Deep Learning (DL) can predict molecular alterations of solid tumors directly from routine histopathology slides. Since the 2021 update of the World Health Organization (WHO) diagnostic criteria, the classification of brain tumors integrates both histopathological and molecular information. We hypothesize that DL can predict molecular alterations as well as WHO subtyping of brain tumors from hematoxylin and eosin-stained histopathology slides.N = 2845 patients.We used weakly supervised DL and applied it to three large cohorts of brain tumor samples, comprising IDH and ATRX, as well as 1p19q codeletion, were predictable from histology with an area under the receiver operating characteristic curve (AUROC) of 0.95, 0.90, and 0.80 in the training cohort, respectively. These findings were upheld in external validation cohorts with AUROCs of 0.90, 0.79, and 0.87 for prediction of IDH, ATRX, and 1p19q codeletion, respectively.We found that the key molecular alterations for subtyping, In the future, such DL-based implementations could ease diagnostic workflows, particularly for situations in which advanced molecular testing is not readily available. Deep Learning can predict both molecular alteration status and subtype of diffuse adult-type gliomas directly from histopathology whole slide images.This is the first study to predict subtype and molecular status according to the 2021 WHO CNS 5th edition.The 2021 update to the World Health Organization (WHO) classification system for central nervous system (CNS) tumors places further emphasis on molecular characterization in the diagnosis of adult-type diffuse gliomas. However, molecular assays are not necessarily available in all healthcare systems. Deep Learning (DL) offers an alternative approach for predicting molecular status directly from digitized histopathology slides. This study demonstrates that DL can accurately predict not only the subtype of glioma but also the status of individual molecular alterations. To our knowledge, this is the first study predicting clinically relevant markers according to the WHO fifth edition. This work provides further evidence supporting the use of digital workflows to support decision-making in clinical medicine.2 Due to the aggressive nature, infiltrative growth and central location, these tumors generally have a poor prognosis. Clinical outcome for these entities largely depends on subtype, the diagnosis of which has historically been based on histological assessment.2Diffuse gliomas are the most frequent primary malignant brain tumors in adults and broad demographic shifts mean that their incidence is expected to increase.3 In addition, methylation profiles can be used to define the glioma diagnosis.2In 2021, the World Health Organization (WHO) released the 5th edition of the diagnostic criteria for adult-type diffuse gliomas. These criteria are based on a combination of molecular alterations and histopathological assessment, which determines WHO CNS subtype and grade.IDH (IDH1 or IDH2) and ATRX mutation status, as well as 1p19q codeletion. Glioblastomas CNS WHO 4 are currently defined by the absence of IDH and H3 mutations and occurrence of at least one of the following histopathological  or molecular  alterations. IDH-mutant gliomas are subclassified into astrocytomas or oligodendrogliomas. The classification of IDH-mutant oligodendrogliomas CNS WHO 2/3 are defined by demonstration of a combined whole arm deletion of chromosomes 1p and 19q. Immunohistochemically retained nuclear ATRX expression as a surrogate of ATRX-wildtype status is a very strong correlate for 1p19q codeletion status in IDH-mutant gliomas.2IDH-mutant astrocytomas CNS WHO 2/3/4 are molecularly defined by retained 1p19q chromosomes and the presence of ATRX-mutation, or its immunohistochemical surrogate of nuclear ATRX-staining loss. The grading of IDH-mutant astrocytomas also relies on a combination of histomorphological, as well as molecular traits: the presence of CDKN2A/B homozygous deletion and/or necrosis and/or microvascular proliferation defines a CNS WHO grade 4 lesion in IDH-mutant astrocytomas.2Based on their molecular profile, a significant proportion of adult-type gliomas can be subtyped by 4 These diagnostic requirements are further confounded by the fact that many advanced genetic tests are not available in all healthcare systems. For a fully integrated diagnosis, multiple different assays may be required to characterize different molecular features. This may further drive up costs associated with molecular testing, serving to widening the already pronounced inequities in precision medicine.5 Furthermore, the requirement for multiple tests could delay treatment by up to several weeks, with adverse sequelae for the patient.6This evolution in the diagnostic approach represents a further shift away from the traditional histopathological process where pathologists typically assign subtypes based on specific tumor features, including morphology. The integration of genetic alterations into the diagnostic process reflects improved understanding of these tumors allowing for greater diagnostic precision. However, the necessity for molecular assessment and in some cases, methylation array profiling, also makes the process more expensive.7 This has the potential to accelerate diagnostic workflows and reduce the costs associated with molecular testing.8 These studies have shown that clinically relevant features, such as biomarkers and subtype, can be predicted directly from histopathology slides for many tumor types,10 including in brain tumors.12 DL is a machine learning (ML) method from the field of artificial intelligence (AI) and is a common and powerful way to extract quantitative information from image data.13 A typical DL workflow involves three main stages; preprocessing of the data, training and testing of an algorithm, and assessment of model performance through interpretation of statistical results.13An increasing body of evidence indicates that Deep Learning (DL) techniques are able to predict the phenotypes linked to individual molecular alterations directly from routine hematoxylin and eosin (H&E) stained histopathology slides.n = 13) followed by grade (n = 10) and subtype prediction (n = 4). This demonstrates that brain tumors remain understudied in the field of computational pathology. This is particularly evident when compared to breast or colorectal cancer, which have been investigated in hundreds of computational pathology studies.7 The brain cancer-related studies identified via literature review were generally performed on small datasets,14–16 without external validation,17–19 or used networks that were not pretrained on histopathology images.20–22 This demonstrates a pressing need to address brain tumor research using larger, more heterogeneous cohorts, employing up-to-date methods. To our knowledge, there are no previously published studies attempting to predict subtype in adult-type diffuse gliomas on the basis of the WHO 2021 5th edition.To assess the current status of DL research in diagnostic neuropathology, we performed a systematic literature review . DL applTo this end, we collected and analyzed three independent datasets of digitized routine histological whole slide images (WSI) with the primary aims of predicting genetic alterations and differentiating brain tumor subtypes by using DL.This study was performed in accordance with the Declaration of Helsinki. The research involved analysis of anonymized archival digital images of human tissue. Ethical approval was obtained at collaborating centers prior to collection and pseudonymization. Data were obtained from University College London as part of the UK Brain Archive Information Network (BRAIN UK), which is funded by the Medical Research Council and Brain Tumour Research. BRAIN UK reference number: 22-011—Artificial intelligence-based reconstruction of the WHO 2021 diagnostic algorithm for adult-type diffuse gliomas. The overall analysis was approved by the Ethics commission of the Medical Faculty of Technical University of Dresden (BO-EK-444102022). The STARD 2015 checklist can be found in n = 1882 digitized H&E histopathology WSIs from n = 1877 patients with corresponding clinicopathological and molecular data. These are nonconsecutive cases collected from routine diagnostic work between 2011 and 2019. The dataset, curated from 40 sites across seven countries, encompasses both typical clinical cases and a small number of rare entities. All cases in which a diagnosis of an adult-type diffuse glioma was made were included in our study. These cases had an integrated molecular diagnosis according to WHO 2021, updated from a diagnosis according to WHO 2016, i.e., all cases were retrospectively assessed to comply with the CNS WHO 2021 classification.The first cohort was obtained from University College London (UCL) via Brain UK (REF:22/011). This cohort consisted of n = 864) network, www.cbioportal.org, for the tumor entities of low-grade glioma  and glioblastoma . The dataset is composed of cases contributed from 38 sites across seven countries. All cases in which a diagnosis of an adult-type diffuse glioma or a relevant genetic alteration was made, and were included in our study. Digitized histopathological WSIs with matching clinical–pathological and molecular data were obtained from www.cbioportal.org. Classification for this cohort was made according to WHO 2016. Molecular alteration data available for this cohort were used to update the subtype diagnosis to comply with WHO 2021, with assistance from an expert neuropathologist (SB). The third cohort was the Clinical Proteomic Tumor Analysis Consortium  from https://www.cancerimagingarchive.net/collections/. All cases that were tested for CDKN2A/B homozygous deletion were included in our study. Matching clinicopathological and molecular data were downloaded from www.cbioportal.org. Data were obtained from these online sources as of March 13, 2022.The second cohort was derived from The Cancer Genome Atlas .27 Preprocessing included normalization28 and tessellation of WSIs before features were extracted from each tile.29 The attMIL model makes predictions for a patient, based on a collection of tiles extracted from the patient’s slides. We call the aggregate of a patient’s features a bag, with the features itself being the bag’s instances. Because it is probable that not all of the instances have the same amount of information on the patient-level label, our model considers the entire bag at once. This enables it to consider information which may only be present in some of the instances while ignoring instances which contain little to no valuable information.30https://github.com/KatherLab/marugoto).We have previously established7 A bootstrap 95% confidence interval (CI) is also given to provide a measure on AUROC accuracy. Furthermore, we provide additional statistics including sensitivity, specificity, accuracy and precision.Model performance was assessed with the Area Under the Receiver Operating Curve (AUROC), which is the primary statistical endpoint in most studies on AI-based medical image analysis.https://www.southampton.ac.uk/brainuk/index.page. Data from the TCGA and CPTAC cohorts can be accessed via the cBioPortal website .Access to the UCL data can be requested via the Brain UK study proposal platform at Our aim was to use DL for subtype and molecular status prediction in adult-type diffuse gliomas