Background: Aerosol-producing dental procedures are of concern in the spread of infections, especially during the COVID-19 pandemic. Periodontal prophylaxis is the most common aerosol-producing procedure conducted in dental practice globally. During COVID-19, many national and international organizations advocated the use of pre-procedural mouth rinsing to prevent the spread of infections from aerosol-generating procedures in the dental setting; however, many questioned the scientific basis for such recommendations.Objective: This systematic review aimed to evaluate the effectiveness of pre-procedural rinsing when preforming periodontal prophylaxis in reducing aerosol contamination in the dental setting.Methods: A comprehensive standardized search strategy was employed, informed by a defined PICO question across four electronic databases. The review of the literature was conducted using the PRISMA framework. Agreement between assessors was determined throughout. Synthesis of study characteristics and key outcomes were conducted. Cochrane's risk-of-bias tool for randomized trials (RoB 2) was employed to assess the quality/bias among studies.Results: The initial search yielded 731 citations across the four databases; 95 potentially effective studies were identified, with 56 effective studies found. Thirty randomized control trial studies were identified, 21 with a focus on effectiveness of pre-procedural mouth rinsing, involving 984 participants (aged 18–70). Agreement between assessors was high (Kappa >0.80). Various pre-procedural mouth rinses were tested, most frequently chlorhexidine (CHX) in 18 studies. The concentrations, volume, and prescribed duration of rinsing varied among studies, hampering meta-analyses. Nonetheless, all studies identified significant reductions in bacterial contamination, as measured by colony forming units (cfu). The effectiveness of CHX over other agents was evident with more than half of the studies (7/15) reporting over a 70% reduction in bacterial contamination (cfu). There were concerns over the risk of bias in most studies (76.2%); 19.0% had a high risk of bias and 4.8% were of low risk of bias.Conclusion: There is substantial evidence to support pre-procedural mouth rinsing, such as with chlorohexidine, to effectively reduce aerosol contamination when performing periodontal prophylaxis compared to mouth rinsing with water or not rinsing. The cornerstone for maintaining periodontal health is effective dental plaque control. This may be achieved through a combination of diligent home care practices and compliance to scheduled dental visits, whereby the dentist or hygienist undertakes the necessary scaling, polishing, and root debridement using ultrasonic scalers and air polishers. For individuals who have been affected with periodontitis, it is particularly crucial that they comply with scheduled dental visits regardless if they are in the active or maintenance phase of periodontal therapy.Interruptions to regular dental services have been unexpectedly imposed due to the COVID-19 pandemic as part of the collective efforts to reduce the risk of transmission within dental clinics. Around the globe, the dental fraternity resolved that only urgent and emergency dental care should be permitted during the pandemic. Amidst dynamic health and community-related updates of the pandemic, the dental profession developed various guidelines to assist dentists to make appropriate clinical decisions in the management of their patients . These gFor patients with periodontal concerns, this disruption to scheduled periodontal therapy may not cause immediate pain or discomfort. Nonetheless, postponement of regular care can aggravate or be detrimental to their oral health status, may increase their risk of non-communicable diseases, or worsen their health status, particularly those with underlying systemic conditions . Non-surOne of the most widely advocated methods to reduce the level of contamination in the aerosol during dental procedures is pre-procedural mouth rinsing . The aimP) was periodontitis patients receiving interventions (I) for reducing aerosol contaminations during non-surgical prophylaxis including dental scaling and tooth polishing, root planning or debridement, and air polishing using powered instruments with/without use of adjunctive antimicrobials. Findings from the relevant studies were to be compared (C) to the subjects or patients that did not receive similar interventions, with the primary outcomes (O) of interventions being reduced aerosol contamination.The PICO strategy  was emplElectronic database searches were conducted on Scopus, MEDLINE via PubMed, Cochrane Library, and Web of Science up to 8th April 2020 using the predefined keywords “aerosol” and “dental prophylaxis”  and Dental Public Health , and a dental graduate (N.S.). Initially, titles and abstracts were independently screened by two reviewers (S.M-S. and N.S.) to identify potential effective studies. Then, full-text articles were retrieved for the secondary screening by two additional reviewers  for consensus on the eligibility. Disagreement between reviewers were resolved with the supervising author (C.M.) and Kappa statistics was used to assess the agreement between assessors throughout.In synthesis of evidence from ‘effective’ studies, details included authors, article publication year, design of study, sampling size and allocation of test and control groups, details of intervention, type and description of periodontal prophylaxis procedures, and primary outcomes in terms of statistically significant findings and reduction of aerosol contamination between groups measured by colony forming units (cfu) using means and percentages. If permissible, mean cfu reduction % was calculated by Mean cfu reduction (%) = [/total amount of mean cfu at baseline] x 100%. When baseline data were not provided in the articles, mean reduction percentage was calculated as Mean cfu reduction (%) = /total amount of mean cfu for control group] x 100%.Yes (Y), Probably yes (PY), Probably no (PN), No (N), and No information (NI) available were made. Finally, the overall risk-of-bias judgement was made for each article based on the criteria for Low Risk, judged to raise Some Concerns, or High Risk of bias. Agreement between evaluators for this section  were discussed with the supervising author (C.M.) and the inter-evaluator reliability was calculated using Kappa statistics.An assessment of quality and risk of bias assessment was conducted on effective studies that informed the review employing the revised Cochrane risk-of-bias tool for randomized trials (RoB 2) . DomainsK = 0.96). The full texts of potentially effective studies were then assessed, and 56 studies were identified as “effective studies” (Agreement between assessors was high K = 0.88). No additional studies were identified through “reference linkage” and among the effective studies 30 randomized control trial (RCT) studies were identified, 21 with a specific focus on pre-procedural rinsing  , 22, 26 Among the 21 studies, the majority  assessed bacterial contamination for aerosols . For theRisk of bias varied among studies, As the number of fatalities and morbidities due to COVID-19 continues to rise it is important to acknowledge that oral health remains an integral element of overall health and well-being . DespiteOur review is limited to aerosol contamination. Although our search was focused on microbial contamination in dental aerosol, there was no literature found on virus, which would provide more insight on possible COVID-19 cross-contamination in dental clinics. A majority of these studies included the use of CHX as the main antiseptic agent to reduce aerosol contamination, as it is a well-established antimicrobial agent used in dentistry. CPC has also been widely tested in anti-plaque and anti-gingivitis studies , 45 as win vivo viricidal studies in part relates to the challenges and associated risk in culturing the virus (In vitro studies have shown chlorhexidine (0.12%) to be effective against most viruses, in as little as 30 s  that could help provide safe alternatives to dental practices and healthcare for the public at large.A recent publication by Meister et al.  reportedContinuous periodontal care for periodontitis patients is of the utmost importance and must be done in a timely manner to prevent progression and reinfection of the disease, especially among high-risk individuals –56. ProgPre-procedural rinsing for clinical dental procedures has long been advocated for and, during the current pandemic, its importance to practice is more relevant than ever. A number of acceptable quality RCTs of pre-procedural mouth rinsing have been conducted. The focus has been on bacterial contamination rather than viral or fungal assay. A range of different pre-procedural mouth rinses have been tested (mostly chlorhexidine), of varying concentrations and amounts, and for dental procedures of different durations and using different techniques with and without additional means. We acknowledge that these systematic review findings do not differ much from the previous reviews, nevertheless it emphasizes on the importance of pre-rinsing particularly in periodontal prophylaxis procedures. This is especially important during the current pandemic where transmission bacterial-borne diseases could well be avoided by reducing the risk on dental personnel as well as the patients. This also supports the need to include the pre-rinsing procedure for dental patients as one of the mandatory SOP in current practice.Our systematic review found no remarkable new evidence on the effectiveness of pre-procedural rinse in periodontal prophylaxis, but it highlights the importance of the procedure to be given a mandatory emphasis in the current pandemic SOP in dental clinics. Pre-procedural rinsing for 30 s to 2 min with selected antimicrobial solutions compared to water or no rinsing were found to effectively reduce aerosol contamination in periodontal prophylaxis on dental patients. There is evidence that chlorhexidine (either 0.12 or 0.2%) is an effective antimicrobial solution for this purpose. The use of HVE during the procedure also helps to reduce the aerosol contamination.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.CM, TM-D, and SM-S: conceptualization. CM, TM-D, and SM-S: methodology. SM-S, HR, TM-D, and CM: analysis. SM-S, NS, and TM-D: writing—original draft preparation. SM-S, HR, TM-D, and CM: writing—review and editing. CM: supervision. All authors contributed to the article and approved the submitted version.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.


