With the development of molecular techniques, environmental DNA (eDNA) methods are increasingly being applied to assess marine fish biodiversity. Fish biodiversity survey methods have been difficult to standardize due to the diversity and complexity of reef habitats. Traditional surveys can damage island ecosystems. A rational method for surveying fish biodiversity in different habitats is urgently needed. This study aimed to investigate the practical validity of the eDNA method for evaluating the fish composition and diversity in different habitats. Additionally, compared with traditional surveys, the eDNA method includes the same results, but also includes species that would not be caught by traditional surveys due to the technical limitations of traditional surveys. This is significant for continuous biodiversity monitoring and management in protected areas.Larimichthys crocea, Paralichthys olivaceus, and Lateolabrax maculatus. The mussel culture area had the highest number of species, with 19 fish species, while the offshore bulk load shedding platform had the lowest number of species, with 12 fish species. The rest of the habitat was not significantly different. The results showed that the mussel culture area had the highest diversity index , and the offshore bulk load shedding platform had the lowest diversity index ; the rest of the habitat diversity indices did not differ significantly. A comparison with historical surveys showed that the eDNA technique can detect species not collected by traditional methods such as gillnets and trawls. Our study demonstrates the role of eDNA technology in obtaining fish diversity in different habitats and provides a theoretical basis for the continuous monitoring and management of fish biodiversity in protected areas.This study aimed to investigate the practical validity of the environmental DNA (eDNA) method for evaluating fish composition and diversity in different habitats. We evaluated the fish composition and diversity characteristics of seven different habitats in the Ma’an Archipelago Special Protected Area in April 2020. The results showed that a total of twenty-seven species of fishes belonging to six orders, eighteen families, and twenty-three genera of the Actinopterygii were detected in the marine waters of the Ma’an Archipelago Special Protected Area. The dominant species in each habitat were  Inshore islands and reefs are important components of marine ecosystems, often possessing a variety of habitat types with high primary productivity and abundant bait, and thus providing suitable habitats for many marine organisms ,2 as welEnvironmental DNA (eDNA) is obtained directly from the DNA of various organisms extracted from environmental samples such as soil, water, glaciers, and sediments . eDNA caSebastiscus marmoratus with trawls and we also had difficulties in catching Harpadon nehereus with gillnets [The Ma’an Archipelago Special Protected Area is the core part of the Zhoushan Fishing Ground, which is located in the northeastern part of the Zhoushan Islands in the Zhejiang Province. The Yangtze River water injects into the reserve, bringing many nutrients to the reserve and providing more food for fish . The Taigillnets . In summWith the development of massively parallel sequencing technologies, eDNA is being used in a large number of studies for species diversity analyses of monitored areas. Boussarie et al.  confirmeThe survey was conducted in April 2020 (spring) in seven different habitats in the Ma’an Archipelago Special Protected Area . These wAt each survey site, three replicates of 1 L of surface water (1 m below the water surface), 1 L of mid-water, and 1 L of bottom water (1 m above the bottom) were collected in sterile sampling bags. The water temperature (T), salinity, pH, and dissolved oxygen (DO) were measured for each layer using a multiparametric probe. The eDNA in seawater was filtered in the laboratory using a mixed-fiber membrane  immediately after water sample collection. To avoid filter clogging and contaminations, we used pre-filtration with a 1 µm pore size membrane, followed by filtration with a 0.45 µm pore size membrane. Three blank water samples (pure water) were filtered before filtering the experimental water samples to avoid any contamination, and then the experimental water samples were filtered. As we used the mixed-tissue method, water samples from different layers of the same habitat had to be mixed. Therefore, a volume of 3 L of the mixed water samples was filtered from each site by each membrane per replicate, and the filtered membranes were sealed in 1.5 mL sterile centrifuge tubes by curling them with forceps, marked with the sites and sampling times, and stored in liquid nitrogen at −80 °C until DNA extraction. To avoid cross-contamination, the experimental equipment was disinfected and cleaned before and after each filtration.We used the DNeasy Blood & Tissue Kit  and followed the instructions to extract the DNA from the filter membrane. The extraction process was carried out according to the kit instructions and repeated three times for each sample. The eDNA was quantified using the Qubit 3.0 DNA Assay Kit , and the quantification method was adjusted according to the kit instructions. The extracted eDNA was mixed and dispensed, and its quality was checked using agarose gel electrophoresis and a NanoDrop2000 spectrophotometer .