly classified into three categories, namely classical physical methods17. Extensive use of feedforward neural networks for fusing influential features from other relevant stations. In addition18, consider the correlation of inequality between stations and calculate the weights of the hidden states of stations within a certain geospatial distance using an advanced attention mechanism. Some researchers13 have treated the spatial dependence between stations as non-Euclidean and used them to model the correlation between air quality stations. Although existing models such as ATGCN11 describe multiple spatial dependencies by constructing multi-graph, it is suboptimal due to the missing analysis of shared space dependencies.Several recent studies have made significant progress that used deep learning methods to model spatiotemporal dependence20. The core of the multi-graph approach is to simultaneously learn multiple spatial dependencies by constructing multiple graphs. Existing multi-graph methods can be roughly divided into two categories: generate more graph structures to capture more detailed spatial dependencies and integrate multiple relational features more effectively.Multi-graph based methods have been widely used in spatiotemporal prediction for constructing multiple spatial dependencies21 believes that besides spatially adjacent, spatial dependencies also include functionality similar and transportation connected, multiple corresponding graph structures are constructed to describe a variety of spatial relationships. ATGCN11 coded the relationships among air quality stations as spatial adjacency, functional similarity, and temporal pattern similarity into multiple graphs, a parallel codec architecture is used for multi-step prediction.In the first category, there are multiple correlations between stations, and pair-wise correlations are encoded as multiple graphs to capture more detailed spatial relationships. ST-MGCN22 explores Multi-layer stacked information fusion method in graph convolution, where dynamic graph features are used to automatically fuse information from each layer of static graph. DMGA-GNN13 first uses Spatial Attention to capture the contextual correlation of nodes in different graphs and then uses Graph Attention to obtain autocorrelation of nodes in different graphs. Finally, the gating mechanism is used to consider further the above two effects of node correlation in different graphs.In the second category, STAG-GCNTable18. Similarly, we use air quality, weather, points of interest (POI), and temporal information data for the city-wide station air quality prediction. In this section, we first describe the multi-source heterogeneous data and then formally represent the prediction.We consider the multi-source heterogeneous data used in previous research3, NO2, SO2) and multiple weather data  at hourly intervals. We use Suppose there are Besides, each monitoring station Each timestamp contains rich temporal information, which can respond to people's travel situation time information and thus assist in air quality prediction. We collected three kinds of time information: hour/day, day/week, and month. Given historical time data with a time window of length This section describes in detail the modules of each part of our proposed STAA-GCN, and the overview framework of STAA-GCN is shown in Fig. 11.This section describes the graph generation of three specific types of inter-station relationships. We no longer generate new relationship graphs for the shared inter-station relationships, which rely on the three specific graphs to extract spatial dependencies. Formally, we generate graphs In general, the closer two things are to each other, the stronger the correlation. We generate a spatial proximity graph using the physical spatial distance between two stations to achieve edge connectivity and assign weights.Intuitively, when the POI around the two stations is relatively similar, there is a strong correlation between their air quality. When two stations are surrounded by industrial parks, they must have high air quality readings. And they may be strongly correlated on certain pollutants, such as industrial parks with similar production. We consider the similarity between stations with similar functions, then generate a functional similarity graph.In addition to the two inter-station relationships mentioned above, there are potential temporal correlations between stations. We use the monthly average as a criterion for correlation:We focus on complete spatiotemporal dependencies interaction modeling. Interactions between stations can be associated with one or more spatiotemporal dependencies. Using seven sets of encoders, we first capture the seven spatial relationships' spatiotemporal dependencies. Then, we employ a station-level attention mechanism to learn the interaction process between monitoring stations. In addition, air pollution's easy diffusion and dynamics cannot be ignored, and we design multi-angle soft attention to capture the pairwise dependencies between stations.First, air pollution is an aerosol mixture that exhibits a dynamic and easily diffusible nature in space, and it is inappropriate to ignore the unequal dynamic dependencies between stations. Second, the dependencies between stations are complex and diverse, and it is unconvincing to consider them simply from the intrinsic station data. To this end, we propose to capture the dynamic influence relationships between stations from multi-angle adaptively. Specifically, we first project station inherent data into multiple semantic spaces for enriching their semantic representations. Second, the influence of stations may vary nonlinearly. We nonlinearly map different semantic space information and obtain the final influence weights by computing them as the mean of the influence weights of all semantic spaces between stations.For all stations 23 on graph convolution, combined with the weight matrix E, we use graph convolution in the vertex domain to aggregate K-hop neighbor information.We capture specific types of inter-station relationships with their graphs, which Given the observation Intuitively, there are shared inter-station relationships in air quality prediction. We design a Bi-level sharing strategy to extract their relational features effectively. In this paper, shared inter-station relationships are divided into two categories, i.e., the combination of two arbitrary specific types of shared inter-station relationships and the combination of three specific types