according to the WHO 2021 5th edition. We investigated two approaches for subtype prediction:Direct prediction: The tumor subtype is predicted from the WSI directly, according to both 2016 and 2021 WHO approaches. The 2016 results were used as the baseline from which to compare the 2021 results.Sequential prediction: Each alteration in the diagnostic pathway is predicted separately. As a result of this output, the diagnostic pathway can be reproduced in a stepwise fashion, where the combination of alteration present and/or absent would then determine the subtype.CDKN2A/B, as described in Both experimental approaches were run with internal cross-validation and external validation settings. The UCL cohort was chosen as the primary cohort for internal cross-validation and as a training set for external validation, due to having a larger number of patients and more balanced classes, with the exception of Ch +7/−10 and The 2016 WHO subtypes of brain tumors are based on molecular alteration status plus histomorphology. DL utilizes the characteristics of an image to inform classification decisions. Hence, a DL model should be able to predict these subtypes from images more readily than the other targets. Once established, these results can be used as a baseline from which to assess our further experiments.In the cross-validation setting, our approach yielded AUROCs of 0.89 (CI ± 0.02), 0.94 (CI ± 0.01), and 0.93 (CI ± 0.01) for detection of astrocytoma, glioblastoma, and oligodendroglioma, respectively , 0.95 (CI ± 0.02), and 0.93 (CI ± 0.02) for astrocytoma, glioblastoma, and oligodendroglioma, respectively , 0.90 (CI ± 0.02), and 0.91 (CI ± 0.03) for prediction of astrocytoma, glioblastoma, and oligodendroglioma, respectively  were within 0.02, and the internal validation AUROCs were within 0.03 and 0.01 for astrocytoma and glioblastoma, respectively  and 2G. IDH, 1p19q, and ATRX) in a cross-validation setting. Prediction of IDH within this cohort was highly successful giving an AUROC of 0.95 (CI ± 0.02), with ATRX and 1p19q giving AUROCs of 0.91 (CI ± 0.03) and 0.80 (CI ± 0.03), respectively. For external validation of the core molecular alterations, results were similar to external validation for subtype prediction, with AUROCs of 0.90 (CI ± 0.02), 0.79 (CI ± 0.04), and 0.87 (CI ± 0.03) for IDH, ATRX, and 1p19q prediction, respectively . For EGFR amplification, internal validation gave an AUROC 0.83 (CI ± 0.04) and external validation yielded an AUROCs of 0.85 (CI ± 0.03)  focused on areas of microvascular proliferation and pseudopalisading necrosis. Interestingly, ATRX mutation heatmaps were mostly “cold.” In some cases, gemistocytes appeared to be relevant to classification, but these cells were not present in all cases  were consistently highlighted in our heatmaps, indicating that they are important for the prediction of these targets. This was the case for oligodendroglioma subtype prediction in both 2016 and 2021 experiments. Similarly, the ll cases .The aim of this work was to recreate the diagnostic neuropathological workflow for diffuse adult-type gliomas with DL. Hence, we utilized a large dataset acquired through the Brain UK consortium—the UCL dataset—and demonstrated the efficacy of an attention-based DL pipeline on this task. We subsequently illustrated the general applicability of our pipeline by deploying on independent data using the publicly available TCGA and CPTAC datasets.32 Thus, we postulate that our model successfully identified and leveraged relevant phenotypes for these targets.Through these methods, we were able to demonstrate that DL can infer both genetic alteration status and WHO subtype directly from WSI. Our results for subtype prediction and the three core diagnostic alterations were consistently above 0.79 in both internal and external validation. While an AUROC of 0.5 would indicate a random classifier, AUROCs of 0.7–0.8 indicate that a phenotype associated with the target is detectable to some degree by DL.33 Domain shift can occur due to multiple factors, particularly due to variations in tissue processing methods, staining and scanner properties between cohorts.33 While we employed multiple approaches to alleviate this problem, including training with a diverse cohort and normalizing tiles, model generalization remains an open problem in the field.33A variance in AUROC between internal- and external-validation experiments was notable for some targets. This is a common issue termed domain shift.IDH-wildtype and glioblastoma predictions, while gemistocytes and Rosenthal fibers correlated with astrocytoma and IDHmut predictions (2 This indicates that our network used morphology in decision-making similarly to that of a neuropathologist and supports the correctness of our model.Visual interpretability performed as part of this study highlighted morphologies that are recognized as being associated with specific subtypes as important to model decision-making. Fried egg cells and chicken-wire vasculature were found to correlate with prediction of 1p19q codeletion and oligodendroglioma subtype. Microvascular proliferation and necrosis correlated with dictions . These f34Our study assessed two experimental approaches. Our direct approach predicted tumor subtype directly from WSI, whereas the sequential approach predicted mutational alteration statuses. Statistical analysis of these approaches indicated that overall, the sequential approach performed better. The sequential approach provides the added benefit of interpretability, as it allows pathologists to understand which alterations are present in a WSI, and thus support the final predicted subtype diagnosis. However, the excellent specificity of the direct approach for glioblastoma prediction should be noted. Glioblastoma is important to rule out in the clinical setting due to the poor prognosis.CDKN2A/B in the UCL cohort and TERT in the TCGA and CPTAC cohorts, which can create biased predictions. Similarly, we were not able to externally validate ch +7/−10 as we only had data in the TCGA cohort. Furthermore, our study only included adult-type diffuse gliomas without considering other differential diagnoses, as would take place in clinical practice.The limitations of our study primarily relate to our data. We were unable to evaluate all targets in the 2021 WHO CNS classification due to lack of data. Classes were particularly unbalanced for alterations such as Further work would include acquiring more data from different sites. This would allow us to improve balance in our classes and provide more varied training data, including common differentials. This should help address the issue of domain shift and make our network more applicable to additional cohorts.In conclusion, our study demonstrates that DL can predict the WHO CNS 2021 subtypes with high accuracy in a single and external cohort. Although a small number of studies have previously predicted WHO 2016 subtype and some molecular alterations, to our knowledge, no external validation experiments for 2016 subtype prediction have been previously performed. Furthermore, this is the first study to predict WHO 2021 subtype and the three core diagnostic alterations in one study.vdad139_suppl_Supplementary_Figures_S1-S7_Tables_S1-S2Click here for additional data file.


Kaposi sarcoma (KS) is a low-grade tumor of the vascular endothelium. The majority of individuals affected have advanced human immunodeficiency virus (HIV) or acquired immunodeficiency syndrome (AIDS). The disease typically manifests as cutaneous lesions but reports have shown that systemic disease is not uncommon. Because gastrointestinal KS is often asymptomatic, it is likely underdiagnosed. Those with symptoms can present with vague abdominal pain, nausea/vomiting or anemia. Rarely the tumors can cause bowel obstruction or perforation. We present a case of small bowel obstruction cause by KS tumors in a young transgender male to female patient with poorly controlled AIDS, supported by literature review of the clinical presentation, diagnosis and treatment recommendations. Kaposi sarcoma (KS) is the most common tumor seen in acquired immunodeficiency syndrome (AIDS) with typical findings of cutaneous lesions. Previous reports have shown that 10–66% of those patients with skin lesions also had systemic disease . We presPatient is a frail 28-year-old transgender male to female with poorly controlled human immunodeficiency virus (HIV)/AIDS  who presented to the ED with one day of abdominal pain. History was limited as the patient was uncooperative. Vitals on arrival in the ED were: Temp 98°F, Pulse 78, BP 106/55, RR 18, SpO2 97%. The physical exam was significant for cachexia (BMI 17.3), skin changes compatible with skin type Kaposi’s Sarcoma, abdominal distention, and peritoneal signs. Computed tomography (CT) abdomen and pelvis showed multiple dilated small bowel loops with swirling of mesentery  and possThe patient was taken to the operating room for exploratory laparotomy given concerns for peritonitis in the context of closed loop obstruction. Intra-operative findings consisted of three ileal tumors spanning an approximately 40-cm segment, located 45 cm proximal to cecum accompanied by significant lymphadenopathy –3.A palliative en bloc resection of the segment containing the three tumors was carried out with creation of an end ileostomy and mucous fistula. Final pathology showed malignant vascular tumor with extensive ulceration involving all layers of bowel wall, positive staining for CD-34 & HHV-8 consistent with KS.The patient was started on HAART therapy post-operatively, had an uncomplicated hospital course and was eventually discharged to rehab. Unfortunately, the patient refused chemotherapy on discharge and was lost to follow up.KS is a vascular angio-proliferative neoplasm which usually presents on mucocutaneous sites but may involve visceral organs. In the US, KS predominantly affects HIV infected patients, with the majority of individuals having advanced immunosuppression (CD4 count <150 cells per cubic millimeter) and a high viral load (>10 000 copies per millimeter) at the time of diagnosis . While gGiven the indolent nature of GI KS, it raises the question of whether routine endoscopic screening should be done. Endoscopically KS appears as red or purple patchy lesions of different sizes and shapes of polypoid/nodular morphology. These lesions are typically identified in the stomach, esophagus, and duodenum . The rolIn patients with AIDS-related KS with systemic manifestations, combined HAART and systemic chemotherapy is indicated. Liposomal doxorubicin is the agent of choice, and trials have shown that delivery via pegylated doxorubicin is effectively concentrated in lesions with greater tolerability and less toxicity than other chemotherapeutic agents .The surgical management of small bowel obstruction due to KS remains controversial– the decision to perform surgery should be made on a case-by-case basis. In general, surgery may be indicated in cases of complete or high-grade partial obstruction, perforation, or ischemia, which are associated with a high risk of morbidity and mortality . HoweverKS should be included in the differential diagnosis of small bowel obstruction in patients with AIDS. Although this extent of progression of GI KS is rare, high index of suspicion and perhaps increased endoscopic screening is warranted when dealing with this vulnerable demographic.