The volume of biomedical literature and clinical data is growing at an exponential rate. Therefore, efficient access to data described in unstructured biomedical texts is a crucial task for the biomedical industry and research. Named Entity Recognition (NER) is the first step for information and knowledge acquisition when we deal with unstructured texts. Recent NER approaches use contextualized word representations as input for a downstream classification task. However, distributed word vectors (embeddings) are very limited in Spanish and even more for the biomedical domain.In this work, we develop several biomedical Spanish word representations, and we introduce two Deep Learning approaches for pharmaceutical, chemical, and other biomedical entities recognition in Spanish clinical case texts and biomedical texts, one based on a Bi-STM-CRF model and the other on a BERT-based architecture.Several Spanish biomedical embeddigns together with the two deep learning models were evaluated on the PharmaCoNER and CORD-19 datasets. The PharmaCoNER dataset is composed of a set of Spanish clinical cases annotated with drugs, chemical compounds and pharmacological substances; our extended Bi-LSTM-CRF model obtains an F-score of 85.24% on entity identification and classification and the BERT model obtains an F-score of 88.80% . For the entity normalization task, the extended Bi-LSTM-CRF model achieves an F-score of 72.85% and the BERT model achieves 79.97%. The CORD-19 dataset consists of scholarly articles written in English annotated with biomedical concepts such as disorder, species, chemical or drugs, gene and protein, enzyme and anatomy. Bi-LSTM-CRF model and BERT model obtain an F-measure of 78.23% and 78.86% on entity identification and classification, respectively on the CORD-19 dataset.These results prove that deep learning models with in-domain knowledge learned from large-scale datasets highly improve named entity recognition performance. Moreover, contextualized representations help to understand complexities and ambiguity inherent to biomedical texts. Embeddings based on word, concepts, senses, etc. other than those for English are required to improve NER tasks in other languages. Efficient information extraction off biomedical data described in scientific articles, clinical narrative, or e-health reports is a growing interest in biomedical industry, research, and so forth. In this context, improved biomedical name mentions identification in the biomedical texts is a crucial step downstream tasks such as drug and protein interactions, chemical compounds, adverse drug reactions, among others. Named Entity Recognition (NER) is one of the fundamental tasks of biomedical text processing, intending to automatically extract and identify mentions of entities of interest in running text, typically through their mention boundary or by classifying tokens to match specific entity mentions. Traditionally, there are three phases in recognizing concepts in texts: 1) to identify the limits of the term or phrase that represents the concept in the text (char offsets in the text), (2) to classify the term or phrase on a class  and (3) to normalize the concept by assigning it an identifier in a specific domain resource such as UMLS [ to identThe NER task has been accomplished by three types of methods. Dictionary-based methods require having specific resources integrating terminology such METAMAP tool  that incMore recently, deep learning methods started to obtain better results in NER based on the use of pre-trained models (word embeddings) obtained from a huge volume of unlabelled texts . Word embeddings have been evolving from static representations that do not model the dynamic nature of words to contextualized representations that allow word embeddings to adapt to the context it appears, see  for a deIn this paper, we propose two deep learning approaches to face the recognition of pharmacological and chemical entities in Spanish texts. The approaches are evaluated using the Spanish biomedical PharmaCoNER and English biomedical CORD-19 datasets. Our main goal is to evaluate the performance impact of cross-domain  and cross-language (Spanish and English) pre-trained embeddings models. Firstly, for entity identification and classification, we implemented two bidirectional Long Short Memory (Bi-LSTM) layers with a CRF layer based on the NeuroNER model proposed in . SpecifiWe evaluate our deep learning models using the train, validation and test datasets provided by the task organizers of the PharmaCoNER Shared Task . The PhaMoreover, we evaluate our deep learning models on the train, validation and test subsets of the CORD-19 dataset . F-measuThe NER task is addressed as a sequence labeling task. For NER we tested different configurations with various pre-trained word representation models.For our Bi-LSTM CRF model we test various pre-trained and trained from the scratch word embeddings models  with the baseline NeuroNER model (without sense embeddings and BMEWO-V format encoding) using the same FastText-SBC embedding and configuration. Table Furthermore, we tested the FastText-2M English general domain and the Pubmed and PMC English domain-specific non-contextualized pre-trained embeddings models  and domain-specific English pre-trained (SBC-BERT) contextualized word embeddings. As shown in Table We compare our deep learning approaches with the participating systems presented in the PharmaCoNER task. A detailed description of the evaluation and the participant systems is provided in . As can Moreover, we test different contextualized word pre-trained models on the CORD-19 test dataset. As shown in Table For concept indexing or normalization, we applied the same approach described for SNOMED-SBC model training, replacing each entity detected in the entity recognition and classification step with their unique SNOMED-CT Spanish Edition identifier. First, we applied a lowercase conversion, then we replace abbreviations with their corresponding full concept name using the Spanish Medical Abbreviation DataBase (AbreMES-DB)  and the Our results for concept indexing are low due to a large number of misspellings entities, abbreviations ambiguity, drug names where the identifier corresponds to the active substance as “durogesic” (“Duragesic”) active ingredient “fentanyl” (“fentanyl”), identifiers not existing in SNOMED CT, such as CHEBI:135810 and 373757009 and false positives, such as diseases identified as NORMALIZABLE entities and PROTEIN entities not annotated in the PharmaCoNER corpus.We used different pre-trained models and investigated their effect on performance. For Extended NeuroNER, we used general and specific-domain pre-trained word embedding models, likewise we used pre-trained multi-language and language-specific models. We found that the use of a domain-specific  and language-specific pre-trained models highly improve the NER task. In addition, to the best of our knowledge, there is no open pre-trained biomedical Spanish model for context-dependent word representations (pre-trained BERT). The base BERT model without extensions outperforms Extended NeuroNER model and other PharmaCoNER participant approaches, mainly due to its capability to deal with ambiguity problems.We found that the text pre-processing (sentences split and tokenization) step had a significant impact on the entity offset recognition and classification mainly due out-of-vocabulary words. Additionally, we analyzed the confusion matrices for PharmaCoNER  and concept embeddings have been generated for this work.More initiatives to foster the availability of sufficiently large clinical narrative corpora in Spanish from hospitals or regional health systems are necessary. This will allow us to train embeddings of different types such as knowledge enhanced word embeddings that combine text corpora with terminology resources. Resources similar to clinical concept embeddings (cui2vec) obtained in  from 20 As future work, we plan to enhance the SNOMED-CT concept representations in concept indexing step. Furthermore, we plan to generate contextualized word representations integrating biomedical knowledge into our system such as SNOMED-CT or UMLS. The motivation would be to see whether contextualized word representations generated with biomedical knowledge can help to improve the results and provide a deep learning model for biomedical NER and concept indexing.In this section, we described our NER approach. Additionally, we introduce the corpora used to generate our train from the scratch contextualized and no-contextualized word representations. Furthermore, we described our deep learning approaches. We first present a deep network with a pre-processing step, a learning transfer step, then two recurrent neural network layers and the last layer with CRF classifier and a deep learning model based on a multi-layer bidirectional transformer encoder. Finally, the datasets used for training, validating, and evaluating our deep learning models performance.In order to train our model, first texts must be preprocessed to create the input for the deep network. Sentences are split and tokenized using Spacy , an openThe BMEWO-V encoding distinguishes the B tag to indicate the start of an entity, the M tag representing the continuity of an entity, the E tag as the end of an entity, the W tag for indicating a single entity, and the O tag to represent other tokens that do not belong to any entity. The V tag allows representing overlapping entities. BMEWO-V is similar to other previous encodings ; howeverThe Spanish Bibliographical Index in Health Sciences (IBECS) corpus  that colScientific Electronic Library Online (SciELO) corpus  gathers MedlineNLM corpus obtained from the PubMed free search engine .The MedlinePlus corpus   and TXT format files. XML files were processed for extract only raw text from specific XML tags such as “title” and “description” from Spanish labels, based on the Dublin Core format as shown in Fig. Our proposal involves the adaption of a NER model named NeuroNER  based onTo facilitate our model training, we first perform a learning transfer step. Learning transfer aims to perform a task on a dataset using knowledge learned from a previous dataset . As is sAlthough word embedding models are able to capture syntactic and semantic information, other linguistic information such as morphological information, orthographic transcription or POS tags are not exploited in these models. According to , the useIn this work, we used various Spanish and English pre-trained embedding models. The Spanish Billion Words Corpora (SBWC)   datasets. The PharmacoNER dataset is a manually annotated corpus of 1000 clinical cases written in Spanish and annotated with mentions of chemical compounds, drugs, genes, and proteins. The dataset consists of Normalizables 4398), No Normalizables (50), Proteins (3009), and Unclear (167) labels. Further details can be found in [, No NormThe CORD-19 dataset consists of over 181,000 scholarly articles written in English about COVID-19, SARS-CoV-2, and related coronaviruses. The dataset is manually annotated with disorder , species , chemical or drugs , gene and protein , enzyme (1480), anatomy , biological process (7765), molecular function (1722), cellular component (1099), pathway (517) and microRNA (690) unique entities. Further details can be found in . In orde