® Robust PCR Master Mix, 15 μL; PCR products, 10–20 ng; 1 μL each of forward and reverse primer; 2 μL of template DNA; 12 μL of distilled water; and 30 μL of total volume. Paired-end libraries were prepared using a two-step PCR method. PCR thermal cycling was performed after an initial denaturation at 94 °C for 3 min, followed by 35 cycles of (1) denaturation at 94 °C for 30 s, (2) annealing at 45 °C for 20 s, (3) extension at 72 °C for 10 s, followed by one cycle, and finally extension at 72 °C for 5 min, followed by cooling at 10 °C for 5 min. The solution was then kept at 10 °C and transferred to a refrigerator until further use. A negative control was also established to detect microbial contamination from the environment or reagents. Finally, the libraries were double-end sequenced on the Illumina MiSeq platform .We used 18S universal primers (18SF: 5′-GGCAAGTCTGGTGCCAG-3′ and 18SR: 5′-ACGGTATCTRATCRTCTTCG-3′) for the PCR amplification of the environmental DNA samples . The amphttps://github.com/OpenGene/fastp/, accessed on 21 October 2022) on the double-ended raw reads data, and clustered by sequences ≥97% using the UPARSE software [https://www.ncbi.nlm.nih.gov/, accessed on 21 October 2022) [The raw high-throughput sequencing data were quality clipped and spliced using fastp software (software . The clusoftware . The obter 2022)  for sequer 2022) ,33. In tThe fish orders, families, genera, and species detected by eDNA sequencing were counted using Excel . We used the QIIME2 software to count the number of species, the number of OTUs, and the number of sequences at different taxonomic levels and to calculate the relative abundance of the species sequences. To analyze the compositional characteristics of fishes in different habitats, all fishes were classified into warm-temperature species, cold-temperature species, and cold-water species according to their temperature suitability . All of In this study, we also used the R programming language  to analyze the sequence abundance of the species, and a diversity analysis was calculated to comprehensively evaluate fish diversity among different habitats. We used the Shannon–Wiener index to evaluate the diversity of the fish community distribution: the higher the Shannon value, the higher the community diversity. The Simpson index was often used to quantify the biodiversity of an ecoregion: the greater the Simpson index, the lower the community diversity. The Chao1 index was used to calculate the abundance of fish community distribution. The Pielou index reflected the evenness of the distribution of different species in the community.A total of 21 water samples from seven different habitats in the Ma’an Archipelago Special Protected Area were analyzed, and a total of 697,534 raw sequences were obtained by processing the raw double-ended sequencing data, including 516,785 high-quality sequences with an average length of 265 bp. A total of 1076 OTUs were obtained by clustering at ≥97% sequence similarity. The high-throughput sequencing statistics of the eDNA samples are shown in The OTUs were compared with databases (NCBI); the OTUs of annotated sequences were classified and their taxonomic statuses were identified. A total of twenty-seven species of marine fishes in twenty-three genera of eighteen families and six orders were detected, among which Perciformes was the most abundant with nineteen species (accounting for 70.37% of the detected species), followed by Cypriniformes with three species , the habitat index values were highest for B and lowest for C, and three habitat index values were above the mean. The mean Shannon–Wiener index was 2.12 (range: 1.865–2.352). The Shannon index was highest in habitat B and lowest in habitat C, with significant differences between habitats. The high-throughput sequencing of all samples showed that the Chao1 index ranged from 74,144 to 96,912, with a maximum B-habitat index of 95.912 and a minimum C-habitat index of 74,141. The Pielou evenness index varied between habitats, with the highest index values in habitat G  and the lowest in habitat C .Larimichthys crocea, Lates calcarifer, Oplegnathus fasciatus, Lateolabrax maculatus, Pagrus major, Mugil cephalus, Scophthalmus maximus, Paralichthys olivus, Conger myriaster, and Sebastiscus marmoratus , C (offshore bulk load shedding platform), and G (marine ranching) were close together and had similar fish structure compositions, and thus could be viewed as one community . The remThe RDA analysis using environmental factors  and the sequence richness of dominant species showed that the eigenvalues of the first ordination axis (RDA1) explained 72.57% of the variance . The eigL. crocea, L. maculatus, and O. fasciatus were higher, followed by S. maximus and P. olivaceus.The Ma’an Archipelago Special Protected Area has a wide variety of economic fish species , and dueS. ghobban, L. calcarifer, and S. lalandi. S. ghobban, especially the juveniles, mainly inhabit algal thickets [The eDNA technique is highly sensitive, not only for the identification of fish diversity in the water column, but also for testing the morphological identification results of some fish species, as well as for the detection of species with very narrow distributions and exotic species . In thisthickets  and has thickets . As a reThe Shannon–Wiener and Simpson indices can indicate the level of fish diversity . The SimLin et al.  studied Harpadon nehereus, Psenopsis anomala, Collichthys niveatus, and Cynoglossus robutus were the dominant species in the reserve in spring; compared with the survey by Wang [Thryssa kammalensis did not appear among the dominant species in the area. This study showed a change in the dominant fish species in the reserve. L. crocea had become the new dominant species, and species such as H. nehereus did not appear in this study.