of shared inter-station relationships.We first train a set of shared graph convolution kernels to extract the features of shared inter-station among different spaces. For the combination of three, which Not all stations have combinatorial shared dependencies with each other, and we use a set of shared diagonal matrices to remove irrelevant station feature information. The intuition behind the shared diagonal matrix is that using the shared graph convolution kernel, the feature information with shared dependencies dominates. We remove the sites with common weaker representation information by a set of shared diagonal matrices.Specifically, diagonal matrix sharing is a set of shared sparse diagonal weight matrices. We first apply Finally, we obtain the shared relation inter-station dependency feature representation as follows:We adopt the Bi-level sharing strategy similar to that used for the three specific types of shared inter-station relationships for extracting the relationship between two arbitrary combinatorial stations. The difference is that two arbitrary are about sharing two arbitrary graphs, not three graphs, such as the spatial proximity graph and the functional similarity graph.We use the Gated Recurrent Units (GRU) on the temporal dependencies to capture the correlations on the temporal domain of each spatial dependence. Given any station spatial dependencies after learning at the current time step Now we have seven spatiotemporal dependencies hidden states 10. Therefore, before capturing the spatiotemporal dependence, we use temporal-level attention to capture the correlation between The correlation between the target series and its associated historical series is dynamicFor the multi-step predictions, we aggregate multiple spatiotemporal dependencies With the seven inter-station relationships modeling, which will lead to large number of parameters in the graph. To ensure the trainability of the model while reducing the risk of overfitting, we introduce basis decomposition:To get closer to the set threshold more efficiently, we apply For shared inter-station relationships, we obtain the shared feature with Essentially, air quality prediction is a matter of regression. Therefore we use the root mean square error (MSE) as a loss function between the predicted and ground truth.The total loss of the proposed architecture as following:https://quotsoft.net/air/. Tianjin air quality dataset is available in Urban Computing, http://urban-computing.com/data/Data-1.zip. And London air quality dataset is available in the Artificial Intelligence Competition Learning Platform, https://www.biendata.net/competition/kdd_2018/. The datasets widely used in the air quality prediction literature. The Beijing air quality dataset is from 01/2016 to 01/2018, including PM2.5, PM10, SO2, NO2, and O3; The Tianjin air quality dataset is from 01/2014 to 04/2015, including PM2.5, PM10, SO2, NO2, O3, and CO; The London air quality dataset is from 01/2017 to 03/2018, including PM2.5, PM10, and NO2.We conducted experiments on three real-world air quality datasets. Beijing air quality dataset is available in Chinses Air Quality Historical Data, 11. We selected five attributes of grid weather datasets, including temperature, humidity, wind speed, wind-u, and wind-v. The gridded weather dataset for Beijing is available in the Global Data Assimilation System, https://www.ncdc.noaa.gov. The gridded weather dataset for Tianjin is available in Urban Computing, http://urban-computing.com/data/Data-1.zip. The gridded weather dataset for London is available in the Artificial Intelligence Competition Learning Platform, https://www.biendata.net/competition/kdd_2018/. Following the ATGCN11, POI data for all three datasets are available in Amap, https://lbs.amap.com/api/webservice/download. POI data including 12 categories from Beijing, 20 from Tianjin and 8 from London. In addition, Following previous work6, we also used temporal information features to support air quality prediction, including hours/day, days/week, and months. The statistics of the datasets are shown in Table Historical meteorology data and weather forecasts are used to improve the accuracy of predictionsWe use linear interpolation extensively for data pre-processing. To ensure the authenticity and reliability of the experiments, we used a non-overlapping 8:1:1 ratio for the training, validation, and test sets.STAA-GCN and all the deep learning model experiments were run on Python 3.7.4, PyTorch 1.9.1 environment. For STAA-GCN, the cell size of GRU and the hidden states size in the graph convolution layer to 64. We aggregate 2-hop neighbor information with K = 2.https://github.com/349117955/STAAGCN.The learning parameters used initialize with uniform distribution. Our model is trained by objective function with the Adam optimizer, and the learning rate is set to 0.001. We set the batch size to 256 for Beijing, Tianjin and London datasets. We prevent overfitting the training data with the early stop mechanism in the training phase, in which patience is 15. The time window T we set to 12 and the prediction step τ to 6. We use PM2.5 as the prediction target because it is always the most important of all pollutants. Our code is publicly available at We use Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), widely used for air quality prediction tasks, to measure model performance. The model's validity is verified by comparing it with the following seven methods.HA The average of the historical time steps was used as the predicted value of PM2.5.SVR Learned linear relationships between historical time series to perform multi-step forecasting.Seq2seq Encoder-Decoder based methods have been widely used in multi-step forecasting.MGED-Net6 Air quality prediction model for multi-feature relationship learning using multi-group feature fusion approach.Graph WaveNet24 In spatiotemporal prediction, GCN models spatial dependencies with spatiotemporal data.ATGCN11 Multi-graph based air quality prediction methods, using multiple sets of Encoder-Decoder to model the Inter-station relationships.Table To verify that multiple inter-station relationships exist, we conduct experiments on multiple sets of variants of STAA-GCN on three datasets. We remove different types of inter-station relationships, including spatial proximity (S), functional similarity (F), temporal pattern similarity (T), spatial adjacency-functional similarity (S-F), spatial adjacency-temporal pattern similarity (S-T), functional similarity-temporal pattern similarity (F-T), and spatial adjacency-functional similarity-temporal pattern similarity (S-F-T). We observed that the removal of each inter-station relationship causes a degradation of the model performance, which suggests the validity of these inter-station relationships.To study the effectiveness of each component in our model, we conduct experiments on four variants of STAA-GCN on three datasets. (1) STAA-GCN w/o STA-attn, which removes the station-level attention mechanism for building complete spatiotemporal interaction; (2) STAA-GCN w/o Mul-attn, which removes the multi-angle soft attention for capturing unequal weights between stations; (3) STAA-GCN w/o SA-matrix, which removes shared diagonal matrix for removing stations features without shared dependencies; (4) STAA-GCN w/o similar loss, which removes similarity constraints for extraction sharing representation. We tested the performance of different variants in agreement with the parameters of the STAA-GCN.Figure We conducted extensive experiments on three datasets to further study the sensitivity of parameters in our model.First, we vary the number of multi-angle from 1 to 8 on three datasets. The results are shown in Fig. Then, we vary the number of the graph convolution step K from 1 to 4 on three datasets. The results are shown in Fig. After that, we vary the dimension of hidden states as {16, 32, 64, 128} on three datasets. The results are shown in Fig. Finally, we vary the threshold for removing irrelevant station feature information. We experiment by setting the thresholds to α for triple-shares, which α from 0.15 to 0.95. We test the validity of the parameters α, the results are shown in Fig. This paper proposes a new deep learning model, the spatiotemporal adaptive attention graph convolution model STAA-GCN, for city-level air quality prediction. The core of STAA-GCN is its efficient extract multiple spatiotemporal features, which are extracted from the complete inter-station interactions between stations. Furthermore, this model also considers the inherent inequality relationship between stations. Our model achieves the best experimental results in a broad range of tests on all three publicly available datasets and demonstrates the importance of the complete spatiotemporal interactions for the extraction of spatiotemporal dependencies and the effectiveness of the seven inter-station relationships.In the future, we would like to fully use more datasets from areas without air pollutant monitoring, such as fine-grained gridded weather datasets and POI datasets. The spread and dispersion of air pollution are unstable and mutable, related to the weather changes and environmental characteristics during transmission. Therefore, we will use the tensor decomposition as a basis, combined with a large amount of gridded influence factor data, to fill the areas where no monitoring data are available. That will further clarify the process of pollutant dispersion, rather than just passing information between monitoring stations, and we will achieve the improvement of the accuracy of existing models, thereby enabling a more accurate basis for people's healthy outdoor travel.


Arsenic trioxide (ATO) is a promising anticancer drug for hematological malignancy. Given the dramatic efficacy of acute promyelocytic leukemia (APL), ATO has been utilized in other types of cancers, including solid tumors. Unfortunately, the results were not comparable with the effects on APL, and the resistance mechanism has not been clarified yet. This study intends to identify relevant genes and pathways affecting ATO drug sensitivity through genome-wide CRISPR-Cas9 knockdown screening to provide a panoramic view for further study of ATO targets and improved clinical outcomes.A genome-wide CRISPR-Cas9 knockdown screening system was constructed for ATO screening. The screening results were processed with MAGeCK, and the results were subjected to pathway enrichment analysis using WebGestalt and KOBAS. We also performed protein-protein interaction (PPI) network analysis using String and Cytoscape, followed by expression profiling and survival curve analysis of critical genes. Virtual screening was used to recognize drugs that may interact with the hub gene.We applied enrichment analysis and identified vital ATO-related pathways such as metabolism, chemokines and cytokines production and signaling, and immune system responses. In addition, we identified KEAP1 as the top gene relating to ATO resistance. We found that KEAP1 expression was higher in the pan-cancer, including ALL, than in normal tissue. Patients with acute myeloid leukemia (AML) with higher KEAP1 expression had worse overall survival (OS). A virtual screen showed that etoposide and eltrombopag could bind to KEAP1 and potentially interact with ATO.ATO is a multi-target anticancer drug, and the key pathways regulating its sensitivity include oxidative stress, metabolism, chemokines and cytokines, and the immune system. KEAP1 is the most critical gene regulating ATO drug sensitivity, which is related to AML prognosis and may bind to some clinical drugs leading to an interaction with ATO. These integrated results provided new insights into the pharmacological mechanism of ATO and potentiate for further applications in cancer treatments. ATO degGenome-wide CRISPR-Cas9 screening is a revolutionary method appearing in biomedicine in the last decade. Based on the CRISPR-Cas9 system, this screening system effectively creates a genome-wide knockout cell library, and the cells are further challenged with certain drugs, toxins, or virals. The depleted genes will contribute to a specific phenotype, such as cell death or survival, and thus are selected as essential targets of chemical and biological agents. Taking advantage of its high throughput  and unbiased nature, the CRISPR-Cas9 screening has emerged as a handy technique for identifying the genes or pathways relevant to the sensitivity or resistance of existing treatments. The determinants of cell response to a list of anticancer drugs have been identified successfully through this method, including traditional chemo agents  and identified a total of 151 genes with significance . We specp < 0.05)  patients compared with normal  can be found below: J-ZC and Y-LT designed the study. J-ZC performed the experiments. L-NW and X-QL contributed to the study design and scientific discussions. J-ZC drafted the manuscript. X-QL and Y-LT revised the manuscript critically. All authors contributed to the article and approved the submitted version.