Usingexperimental blood-based infrared spectra from two cancer-detectionapplications, we validated the model and subsequently adjusted modelparameters to simulate diverse experimental settings, thereby yieldinginsights into the framework of molecular fingerprinting. Intriguingly,the model revealed substantial improvements in classifying clinicallyrelevant phenotypes when the biological variability was reduced froma between-person to a within-person level and when the chemical complexityof the spectra was reduced. These findings quantitively demonstratethe potential benefits of personalized molecular fingerprinting andbiochemical fractionation for applications in health diagnostics.Molecular fingerprinting via vibrational spectroscopycharacterizesthe chemical composition of molecularly complex media which enablesthe classification of phenotypes associated with biological systems.However, the interplay between factors such as biological variability,measurement noise, chemical complexity, and cohort size makes it challengingto investigate their impact on how the classification performs. Consideringthese factors, we developed an  Statistical or machinelearning methods can identify spectral patterns specific to molecularphenotypes and consequently classify samples.Vibrational spectroscopy bymeans of Raman or infrared techniques is a powerful analytical platformcapable of characterizing molecular samples at any state of matterin a label-free manner.4 The approachhas been used to classify bacteria and cell (sub)types,6 distinguish between benign and malignant tissues,2 andidentify diseases based on fingerprint spectra of biofluids.7Molecular fingerprintingby vibrational spectroscopy has been increasinglyapplied to biomedical problems.8 The challenge is that a variety of technical and data acquisitionaspects impact the measured spectra and thus can affect the classificationaccuracy.11 At the same time, every living system exhibits aninherent level of biological variability,13 further challenging the unambiguous identificationof different molecular states. While previous work has examined thevariations caused by technical, data acquisition, and biological aspectson recorded spectra,14 disentangling their individual contribution on the classificationaccuracy remains experimentally challenging. This often requires large-scalestudies involving different measurement instruments, different laboratories,and different protocols for sample handling, which may be difficultto realize due to resource limitations.15Although successfully applied, the prospects and fundamentallimitationsof vibrational fingerprinting for certain applications, such as forclinically relevant questions, remain largely unexplored.In silico investigations performed via computersimulations are particularly powerful in this respect as they canaddress problems that are practically or experimentally challenging.17 By creating computational models capable of mimicking the behaviorof biological systems, considering various sources of noise or heterogeneity,one can rapidly gain insights into the underlying mechanisms thatdefine the behavior of a system under differing simulated conditions.18in silico approach that generates artificialinfrared spectra ,19 including the generated data, to allow for convenientapplications of the proposed model. Although applied here to infraredspectra, the model can be applied to other molecular fingerprintingapproaches, such as Raman20 or nuclearmagnetic resonance21 spectroscopy.The model is described and tested on a previouslypublished application—infraredfingerprinting of blood sera to detect lung and prostate cancer.Altogether, we obtain excellent agreement between the results ofexperimental and simulated data. By systematically adjusting all modelparameters, we explored the potentials and limitations of molecularfingerprinting and thus contribute to accelerating its real-worldapplications.in silico modelshould be able to generate spectra which meet the following requirements:Reflect the biological or sample variability of a givensample pool.Incorporate differencesbetween different molecularstates .Considercharacteristics of the physical measurement,namely the noise introduced by the measurement device.In essence, the problemstackled by molecular pattern recognition using machine learning areoften very related. Depending on the application, by detecting changesin qualitative or quantitative aspects, a distinction between twoor more molecular states is desired. In practice, this distinctionis only possible to a certain extent due to the inherent biologicalor sample variability as well as measurement-specific errors. Additionally,often a limited amount of data is available to robustly validate conclusionsdrawn by analyzing differences between the studied states. Therefore,it can be difficult to make conclusive statements about the potentialand limitations of the method in question or to isolate the molecularsignal of interest. Even in a scenario where the signal is alreadyknown , the expected accuracyof distinguishing between different states often requires numerousmeasurements due to the multiple sources of variation. To facilitatethe investigation of these points, the We describe two approaches to generate the artificialspectra. In a so-called bottom-up approach, the contribution of individualmolecular species in a given sample is considered. Although intuitive,this approach cannot be implemented in the case of infrared spectroscopyof highly complex samples as we discuss below. Instead, it providesthe motivation and basis for implementing another so-called descriptiveapproach which follows a similar mathematical formulation. Althoughthe model can be applied to study different samples types with varyingchemical complexities and to several molecular fingerprinting techniques,infrared molecular fingerprints of blood serum will serve as the guidingprinciple for assembling the model.22 Thereby,each molecular spectrum xi is scaled to its respective concentration ic with ∑i = 1Mci · xi.It has been shown that by breakingdown a molecularly complex sample into its individual components,the corresponding infrared spectrum can be reasonably well approximatedwith a linear combination of individual component spectra of the mostabundant molecular species.24 Using these facts, the concentration parameter ic could be replaced with a random variable c that models the distribution of each component for a healthycohort. Thereby, a set of spectra that model the molecularly complexsamples could be simulated where each spectrum is modeled as a statisticaloutcome Y with:Thisidea can be also utilized to create spectra of a given population.The normal concentration ranges for the most abundant blood componentsin healthy individuals—which in total make up more than 99%of the molecular mass—can be obtained from the literature.We term this modelformulation as the bottom-up approach.25 In cases where these correlations are well known, introducing dependenciesbetween the corresponding random variables would, in principle, accountfor this behavior.It is important to notethat the bottom-up approach contains severalsimplifications and assumptions. Specifically, it is assumed thatthe concentrations of individual molecules or molecular classes areindependent, which is not the case. In reality, biological networksare interconnected, and molecular changes within living organismsare occurring in a correlated fashion.27 and environmental conditions , severalstudies suggest that complex blood spectra can be described by linearsuperposition of individual single component spectra. For infraredspectroscopy, it is known that the strength of a molecular spectrumscales linearly with concentration over many orders of magnitude.28 Furthermore, it has been shown that the concentration of severaldifferent molecules in blood can be determined by linear regression.32 Moreover, it was shown that a linear combination of the spectraof the most abundant molecular species can be used to describe experimentallyobserved differences in the spectra of blood serum from a healthycohort and from patients with lung cancer.22The assumption that an infrared spectrumof a complex sample canbe described by a linear combination of individual component spectrascaled to their concentrations can be regarded as sufficiently fulfilled.Although molecular spectra are influenced by inter-molecular interactions22 our results showthat significantly more are required to represent the molecular complexityand biological variability adequately. Taken together, from the currentperspective, it is unlikely that the bottom-up can be implementedwhen studying a complex blood-based matrix. In principle, however,it can be used to simulate the spectra of simpler systems, such asfor pharmaceutical samples that can be described as a mixture of afew well-known substances.33While the bottom-up approach is straightforward to envision,accessto the associated molecular spectra is essential for creating themodel. This is a crucial limitation since, to our knowledge, thereexists no consistent database with infrared spectra of the majorityof molecules contained in complex biological systems. While previouswork suggests that 12 selected protein spectra can model the shapeof blood serum spectra,m experimentallyderived spectra si that describe the samples of a particular biological matrix. By utilizing a randomvariable β assuming a Gaussian distribution, the spectrumof a measured biological matrix can now be expressed as another statisticaloutcome Y with:To mitigate the limited accessto individual component infrared spectra, we introduce an alternativedescriptive approach. Here, we use a set of Thereby, the mathematicalstructure of the formulation is similar to the bottom-up approach,but utilizing a different set of calibration vectors to realize themodel.One difference between the two model formulations isthat we cannotrely on the literature to obtain the concentration ranges for individualmolecules and construct a realistic behavior for the biological variability.Therefore, a calibration procedure is required to adjust the statisticalvariables and select the experimentally derived spectra such thatthe resulting spectra reflect the biological variability of the measuredsamples. In addition, we must validate that assuming a Gaussian distributioncreates spectral cohorts that adequately match experimentally measuredcohorts.bi and calculate the expected value and variance ofthe descriptive approach:E(Y)should correspond to the mean of the experimentally measured spectra b®, we can replace the sum of the scaledvectors ∑i=1m μi · si in b®. Additionally,by comparing the variance of si = bi – b®.With this, the expected value and variance of the generated spectrabecome equal to the experimentally determined values.To calibrate the biological variability, we use a setof experimentallymeasured blood serum spectra m may besmaller than the number of different molecular species M within the sample, this alternative representation maynot be able to reproduce the complete molecular complexity. However,considering that we can often use hundreds of measurements for modeling,this approach should be able to capture variations related to a similarnumber of molecular species and thus asymptotically approximate mostof the biological variability.Sincethe considered number of measured spectra In addition to modelingthe biological properties, it is important to factor in the contributionsof noise introduced by the measurement device to assess its influenceon the application of interest.