Multi-modal three-dimensional (3-D) image segmentation is used in many medical applications, such as disease diagnosis, treatment planning, and image-guided surgery. Although multi-modal images provide information that no single image modality alone can provide, integrating such information to be used in segmentation is a challenging task. Numerous methods have been introduced to solve the problem of multi-modal medical image segmentation in recent years. In this paper, we propose a solution for the task of brain tumor segmentation. To this end, we first introduce a method of enhancing an existing magnetic resonance imaging (MRI) dataset by generating synthetic computed tomography (CT) images. Then, we discuss a process of systematic optimization of a convolutional neural network (CNN) architecture that uses this enhanced dataset, in order to customize it for our task. Using publicly available datasets, we show that the proposed method outperforms similar existing methods. Although vast amounts of medical images are produced each day, most are still interpreted through visual analysis on a slice-by-slice basis . This reSegmentation is a standard method of image analysis where an image is separated into different regions, typically based on the characteristics of its pixels  images) or voxels  volumes) -D volume and pathDeep learning techniques have gained considerable interest in the field of multi-modal medical image segmentation due to their self-learning and generalization ability over large-scale data . FurtherFusion methods for deep-learning-based multi-modal image segmentation can broadly be categorized as: input-, layer-, and decision-level fusion . In inpuZhou et al.  used fouSimilarly, Wang et al.  also prok represented the number of modalities in the input layer. In layer-level fusion, every modality’s network layers were learned from corresponding features independently. Then, they were used to train a single network to perform the segmentation task. For decision-level fusion, the output of each network’s prediction was used to decide the final segmentation through voting.Guo et al.  proposedChoudhury et al.  investigSun et al.  investigIn this paper, we explore different deep learning architectures and training/testing strategies for the multi-modal segmentation of brain tumors using a publicly available dataset. We enhance this dataset by developing a method of generating CT images from MRI. Using experimental results, we show that using generated CT images improves the performance of deep learning models.Here, for the task of segmenting brain tumors using 3-D multi-modal images, we used data from a publicly available dataset that contained four types of MRI images. We investigated whether the segmentation accuracy could be improved by introducing an additional modality to this dataset, namely CT images. For this purpose, we developed a method of generating CT images from MRI. The MATLAB numeric computing environment (academic), including the image processing and deep learning toolboxes, was used to implement all methods. An Intel Xeno Silver 4108 CPU (1.80 GigaHertZ) processor with 5 gigabytes of NVIDIA QuADro P2000 GPU graphics memory and 16 gigabytes of physical memory, running the education version of 64 bit Microsoft Windows, was used as the main experimental platform. Some experiments were conducted on the University of Melbourne’s high-performance computing (HPC) system .http://www.insight-journal.org/rire/download_data.php, accessed on 1 October 2021) containing the 3-D MRI and CT pairs of 16 patients to train them [Generating one (synthetic) image modality from another is a convenient way to avoid intricacies involved in obtaining multiple images of the same patient. Pre-processing steps that enable the analysis of multiple images, such as co-registration, are also not required in this case. As such, there has been some interest in the field of synthetic data generation ,26,27,28ain them . We usedain them ,31 archiain them ,33,34,35ain them . To traiAs the existing dataset was relatively small, we augmented it by generating synthetic data based on the original data. To this end, we performed one of five different transformations  on each image, using the same transformation for each corresponding MRI and CT pair. To further augment the 2-D dataset of axial slices, we used random transformations  with each stage having multiple layers. There are two sets of convolutional  and rectified linear unit (ReLU) layers , followeTo optimize our network, we first tested depths from two to five and selected a depth of three, as it showed the best performance. Then, we used different training optimizers (stochastic gradient descent with momentum (SGDM) , root meSeveral state-of-the-art deep learning network architectures, mostly CNNs, dominate the field of medical image segmentation ,41,42. Whttp://medicaldecathlon.com/, accessed on 1 October 2021) [We used the publicly available BraTS dataset (this dataset can be downloaded from: er 2021)  in this For the 3-D CNN, we used volume patches of 32-by-32-by-32 as input and output. For the 2-D CNN, we pre-processed the data to remove slices with a large number of background pixels (those with less than 120 labeled pixels). We used the remaining slices  in the training and testing of the 2-D U-Net.First, we used the four different MRI scan modalities  together to perform the segmentation task. Then, we paired each of the MRI modalities with the synthetic CT to obtain four combinations of input. Finally, we combined all five modalities to form a new set of inputs. We modified the input layer to allow for the respective number of channels . The 2-D and 3-D U-Net architectures used for segmentation were the same as those shown in Additionally, we explored different fusion strategies: input-, layer-, and decision-level. For input-level fusion, we used one network with input images fused  index, peak signal-to-noise ratio (PSNR), and mean-squared error (MSE)  provide https://au.mathworks.com/help/vision/ref/evaluatesemanticsegmentation.html, accessed on 1 October 2021).The confusion matrix compares segmentation results with ground truth data and presents true positives (quations  to 3)) ) TP), trGlobal accuracy, the accuracy over all classes, and mean accuracy, the average over all classes, provide computationally inexpensive and quick estimations. IoU (or Jaccard similarity coefficient) is one of the most commonly used metrics, with the mean IoU representing the average IoU over all classes. Weighted average IoU (weighted by the number of pixels per class) is useful when class sizes are imbalanced. The BF score has been seen to correlate better with human perception, as the contour of the BF matching score shows how well the predicted boundary of each class aligns with the actual boundary . A mean We observed that the Adam optimizer performed best in the tumor segmentation task. As such, we tested its performance on different loss functions with all four MRI modalities of the original dataset provided as different input channels. Using the network that showed the best results from the previous section (3-D U-Net with Adam optimizer and Dice loss function), we compared the effect of different fusion strategies on tumor segmentation performance. To this end, we considered all five image modalities  together. As seen in Here, we evaluated the segmentation performance for the different MRI and synthetic CT combinations for input-level fusion, as it performed best in the previous experiment. We modified several state-of-the-art deep neural network architectures . We re-implemented these methods and in some cases, modified their architectures to suit our dataset. For example, for the network presented by Guo et al. , we usedWe discussed how a segmentation task, namely brain tumor segmentation, can be improved by (1) adding a synthetic CT modality to an existing MRI dataset and (2) optimizing network configurations. To generate synthetic CT images, we used a modified U-Net architecture. We experimentally demonstrated that the proposed method outperformed other similar state-of-the-art methods. In future work, we will explore how this work can be extended to other segmentation tasks/datasets. We will also investigate how other complex CNN architectures can be utilized for this purpose. Furthermore, we will consider multi-class segmentation, for example, to identify different regions of a tumor. A natural extension to this would be to classify segmented areas . We also aim to explore how our synthetic image generator could be utilized for other modalities .


Treatment of multidrug-resistant or rifampicin-resistant tuberculosis (MDR/RR-TB), although improved in recent years with shorter, more tolerable regimens, remains largely standardized and based on limited drug susceptibility testing (DST). More individualized treatment with expanded DST access is likely to improve patient outcomes. To assess the potential of TB drug resistance prediction based on whole-genome sequencing (WGS) to provide more effective treatment regimens, we applied current South African treatment recommendations to a retrospective cohort of MDR/RR-TB patients from Khayelitsha, Cape Town. Routine DST and clinical data were used to retrospectively categorize patients into a recommended regimen, either a standardized short regimen or a longer individualized regimen. Potential regimen changes were then described with the addition of WGS-derived DST. WGS data were available for 1274 MDR/RR-TB patient treatment episodes across 2008 to 2017. Among 834 patients initially eligible for the shorter regimen, 385 (46%) may have benefited from reduced drug dosage or removing ineffective drugs when WGS data were considered. A further 187 (22%) patients may have benefited from more effective adjusted regimens. Among 440 patients initially eligible for a longer individualized regimen, 153 (35%) could have been switched to the short regimen. Overall, 305 (24%) patients had MDR/RR-TB with second-line TB drug resistance, where the availability of WGS-derived DST would have allowed more effective treatment individualization. These data suggest considerable benefits could accrue from routine access to WGS-derived resistance prediction. Advances in culture-free sequencing and expansion of the reference resistance mutation catalogue will increase the utility of WGS resistance prediction. The last several years have seen radical changes in the way that persons living with multidrug-resistant or rifampicin-resistant tuberculosis (MDR/RR-TB) are treated for their disease . In the Mycobacterium tuberculosis strains have documented fluoroquinolone resistance or those whose strains may be at risk for fluoroquinolone resistance (including patients with previous exposure to second-line antituberculosis medications), documented resistance to injectable agents, extensive pulmonary disease, and factors that may predict more extensive second-line resistance, for example, the presence of both the inhA and katG mutations conferring isoniazid resistance in South Africa (based on unpublished surveillance data).While the use of all-oral regimens lasting 9 to 12 months has the potential to revolutionize the treatment of MDR/RR-TB, few countries have moved to implement them on a programmatic level . Those tThere are several challenges with this approach. First, factors associated with fluoroquinolone resistance do not correlate with actual resistance in many instances, which could lead to excluding patients who would benefit from shortened treatment regimens, as well as including patients who are unlikely to be successfully treated . Second,M. tuberculosis drug resistance , where all known resistance-conferring mutations are identified simultaneously, is increasingly being used to provide comprehensive DST to individualize MDR/RR-TB treatment in well-resourced settings , 12. In sistance  and, mor emerges . However emerges , 15. Sou emerges . In ordeM. tuberculosis isolates held in a biobank at Stellenbosch University. Ethical approval for collection of M. tuberculosis isolates in the biobank was granted by the Stellenbosch University Research Ethics Committee and approval for linking WGS data to clinical data were granted by the University of Cape Town Human Research Ethics Committee. All patients routinely diagnosed with MDR/RR-TB between 2008 and 2017 and with pretreatment (defined as up to 1 month after second-line treatment initiation) M. tuberculosis isolate WGS data available were included in the study cohort.This analysis applied current treatment recommendations to a retrospective cohort of MDR/RR-TB patients diagnosed between 2008 and 2017 in Khayelitsha, a subdistrict of Cape Town. Khayelitsha has a population of approximately 450,000, with high burdens of HIV, TB, and MDR/RR-TB. A detailed prospective clinical database was established in Khayelitsha to evaluate a decentralized program to diagnose and treat RR-TB from late 2007 onward . AnonymiM. tuberculosis strains develop resistance or if the patients develop intolerance to pyrazinamide, ethambutol, or isoniazid, these drugs can be removed from the regimen without replacement. If more than one drug is removed, either bedaquiline or linezolid treatment is lengthened.The standardized short (9 to 12 month) regimen for MDR/RR-TB in South Africa is composed of an intensive phase of 4 to 6 months duration containing; bedaquiline (minimum 6 months), linezolid (2 months), high-dose isoniazid, levofloxacin, clofazimine, pyrazinamide, and ethambutol. The 5-month continuation phase is composed of levofloxacin, clofazimine, pyrazinamide, and ethambutol. If patients’ katG and inhA isoniazid resistance-conferring mutations; and suspected or confirmed resistance to fluoroquinolones, injectable agents, bedaquiline, clofazimine, or linezolid. Patients fulfilling any one of these criteria receive an individualized long regimen ; close contact with XDR-TB (based on the pre-2021 definition of MDR-TB with both fluoroquinolone and second-line injectable resistance) or pre-XDR (MDR-TB with either fluoroquinolone or second-line injectable resistance); age <6 years; extensive TB disease on chest X-ray; the presence of both plus, Tübingen, Germany) is used for isoniazid DST. If isoniazid susceptibility is demonstrated by LPA, phenotypic INH DST is conducted on a cultured specimen . A second-line LPA  is used to detect resistance to fluoroquinolones and second-line injectables, with isolates that test susceptible subjected to phenotypic fluoroquinolone (levofloxacin) DST. Isolates with fluoroquinolone resistance are subjected to extended DST to a wider range of second-line drugs, including bedaquiline and linezolid. All DST conducted programmatically, both phenotypic and genotypic, is referred to as routine DST. Rifampicin monoresistant TB (RMR-TB) was defined as