Despite being effective in schizophrenia, second-generation antipsychotics are potent histamine-H1 receptor antagonists associated with weight gain. Histaminergic agonists can potentially counteract the weight gain effects of antipsychotics. Betahistine is a centrally acting histamine-H1 agonist and, therefore, may reduce antipsychotic-induced weight gain, but it has never been examined in a meta-analysis.This meta-analysis aims to examine the efficacy of betahistine in counteracting the weight gain effects of antipsychotics.We searched PubMed, Scopus, Web of Science, and Cochrane Controlled Register of Trials (CENTRAL) for all relevant trials. We used Hedges’ g with its confidence interval as our effect size to correct for the small sample size. The primary outcomes of this study were changes in weight and body mass index (BMI). Changes in insulin resistance and lipid parameters were secondary outcomes.165 studies were included in the title/abstract screening, and 5 studies with 217 patients were finally included. Betahistine led to statistically significant changes in weight , BMI , and waist circumference . Nevertheless, betahistine did not lead to any significant changes in fasting glucose  or insulin levels .Betahistine is an effective add-on treatment for second-generation antipsychotics to counteract weight gain experienced with these medications. Further trials are recommended to examine its effect on blood lipids and side effects.None Declared


N = 176) HCPs were included in the data analysis. Spiritual well-being and organizational support were each significantly associated with reduced total MI (p’s < 0.001), shame-related MI  and trust-violation-related MI (p’s < 0.001). Notably, comparison of the standardized beta coefficients suggests that the association between trust-violation-related MI and both spiritual well-being and organizational support was more than twice as great as the associations between these variables and shame-related MI, emphasizing the importance of these supports and the trust-violation outcomes particularly. Mental health history (p = 0.02) and self-compassion (p = 0.01) were additionally related to shame-related MI only. Our findings indicate that heightened levels of spiritual well-being and organizational support were associated with reduced MI among HCPs during the COVID-19 pandemic. Rather than placing sole responsibility for mental health outcomes on HCPs individually, organizations can instead play a significant role in mitigating MI among staff by implementing evidence-informed organizational policies and interventions and by considering how supports for spiritual well-being may be implemented into existing models of care where relevant for employees.Healthcare providers (HCPs) have described the onset of shame- and trust-violation-related moral injuries (MI) throughout the COVID-19 pandemic. Previous research suggests that HCPs may turn to various coping methods and supports, such as spirituality/religiosity, substance use, friends/family or organizational support, to manage workplace stress. It remains unknown, however, if similar coping methods and supports are associated with MI among this population. We explored associations between MI  and coping methods and supports. Canadian HCPs completed an online survey about their mental health and experiences during the COVID-19 pandemic, including demographic indices  and measures of MI, organizational support, social support, spiritual well-being, self-compassion, alcohol use, cannabis use and childhood adversity. Three hierarchical multiple linear regressions were conducted to assess the associations between coping methods/supports and (i) MI, (ii) shame-related MI and (iii) trust-violation-related MI, when controlling for age, mental health history and childhood adversity. One hundred and seventy-six ( The COVID-19 pandemic has highlighted the widespread experience of moral injury (MI) in the healthcare community, in relation to both shame- and trust-violation-based morally injurious events encountered during this historic period ,3,4,5,6.Throughout the COVID-19 pandemic, healthcare providers (HCPs) faced widespread PMIEs, including witnessing and having to provide sub-optimal patient care due to a lack of resources and staffing, making difficult time-limited decisions about who would receive potentially life-saving care and having to enforce visitor restrictions for patients at end-of-life ,3,4. AltThe nature of healthcare service exposes HCPs to diverse occupational stressors  was associated with reduced anxiety and depression, and negative religious coping  was associated with increased anxiety and depression .In line with Litz et al.’s  earlier An understanding of factors associated with MI is necessary to address MI among HCPs beyond the COVID-19 pandemic. Accordingly, the aim of the present study was to identify coping methods and supports associated with MI among Canadian HCPs, with particular emphasis on the shame and trust-violation components of MI. Based on the available literature on coping and supports in relation to the adverse psychological outcomes reviewed above, social and organizational support, self-compassion, spiritual well-being and substance use were considered in our investigation. Age, mental health history and childhood adversity were additionally considered in our predictive models based on evidence of a relation between these variables and MI and/or PTSD ,46,47,48This study is part of a larger study on HCPs’ mental health and experiences during the COVID-19 pandemic. HCPs from across Canada aged 18 and older who worked in a healthcare setting during the COVID-19 pandemic were invited to participate in an anonymous online survey housed on Research Electronic Data Capture (REDCap) ,50 softwSociodemographic Questionnaire. The sociodemographic questionnaire included demographic  and occupational information . Mental health history was assessed by asking participants to indicate yes (1) or no (0) as to whether they had ever been given a mental health diagnosis. Participants had the option to indicate the diagnosis if they wished. A dichotomous variable for mental health history was used in predictive models to account for the association between lifetime mental health concerns and MI.Moral Injury Outcomes Scale (MIOS). The MIOS  is a selAdverse Childhood Experiences Scale (ACES). The ACES  is a 10-Multidimensional Scale of