ϵ:When using a commercial Fourier-transforminfrared (FTIR) spectrometer, the dominant noise type inherent tothe spectral measurement itself is additive white noise. Such noisecan be added to a modeled spectrum as a vector ϵ, werepeatedly measured water samples with the FTIR device used for theblood sera measurements and calculated the standard deviation observedacross the spectral features . The measurement noise coefficient ϵ was thus calibrated to be a random Gaussian vectorwith a mean 0 and a spectrally dependent standard deviation.To determine a realisticestimate for 3 or sample delivery andpreparation14 can also be considered by introducingmultiplicative noise and drift vectors in the model.28 When transferring this model to Raman-based fingerprintingapplications, it may be necessary to consider further, partly nonlinearnoise sources34 to obtain realistic results.As we show below, for the application of the model presented in thiswork, it is sufficient to consider only additive white noise to obtainrealistic results.Other sources of error in infrared spectroscopy which result frombaseline driftbi* that characterize the new molecularstate Y* and use that as a basis forcalibration. As later shown, for certain applications, the discriminantfeatures can be largely explained by a single vector d. By utilizing an additional Gaussian variable δ which scales the introduced vector d and accounts for its variability, a modeledspectrum of an alternative class can be reduced to:Changes in the physiology of an organism, e.g., due to the onsetof a disease, may change the molecular composition of the analyzedsample. Thus, we can choose a new set of experimentally measured spectra b1, ..., bm and modeled measurement noise ϵ. The statisticalquantities β and ϵ can then be scaled byadditional factors to simulate different levels of variability forthese coefficients in a created cohort of n measurements.In our application, the molecular outcome Y represents an artificial measurement of a control sampleand Y* represents an artificial measurementof a cancer case sample. Unless explicitly stated otherwise, the descriptiveapproach is used where independent experimental measurements of casesand controls form the basis of biological variability calibrations.With this modelingplatform, an arbitrary number of samples can be generated for differentmolecular states, resulting in a generated cohort that reflects thestatistical properties of the measured sample pool 7 Newly measured blood sera samples from different individuals wereincluded in this study to increase the sample size and thus improvethe statistical robustness of the results. All participants providedwritten informed consent for the study under research study protocol#17-141 and under research study protocol #17-182, both of which wereapproved by the Ethics Committee of the Ludwig-Maximillian-University(LMU) of Munich. Our study complies with all relevant ethical regulationsand was conducted according to Good Clinical Practice (ICH-GCP) andthe principles of the Declaration of Helsinki. The clinical trialis registered (ID DRKS00013217) at the German Clinical Trials Register(DRKS).The experimental FTIR spectra used in this study largely overlapwith spectra obtained from a previous study that involved the detectionof lung and prostate cancer from blood serum.Table S1 in the Supporting Information. Information regarding thestudy design, statistical matching, sample collection, sample handling,FTIR measurements, and spectral preprocessing is detailed in the previousstudy.7 Machine learning analysis performedon the experimental and simulated data is detailed in Section 4 in the Supporting Information. In essence,an L2-regularized logistic regression was used as a classificationalgorithm. Classifications were assessed using the receiver operatingcharacteristic (ROC) curve, and the area under the curve (AUC) wasused as a summary metric of predictive performance.In total, we considered spectra from 523 lung cancerpatients, 411 prostate cancer patients, and nonsymptomatic referencespair-matched to each individual from each cancer entity. A detailedbreakdown of the cohorts is provided in Sections 6–9 in the Supporting Information.By applying our simulation model to a clinicallyrelevant scenario,we studied the potential and limitations of detecting lung and prostatecancer in binary case–control settings. Using experimentallymeasured FTIR spectra, we validated that comparable results were obtainedbetween artificially generated cohorts and experimental cohorts. Next,we investigated how classification performance was influenced by varyingmodel parameters that control the cohort size, measurement noise,biologically variability, and molecular complexity. Our findings fromthe lung cancer application are provided within the main text andfigures. Figures relating to the prostate cancer conveyed consistentresults with the lung cancer application and are thus provided in After performing the calibrationprocedure described in the methods, we validated that the simulationmodel was able to create cohorts that effectively captured the intrinsicproperties of experimental cohorts for our lung cancer case study2A–D.We In this comparison, we observed a high degree ofagreement in thestatistical features of the spectra between simulated and experimentaldata 2A–C.TheSections 4 and 11 in the SupportingInformation for more details). For the experimental data, model performancewas estimated in a 10-times repeated 10-fold cross-validation—totaling100 folds. For the simulated data sets, a 10-fold cross-validationwas carried out on each of the 10 simulated data sets—thereby,also totaling 100 folds. With this pipeline, we estimated mean ROC-AUCsof 0.88 and 0.87 for the lung cancer classification with the experimentaldata and the simulated data, respectively . Taken together,these results suggest that the proposed descriptive modeling approachis able to capture all relevant properties and features for the classificationof the studied system.As an additional validation, we compared the predictive performanceof a model trained exclusively on simulated data and tested on experimentaldata. To ensure that no experimental data used for testing contributedto the model training, we split the experimental data into 10 folds.For each fold, we held out an experimental test set, fit a predictivemodel on simulated samples calibrated on the remaining experimentalsamples, and tested the predictive model on the held-out set. As previouslydescribed, we created simulated data sets of the same cohort sizesas our experimental data and repeated the simulation 10 times, resultingin a total of 100 ROC-AUC estimates. With this approach, we stillachieved a similar performance to what we expected from the experimentaldata estimates, with a mean ROC-AUC of 0.85 . Althoughthe approach based on modeling the case measurements using experimentalcase spectra  only marginally improved the classification performanceand reduced the relative standard deviation as the plateau of asymptoticperformance was reached. It was apparent, however, that the classificationperformance significantly suffered when smaller cohorts were used compared to using larger cohorts. Moreover, the standarddeviation became intolerably large with smaller sample sizes, makingit difficult to construct conclusive estimates when only limited cohortsizes are available.We observed a similar dependence between the cohortsize and theperformance of detecting lung cancer for simulated and experimentaldata 3. Thisi36 Furthermore, it served as anadditional validation that the simulation model was able to reproducethe results obtained from experimental observations.This result showed that the cohort sizesused were sufficientlylarge, which is reflected by the well-known fact that the predictiveperformance of a machine learning classifier reaches a plateau abovea certain cohort size.ϵ, modeling the biological variability andmeasurement noise in levels similar to that of the experimental observations.The standard deviations of these two coefficients are tunable, thusenabling the scaling of these levels noise factors seen within thespectra and thereby simulating different experimental conditions.We generated artificial cohorts considering varying levels of thesenoise factors and examined their effects on classification performance.For each level of measurement noise and biological variability considered,we simulated 10 cohorts and cross-validated (10-fold) on each simulatedcohort—averaging the ROC-AUC scores across the 100 total testdata splits.After demonstrating that the calibration procedure generated artificialcohorts that captured the properties of our experimental results,we examined the potential effects of measurement noise and biologicalvariability on the performance of detecting lung cancer 4. When dOverall, we found that the dominating source ofnoise, which maskedaway the signal separating the classes, was the level of biologicalvariability 4A. With 14 This allowed us to estimate the potential classification performanceof detecting cancer in a personalized, longitudinal health-monitoringscenario. By tuning our random coefficient β to model the levelof within-person variability , we estimatedthat such a classification would yield a ROC-AUC of 0.98 for lungcancer detection  yielded an estimated ROC-AUCof 0.92. Nevertheless, varying both the biological variability andmeasurement noise simultaneously revealed that the effect of the measurementnoise on the retrieved ROC-AUC also depended on the level of biologicalvariability. For instance, when the level of biological variabilitywas highest, reducing the measurement noise resulted in substantialclassification performance gains (in terms of ΔROC-AUC). However,when the biological variability was lowest, measurement noise hadlittle effect on the retrieved ROC-AUC—with the classificationperforming with perfect efficiency. Thus, advances in spectroscopicmethods which result in further reductions of measurement noise maybe particularly promising for problems with low classification efficiencies.Compared to the effect of biological variability,varying the levelof measurement noise was found to have a substantially smaller effecton the classification performance 4A,C. Elim unique and independentcalibration vectors bi of a given sample pool. Each vector represents the absorbancespectrum combining all molecular constituents in a given molecularsample, combining all their respective concentrations. As previouslydescribed, changing the number of independent vectors considered whencreating an artificial sample would mathematically correspond to changingthe number of unique molecular species creating the full blood-serumspectrum.Within the proposedsimulation model, the biological variability of the molecularly complexblood-based spectra was modeled according to two aspects: the totalstrength of biological variability, determined by the statisticalvariable β, and the structure of the underlying biological variabilityas determined by the set of b® for each sample pool constant andrandomly selected m different experimental calibrationvectors bi to create artificial cohorts at varying values of m  on each simulated data set. We observed that when the molecularcomplexity was modeled with m ≤ 20 independentbiological calibration vectors for each sample pool, perfect classseparation can be achieved with ROC-AUCs of 1.0  and can potentially be exploited with next-generationinfrared spectrometers.37 A greater advantagecame from reducing biological variability, which is more challengingto realize as it is inherent to any biological setting—froma single gene38 to an organismal level.13 A possible strategy here is populational stratification, or groupingindividuals into defined strata , to reduce the biological variability within eachstratum. Although widely established in genomics,39 it needs to be carefully controlled to not lead to spuriousassociations. Furthermore, the concept of longitudinal self-referencingcould be incorporated in fingerprinting for disease diagnostics andhealth monitoring.40 The within-personbiological variability of blood-based infrared fingerprints is neara factor of 2 lower than the between-person variability when monitoredover a span of 6 months.14 Using our model,we estimated that this would improve the ROC-AUC from 0.88 for groupcomparison to 0.98 for longitudinal self-referencing when detectinglung cancer.To investigate these effects, we systematicallyadjusted the parametersof our validated Further investigations showed that identifyingspectral patternsusing infrared molecular fingerprinting may be fundamentally limitedby the molecular complexity of the analyzed samples. By reducing thenumber of different molecular species considered for generating thecohorts, perfect class separation was achievable when less than 20unique spectra were used for each sample pool—regardless ofthe level of biological variability. By increasing the cohort size,near-perfect separation could be achieved even with increasing molecularcomplexity—albeit, this requires exponentially larger cohortsizes. This indicated that infrared fingerprinting of complex biologicalsamples  is only able to unambiguously determinea certain number of independent variables, or molecular species, withincomplex samples. The actual number of detectable independent variablescertainly depends on measurement properties such as the spectral bandwidth,spectral range, and the type of sample under investigation. Such questionscan be investigated with the presented model but would exceed thescope of this study. Considering our applications using blood-serum,we found that reducing the chemical sample complexity could be a viable option