rifampicin resistance and isoniazid susceptibility.Currently, all individuals under investigation for pulmonary TB are tested with Xpert MTB/RIF Ultra , and culture if Xpert is negative for individuals living with HIV. If rifampicin-resistance is detected, a first-line line probe assay  to determine M. tuberculosis drug resistance-conferring mutations (Stored utations .katG and inhA). Data were not available for situations where second-line resistance was suspected, extensive disease on X-ray, or complicated EPTB. While EPTB was recorded on the clinical database, the disease site was not consistently recorded. Therefore, all EPTB was considered complicated, with consequent exclusion from the standardized short regimen. Data on the presence of isoniazid resistance-conferring mutations, derived routinely from the LPA, were also not consistently available. Where routine mutation data were missing, mutation data based on WGS were used instead. These data, related to the exclusion criteria, were used to categorize patients as receiving either the currently recommended standardized short or longer individualized regimen.Clinical and laboratory data pertaining to the exclusion criteria for the standardized short regimen were extracted from the routine database. Data were available for: previous second-line TB treatment, routinely diagnosed second-line drug resistance (fluoroquinolones and injectable agents), age, the presence of extrapulmonary disease, and the presence of both isoniazid resistance-conferring mutations .All sequencing data are available via online repository (European Nucleotide Archive) under accession number M. tuberculosis isolate WGS data were available for 1,274 patient treatment episodes between 2008 and 2017 inclusive. The mean read depth at drug resistance-conferring sites was 82 . There were no major differences between patients with WGS data available and those without, as previously described  could potentially have received the shorter regimen . These aThese data demonstrate that a significant proportion of MDR/RR-TB patients may benefit from more appropriate treatment regimens with access to expanded DST through WGS, even in a setting with relatively high access to routine line probe assay and phenotypic DST. Among patients who would have been eligible for the short regimen, 46% may have had their regimen adjusted through reducing dosage or removing ineffective drugs. A further 22% may have benefited from regimens that were adjusted to provide more effective treatment. Importantly, a third of patients not eligible for the short regimen could potentially have been re-allocated to the shorter regimen, with substantial patient benefits. These data also demonstrate that more complete DST data could be incorporated into an algorithmic approach to regimen allocation, such as that currently followed in South Africa, without adding significant complexity for clinicians in high MDR/RR-TB burden settings. They also show the potential importance of WGS data for providing information on resistance to medications such as ethambutol and pyrazinamide that are not commonly assessed in high-burden settings such as South Africa.M. tuberculosis strains that have fluoroquinolone resistance. Fluoroquinolone resistance is strongly associated with poor treatment outcomes (Overall, just over a fifth of all patients may be considered to have more complicated MDR/RR-TB requiring more individualized treatment. This includes young children, patients with EPTB and those with outcomes , and remoutcomes . For theoutcomes .There are a number of challenges for the implementation of WGS for patient treatment in high-burden settings , 21. TheMDR/RR-TB treatment, while much improved, remains lengthy and difficult to adhere to for patients with concomitant poor overall patient outcomes , 24. GloM. tuberculosis isolates and is therefore subject to the same delays as for M. tuberculosis culture as a diagnostic tool. However, experience has demonstrated that replacing a series of molecular diagnostic tests with WGS resulted in shorter turnaround times (–Currently, WGS is predominantly performed on DNA isolated from cultured nd times . Encourand times –28. IndeAmong the limitations of this study was the considerable discordance in rifampicin susceptibility detected by WGS and that from routine DST, with 9% of sequenced isolates predicting rifampicin susceptibility. There are several potential explanations for this, including the capacity of sequencing to detect heteroresistance which is highly dependent on the depth of coverage obtained and the cutoff percentages of resistance-conferring mutations used to predict resistance. In addition, mixed strain infections, where drug-susceptible strains outgrow more resistant strains in culture, may result in discordance . DiscordWe considered some of the key factors used to allocate treatment regimens based on the South African guidance. However, there are several other factors considered by clinicians in deciding treatment regimens, including drug intolerance, heightened risk of adverse events associated with particular drugs, extent of disease, HIV treatment regimens, and patient preference. In addition, we did not consider pretreatment laboratory tests such as those for anemia and hepatic or renal function. Low hemoglobin levels are a relatively common exclusion criteria for the short regimen since these patients are likely not to tolerate linezolid. However, more comprehensive DST may enable patients with moderate or severe anemia to continue a short regimen that excludes linezolid. Our analysis was therefore not intended to provide definitive treatment regimens for individual patients. In addition, there are multiple shorter regimens, other than that used in South Africa, being considered for routine use or use under operational research conditions that do not contain isoniazid, pyrazinamide, and/or ethambutol, including the Nix-TB regimen, the ZeNIX-TB regimen, the endTB regimens, the PRACTECAL regimen, and the NEXT TB regimen . As a reDespite these limitations, these data from a very large patient cohort suggest that a significant proportion of patients would benefit from access to more detailed DST through WGS. While the observed discordance may preclude WGS completely replacing routine DST at this point, targeted sequencing may enable culture-free testing direct from specimens, providing more timely and accurate drug resistance prediction. In this study setting, many MDR/RR-TB patients are treated in primary care; this raises questions as to what types of support they might need to optimize the information received through WGS. These implementation questions can be addressed in further research. South Africa has already invested in WGS for management of MDR/RR-TB, which is currently performed at the National Health Laboratory Services National TB Laboratory upon specimen referral. For scale-up to occur, capacity is required at a provincial level and a standardized method for reporting results is needed.


Alpha-synucleinopathies, such as Parkinson’s disease (PD), dementia with Lewy bodies (DLB), and multiple system atrophy (MSA), are a class of neurodegenerative diseases. A diagnosis may be challenging because clinical symptoms partially overlap, and there is currently no reliable diagnostic test available. Therefore, we aimed to identify a suitable marker protein in cerebrospinal fluid (CSF) to distinguish either between different types of alpha-synucleinopathies or between alpha-synucleinopathies and controls. In this study, the regulation of different marker protein candidates, such as alpha-synuclein (a-Syn), neurofilament light chain (NfL), glial fibrillary acidic protein (GFAP), and total tau (tau) in different types of alpha-synucleinopathies, had been analyzed by using an ultrasensitive test system called single-molecule array (SIMOA). Interestingly, we observed that CSF-NfL was significantly elevated in patients with DLB and MSA compared to patients with PD or control donors. To differentiate between groups, receiver operating characteristic (ROC) curve analysis resulted in a very good diagnostic accuracy as indicated by the area under the curve (AUC) values of 0.87–0.92 for CSF-NfL. Furthermore, we observed that GFAP and tau were slightly increased either in DLB or MSA, while a-Syn levels remained unregulated. Our study suggests NfL as a promising marker to discriminate between different types of alpha-synucleinopathies or between DLB/MSA and controls. Alpha-synucleinopathies are a group of neurological disorders, including Parkinson’s disease (PD), dementia with Lewy bodies (DLB), and multiple system atrophy (MSA). They share common overriding symptoms such as rigidity, rest tremor, akinesia, autonomic, behavioral, cognitive-motor dysfunctions like freezing, and speech problems . PD is tAlpha synucleinopathies are mainly characterized by alpha-synuclein (a-Syn) aggregates in the brain tissue. While PD and DLB possess the formation of Lewy bodies (LB) in neurons , MSA is The misdiagnosis of alpha-synucleinopathies may occur due to a clinical overlap of symptoms and the lack of specific diagnostic tests or biomarkers. A confirmed diagnosis requires postmortem examination by autopsy, which confirms approximately 80% of premortal clinical diagnoses .Until present, the colorimetric ELISA system and western blotting are commonly used for protein analysis in a diagnostic context. However, small changes in the protein concentration cannot be detected effectively by these standard methods for protein analyses. The high technological progress of protein analytics had been achieved by the development of the digital ELISA concept, such as the ultrasensitive single-molecule array (SIMOA) . SIMOA mIn this study, we analyzed the concentration of potential marker protein candidates, such as neurofilament light chain (NfL), glial fibrillary acidic protein (GFAP), a-Syn, and total tau (tau) in patients with different types of alpha-synucleinopathies. Diagnostic accuracies were estimated by the receiver operating characteristic (ROC) curve analysis.This study includes patients with alpha-synucleinopathies that were classified as PD , DLB  upregulWe observed no significant alterations of CSF-a-Syn levels between different diagnostic groups. Previous studies using a colorimetric a-Syn assay or an assay based on the Mesoscale technology revealed a moderated decrease of CSF-a-Syn in alpha-synucleinopathies compared with controls. No significant regulation was reported between different subgroups of synucleinopathies, such as PD, DLB, and MSA, which is in line with our current study .The strength of our study is that we applied the novel and ultrasensitive SIMOA to measure potential CSF protein marker candidates for alpha-synucleinopathies. The assays are commercially available and well-validated. In future studies, we also plan to develop a Homebrew assay for the detection of epigenetically modified a-Syn or tau species, such as different phosphorylated forms. A limitation of this analysis is the low number of available DLB cases that impeded a proper calculation of diagnostic accuracy. Therefore, a confirmation of our observations in larger cohorts or with alternative methodologies  is suggested.Our study applied an innovative digital methodology , which enabled us to detect even subtle regulations in CSF, suggesting NfL as a potential diagnostic marker for a reliable discrimination either between other synucleinopathies  and PD or between other synucleinopathies and controls. This is of particular interest because, in the daily clinical routine, it is more difficult to differentiate the PD-type of MSA (MSA-P) and PD than to differentiate MSA and DLB. Therefore, NfL may become important as a marker to identify those patients, allowing to include them in disease-modifying trials early in the disease course and to inform them about the quite different prognosis and characteristics of MSA disease.The raw data supporting the conclusions of this article will be made available by the authors, on request.The study was conducted according to the revised Declaration of Helsinki and Good Clinical Practice guidelines and has been approved by the Local Ethics Committee of the University Medicine Göttingen, No. 19/11/09 “Liquormarker als Prädiktoren für die Entwicklung einer Demenz bei Patienten mit Morbus Parkinson, Demenz mit Lewy Körperchen und Morbus Alzheimer” and by the Ethics Committee of the University Medicine Göttingen, No. 13/11/12 “LIX – Liquormarker zur Frühdiagnose und Krankheitsprogression bei Patienten mit Parkinson-syndromen und Motoneuronerkrankungen.” The patients/participants provided their written informed consent to participate in this study.MS and IZ designed the study and wrote the manuscript. SC performed the experiments. SC, MS, AV-P, and PH analyzed and interpreted the data. AV-P, KG, PL, PH, FM, and FL critically revised the manuscript. KG, FM, and DV provided the samples and the clinical data. All authors contributed to the article and approved the submitted version.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.