Perceived Social Support (MSPSS). The MSPSS  is a selSelf-Compassion Scale (SCS). The SCS  is a selSurvey of Perceived Organizational Support (SPOS). The SPOS  is a selFunctional Assessment of Chronic Illness Therapy—Spiritual Well-Being (FACIT-Sp-12). The non-illness version of the FACIT-Sp-12  is a selAlcohol Use Disorder Identification Test (AUDIT). The AUDIT  is a selCannabis Use Disorder Identification Test (CUDIT). The CUDIT  is a selN = 176). Missing data were assessed and imputed using the estimation maximization procedure. Only 0.23% (43) of values pertaining to the analysis were missing. Little’s MCAR test . Average scores were compared between the complete case and imputed samples  HCPs were included in data analysis (SD = 11.9). Most participants were female (91%) and registered nurses or registered practical nurses (56.8%). Almost half (45.5%) of the participants reported being diagnosed with a mental disorder in their lifetime. Descriptive statistics for all self-report scales are provided in One-hundred and seventy-six (R2 = 0.466) in MI , representing a 38% increase from the variance in MI explained by age, mental health history and childhood adversity alone . Perceived organizational support  and spiritual well-being  were each significantly associated with reduced MI. The positive association between cannabis use and MI approached significance (b = 0.29 p = 0.07).Hierarchical multiple linear regression was run to determine the associations between coping methods/supports and MI, when controlling for age, mental health history and childhood adversity . The fulR2 = 0.346) in MI , representing a 24.1% increase from the variance in MI explained by age, mental health history and childhood adversity alone . Mental health history was significantly associated with shame-related MI, such that having a lifetime history of a mental health diagnosis was associated with an increase in shame-related MI . Self-compassion , spiritual well-being  and perceived organizational support  were each significantly associated with reduced shame-related MI. The positive association between cannabis use and shame-related MI approached significance .Hierarchical multiple linear regression was run to determine the associations between coping methods/supports and shame-related MI, when controlling for age, mental health history and childhood adversity . The fulR2 = 0.430) in MI , representing a 40.4% increase from the variance in MI explained by age, mental health history and childhood adversity alone . Perceived organizational support  and spiritual well-being  were each significantly associated with decreased trust-violation-related MI.Hierarchical multiple linear regression was run to determine the associations between coping methods/supports and trust-violation-related MI, when controlling for age, mental health history and childhood adversity . The fulThe primary aim of this study was to investigate the relation between MI and coping methods and supports among Canadian HCPs during the COVID-19 pandemic. Higher levels of spiritual well-being and perceived organizational support were each associated with a reduced endorsement of MI. Cannabis use approached significance as being associated with an elevated endorsement of MI. The addition of coping methods and supports to a predictive model explained an additional 38% of the variance of MI, above the contributions of age, mental health history and childhood adversity to MI. Indeed, in the final step of the model in which coping methods/supports were added, the significant associations between age, mental health history and childhood adversity were no longer significantly associated with MI.Our secondary aim was to elucidate associations between the coping methods and supports and shame and trust-violation presentations of MI, separately. We found that the inclusion of coping methods and supports explained an additional 24.1% variance in shame-related MI and an additional 40.4% of the variance in trust-violation-related MI, beyond the contributions of age, mental health history and childhood adversity. Notably, in the final step of the model, childhood adversity was no longer significantly associated with shame-related MI, yet mental health history maintained a significant association with increased shame-related MI. Alternatively, neither age, mental health history, nor childhood adversity were significantly associated with trust-violation-related MI in the final step of the model. Spiritual well-being and organizational support were each associated with increased endorsement of shame-related and trust-violation-related MI. Comparison of the standardized beta coefficients in our models revealed that the strength of the association between trust-violation-related MI and both spiritual well-being and organizational support was more than twice as great as the associations between these variables and shame-related MI, emphasizing the potential importance of these supports and the trust-violation outcomes of MI compared to the shame-related outcomes of MI. Higher levels of self-compassion were also significantly associated with reduced shame-related MI, but not trust-violation-related MI. Cannabis use approached significance in predicting higher levels of shame-related MI, but not trust-violation-related MI. These results suggest that the utility of self-compassion and the use of cannabis may depend on the type of PMIE exposure. Specifically, individuals exposed to self-perpetrated PMIEs, which tend to give rise to the shame-related presentation of MI, may benefit from self-compassion training and may be more likely to utilize cannabis as a coping method than those exposed to PMIEs perpetrated by others, commonly associated with the trust-violation presentation of MI ,9,51. FuTaken together, these findings deepen our understanding of factors associated with MI among HCPs by identifying the additional impact of coping methods and supports beyond demographic and life history  variables alone. This observation is particularly germane for trust-violation MI, in which demographic and life history factors alone explained only approximately 5.5% of the variance in MI. Critically, these findings also suggest that where exposure to workplace PMIEs may be inevitable for many HCPs, the provision of coping methods and supports appears beneficial in warding against MI. Here, emphasis on supports that enhance spiritual