to improve the classification performance.33 to investigate the effects of factors like molecular mixing andvariance of molecular concentrations introduced through manufacturing.Critically seen, we initially developed our approach to calculaterealistic fingerprint spectra of blood serum by considering the individualspectral contributions of the majority of molecular species containedtherein. Since this requires information on individual molecular concentrationsin blood for different groups of the human population and also theuse of a large number of individual component spectra, with the majorityunavailable, we could not realize this approach. Instead, we chosea descriptive approach that relies on measured spectra of the molecularlycomplex samples from observational studies. We have, however, shownthat both approaches follow a similar mathematical formulation. Toassess whether they have similar explanatory power, a direct comparisonis necessary and which is already ongoing. At the same time, we believethat the bottom-up approach can facilitate the analysis of simplersystems, such as pharmaceutical samples,Figure S8 in the Supporting Information).Nevertheless, this approach has its advantages. It is well suitedfor exploratory analysis of any fingerprinting settings and couldhelp estimate to what extent a change in a certain molecule  would be detectable by fingerprinting. For suchinvestigations, one would only need to record the spectra of the targetmolecule independently and then use the calibrated data from thiswork to generate simulated spectra with and without contributionsfrom the target spectra.A disadvantage of the descriptive approach is that the best agreementwith experimental results is only achieved when healthy and diseasedsample pools were calibrated separately. Choosing a simplified approachthat describes the occurrence of a disease by a single discriminantvector, but otherwise only considers the biological variability ofthe healthy cohort, reduces the agreement with the experimental results to how different classification algorithmsperform under different simulated conditions. To facilitate the useof the model, we provide a Python toolbox alongside executable scriptsto reproduce and extend the results of this study (Sections 2 and 3 in the Supporting Information).In general, the core idea of the proposed insilico approachcapable of modeling molecular fingerprints of complex biological systems.We calibrated our model using experimentally measured infrared spectraof blood sera and applied it to the detection of lung and prostatecancer. Excellent agreement between the statistical properties andclassification results of simulated and experimental data was achievedfor both applications. The validity of the approach was further supportedby the finding that machine learning models trained on simulated dataand tested on experimentally measured data delivered comparable results.We demonstrated that the newly developed model enables investigationsof the potential and limitations of the molecular fingerprinting frameworkand that it can serve as a platform for exposing opportunities toadvance molecular fingerprinting applications.In this study, we describe an


The improvements due to small incision lenticule extraction (SMILE) in vision, e.g., in spherical equivalent (SE) and visual acuity (VA), has been widely recognized. However, the contrast sensitivity (CS) change after SMILE was not certain. Here, we investigated the effect of SMILE on CS before, 1 day after and 7 days after surgery and then clarified the corresponding mechanism by using a perceptual template model (PTM). In addition, the relationship among SE, VA, and CS was discussed. The quick contrast sensitivity function (qCSF) was applied to measure CS with high precision and accuracy. We found that (1) CS was significantly improved 1 day after SMILE and was also increased 7 days after the surgery, (2) CS improvements were dependent on spatial frequency and external noise, (3) the increase in CS was due to the decreased internal additive noise and an enhanced perceptual template, and (4) Greater SE improvements predicted better VA improvements 1 day after SMILE, and a positive correlation between SE improvements and AULCSF improvements 7 days after SMILE was observed. These findings help us better understand the effect of SMILE and provide effective indicators for future visual research. Myopia has become a global issue. To correct myopia, patients are increasingly inclined to have corrective surgery, especially small incision lenticule extraction (SMILE), which has been proven to be a safe, effective and stable method to improve visual performance ; for exaTwo indicators, diopter (D) and visual acuity (VA), are the characteristics most commonly used in studies that assess visual function after SMILE: D is a measure of the refractive error, and myopia is defined as ≤−0.50 D ; VA is aUntil now, how CS changes after SMILE remained uncertain. For example, using the CSV-1000 test, Second, prior studies hardly accounted for the influence of external noise. The complexity of the real-world environment can affect visual performance. For example, bad weather conditions  limit a driver’s vision and pose potential driving risks . The extIn brief, the objectives of this study included (1) determining how SMILE affects D, VA, and CS, (2) examining whether or not CS increase depends on SF, external noise, and recovery time, (3) investigating the possible relation among improvements in vision indicators, and (4) revealing the relevant mechanisms using the PTM.f) = 0.25, α = 0.05, and the power of the tests (1 − β) = 0.95, the required sample size for a repeated-measures analysis of variance (ANOVA) was six. We recruited fourteen participants (aged 18 years or older) from Shijiazhuang People’s Hospital. Before the surgery, they all voluntarily signed informed consent for data collection. Patients were included when they maintained stable refraction for at least 1 year, used soft contact lenses but discontinued use for 1 week or used rigid gas permeable lenses but discontinued use for 3 weeks prior to the procedure; their minimum corneal thickness was 480 μm and residual stromal bed was at least 250 μm. Patients were not included if they had other serious eye diseases except myopia and astigmatism. This study was authorized by the Ethics Committee of Hebei Normal University and the Ethics Committee of Shijiazhuang People’s Hospital. We adhered to the tenets of the Declaration of Helsinki.To determine the required sample size for this study, we used G*Power 3.1.9.7 . Based o2 background brightness. The viewing distance was 176 cm. Only one eye of each participant was tested.All stimuli were produced by MATLAB + PsychToolbox and presented on a luminance-calibrated Apple (CRT) monitor with a resolution of 1,280 × 1,024, a refresh rate of 85 Hz and 36.3 cd/mThe experiment consisted of target stimuli and masks: target stimuli were vertical gratings at 10 SFs . All gratings were displayed for three cycles, and they were covered with truncated Gaussian envelopes to blur their edges. Masks were noise images that were obtained from Gaussian distributed pixel contrasts and were composed of the same number of noise elements (15 × 15). In each trial, the sizes of the signal and noise images were identical.Spherical equivalent (SE) refraction, which was measured by a TOPCON RM-8900 autorefractor, was the indicator of D, and it was equal to spherical power (DS) plus half the cylinder power (DC). VA was measured by using the E-chart at a distance of 5 m and was recorded as the logMAR value for statistical analyses. The smaller the logMAR value was, the better the VA outcome was.The qCSF method was used to measure the CSF . Each trThe whole experiment was divided into 3 stages: pretest, posttest 1 (the first day after surgery), and posttest 2 (the seventh day after surgery). Each test included measurements of VA, CS, and SE. VA and SE tests were performed in an illuminated room with the lights on. After a 5 min period of dark adaptation, CS was assessed by the qCSF method within 15 min.Repeated-measures ANOVA was used to analyze the influence of time point, external noise, and SF on CS and its improvements; repeated-measures ANOVA was used to analyze the influence of time point and external noise on the area under the log CSF (AULCSF) and its improvements; least significant difference (LSD) test was used for multiple comparisons; and Pearson correlation analysis was used to analyze the correlation among SE, VA, and CS.d′ represents the perceptual sensitivity of the patients and is expressed by The PTM was applied to effectively clarify the potential mechanisms of CS improvements. First, c is the signal contrast; β signifies the perceptual template gain; and γ implies the nonlinearity of the system. Nadd is the equivalent internal additive noise, Nmul is the equivalent internal multiplicative noise, and Next is the contrast of external noise. When d′ is a given value, τc can be calculated from where aA, mA and fA were utilized to adjust Nadd, Nmul and Next, respectively. Therefore, Then, for simulating CS changes due to SMILE, Nadd and β were dependent on SFs, while Nmul and γ were independent of SFs. In brief, the effects of SMILE on visual perception can be explained by two possible mechanisms: lower internal additive noise and enhanced perceptual template .The multiplicative noise was set as a constant when the slopes of psychometric functions were not altered from pretest to posttest 1 and posttest 2 . In addiF = 93.526, p < 0.001]. The LSD analysis found that SE at posttest 1 and posttest 2 was significantly better than that at pretest , reflecting the improvements in SE due to SMILE. In addition, due to the ceiling effect, there was no significant difference in SE between posttest 1 and posttest 2 (p = 0.787).A repeated-measures ANOVA was used to test SE. The main effect of time point  was significant . Specifically, the LSD test showed that VA at posttest 1 was significantly better than that at pretest (p < 0.001). VA at posttest 2 was also better than that at pretest (p < 0.001). Moreover, VA was comparable between posttest 1 and posttest 2, indicating that VA tended to be stable (p = 0.051).The result of a repeated-measures ANOVA revealed that SMILE could improve VA effectively. Time point had a significant main effect on VA . The two-way interactions between external noise and time point, between time point and SF, and between external noise and SF were all significant . The three-way interaction among the above variables was significant . The LSD test was used to analyze the influence of time points on CS under each SF when noise conditions changed (see p < 0.013). At 8 cpd, CS at posttest 2 was significantly greater than that at pretest and posttest 1 ; CS was comparable between posttest 1 and pretest (p = 0.131). At 16 cpd, there was no difference in CS among the three time points , which may be due to the floor effect. These analyses suggested that in the zero-noise condition, CS at 0.5–8 cpd could be raised through SMILE, and CS increased gradually with time. In the low noise condition, at 0.5 cpd, CS at posttest 2 was not significantly different from that at pretest and posttest 1 ; CS at pretest was significantly higher than that at posttest 1 (p = 0.039). At 0.67 and 8 cpd, there were no significant differences in CS among the three time points . At 1 cpd, CS at posttest 2 was better than that at posttest and posttest 1 ; no significant difference in CS was detected between pretest and posttest 1 (p = 0.136). From 1.33 to 8 cpd, CS at posttest 2 was the best, followed by CS at posttest 1 and CS at pretest was the worst . At 16 cpd, there were no significant differences among all time points, which may be due to the floor effect. The results indicated that in low noise conditions, the improvements due to SMILE were effective at 1–8 cpd, and postoperative CS improved continuously due to postoperative recovery. In the high noise condition, at 0.5 and 0.67 cpd, CS at pretest and posttest 2 was significantly better than that at posttest 1 ; CS was not significantly different between pretest and posttest 2 . At 1 cpd, CS at pretest was comparable to that at posttest 1 and posttest 2 ; CS at posttest 2 was greater than that at posttest 1 (p = 0.018). From 1.33 to 8 cpd, CS at posttest 2 was the best, followed by CS at posttest 1 and CS at pretest was the worst . At 16 cpd, CS at posttest 2 was significantly higher than that at pretest (p = 0.026); CS at posttest 1 was comparable to that at pretest and posttest 2 . These data revealed that at 1.33–16 cpd, CS