Metastatic non-small cell lung cancer (NSCLC) patients treated with immune checkpoint inhibitors (ICIs) may suffer from heavy side effects, and not all patients benefit from the treatment. Therefore, it is crucial to gain knowledge about possible (bio-)markers for response to ICIs. We used retrospective data acquired from NSCLC patients treated with ICIs in first- or further-line therapy settings, including 16 possible markers. We conducted a comprehensive statistical analysis study to find markers for response to treatment, assessed the robustness of our results, and discussed often encountered statistical pitfalls. Our study yielded hypotheses for various predictive and prognostic (bio-)markers for response to ICIs in NSCLC patients. In particular, we found that high basophil counts may be predictive for treatment response in patients in further-line therapy settings.Metastatic non-small cell lung cancer (NSCLC) patients treated with immune checkpoint inhibitors (ICIs) may suffer from heavy side effects and not all patients benefit from the treatment. We conducted a comprehensive statistical analysis to identify promising (bio-)markers for treatment response. We analyzed retrospective data from NSCLC patients treated with ICIs in first- or further-line therapy settings at the University Hospital Zurich. We investigated 16 possible prognostic markers with respect to overall survival, tumor size reduction, and the development of an immune-related adverse event (irAE) and assessed the robustness of our results. For the further-line patient group, the most significant result was that increased basophil counts were associated with increased odds of tumor size reduction within three months and with the development of an irAE. For the first-line patient group, the most significant results were that increased lymphocyte counts, the histology of adenocarcinoma, and the intake of non-steroidal anti-rheumatic drugs (NSAR) were associated with decreased hazards of dying. Our study yielded new hypotheses for predictive (bio-)markers for response to ICIs in NSCLC patients. The possibly beneficial role of high basophil counts is a particularly interesting finding. Our results should be tested on independent data in a prospective fashion. Non-small cell lung cancer (NSCLC) is one of the most frequently diagnosed cancers worldwide. Approximately 70% of the patients are diagnosed with advanced and unresectable tumors with limited treatment options . Platinu+ T cells), or a specific immune gene signature correlating with response [A number of blood and tumor markers have been evaluated in clinical studies, such as PD-L1 expression in the tumor cells (PD-L1TC), tumor mutational burden (TMB), neutrophil to lymphocyte ratio (NLR), the presence of tumor-infiltrating lymphocytes (TIL)  who received treatment with anti-PD-1/PD-L1 checkpoint inhibitors  at the University Hospital Zurich (USZ) as standard of care between 2015 and 2019. Treatment with ICIs and chemotherapy was applied in the first- or further-line of treatment. These two patient groups (first- and further-line patient group) are inherently different . TherefoWe investigated possible (bio-)markers with respect to how predictive and prognostic they are for three different responses to ICIs, which can be divided into two categories: (i) binary responses, consisting of two outcomes , including response to treatment within the first three months (Response3mt) and the development of immune-related adverse events (irAE); and (ii) overall survival calculated from the start of ICI therapy. Response3mt was determined by radiological assessment, according to the response evaluation criteria in solid tumors (RECIST), performed after three months of therapy using PET/CT imaging , where w2), lymphocytes (measured as 109 cells per liter (G/l)), neutrophils (G/l), monocytes (G/l), basophils (0.01 G/l), eosinophils (0.01 G/l), and age at the start of ICI therapy (yrs). We note that the units of basophils and eosinophils were given in 0.01 G/l instead of 1 G/l, as the range of these predictors often did not exceed 1 G/l. The predictors antibiotics and steroids were excluded from the analysis related to irAE because these medications were given to treat the irAE.We considered the following 16 variables as possible prognostic and predictive markers (predictors). These included 9 factor predictors: sex , histology , smoking status , metformin use , steroids use , antibiotics use , non-steroidal anti-inflammatory drugs use (NSAR) , PD-L1 expression in the tumor cells (PD-L1TC) (not available (NA), <1%, 1–50%, >50%) and treatment . The remaining 7 predictors were continuous: body mass index (BMI) (kg/mn = 23). An exception was made for patients only missing the predictor PD-L1TC (n = 20), for which we created an additional category “NA”. The resulting number of first-line patients was n = 71 and the number of further-line patients was n = 111. The missingness plots of the initial data of both patient groups can be found in At baseline, PD-L1 expression was evaluated on FFPE tumor sections using the E1L3N clone and scored if at least 50 tumor cells were available for analysis. Smoking status, age, BMI, and histology were determined at the start of therapy. Immune cell concentrations in the blood  were measured at the day of treatment start. The administration of co-medications  was reported as “yes” if the medication was taken within one month before the start of IT and two months after the start of IT. For the statistical analyses, we excluded patients with undetermined histological subtypes and/or ongoing infections. Patients with missing values were excluded as well  multiplied by the sign of the regression coefficient .Before conducting the main statistical analyses, we assessed the pairwise associations between the predictors in both patient groups separately. For two continuous predictors, we used Spearman correlation ; for two discrete predictors, we used Cramers’ V, which is based on a Chi-squared statistic ; and for a pair consisting of a discrete and a continuous predictor, we regressed the continuous predictor on the discrete predictor and took the square root of the explained variance  and assessed the association between the two binary responses with a Fisher test.p-value for the predictor (using the Fisher test if the predictor was binary and the likelihood ratio test (LR test) if the predictor was not) and the multiple-testing adjusted p-value ; and 15 tests for irAE (excluding steroids and antibiotics)) [n, where n is the number of observations.First, we ran univariate logistic regressions of the respective response on each predictor (including an intercept), and reported the following quantities: an estimate of the odds ratio (OR) for a one-unit increase in the predictor , including 95% confidence intervals (CIs); the raw iotics)) . We assep-value and the multiple-testing adjusted p-value . The Wilcoxon test is robust against outliers.Second, we ran two-sample Wilcoxon tests for each continuous predictor, where the two groups were defined by the binary response. We reported the resulting ranger [p-value of the impurity variable importance of a permutation test with 15,000 permutations. Again, we reported the multiple-testing adjusted p-value ).Last, we fitted a multivariate classification random forest, using the R-package ranger ,18. For survival [p-value for the predictor (LR-test), and the multiple testing adjusted p-value . In each Cox proportional hazard regression, we checked the proportional hazards assumption. We evaluated the sensitivity of the univariate Cox proportional hazard regression results with respect to removing influential observations. Here, influential observations were defined based on “dfbetas”, which are the estimated changes in the coefficients divided by their standard errors. We considered an observation to be influential if its absolute dfbetas value was greater than 0.3.Similar to the binary responses, the main analyses conducted for the overall survival analysis were the following. First, we ran univariate Cox proportional hazard regressions of overall survival on each predictor (including an intercept), using the R-package survival . This alranger. For each predictor, we reported the p-value of the impurity variable importance of a permutation test with 15,000 permutations. Again, we reported the multiple-testing adjusted p-value ).Second, we fitted a multivariate survival random forest, using the R-package Finally, we summarized the results of the univariate logistic and Cox regressions by plotting the log-OR and log-HR, including the respective CIs for each predictor, after standardizing the continuous predictors . This summary is suitable to compare effect sizes of different predictors.In clinical studies, it is often of interest to divide the patients into treatment groups according to a predictor. These groups are usually determined based on a threshold t: one group consists of patients with predictor values ≤ t and the other group consists of patients with predictor values > t.maxstat [p-value that corresponds to testing the null hypothesis that the distribution of the response is the same in both groups. In addition, for comparison and the sake of completeness, we binarized those continuous predictors that have a medically accepted healthy and corresponding non-healthy range and reported the p-values of the refitted models.In both analyses, i.e., binary events and overall survival, we estimated these cut-offs with respect to the univariate logistic and univariate Cox regressions, using the R-package maxstat . We repoAll methods with corresponding context and explanation are summarized in n = 71) or further-line (n = 111) treatment at the University Hospital Zurich between 2015 and 2019. Patient characteristics of both groups are presented in p-value of the log-rank test of < 0.0001)  .The associations between pairs of predictors within the two patient groups were all below 0.6  and did p = 0.0132; HR: 0.3652; CI: 0.1586, 0.8407)    (p = 0.041) in the further-line patient group but not in the first-line patient group (p = 1)  were also assessed for both patient groups. First, we fitted univariate Cox proportional hazard regressions of overall survival on Response3mt and irAE. First-line patients showed significantly prolonged survival if they responded within three months ( 0.8407) , but no  2.1324) . Further 0.4623)  or if th 0.6773) . The rel (p = 1) .p = 0.0235, adjusted p-value not significant)  .p-values for basophils, eosinophils, and steroids . Increased basophil  and eosinophil counts  as well as steroid intake  were associated with increased odds of responding within the first three months , and the p-value of basophils remained significant after multiple-testing correction. Only basophils showed a significant result in the multivariate classification random forest (p = 0.0177), fitted on all 16 predictors (not significant after multiple testing correction).In the further-line patient group, the univariate logistic regressions of Response3mt on each predictor yielded significant e months . The p-vp = 0.0049), where increased basophil counts were associated with increased odds of developing an irAE   and in the multivariate classification random forest (p = 0.004); however, both p-values were not significant after multiple testing correction. Thus, for further-line patients, the results suggest that basophil counts appear to be prognostic for Response3mt and for irAE, both marginally and when given all the other predictors.In the further-line patient group, the univariate