well-being and bolster perceived levels of organizational support may serve as key avenues to reducing MI in this vital workforce.In the present study, spiritual well-being was strongly associated with reduced MI among HCPs, especially with trust violation compared to the shame outcome. Notably, spiritual well-being was conceptualized through the FACIT-Sp-12 as a measure not specific to any one religious or spiritual tradition . SpirituAlthough our findings are consistent with previous evidence suggesting that spirituality and/or positive religious coping  is associated with decreased MI, depression and anxiety, it is worth noting that prior evidence also suggests that spirituality and/or negative religious coping  may exacerbate HCPs’ MI, PTSD, depression and anxiety and even reduce quality of life ,45,64. FAdditionally, although our results demonstrate an association between spiritual well-being and MI, we are unable to make conclusions about the temporal relationship between these variables. As discussed above with cannabis use, it remains unclear whether individuals specifically turned to resources to enhance spiritual well-being after PMIE exposure, or if individuals previously strong in spiritual well-being were less impacted by MI after experienced a PMIE. Future research is needed to better understand this complex relationship.Consistent with previous work ,65,66,67Notably, our results suggest that mental health history and self-compassion relate to shame-related MI among HCPs. Specifically, whereas a lifetime history of a mental health diagnosis was associated with increased shame-related MI, increased self-compassion was associated with decreased shame-related MI in the final step of the model. This finding is in line with Hall et al.’s  review oAlthough this research yields novel evidence of the relation between MI and coping methods/supports, interpretation of the present results must be tempered according to methodological limitations. The results of this study may not be generalizable to HCPs across Canada or globally due to the sample being composed mainly of female, Caucasian nurses in Ontario with nearly half reporting a lifetime history of at least one mental health diagnosis. Moreover, although these data were collected during the COVID-19 pandemic, the present results may not be specific to experiences during the pandemic as the instrument used (MIOS) did not specify a time frame for participants to select a PMIE. Additionally, although the coping methods and supports used in our analyses map on well to the types of supports and coping methods reported by HCPs during the pandemic, we did not include an exhaustive list of any and all supports and coping methods in our models  that may also relate to MI. Further, clinically relevant differences in scores on the SPOS and FACIT-Sp were not accessible, thus rending it unclear as to what extent our statistically significant results indicate meaningful clinical or psychological differences. In addition, our work is correlational in nature and cannot comment on the potentially complex, temporal relationships between coping/supports and MI. Finally, a lack of consensus on what constitutes MI and PMIEs in the healthcare context remains a limitation for all work in this area. Nonetheless, the present study contributes to a growing body of knowledge surrounding MI among HCPs during the COVID-19 pandemic and points towards coping/supports associated with MI, which may be useful targets in interventions.Overall, our findings suggest that higher levels of spiritual well-being and perceived organizational support were associated with reduced MI among HCPs during the COVID-19 pandemic, a relation strongest for trust-violation-related MI. This knowledge should inform discussions on how to best support HCPs beyond the pandemic period. Instead of placing sole responsibility on HCPs for their mental health, organizations have a significant role to play in mitigating MI among staff by offering appropriate supports and attitudes and by considering how, for some HCPs, spiritual well-being may be implemented into existing models of care for staff. Future research is needed to better understand the potentially complex temporal relationships between coping/supports and MI and to develop evidence-informed resources for mitigating MI among HCPs beyond the pandemic period.


Objectives: The dissemination of Escherichia coli–producing extended-spectrum β-lactamase (ESBL-Ec) is evident in the community. In this population-based spatial analysis, we sought to describe the distribution of ESBL-Ec and to identify predictors of incidence in the community. Methods: The study population was defined as individuals with the ESBL-Ec isolate in Queensland, Australia, from 2010 to 2019. Annual choropleth maps and a global Moran index were constructed to describe ESBL-Ec distribution. Getis-Ord Gi* was performed to identify “hot spots” of statistical significance. Using demographic factors and incidence per postal area from 2016, multivariable analyses with or without spatially structured random effects were performed. Results: In total, 12,786 individuals with ESBL-Ec isolate were identified. The incidence rate increased annually from 9.1 per 100,000 residents in 2010 to 49.8 per 100,000 residents in 2019. The geographical distribution changed from random to clustered in 2014. Hot spots were more frequently identified in the Outback and Far North Queensland, where remote communities and hotter weather are prevalent. Multivariable spatial analysis suggests that communities with higher socioeconomic status  and employment in the agricultural industry  were protective of lower ESBL-Ec incidence. After accounting for multiple demographic factors, the residual, structured, random-effects model indicated that hot spots were still detected in more remote communities but also in several city regions. Conclusions: The change in distribution of ESBL-Ec across Queensland suggests the presence of area-level specific risk factors that enhance spread in the community. Risk factors for spread appear different between remote and city settings, and future research should be tailored to understand the respective area-level risk factors. Factors such as local temperature, antibiotic consumption, and access to services should be validated. Future public health measures to reduce transmission should be focused on the identified hot spots.