increased gradually. In summary, SMILE improved CS effectively, but the effects were modulated by the time points, SF, and external noise. Specifically, under the three noise conditions, CS increased immediately at 1.33–5.33 cpd (posttest 1 vs. pretest), further improved with recovery from surgery at 1–8 cpd (posttest 2 vs. posttest 1), and generally improved after SMILE at 1.33–8 cpd (posttest 2 vs. pretest). In noise condition, SMILE temporally reduced CS at 0.5 and/or 0.67 cpd. In addition, SMILE was unable to increase CS at the highest SF (16 cpd), which may be due to the floor effect.The curves in nged see . In the F = 56.844, p < 0.001; F = 48.664, p < 0.001; F = 44.686, p < 0.001, respectively]; two-way interactions between time point and SF, between time point and external noise, and between SF and external noise were significant ; additionally, three-way interaction was significant . Additionally, The LSD tests revealed that the CS improvements first increased with SF, then after 2 or 2.67 cpd, it decreased with SF. Detailed p values of comparison were listed in To elucidate how the CS improvements were modulated by SF, we calculated it at posttest 1 (CS at posttest 1 minus that at pretest) and posttest 2 (CS at posttest 2 minus that at pretest). A repeated-measures ANOVA was conducted to assess CS improvements with three within-subject factors . The results showed that the main effects of time point, SF, and external noise were significant . The LSD tests revealed that among the three external noise conditions, AULCSF at posttest 2 was the best, followed by CS at posttest 1 and CS at pretest was the worst , indicating that AULCSF increased over time.To measure the effect of SMILE on CSF over all SFs, the AULCSF was calculated as shown in F = 58.454, p < 0.001; F = 41.402, p < 0.001; F = 11.271, p < 0.001, respectively]. The LSD tests showed that at two time points, AULCSF improvements decreased as noise increased , indicating the external noise depended AULCSF improvements. Among the three external noise conditions, AULCSF improvements at posttest 2 were significantly larger than those at posttest 1 , indicating that the recovery of CS required time. These findings indicated that AULCSF improvements were dependent on time point and external noise, with larger improvements observed as postoperative recovery progressed and at lower external noise levels.To explore whether the AULCSF improvements were dependent on external noise. They were computed at posttest 1 (AULCSF at posttest 1 minus that at pretest) and posttest 2 (AULCSF at posttest 2 minus that at pretest). Repeated-measures ANOVA was performed on the AULCSF improvements with time point (posttest 1 and posttest 2) and external noise  as two within-subject variables. The main effects of time point and external noise and the interaction between them were all significant . Thus, we removed mA from aA < 1) and enhanced the perceptual template (fA < 1); reduced model 1 (M1), which assumed that SMILE decreased internal additive noise (aA < 1); reduced model 2 (M2), in which SMILE was considered to improve the perceptual template (fA < 1); and the most reduced model (M3), in which none of the parameters were changed by SMILE. The best-fitting model had to satisfy both conditions of a comparable 2r value with the full model and the fewest free parameters covered. From the full model to the most reduced model, in order, the 2r values were 91.8% (aA and fA change), 60.6% (aA change), 37.9% (fA change), and 33.0% (no change). An F test was used to compare models. The 2r of the full model was significantly better than that of M1, M2 and M3 . Thus, the full model was determined to be the best-fitting model. To explore the relationship among SF, aA, and fA, we conducted two Pearson correlation analyses. SF was negatively correlated with aA and fA at posttest 1 ; similarly, SF had a significant negative correlation with aA and fA at posttest 2 . These results indicated that the changes in internal additive noise and perceptual template were larger as SF increased.To recognize the mechanisms underlying the postoperative CS improvements, the average data of all observers was fitted to the PTM . AULCSF improvement was not related to SE improvement . At posttest 2, increased SE was not correlated with increased VA but was positively correlated with AULCSF improvement . In summary, at posttest 1, the larger the SE improvement was, the larger the observed VA improvement was; at posttest 2, a larger SE improvement predicted a larger AULCSF increase.To determine the relation of visual indicators, we calculated improvements in SE, VA, and CS at posttest 1  and posttest 2 . CS was represented by AULCSF in the zero-noise condition. VA was measured by the LogMAR vision chart, and a lower LogMAR denoted better VA. At posttest 1, the correlation between SE improvement and VA improvement was strong  and SE improvement (X) were significantly correlated, which fitted the regression equation Y = −0.291–0.122X. Similarly, at posttest 2, according to the significant correlation between increased AULCSF (Y) and increased SE (X), the regression equation was established as Y = 1.583 + 0.477X.This study demonstrated that SMILE led to improvements in vision, including changes in variables  and the relationship among them; then, this study investigated the mechanisms of perception alteration with the PTM framework. CS improvements after SMILE were observed and dependent on external noise levels and SF conditions. CS recovery required time. Furthermore, SMILE reduced internal additive noise and enhanced the perceptual template, and both changes were SF dependent.Prior studies focused on the influence of SMILE and found improvements in VA and SE; however, the trend of CS change was uncertain. To clarify the CS change, the current study applied a Bayesian Algorithm based procedure called qCSF. Compared with traditional tests that need to measure CS at a limited SF and require a long test time, qCSF extends the adaptive stimulus search to both grating stimulus dimensions (frequency and contrast), which makes it less time-consuming  and avoiThe inclusion of external noise when analyzing CSF is another innovation of this research. The AULCSF is an overall assessment of CS at 10 SFs. We found that AULCSF increased with time  under different external noise levels, showing the effectiveness of SMILE and the quick recovery of wounds. In addition, AULCSF improvements decreased as external noise increased. Interestingly, when external noise was present, the CS at low SF  became significantly worse on the 1st day after SMILE and then was restored to normal levels on the 7th day after SMILE. This suggested that healing wounds requires time, and this phenomenon was sensitive to external noise levels.In the current study, we analyzed the underlying mechanisms of visual perception changes. The perceptual system is limited by an equivalent internal noise source; in other words, internal additive noise is associated with the efficiency of the visual processor. We found that CS improvements after SMILE could be attributed to lower internal additive noise and an enhanced perceptual template. In addition, the changes in internal additive noise and perceptual template were larger as SF increased. However, the PTM could not be used to analyze the data at intermediate and high SFs . This is because the CS at those SFs of myopia subjects was quite poor before SMILE, and a floor effect was observed. In other words, CS improvement was underestimated when SFs were higher than 2 cpd.Finally, although this study had the limitation of the relatively short follow-up time, our design mainly focused on recovery in the early stage after SMILE. According to previous studies, refractive stability was observed by one week postoperatively, which meant that vision tended to be stable instead of changing with time . Thus, wIn summary, SMILE is a promising newer technique that results in effective improvements in vision characteristics, including SE, VA, and CS; thus, the above factors should be taken into account when assessing visual changes. In addition to the improvements in vision, the influence of SMILE on quality of life is an important issue that should be explored in future studies.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the Ethics Committee of Hebei Normal University and the Ethics Committee of Shijiazhuang People’s Hospital. The patients/participants provided their written informed consent to participate in this study.PZ and HZ design and collected the data. PY and PZ analyzed the data. PY, PZ, and HZ wrote the manuscript. All authors revised the manuscript, contributed to the article, and approved the submitted version.This work was supported by the Social Science Foundation of Hebei Province , Science and Technology Project of Hebei Province (22556202K to ZW), Social Science Development Project of Hebei Province (20220202304 to ZW) and Natural Science Foundation of Hebei Province (C2012205046 to ZW and C2021205005 to PZ).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.https://www.frontiersin.org/articles/10.3389/fnins.2023.1132681/full#supplementary-materialThe Supplementary material for this article can be found online at: Click here for additional data file.


Promoting use of pharmacoeconomic models by formulary reviewers is a goal of the Academy of Managed Care Pharmacy (AMCP) Format for Formulary Submissions, but relatively few decision makers use such models, and many doubt that they provide meaningful input.To demonstrate how sophisticated disease-based pharmacoeconomic models can aid formulary decision makers when long-term outcomes data are lacking.The Center for Outcomes Research (CORE) Diabetes Model (CDM), a published, validated Markov pharmacoeconomic model that projects clinical and economic endpoints, was used to model the cost-effectiveness of exenatide, a new injectable antidiabetic agent that enhances glucose-dependent insulin secretion, in a standard cohort of type 2 diabetes patients (mean body mass index [BMI] = 27.5 ± 3 kg/m2), compared with a modified obese cohort (mean BMI = 35 ± 3 kg/m2) that was otherwise demographically identical at baseline to the standard cohort. The standard cohort was assumed to maintain baseline weight during treatment, and the modified obese cohort was assumed to experience weight loss of approximately 9% (mean = 3 kg/m2), with corresponding improvements in blood pressure, low density lipoprotein cholesterol, and triglycerides. We selected a 30-year time horizon because it was the time interval during which the CDM predicted most of the subjects would have died, and the costs obtained thus reasonably projected lifetime total direct medical costs for these cohorts. While treatment options certainly will change over a 30-year period, our goal was to estimate the incremental effect of exenatide over other available therapies.The model predicted reduced long-term treatment costs in obese patients, driven by an 11% decrease in cardiovascular disease burden and derived from the presumed weight loss. The incremental cost-effectiveness ratio (ICER) for adding exenatide over 3 years was $35,000/quality-adjusted life-year (QALY). Using a 30-year horizon, ICER values were $13,000/QALY versus insulin, $32,000 versus generic glyburide, and $16,000 versus no additional treatment. Exenatide dominated pioglitazone. By comparison, the 30-year ICER for exenatide versus insulin in the nonobese cohort was $33,000. These results were presented to the pharmacy and therapeutics (PandT) committee and influenced its decision to add exenatide to the drug formulary. While our modeling assumed certain patient characteristics , the PandT committee imposed only a step-therapy requirement to try either metformin or a sulfonylurea before trying exenatide and did adopt a nonspecific requirement for physician reauthorization of refills before the fourth pharmacy claim for exenatide.Disease-based pharmacoeconomic models may help third party payers project costs and be particularly useful when only data from short-term clinical trials are available. In the present case, the pharmacy staff of a health plan used a pharmacoeconomic model for drug treatment of type 2 diabetes provided by the manufacturer as part of the AMCP Format dossier process to project cost outcomes for exenatide, adjunct injectable therapy for patients taking metformin and/or sulfonylurea. The PandT committee approved the drug for inclusion in the drug formulary based in part on the results of the pharmacoeconomic model produced from the cost inputs entered into the model by the health plan pharmacists.