logistic regressions of irAE on each predictor also found basophils to be significant ( 1.6008) . Howeverp-value was significant before and/or after removing influential observations, p-values. Interestingly, the p-values only got more significant after removing influential observations. Basophil counts now remained significant in the further-line patient group even when adjusting for multiple testing for Response3mt  as well as for irAE .Next, we were interested in the sensitivity of the univariate logistic regression results with respect to outliers. For all the tests where the p = 0.005) and decided to still use the model in this case.We checked the proportional hazards assumption for each univariate Cox proportional hazards model. We found a significant violation only for basophil counts in the first-line patient group . None of these p-values remained significant after adjusting for multiple testing. Increased lymphocyte counts were associated with decreased hazards of dying . The hazards of dying were also decreased for the histology of adenocarcinoma compared to squamous cell carcinoma  and for the intake of NSAR . In the multivariate survival random forest, histology, neutrophils and NSAR were significant , while ICI & Chemo did not change the risk of dying compared to ICI alone   significant p-values only for monocytes and smoking , which were not significant after adjusting for multiple testing.In the further-line patient group, the univariate Cox proportional hazard regressions yielded significant ctively) . Increasp-values becoming smaller. In the first-line patient group, the two predictors NSAR  and lymphocytes  now remained significant even when adjusting for multiple testing.We again assessed the sensitivity of our results with respect to the removal of influential observations. As before, we found that removing influential observations only strengthened the univariate Cox regression results, with We estimated cut-off values for continuous predictors and tested if the resulting subgroups behaved differently with respect to the binary Response3mt or irAE and overall survival.p = 0.0266) in monocyte counts considering irAE, where high monocyte counts were associated with increased odds of developing an irAE. Considering overall survival, we found the cut-off of 1.78 G/l in lymphocyte counts to be significant (p = 0.029), where high lymphocyte counts were associated with decreased odds of dying. The only significant result we found in the healthy range analysis was the one of neutrophils (p = 0.0346), where neutrophil counts in the healthy range were associated with increased odds of developing an irAE  in basophil counts considering Response3mt and a significant cut-off of 0.05 G/l (p = 0.013) considering irAE. High basophil counts were associated with increased odds of responding within three months and developing an irAE. The only significant result we found in the healthy range analysis is the one of lymphocyte counts, where lymphocyte counts in the healthy range were associated with increased odds of responding within three months (p = 0.0466) and decreased hazard of dying (p = 0.0415)  .The implementation of ICI therapies targeting the PD-1/PD-L1 axis has revolutionized the treatment of metastatic NSCLC and improved overall survival rates in these difficult-to-treat patients. However, patients can suffer from heavy side effects and only a subgroup of patients benefits from the treatment with ICIs. Thus, it is crucial to find (bio-)markers of response, to aid selection of patients that will likely benefit from the treatment and to stratify patients in future clinical trials.Many studies have discussed clinical markers as prognostic factors for NSCLC patients treated with ICI therapy (reviewed in ). HoweveIn this study, we used retrospectively collected clinical baseline markers of two cohorts of patients with stage-IV NSCLC, treated in either a first-line or further-line therapy setting. The primary aim of this study was to find predictive and prognostic markers for response to ICIs, quantified by tumor size reduction within the first three months of treatment (Response3mt), development of immune-related adverse events (irAE), and overall survival, where the analyses included robustness checks of the results.In order to find prognostic markers for the binary Response3mt and development of irAE, we performed univariate logistic regressions for each predictor assuming a linear relation between each predictor and the log-odds of the response. The robust Wilcoxon test was used as an additional univariate test that does not rely on any parametric assumption. In addition, we analyzed our data using the classification random forest, which is both multivariate and nonparametric, providing yet another perspective. Similarly, for the overall survival analysis, we performed univariate Cox proportional hazard regressions, assuming a linear relationship between each predictor and the log-hazard, resulting in the well-known proportional hazards assumption. In accordance with the binary response analyses, we also analyzed our data using a survival random forest, which is again both multivariate and nonparametric.p-values of the best sub-model are biased , if the sub-model is fitted on the same data on which it was selected. The latter is known as the problem of post-selection inference. Another problem is the one of multiple testing. The probability of incorrectly rejecting a null-hypothesis of no effect increases with the number of tests. Therefore, proper inflation of the p-values is required to get guarantees on the rate of wrongly rejected null hypotheses [p-values with the Holm method to correct for this multiple testing problem. Often, this is not performed in studies analyzing clinical data sets with many tests, leading to overly optimistic findings and wrong conclusions. Finally, we also checked whether the model assumptions were met, and we checked the sensitivity of our results with respect to the removal of influential observations or outliers.In the multivariate analyses, we did not conduct the often-used best sub-model selection because it is problematic. First, the definition of the best sub-model is quite arbitrary, due to the fact that a few models often have a very similar performance. Second, the potheses . We perfn = 71) and further-line (n = 111) patient group, indicating that these groups may be fundamentally different. For example, first-line patients had a better overall survival than further-line patients. Further-line patients had a prolonged survival in case they responded within three months or developed an irAE, whereas first-line patients only showed a positive association between responding within three months and survival. Several studies reported association of irAE development and longer overall survival in late-stage NSCLC patients [Overall, we obtained different results for the first-line markers for treatment response, which need to be validated in independent and larger cohorts. These (bio-)markers could be of help as predictors of ICI therapy response or for patient stratification in clinical trials. Our strongest result suggests that an increase in basophil counts may be beneficial for treatment response in further-line patients.


Background: The long-term effects of statin use on rehospitalization due to ischemic stroke (reHospIS) in hyperlipidemic patients are still unknown. Therefore, we aimed to assess the long-term risks of reHospIS for hyperlipidemic patients who were taking statins and nonstatin lipid-lowering medicines on a regular basis.Methods and Materials: The National Health Insurance Research Database in Taiwan was used to conduct a 6-year cohort study of patients >45 years old  who were newly diagnosed with hyperlipidemia and hospitalized for the first or second time due to ischemic stroke (IS). The risk of reHospIS was assessed using Cox proportional hazards regression model.Results: Nonstatin lipid-lowering medicines regular users were associated with a higher risk of reHospIS compared to stains users . Rosuvastatin was the most preferred lipid-lowering medicine with lower HRs of reHospIS in hyperlipidemic patients whether they developed diabetes or not. Bezafibrate regular users of hyperlipidemic patients developing diabetes  had nearly 50% lower reHospIS risks than those without diabetes . Age, gender, drug dosage, comorbidities of diabetes and heart failure (HF), and characteristics of the first hospitalization due to IS were all adjusted in models. Moreover, increasing trends of HRs of reHospIS were observed from Rosuvastatin, nonstatin lipid-lowering medicines, Lovastatin, and Gemfibrozil to Bezafibrate users.Conclusion: Statins were associated with long-term secondary prevention of reHospIS for hyperlipidemic patients. Rosuvastatin seemed to have the best protective effects. On the other hand, Bezafibrate appears to be beneficial for hyperlipidemic patients developing diabetes. Further research into the combination treatment of statin and nonstatin lipid-lowering medicines in hyperlipidemic patients developing diabetes is warranted. Hyperlipidemia is one of the most prevalent risk factors for atherosclerosis and cardioembolic stroke , particuStatins (3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitors) are the most commonly prescribed lipid-lowering medicines . They haHowever, most previous studies  comparedThe coverage rate of National Health Insurance is nearly 100% in Taiwan. The analysis data is from the National Health Insurance Research Database (NHIRD) and capable of representing the whole nation. This is a cohort study and we included the medical claims from 2005 to 2010 in the ICD-9-CM system. During the study period in Taiwan, the clinical description guideline of lipid-lowering medicines was consistent. The eligible criteria of the study population were 1) the new patients with newly diagnosed disorders of lipid metabolism (ICD-9-CM code: 272), 2) the first hospitalization due to IS  from 2005 to 2009, 3) taking monolipid-lowering medicine over 90 days and at least three times outpatient visits for hyperlipidemia after the first hospitalization due to IS, 4) age larger than 45 years at which IS likely to occur , 5) the Nine blood lipid-lowering medicines were included . In order to understand the effects of various types of lipid-lowering medicines on the risks of reHospIS, all hyperlipidemic patients were divided into four subgroups according to the type of monolipid-lowering medicine they used on a regular basis: (A) nonstatin lipid-lowering medicines and statins regular users; (B) high-density statins , nonstatin lipid-lowering medicines , and statins regular users; (C) Rosuvastatin, Simvastatin, Lovastatin, Pravastatin, Fluvastatin, Bezafibrate, Gemfibrozil, Fenofibrate, and Atorvastatin regular users; (D) five individual statins  and nonstatin lipid-lowering medicine regular users.Patients who had been prescribed a single type of lipid-lowering medicine for more than 90 days and had at least three times records of outpatient visits from the first hospitalization due to IS to the end of follow-up were classified as monolipid-lowering medicine regular users. In order to clarify the effect of every single type of medication on rehospitalization, patients who used different types of blood lipid-lowering medicines in combination were excluded.The definition of DDD from WHO is the assumed average maintenance dose per day for a drug used for its main indication in adults. The DDD is a unit of measurement and does not necessarily reflect the recommended or prescribed