BackgroundClinical case vignettes are a widely adopted pedagogical approach in medical education. The cases may be presented to students with a closed response option for objectivity. While solving clinical cases has demonstrated its effectiveness in enhancing medical students' clinical reasoning, there is an ongoing debate regarding the most effective approach: individual problem-solving or team-based problem-solving.ObjectiveTo observe and compare the score obtained from individual clinical problem-solving approaches versus team-based clinical problem-solving approaches.MethodsAfter obtaining consent, a total of 100 students were randomly selected for the study. The participants were divided into two groups: an individual approach group (IAG) (n=25) and a team-based approach group (TAG) comprising 25 groups of three students each. Both groups were presented with a set of 10 clinical problems, each requiring a closed-answer response of "yes", "no", or "don't know". The participants' responses were recorded and analyzed to evaluate their problem-solving efficacy.ResultsA total of 25 responses were obtained from 25 students from the IAG group and 25 responses from 25 groups from the TAG group. There was no difference between the score in IAG (7.44±1.12) and TAG (7.52 1.66) p-value=0.58. There was no difference between individual scores in 10 questions between IAG and TAG groups.ConclusionThe study found no significant score differences between individual and team-based clinical case-solving groups. Hence, for the objective type of case-solving pattern used in this study, a team-based approach may not be necessary. Further research is needed to explore factors for such findings in future studies. Clinical case vignette-based learning is a widely adopted pedagogical approach in medical education that emphasizes active learning, critical thinking, and collaborative problem-solving. In case-based learning, students are presented with real-world clinical scenarios or problems and are tasked with finding solutions based on their existing knowledge and resources , 2. WhilIndividual problem-solving in case-based learning allows students to work independently, relying solely on their own knowledge and problem-solving abilities. In contrast, team-based case-based learning encourages students to work collaboratively in groups, pooling their collective knowledge and skills to arrive at solutions. The combination of team-based learning principles within the framework of case-based learning offers a synergistic approach to medical education . In thisHowever, clinical problem-solving may not always benefit from a team-based approach due to potential challenges such as differing opinions leading to debates, the risk of groupthink, uneven participation, communication issues, decision-making delays, accountability concerns, and compatibility issues within the team. In certain situations, especially those requiring quick and decisive actions or clear individual responsibility, individual problem-solving approaches may be more efficient and effective in ensuring optimal patient care , 6. The The gradual introduction of case-based questions in preclinical subjects for both objective and written examinations represents a forward-thinking pedagogical approach. This shift benefits students by encouraging the practical application of theoretical knowledge, fostering critical thinking, and demonstrating the real-world relevance of their studies. With careful implementation, case-based questions can enhance the overall educational experience and better equip students for their future roles in healthcare or related fields , 8. HoweGiven these considerations, this study aimed to observe and compare the efficacy of individual and team-based problem-solving capability for objective types of questions related to a clinical scenario in physiology among first-year medical students.Type and settingsThis cross-sectional study was conducted in a medical school of national importance in the eastern part of India. The study was conducted with first-year medical students studying pre-clinical subjects. The study was conducted on the subject of physiology. This study was approved by the Institutional Ethics Subcommittee of All India Institute of Medical Sciences, Deoghar, Jharkhand, India as an individual project (2023-210-IND-03).Sample and recruitmentTotal number of students in the medical school is 125. All the students were approached for the study, and 121 students consented to participate in the study. Among the willing students, a randomly selected 100 students were recruited for the study. They were randomly assigned to two groups: the individual approach group (IAG) (n=25) and the team-based approach group (TAG) . The process is shown in Figure Case-based learning questions preparation and validationTo ensure the validity and reliability of the case-based learning questions, a panel of subject matter experts (n=3) who are experienced in medical education (more than 10 years) in physiology developed the questions and answers. These scenarios and questions are reviewed for face and content validity and clarity by another two experts. Additionally, a pilot test was conducted with a small group (n=10) of second-year undergraduate medical students to find the suitability and comprehension of the questions. Based on feedback and pilot results, necessary adjustments were made to the questions, and finally, 10 questions were prepared and printed on white paper for application. One such question is shown in Figure Data collectionParticipants from both the IAG and TAG engage in case-based learning sessions where they work on the provided scenarios and answer associated questions. Their responses were analyzed with pre-defined answer keys. The correct answer was scored 1, the wrong answer was -1, and don't know was 0. The grading system incentivizes correct responses, penalizes incorrect ones, and recognizes instances where students openly admit their lack of knowledge, contributing to a more nuanced understanding of their problem-solving skills.Statistical analysisDescriptive statistics were used to summarize the data. The scores obtained by the IAG and TAG groups were compared by the Mann-Whitney U test. We used GraphPad Prism 9.5.0 for statistical analysis, and a p-value <0.05 was considered statistically significant.A total of 100 undergraduate medical students in their first year of study participated in this study. Among these 100 students, 25 students comprised the individual group, and 75 students were in the team-based