Escherichia coli is a common cause of urinary tract infections. Due to the increase in antimicrobial resistance (AMR) and global differences in antimicrobial susceptibility data, routine assessment of local antimicrobial susceptibility patterns is necessary to guide the selection of appropriate empirical therapy. The aim of this study was to evaluate the antimicrobial susceptibility patterns of community-acquired uropathogenic Escherichia coli within a catchment area in Dublin over a 13 year period, 2010–2022.E. coli bacteriuria during the study period, 2010–2022, were included in the analysis. Antimicrobial susceptibility testing was performed by disc diffusion as per the European Committee on Antimicrobial Susceptibility Testing recommendations. All mid-stream urine samples received from local general practitioners in which there was significant E. coli accounting for an average of 67 % of those. Overall AMR rates were highest for ampicillin (53.9 %), followed by trimethoprim (32.4 %), gentamicin (18.6 %), co-amoxiclav (16.5 %), ciprofloxacin (12.3 %), cephalexin (8.3 %), cefpodoxime (6.8 %) and nitrofurantoin (2 %). While rates appeared grossly static, statistically significant reduced resistance rates were noted for co-amoxiclav , cephalexin prior to 2019  and trimethoprim , with a statistically significant increase in non-susceptibility to cefpodoxime . An average of 11 407 urine samples per month had significant bacteriuria, with  In order to generate efficient empirical antimicrobial prescribing guidelines, knowledge of region-specific contemporaneous antimicrobial susceptibility patterns is pivotal. Our findings support the use of nitrofurantoin or cephalexin as empirical antimicrobial therapy within our setting. The authors confirm all supporting data, code and protocols have been provided within the article.Escherichia coli identified as the most common primary causative bacteria [Urinary tract infections (UTIs) are among the most common bacterial infections worldwide, with uropathogenic bacteria .Acute community-acquired (CA) uncomplicated cystitis or a lower UTI are typically a clinical diagnosis, characterized by suprapubic discomfort, dysuria and urinary frequency or urgency. Complicated or upper UTIs produce more systemic symptoms such as general unwellness, pyrexia, fatigue and flank tenderness .Clinicians generally prescribe empirical antimicrobial therapy for CA infections; with urine microbial culture and antimicrobial susceptibility testing (AST) requested in cases of complicated or upper UTIs, recurrent UTIs, resistant infections or failure of empirical therapy. Furthermore, a pretreatment mid-stream urine (MSU) sample from all patients and from those having recently completed a course of antimicrobial therapy where the risk of antimicrobial resistance may be higher are endorsed by the Health Service Executive (HSE), Ireland, in addition to the aforementioned scenarios . The ratHowever, the misuse of antimicrobials is a chief driver of acquired antimicrobial resistance (AMR) to regularly prescribed agents. AMR has been recognized by the World Health Organization (WHO) as an international emergency and one of the greatest potential threats to human health. Many first-line antimicrobials are now ineffective, with alternatives scare, more expensive and potentially more toxic . It is nDue to the rise in AMR and global differences in antimicrobial susceptibility data, routine assessment of local antimicrobial susceptibility patterns is necessary to guide the selection of appropriate empirical therapy .E. coli within a catchment area in Dublin over a 13 year period.The aim of this study was to evaluate antimicrobial susceptibility patterns of community-acquired uropathogenic E. coli in a single Dublin teaching hospital over a 13 year period, 2010–2022 inclusive, was performed.A retrospective analysis of the antimicrobial susceptibility patterns of community-acquired uropathogenic St Vincent’s University Hospital is a 600-bed academic teaching hospital providing care across over 50 different medical specialities in Dublin 4, Ireland.E. coli was isolated as the cause of significant bacteriuria were noted for analysis.All MSU samples received for microbiological analysis by general practitioners working within the catchment area were identified, as it was inferred that these samples represent CA-UTIs. Samples from which −1, associated with microscopic findings of at least 10 white blood cells per high power field, or a pure growth of 10 000 c.f.u. ml−1 when the white blood cell count was greater than 100 per high power field [Significant bacteriuria was defined as culture of a single bacterial species, i.e. pure growth, from the urine sample at a concentration of 100 000 colony-forming units (c.f.u.) mlE. coli appears as burgundy or pink colonies [Microscopy is performed on all MSU samples to determine the presence and quantity of red and white blood cells. Specimens with a white blood cell count ≥10 are routinely cultured using a sterile 1 µl calibrated loop. Chromogenic CPSO (bioMérieux) agar plates are inoculated and incubated in air for 16–24 h at 35–37 °C. The majority of organisms are identified based on their appearance – colour and morphology. colonies . SupplemAST is performed using the disc diffusion method according to the European Committee on Antimicrobial Susceptibility Testing (EUCAST) recommendations . AntimicP-value <0.05 was considered statistically significant.Antimicrobial susceptibility data were collected and processed through WHONET. IBM SPSS V.25 was used for further statistical analysis. Continuous variables were described as means and categorical variables as percentages and frequencies. Spearman’s correlation was performed to compare AMR trends. A E. coli accounting for an average of 67 % (IQR 65–67.4 %) of specimens. Proportions per month are depicted in An average of 11 407 (IQR 7919–14 847) MSU samples per month were reported as significant bacteriuria over the study period, with E. coli, were as follows: 58 % for cephalexin prior to 2019, 53.9 % for ampicillin, 32.4 % for trimethoprim, 18.6 % for gentamicin, 16.5 % for co-amoxiclav – uncomplicated, 16 % for co-amoxiclav – systemic, 12.3 % for ciprofloxacin, 8.3 % for cephalexin from 2019, 6.8 % for cefpodoxime and 2 % for nitrofurantoin.Overall, the non-susceptibility rates, in descending order of isolated s=−0.95; P=<0.001), cephalexin prior to 2019  and trimethoprim . However, a statistically significant increase in non-susceptibility to cefpodoxime  was observed. All other trends were not statistically significant.The evolution of antimicrobial susceptibility rates is shown in E. coli accounts for up to 90 % of CA-UTIs worldwide [orldwide . Empiricorldwide .In vitro antimicrobial susceptibility data for uropathogenic E. coli from Spain and Portugal have reported higher AMR rates than other European countries, and similarly the USA when compared to Canada [Bacterial uropathogen epidemiology and resistance patterns show marked inter-regional variability, and rates of AMR are continually changing due to diverse antimicrobial treatment regimens. o Canada . Even ino Canada .E. coli urinary isolates obtained from various regions in the USA and Canada. AMR rates to trimethoprim, nitrofurantoin and ciprofloxacin were found to be 21.3, 1.1 and 5.5%, respectively [The North American Urinary Tract Infection Collaborative Alliance (NAUTICA) study evaluated antimicrobial susceptibility data for outpatient ectively .E. coli AMR rates of 30 % for ampicillin, 15 % for trimethoprim and <3 % for co-amoxiclav, nitrofurantoin, gentamicin and ciprofloxacin [The later ECO.SENS study conducted in Europe and Canada reported floxacin .Our findings report higher AMR rates across all antimicrobials compared to the abovementioned, likely due to differing prescribing habits, antimicrobial stewardship (AMS) interventions and local and national policy .E. coli isolates studied in 2021, aminopenicillin – including ampicillin – AMR was reported at 53.1 %, fluoroquinolone – including ciprofloxacin – at 21.9 % and aminoglycoside – including gentamicin – at 9.6 %; all of which have been decreasing since 2017 [Looking at the most recent report from the European Antimicrobial Resistance Surveillance Network (EARS-Net) for invasive nce 2017 . Our finE. coli antimicrobial resistance patterns from a different Dublin catchment area from 2005 to 2014 [An 11 year retrospective study conducted in Dublin 1999–2009 identified ampicillin and trimethoprim as unsuitable empirical choices for CA-UTIs with a mean AMR rate of 62.1 and 33.5% respectively. Nitrofurantoin was the most active agent, with an AMR rate at 2.7 %, while ciprofloxacin and co-amoxiclav remained reasonable options, but a steady increase in AMR rates over the time period was noted . SimilarWhile these reviews were published from different institutions serving different areas during a different time period, our findings are very similar. Ampicillin and trimethoprim remain unsuitable empirical therapeutic options, with AMR rates of 53.9 and 32.4% respectively. Interestingly, non-susceptibility to trimethoprim decreased steadily over the study time period, at a rate found to be statistically significant. This is likely due to reduction in prescriptions based on knowledge of a high level of circulating trimethoprim AMR. On the other hand, nitrofurantoin continues to boast the lowest AMR rate of 2 %.AMR to fluoroquinolones and co-amoxiclav remains of global concern, with worldwide rates as high as 60 and 70%, respectively . EncouraE. coli are scarce. The initial high AMR rates displayed in Published AMR surveillance data for cephalexin for uropathogenic The AMR rate of 18.6 % for gentamicin is consistent with previously published studies reporting non-susceptibility rates of 0.3–36.6 % , with reCefpodoxime, although not a therapeutic antimicrobial, serves as a marker for β-lactamase production, specifically extended-spectrum β-lactamases (ESBLs), which confer resistance to β-lactam classes of antimicrobials. Non-susceptibility to cefpodoxime results in the isolate being tested for ESBL production using VITEK2 (bioMérieux), which simultaneously assesses of the inhibitory effects of cefepime, cefotaxime and ceftazidime, alone and in the presence of clavulanate, the latter of which inhibits ESBL production .Unfortunately, data regarding the exact proportion of ESBL isolates during this study period are unavailable due to electronic system errors. Nonetheless, a relatively benign overall AMR rate of 6.8 % for cefpodoxime over the 11 years may not be of concern, but the statistically significant increase in this rate should not be overlooked, as it may suggest an increasing proportion of ESBL isolates, usually driven by broad-spectrum antimicrobial use, and emphasizes the importance of AMS.In Ireland, most uncomplicated CA-UTIs are managed by primary care with short courses of empirical antimicrobials. In many cases, urine samples are only sent for microbiological evaluation following treatment failure and recurrent or relapsing infection, and therefore the levels of AMR observed amongst community isolates may overestimate the true rate of AMR in the community.As our study reports on antimicrobial susceptibility data from a single institution, the results are not readily generalizable. Furthermore, as the focus was on CA-UTIs, assessment of healthcare-associated UTIs represents an area of future investigation. Similarly, we did not correlate AMR rates with antimicrobial consumption data, direct community AMS interventions, infection prevention and control initiatives or AMR in animal and environmental reservoirs as part of One Health.E. coli AMR data will not only highlight emerging trends in resistance, but inform empirical antimicrobial prescribing guidelines, as well as recommendations for antimicrobial prophylaxis in urological procedures. Furthermore, it enables clinicians to make informed decisions regarding antimicrobial therapy for all infections likely to be caused by this organism.Surveillance of local AMR among uropathogens is increasing worldwide. Knowledge of antimicrobial susceptibility data in specific geographical locations remains an important factor in determining appropriate evidence-based empirical antimicrobial treatment. Our findings support the use of either nitrofurantoin or cephalexin as appropriate empirical antimicrobials for the treatment of CA-UTIs, as well as the need to continue rigorous AMS initiatives in the fight against AMR. The statistically significant increase in AMR to cefpodoxime warrants ongoing surveillance.