daily dose . The valCompliance rates of lipid-lowering medicines were calculated as the number of days with lipid-lowering medicines supply divided by the total number of days from the first hospitalization date due to IS to the end of follow-up .In NHIRD, there is a risk of misclassification bias due to unverified diagnosis coding . As a rep value less than 0.05. In the sensitivity analysis, all the hyperlipidemic patients were categorized into four patient subgroups of 1) all hyperlipidemic patients, 2) hyperlipidemic patients with diabetes, 3) nondiabetes hyperlipidemic patients, and 4) nondiabetes and non-HF hyperlipidemic patients. The case number of hyperlipidemic patients with HF was limited (n = 99) for further subgrouping and meaningful multivariable statistical analysis , and higher DDD and compliance rate were the risk factors of reHospIS . DDD andHyperlipidemic patients were grouped into four subgroups by the type of monolipid-lowering medicine they took regularly. Four medicine categories were (A) nonstatin lipid-lowering medicines versus statins , (B) high-density statins  and nonstatin lipid-lowering medicines  versus statins (ref), (C) Rosuvastatin, Simvastatin, Lovastatin, Pravastatin, Fluvastatin, Bezafibrate, Gemfibrozil, and Fenofibrate versus Atorvastatin (ref), and (D) five individual statins of Rosuvastatin, Simvastatin, Lovastatin, Pravastatin, and Fluvastatin versus nonstatin lipid-lowering medicines (ref).p < 0.05) than Atorvastatin in subgroups (C) and (D)  . Among s and (D) .We entered all significant factors from univariable Cox proportional hazards regression models  and furtp < 0.05) for all patients subgroups (p < 0.05) as compared with Atorvastatin regular users in subgroups (C) and (D) (p < 0.05) rather than the other types of statins had the highest risk as compared with Atorvastatin regular users (p < 0.05) (In the results of medicine subgroups (A) to (D), Rosuvastatin regular users had the lowest HRs of reHospIS ranging from 0.63 to 0.64 (ubgroups . Statinsubgroups . Diabeteubgroups . Rosuvas and (D) . Lovastaar users . Except  < 0.05) . To rule < 0.05) . The abo < 0.05) .p < 0.05) than none DM patients  by reducing nearly half the risk of reHospIS. There was no statistical difference of risks of reHospIS in the DM patient subgroup who took Lovastatin (HR = 1.31 p = 0.40) or Atorvastatin, whereas Lovastatin was linked to a higher risk of reHospIS for nondiabetes hyperlipidemic patients  when compared to Atorvastatin   was recoIn this study, Bezafibrate regular users with diabetes had nearly 50% lower reHospIS risks than those without diabetes. Bezafibrate is one of the most commonly used molecules in the treatment of hypertriglyceridemia, and statin therapy is often added to achieve lipid profile goals in mixed dyslipidemia . BezafibLovastatin was an unfavorable lipid-lowering medicine for nondiabetes hyperlipidemic patients due to its high risk of reHospIS, but it had no adverse effects on those with DM when compared to Atorvastatin. Moreover, Lovastatin was found to significantly reduce fatty streak lesion formation in the aortic arch of hyperlipidemic-diabetic hamsters . Its othThere were some limitations in the study. Not all the statins were included in the study  because some statins were not commercially available and proven by Taiwan Food and Drug Administration during the study period. Serum lipid-lowering herbal medicines, oral hypoglycemic agents, lifestyle, and dietary factors were not included. The use of lipid-lowering medicines, blood lipid levels (not available in the NHIRD), and diabetes status of study subjects prior to their first hospitalization due to IS were not included in the study. However, we designed 1-year washout period prior to the start of the study to reduce the impact of the aforementioned conditions.We only discussed the risk of reHospIS for users of monolipid-lowering medicines. We are unable to assess the combined effects of statins and nonstatin lipid-lowering medicines. However, we discovered that nonstatin lipid-lowering medicines had a beneficial effect on hyperlipidemic patients with diabetes. It calls for further studies into the effects of combinational treatments on the long-term risks of reHospIS.In comparison to nonstatin lipid-lowering medicines, statins had a longer-term beneficial effect of secondary prevention of reHospIS for hyperlipidemic patients. Rosuvastatin is the most effective treatment for all subgroups of hyperlipidemic patients. On the other hand, Bezafibrate appears to benefit hyperlipidemic patients with diabetes. The combined effects of statins and nonstatin lipid-lowering medicines on diabetes hyperlipidemic patients warrant further studies to understand the beneficial mechanism.


Additional imaging modalities, such as FDG-PET/CT, have been included into the workup for patients with suspected infective endocarditis, according to major international guidelines published in 2015. The purpose of this review is to give an overview of FDG-PET/CT indications and standardized approaches in the setting of suspected infective endocarditis.There are two main indications for performing FDG-PET/CT in patients with suspected infective endocarditis: (i) detecting intracardiac infections and (ii) detection of  disseminated infectious disease. The diagnostic performance of FDG-PET/CT for intracardiac lesions depends on the presence of native valves, prosthetic valves, or implanted cardiac devices, with a sensitivity that is poor for native valve endocarditis and cardiac device-related lead infections, but much better for prosthetic valve endocarditis and cardiac device-related pocket infections. Specificity is high for all these indications. The detection of disseminated disease may also help establish the diagnosis and/or impact patient management.Based on current evidence, FDG-PET/CT should be considered for detection of disseminated disease in suspected endocarditis. Absence of intracardiac lesions on FDG-PET/CT cannot rule out native valve endocarditis, but positive findings strongly support the diagnosis. For prosthetic valve endocarditis, standard use of FDG-PET/CT is recommended because of its high sensitivity and specificity. For implanted cardiac devices, FDG-PET/CT is also recommended, but should be evaluated with careful attention to clinical context, because its sensitivity is high for pocket infections, but low for lead infections. In patients with prosthetic valves with or without additional aortic prosthesis, combination with CTA should be considered. Optimal timing of FDG-PET/CT is important, both during clinical workup and technically . In addition, procedural standardization is key and encompasses patient preparation, scan acquisition, reconstruction, subsequent analysis, and clinical interpretation. The recommendations discussed here will hopefully contribute to improved standardization and enhanced performance of FDG-PET/CT in the clinical management of patients with suspected infective endocarditis. Infective endocarditis (IE) is a serious condition with substantial morbidity and mortality. While it is relatively rare with an incidence of 3–10 per 100,000 per year –5. IE is18F-fluorodesoxyglucose with low-dose or contrast-enhanced computed tomography (FDG-PET/CT), cardiac CT, and leucocyte scintigraphy are the most frequently used imaging modalities for establishing IE [Besides echocardiography, 18F-FDG-PET/CT accuracy and its overall value for the diagnosis.FDG-PET/CT can be used for two reasons when IE is suspected: it can either directly establish the presence of an infection in the endocardium or be used to find evidence for disseminated infection or portals of entry in IE disease. FDG-PET/CT is mainly applied when the diagnosis remains uncertain after other diagnostic tests are performed. Finding extracardiac foci of infection may help establish the diagnosis and can significantly impact treatment decisions . When thThe largest meta-analysis to date that focused on NVE specifically found that FDG-PET/CT showed a rather poor pooled sensitivity for the diagnosis, while pooled specificity was excellent: 36% and 99% respectively •, 12. BeFor PVE, FDG-PET/CT has both high sensitivity and specificity for intracardiac infection. The most recent meta-analysis on this indication found a pooled sensitivity and specificity of 86% and 84%, respectively , 13•. FuPatients with combined prosthetic aortic valve implantation and ascending aorta replacement  constitute a special group. Relatively little is known about the FDG-PET/CT findings for these indications. One study investigated the value for FDG-PET/CT for suspected Bentall infection in 39 patients . The obsIn both patient groups, the indication for a combination with ECG-gated CT angiography (CTA) should be considered with a rather low threshold, since abscess formation can be detected more reliably , 17, preFor CDRIE, FDG-PET/CT has a high overall pooled sensitivity and specificity (87% and 94% respectively) according to the most recent meta-analysis on this indication . HoweverLiterature about the optimal timing of FDG-PET/CT application is scarce. This applies both to the timing in the diagnostic workup in suspected IE and to the interval between cardiac surgery and FDG-PET/CT and suspected IE in the postoperative period. For the optimal timing in the diagnostic process, we recommend using transthoracic and transoesophageal echocardiography (TEE) first whenever feasible, since these are safe, fast, widely available, and cost-effective , 21. TEERegarding cardiac surgery, the European Society of Cardiology guidelines recommend an empirical minimum interval of 3 months between the intervention and FDG-PET/CT before positive findings can be regarded as true positive . This isFDG-PET/CT standardization is important to ensure both repeatability of scan results and their reproducibility across different PET/CT systems, which is important to ensure maximum diagnostic accuracy and optimal (semi-) quantitative scan analysis. Standardization measures can be applied to patient preparation, scan acquisition, and scan analysis. Guidelines for standardization have been established by the European Association of Nuclear Medicine (EANM) and the European Association of Cardiovascular Imaging (EACVI) , 27••. TFasting and medication: Non-diabetic patients should not consume any foods or drinks besides water, preferably for at least 12 h prior to FDG-PET/CT. Medications can be taken as prescribed, with notable exception to corticosteroids, which should either be delayed until after FDG-PET/CT or be used at the lowest possible dose that is clinically feasible because of their interference with glucose metabolism ••.High-fat, low-carbohydrate (HFLC) diet: the myocardium prefers free fatty acids over glucose for its metabolism. Therefore, using a high fatty acid and low-carbohydrate diet preceding FDG-PET/CT reduces cardiac glucose consumption. This will improve the target-to-background ratio resulting in optimal PET reading. Recommended is a 12-h HFLC diet, followed by the aforementioned 12-h fasting period –30.Heparin loading dose: Heparin causes free fatty acid release, which in turn decreases myocardial glucose uptake. There is some evidence that an intravenous heparin injection 15 min prior to FDG injection has an additive effect on physiological myocardial FDG uptake when used in conjunction with the HFLC diet , though The main goal of patient preparation is to maximize tracer uptake in the target tissue against the background. Since FDG is a glucose analogue, it is taken up in non-affected tissue as well, dependent