group (each group was composed of three students). The score between the IAG (7.44±1.12) and TAG (7.52 1.66) was similar . This indicates that a student alone and in a group have similar performance in solving objective-type clinical case solving. The score in the box plot is shown in Figure The case-wise average score is shown in Table TBL is a valuable pedagogical approach with several advantages in case-based learning contexts. TBL fosters collaboration and teamwork skills, which are crucial in healthcare settings where professionals often work in interdisciplinary teams . TBL encHowever, TBL may not be an ideal method for all aspects of case-based learning. The effectiveness of TBL relies on cohesive team dynamics, and if teams experience conflicts or imbalances in participation, it can hinder the learning process. Case-based learning's individual approach may provide students with a more self-directed learning experience, enabling them to tailor their problem-solving strategies to their unique needs . It may In our study, the lack of a significant difference in scores between the IAG and the TAG could be attributed to several underlying factors. The composition of both groups was relatively homogeneous in terms of academic backgrounds, prior knowledge, and problem-solving skills. Such homogeneity could have led to similar performance outcomes for both groups and individuals. Additionally, the level of complexity of the cases could have played a role. As for objectivity, we used choice-based closed answers. Hence, actual collaborative problem-solving might not take place. The dynamics within each TAG team may have also influenced outcomes, as issues with communication, cooperation, or leadership could have hindered their collective performance. Moreover, some students in the IAG may have taken a highly self-directed approach to problem-solving, compensating for the absence of teamwork , 15.The study's limitations include a relatively small sample size, potential variations in the complexity of case scenarios, and the focus solely on academic performance as an outcome measure. Additionally, the study's cross-sectional design may not capture the long-term impact of case-based learning approaches on students' clinical reasoning and problem-solving abilities.In conclusion, this study examined the effectiveness of individual and team-based case-based learning approaches in a medical education context. The results indicate that, within the parameters of this study, there were no significant differences in academic performance scores between the two approaches for objective case-solving in physiology. These findings suggest that the choice between individual and team-based case-based learning methods may not significantly impact immediate academic performance. Further research on other subjects with larger sample sizes may provide deeper insights into the topic.


This series of six articles  is presented by international leaders on peripheral T-cell lymphomas (PTCL). PTCL is a heterogeneous group of neoplasms originating from T cells that migrate to peripheral lymphoid tissues after differentiation and maturation in the thymus, and accounts for 10–15% of all non-Hodgkin’s lymphomas (NHL). Compared with B-cell lymphoma, disease-specific molecular markers for PTCL are scarce, and PTCL comprises many different entities. In addition, compared to B-cell lymphoma, established treatment methods for PTCL are limited and the prognosis of most PTCL cases remains poor. This special issue summarizes recent findings on the molecular pathology and potential therapeutic targets of PTCL and presents new candidate biomarkers. In addition, considering virus-associated PTCL, this issue includes a review of the methods for the prevention of human T-cell leukemia virus type 1 (HTLV-1) infection and a genomic analysis of Epstein–Barr virus (EBV).Adult T-cell leukemia/lymphoma (ATL) is a malignant tumor with poor prognosis caused by HTLV-1 infection. Over 10 million HTLV-1 carriers are estimated worldwide, mainly in southwestern Japan, sub-Saharan Africa, South America, and the Caribbean region, and 2–7% of HTLV-1 carriers develop ATL. The main routes of HTLV-1 infection are mother-to-child transmission through breast milk and horizontal transmission through sexual intercourse and blood transfusion; mother-to-child transmission is known to be involved in the development of ATL. Itabashi and Miyazawa [−/−RHOA(G17V) mice exhibit AITL-like pathology. Cyclophosphamide, hydroxydaunorubicin, vincristine, and prednisone (CHOP) chemotherapy is the first-line treatment option in PTCL-TFH, but the response rate is not satisfactory. Clinical trials that used a combination of various molecular target drugs, such as the anti-CD20 monoclonal antibody rituximab and lenalidomide, with CHOP have also yielded unsatisfactory results. A possible next step would be to identify new molecular targets, and Krug et al. [PTCL with a T follicular helper phenotype (PTCL-TFH), including angioimmunoblastic T-cell lymphoma (AITL), is a major PTCL entity. However, compared to other lymphomas, such as B-cell lymphoma, this subtype is rare, posing challenges in launching clinical trials. As summarized in the review by Krug et al.  in this Regarding the clonal evolution of PTCL, genomic mutations commonly appear in molecules involved in the TCR signaling pathway, which Liu et al.  focused On the other hand, Al-Khreisat et al.  in this In addition to HTLV-1 that can cause ATL, EBV is another virus involved in PTCL development. EBV, a herpesvirus that infects most adults and remains latent, is detected in most Burkitt lymphomas and NKTCL. Regarding the carcinogenic mechanisms induced by EBV, the virus is thought to be reactivated by some factor, inducing infected B-cell carcinogenesis, and EBV infection likely promotes tumor development by altering the immune microenvironment. On the other hand, EBV is detected in approximately 90% of AITL cases, and researchers speculate that it is related to AITL pathogenesis. Bahri et al.  in this Stimulator of interferon genes (STING) plays an important role not only in the regulation of activity against viral infection but also in tumor inflammation and antitumor immune responses. Xagoraris et al.  in this The above series of papers suggest that PTCL entities are associated both with common molecular mechanisms and mechanisms characteristic of each subtype. We expect that further analyses will lead to the identification of disease-specific molecular markers and the establishment of treatment methods.