Three-dimensional (3D) conformations of a small molecule profoundly affect its binding to the target of interest, the resulting biological effects, and its disposition in living organisms, but it is challenging to accurately characterize the conformational ensemble experimentally. Here, we proposed an autoregressive torsion angle prediction model Tora3D for molecular 3D conformer generation. Rather than directly predicting the conformations in an end-to-end way, Tora3D predicts a set of torsion angles of rotatable bonds by an interpretable autoregressive method and reconstructs the 3D conformations from them, which keeps structural validity during reconstruction. Another advancement of our method over other conformational generation methods is the ability to use energy to guide the conformation generation. In addition, we propose a new message-passing mechanism that applies the Transformer to the graph to solve the difficulty of remote message passing. Tora3D shows superior performance to prior computational models in the trade-off between accuracy and efficiency, and ensures conformational validity, accuracy, and diversity in an interpretable way. Overall, Tora3D can be used for the quick generation of diverse molecular conformations and 3D-based molecular representation, contributing to a wide range of downstream drug design tasks.The online version contains supplementary material available at 10.1186/s13321-023-00726-8. Molecular conformation is important for determining a molecule’s chemical and physical properties. Conformation generation is also important in applications such as quantitative structure–activity relationships (QSAR), docking, and virtual screening for drug development –5. The iOver the past decades, generating an accurate 3D structure for a small chemical compound is not trivial. Molecular conformation can be physically determined using X-ray crystallography, but it is prohibitively costly for industry-scale tasks . Ab initRecent deep learning developments hold promise for improving the prediction of the conformation ensembles of small molecules. Generative deep learning can produce structural candidates by predicting possible valid coordinates or distance matrices of a molecule. Since directly generating the 3D coordinates of atoms from the molecular graph like CVGAE  faces thAlthough deep learning models have been explored for molecular conformation generation in the hope of combining high accuracy with fast sampling, they typically have the drawback of generating invalid conformations. Most graph neural network (GNN)-based methods fail to learn long-range interactions in graphs, and thus cannot accurately capture dependencies among dihedral angles, which would lead to conflicts among local structures. In addition, it is difficult for distance geometry-based methods to enforce geometric graph constraint , hence tHere, we build a deep learning model, namely Tora3D, to predict the torsion angles combinations of all rotatable single bonds in a molecule from a 2D molecular graph, to obtain the set of predicted conformations. Like systematic approaches, our methodology follows a basic assumption that the conformational space mainly originates from the rotation of single bonds in the molecule, while keeping bond lengths and angles  constantk denotes the true number of conformations of a molecule.Firstly, the symbols and notations used here were summarized in Table echanism . The \do0) of the molecule from the 2D molecular graph (G) (Eq. 0)   of the molecule from the 2D molecular graph (G) -invariant by focusing on torsional space specific to the molecule (intrinsic coordinates).Thus, by the above process of 0 as input and output the sequence of predicted torsion angle values The Transformer module (Ft) Fig.  is used entclass1pt{minima0 to the sequence of updated torsion angle representations T  and Matching (MAT) score are used to measure the diversity and accuracy respectively. COV score reports the percentage of reference conformers that are produced by the predicted ensemble. MAT score reports the minimum RMSD between a generated conformer and the references. Following the conventional Recall measurement, COV-R and MAT-R can be defined as :13\documHere, We have compared Tora3D with several recent popular models of molecular 3D conformation prediction: CVGAE , GraphDGPosition embedding is devised to capture the position/location of the node within the broader context of the graph structure to tackle the problem that conventional GNN architecture hardly learns long-range patterns in graphs. The importance of the position embedding in Tor3D is verified by an ablation experiment by removing the position embedding from the model or replacing it with a learnable position embedding that represents the position of atoms. It can be seen in Table In addition, Tora3D uses a basic assumption same to systematic methods that the only factor changing the conformation of a molecule is rotatable bonds. The number of rotatable bonds (nRotb) plays a decisive role in molecular flexibility, as the space of possible conformations grows exponentially with it. Thus, nRotb would affect the prediction performance Fig. . We haveGiven that some inherent defects in typical conformation prediction models would cause conformational invalidity and undermine model performance, we have introduced some strategies into Tora3D to improve its validity.The first and most important strategy of Tora3D is its autoregressive algorithm. Tora3D predicts the torsion angles of rotatable bonds in a molecule one by one to avoid arising conflicts among local structures, and thus the current torsional angle value is determined not only by the molecular graph but also by the previously predicted torsion angle values. Most deep learning-based conformation generation models do not consider the dependencies among the local structures of a molecule and predict each dihedral angle as an independent variable, which will inevitably lead to invalidity of the overall molecular conformation. For example, topologically distant fragments of a molecule may conflict with each other in space. Figure At the same time, the autoregressive approach could further ensure the spatial rationality of the whole molecule by attention mechanism. As shown in Fig. The other strategy is to incorporate prior knowledge of local 3D structures of each non-terminal atom, to ensure the validity of conformational generation. The main challenge in molecular conformation generation comes from the enormous size of the 3D structure space consisting of bond lengths, bond angles, and torsion angles. However, the molecular graph imposes specific constraints on the set of possible stable local structures, which can be predicted by fast cheminformatics methods. Thus, Tora3D incorporates the prior knowledge about bond lengths and angles to guarantee validity by assembling fixed local structures directly from an initial conformation.Using fixed local structures can avoid the prediction error for symmetric graph nodes  and ring structures that seriously undermines the accuracy of many 3D prediction model. For example, GeoMol and ConfVAE generate invalid conformation of non-planar rings, such as hexahydropyridine shown in Fig. Hence, as the accuracy of local structures significantly impacts many models’ performance, Tora3D reconstructing the conformation by a two-stage generation procedure that utilizes predicted torsion angles to assemble fixed local structures can be of practical value. Even if a conformation generated by Tora3D is not in the provided conformation set, it still conforms to the chemical rules and is thus valid and usable.0 that allows the model generates multiple conformations, Tora3D can generate a set of conformations with geometrical diversity by varying relative energies as model input. As shown in Fig. Common methods of conformational sampling in machine learning-based models are random initialization and RDKit initialization. An RDKit initialization can achieve better accuracy by providing a more accurate starting point, while a random initialization can achieve better coverage by the sufficient sampling of the space . To promDue to the extension of the application scope of molecular 3D structure in the field of drug development, the methodology of molecular conformation generation continues to develop. Here, combining systematic search methods and advanced deep learning models, we propose a deep learning-based model to predict the torsion angles of rotatable bonds in a molecule, thereby predicting molecular conformations. Tora3D is superior to a series of baseline models with comparatively high accuracy but does not sacrifice efficiency. In the aspect of conformational validity, Tora3D employs an autoregressive approach to predict all torsion angles, so that the problem of the collision between local structures can also be solved in an interpretable way. The autoregressive algorithm could consider every rotatable bond sequentially to avoid clashes among local structures, and further improve the spatial rationality of the whole molecule by attention mechanism. At the same time, reconstructing the conformation by a two-stage generation procedure avoids many invalid local structures. In the aspect of conformational diversity, by varying relative energies as model input, Tora3D can generate energy-specific conformation ensemble with good coverage. In addition, as an improvement in model structure to promote accuracy, we proposed a new method of position encoding on graphs that compensates for the difficulties of traditional GNN long-distance messaging. The ablation test of the position vector verified that Tora3D outperformed traditional GNN to solve the problem of long-distance information passing.Tora3D is a promising tool to generate valid and diverse molecular conformation sets with competitive accuracy and efficiency. Its performance is particularly high for drug-like molecules with rotatable bonds less than 10. Especially, energy-guided conformational generation provides many possibilities for model application in the field of drug design, as conformational energy is crucial to understand how a molecule binds to a specific target protein. In future work, we will do more rigorous explorations and we expect that Tora3D will be applied in a variety of downstream tasks including large-scale virtual screening, molecular property prediction, and drug-target interaction prediction, thus speeding up areas of drug discovery.Additional file 1: Figure S1. The distribution of the number of conformations.Figure S2. The true and predicted conformations for chiralmolecule, spirans and macrocycles. Figure S3. The comparison of thetorsion angles predicted by Tora3D with the corresponding statisticaldistribution provided by Torsion Library. Figure S4. The conformationcomparison between Tora3D and Conformator. Table S1. Hyperparameters.Table S2. The prediction performance for chiral molecules, spiransand macrocycles. Table S3. Performance comparison with TorsionNET onthe GEOM-drugs dataset (Test set II). Table S4. Performance comparisonwith Conformator’s initial conformations on the GEOM-drugs dataset (Testset II). Implementation of Torsion Library. Implementation of TorsionNet.The comparison between the initial conformations and Tora3D’s generatedconformations. Loss Tora3D’s Loss.