on their metabolic activity. This is especially the case in IE, where FDG is normally taken up in the myocardial cells. Patient preparation in IE is therefore aimed at limiting metabolic activity in the myocardium. The following measures are recommended before FDG-PET/CT in the setting of suspected IE, above the normal measures for patient preparation in FDG imaging :FastingThe recommended administered activity for FDG-PET/CT is 2.5–5 MBq/kg according to EANM recommendations, which corresponds with 175 to 350 MBq for an adult weighing 70 kg . The timBecause FDG remains trapped intracellularly for 3.5–4 h after injection, studies have been performed to evaluate whether delayed acquisition could increase FDG-PET/CT diagnostic accuracy. However, while there might be a potential benefit for CDRIE specifically , the incFor the diagnosis of IE, combining FDG-PET with CT angiography (CTA) leads to a combination of metabolic information of FDG-PET with a high anatomical reference, aiding in evaluation of solitary vegetations, soft tissue extension of infection, and/or potential abscesses. Studies using this combination are still scarce, but indicate towards benefit of the combination over single performed techniques ; see alsThere are both general and specific considerations for FDG-PET/CT assessment in the setting of suspected IE. As general considerations, verification of correct activity administration, image quality control, checks of blood glucose level, and PET/CT alignment are vital for a correct interpretation of FDG-PET/CT results. When increased myocardial physiological uptake is present, compliance to HFLC diet should be verified and reported upon. PET/CT images have to be evaluated in all 2D planes and in 3D maximum intensity projection (MIP) cine mode, taking into account both intensity of FDG uptake in the target lesion(s) and the pattern of FDG uptake. The uptake pattern of FDG is extremely important in defining whether there is IE. Homogeneous uptake mostly points to reactive inflammation and not infection. On the contrary, heterogeneous and/or (multi-) focal uptake points to an infection. Also, spread to surrounding soft tissue and/or metabolically active lymph nodes in the surrounding points to an infection ••.The imaging specialist should not only look to the attenuation-corrected images, but should also pay attention to the non-attenuated images, especially when a prosthetic valve or a cardiac device is present. Scatter or beam hardening artefacts caused by either prosthetic valves or cardiac-implanted devices can lead to false-positive findings. Increased FDG uptake should therefore always be confirmed on non-attenuation-corrected images to further confirm suspected prosthetic valve or device infection ••.As mentioned earlier, use of surgical adhesives  and one specific valve prosthesis model (Medtronic Mosaic) can lead to false-positive findings , 24. ConExperience with semi-quantitative metrics using standardized uptake values (SUV) is extensive in oncology, but for IE, its use is less common. However, some promising results were found in a patient-control study when a SUVratio  cutoff value >2.0 was used for diagnosing prosthetic valve endocarditis in EARL accredited, attenuation-corrected reconstructions. When patients with surgical adhesives and those with low inflammatory activity (CRP < 40 mg/L) were excluded, sensitivity and specificity were 100% and 91%, respectively, similar to visual analysis that showed a sensitivity and specificity of 91% and 95% respectively .New technical developments in PET/CT may lead to further improvement of FDG-PET/CT diagnostic accuracy and expansion of its clinical utility. In this section, new PET/CT acquisition protocols, hybrid imaging modalities, FDG-PET/CT treatment monitoring, and possibilities of artificial intelligence approaches are discussed. These are currently being evaluated and might emerge in clinical setting in the not too distant future.One of the limitations of FDG-PET/CT is that it produces a static image. This potentially limits the interpretability of small intracardiac lesions because of cardiac motion artefacts and might be the explanation that isolated valve vegetations are associated with false-negative findings . A potenCurrent state-of-the-art digital PET/CT systems are capable of dealing with progressively lower photon counts, which has made dynamic PET/CT acquisition feasible. This combined with FDG uptake modelling allows for real-time assessment of FDG uptake rate in target tissues, potentially exposing differences in glucose metabolism impossible to detect on static PET/CT images. In oncology, dynamic FDG-PET/CT might be able to differentiate primary tumors from metastatic disease . PotentiWhole-body PET/CT systems incorporate multiple PET detector rings, which allows for a reduction of either scan time, radiation dose, or both, while giving a one-shot image of the whole body, potentially expanding the possibilities of the use of FDG-PET/CT to haemodynamically unstable patients, children, and pregnant women. It also allows further expansion of dynamic PET/CT abilities, leading to potentially significantly improved FDG-PET/CT applicability and sensitivity for a myriad of indications .New PET/MRI systems are slowly finding more adoption around the world. This hybrid imaging modality allows for dynamic motion correction using MR data, excellent soft tissue evaluation, and advanced acquisitions to evaluate tissues for functional changes  . This miThe information provided by FDG-PET/CT can not only be used for the diagnosis of IE, but potentially could also be used to monitor the effect of IE treatment, guiding therapeutic decision making, e.g., changing antibiotic dose, switching to a different therapeutic strategy, or deciding when treatment can safely be stopped. FDG-PET/CT is already adopted for this use in several oncological diseases, e.g., the treatment of multiple myeloma , and eviArtificial intelligence and machine learning approaches are becoming increasingly incorporated in the field of nuclear medicine. The possibilities of artificial intelligence approaches range from data-driven noise reduction strategies , automat18F-Fluorosorbitol [18F-Fluoromaltohexoase [11C-labeled para-aminobenzoic acid (PABA) [Currently, FDG is the only PET radiotracer used in clinical practice for evaluation of IE. New radiotracers with bacteria-specific uptake are currently being evaluated, which could substantially improve PET/CT diagnostic accuracy. A systematic review by Auletta et al. evaluated some of the potential bacteria-specific candidates, e.g., sorbitol , 18F-Fluohexoase , and 11Cd (PABA) , showed d (PABA) . Their cFDG-PET/CT is a valuable tool for the evaluation of infective endocarditis. Its sensitivity is variable: excellent for the diagnosis of PVE and CDRIE-pocket infections, but poor for NVE and CDRIE-lead infections. The high specificity and ability to detect  foci of dissemination gives FDG-PET/CT a broad applicability and clinical utility for this challenging diagnosis. Standardization is of major importance for maximizing FDG-PET/CT diagnostic accuracy. It is recommended to perform the patient preparation and scan acquisition procedures in accordance with EANM guidelines. The clinical interpretation should be performed with attention to the clinical context, FDG-PET/CT image quality, recent cardiac surgery duration of antibiotic treatment prior to FDG-PET/CT, and confirmation of findings on NAC PET images. In the future, new developments in camera systems, developments in more specific tracers, and the use of artificial intelligence may substantially change the field of PET/CT imaging in patients with suspected IE.


Estimating total body fat in public hospitals using gold-standard measurements such as air displacement plethysmography (ADP), deuterium oxide dilution, or dual-energy X-ray absorptiometry (DXA) is unaffordable, and it is challenging to use skinfold thickness. We aimed to identify the appropriate substitute marker for skinfold thickness to estimate total body fat in pregnant women and infants.Kappa statistics to assess agreement between these anthropometric markers with skinfold thickness.The study is part of a prospective cohort study titled MAASTHI in Bengaluru, from 2016 to 19. Anthropometric measurements such as body weight, head circumference, mid-upper arm circumference (MUAC), and skinfold thickness were measured in pregnant women between 14 and 36 weeks of gestational age; while measurements such as birth weight, head, chest, waist, hip, mid-upper arm circumference, and skinfold thickness were recorded for newborns. We calculated Kappa statistic, 0.42; 95 % CI 0.38–0.46) in pregnant women. For newborns, the highest agreement with total skinfold thickness was with birth weight . Our results indicate that MUAC higher than 29.2 cm can serve as a suitable alternative to total skinfolds-based assessments for obesity screening in pregnancy in public facilities. Similarly, a birth weight cut-off of 3.45 kg can be considered for classifying obesity among newborns.We found the highest amount of agreement between total skinfold thickness and MUAC (Mid-upper arm circumference and birth weight can be used as markers of skinfold thickness, reflecting total body fat in pregnant women and the infant, respectively. These two anthropometric measurements could substitute for skinfold thickness in low- and middle-income urban India settings.The online version contains supplementary material available at 10.1186/s12884-021-03915-1. The increasing prevalence of overweight and obesity among children is a significant public health issue attributing to immediate and long-term health problems . Howevern = 12)  followed by the head and chest circumferences, respectively. The cut-offs for circumferences at the waist, hip, and mid-upper arm and BMI showed fair agreement with total skinfold thickness. Therefore, on the basis of the extent of misclassification and agreement, the birth weight cut-off emerged as the best substitute .The extent of misclassification that would result from the use of the newly defined cut-offs instead of the accepted standard, i.e. 85th percentile of total skinfold in newborns, along with the amount of agreement  when they are not so. Further validation studies in India can establish the reliability and validity to steer policy-level actions to prioritise screening obesity in pregnancy. The third limitation is that this study mostly represents the source population comprising low-middle-income women who attend public facilities in Bengaluru. This needs to be validated in an even larger population to prove its wider applicability. However, we were able to capture the measurements among a large sample size of mother-child dyads and thus have shown the use at public facilities for urban populations that can be applied across the country.Mid-upper arm circumference and birth weight can be used as markers of skinfold thickness, reflecting total body fat in pregnant women and infants, respectively. These two anthropometric measurements could substitute for skinfold thickness in low- and middle-income urban India settings. Our results suggest that the MUAC and birth weight be used in pregnant women and infants, respectively, as markers for effective screening tools for detection of obesity in Indian states and other similar settings. The simple technique and low costs associated with measurements can enable the implementation by frontline health workers including in rural areas.Additional file 1Additional file 2Additional file 3Additional file 4