(1) During the long sampling interval, the dominant fish species and community composition in the reserve may have changed. For example, Han et al.  investig by Wang , the domC. niveatus and Collichthys lucidus) will enter the reefs to spawn in the spring and summer seasons when the temperature is higher [(2) Changes in fish habits and environmental factors may have affected the fish diversity, as this survey was conducted in April, while historical surveys have been conducted in May ,25,42. Os higher  used the(3) The primer selection in the eDNA assays could have led to differences in comparative databases; for example, some freshwater fish species were detected in this study. The choice of primers also differed from that of other studies. The results of the different primers should be compared when the corresponding survey is carried out later. A suitable primer should be selected for the detection of fish species in the reserve.L. crocea, L. calcarifer, L. maculatus, P. major, M. cephalus, P. olivaceus, C. myriaster, S. marmoratus, and S. maximus. These results differ from the findings of Wang [M. miiuy, N. albiflora, and S. marmoratus, which had a higher biomass in spring 2009 (April), were less dominant in the present results, and fish such as T. kammalensis and Setipinna tenuifilis did not appear in this study. The abundances of the dominant species in this survey, P. olivaceus and S. maximus, were low in the traditional fishery resource survey. These differences may be related to the habits of the target species and the degradation rate of eDNA in the environment. For example, M. cephalus is a warm-temperate demersal fish with the habit of sinking during the day and surfacing at night [Studies have shown that the eDNA abundance of organisms in aquatic ecosystems is well correlated with biomass, and thus is widely used in biomass assessments ,17,44,45 of Wang ,23 throuat night . These pL. crocea [L. crocea and have achieved certain results [L. crocea in the East China Sea based on eDNA technology and found that its distribution area and water layer were consistent with the results of traditional trawling. The study demonstrated the feasibility of the eDNA method for surveying and monitoring the natural resources of the greater amberjack . In this study, the eDNA test also showed that L. crocea was detected in all the surveyed habitats and was the dominant species in the surveyed area, further indicating that the L. crocea resources in the Ma’an Archipelago area had recovered well. P. olivaceus is an artificially cultured species in the offshore area, and it has a high biomass in the Ma’an Archipelago Special Protected Area [O. fasciatus had a large biomass and was the dominant species in the study area, and this study also showed that O. fasciatus had a large biomass and was the dominant species in the Ma’an Archipelago Special Protected Area. The biomass of the other dominant species also varies from site to site, which is somewhat related to their habits. Overall, there was some increase in the fish biomass in all habitats.The Ma’an Archipelago, located in the center of Zhoushan Fishery, Zhejiang Province, has traditionally been the spring spawning ground of the Daiku Nation of the East China Sea, and the survey period coincided with the spring breeding period of . crocea ,48. As a results . Wang et results  evaluateted Area ,50. Satoted Area  assessedL. crocea, A. schlegelii, and O. fasciatus. The fish community structure was influenced by two environmental factors, pH and DO, and the respiration of the algal species supported by the seaweed farm during the survey period may have affected the changes in pH and DO [O. fasciatus and A. schlegelii. The eDNA results showed that O. fasciatus and A. schlegelii were the dominant species in the rocky reef habitat, while changes in salinity and temperature affected the community structure composition of this habitat. This result is also related to a study by Wang [There are many islands and reefs in the Ma’an Archipelago Special Protected Area, with a variety of habitats such as rocky reefs, seaweed beds, and mussel raft culture areas, forming a marine environment with extremely complex spatial and temporal changes in environmental factors such as temperature and salinity . The behH and DO ,56. ThisH and DO , such as by Wang  on the eA. schlegelii had a larger biomass in the platform, while the stomach content analysis also showed that there were more barnacles and mussels in the stomach, and thus the offshore bulk load shedding platform provided a good habitat for A. schlegelii.Studies have shown that rocky reef waters with an abundant seaweed attachment have higher fish diversity than muddy or sandy habitats without seaweed , and theArtificial reefs are one or more natural or artificial structures placed on the seabed . As an iIn summary, the differences between different areas resulted in the formation of reef ecosystems . BiologiAccurate information on the composition, distribution, and abundance of major fish communities is needed for fisheries management and resource conservation. We found that the fish community compositions and structures differed among ecological types, and that eDNA concentrations of the same species differed significantly among habitat types, indicating a significant horizontal distribution of fish. We also determined the effect of environmental factors such as SST and salinity on fish distribution, which has predictive value for fish distribution. This study verifies the feasibility of eDNA. In the future, eDNA technology will be used in the Ma’an Archipelago Special Protected Area to conduct fishery resource surveys for different island habitat types, to provide information support for fish diversity conservation in the protected area, and to provide a scientific basis for carrying out fish resource management and detection in the protected area. At the same time, the influence of environmental complexity on the detection results of eDNA technology should be fully considered, and the influence of the environment on it should be reduced by improving the precision and increasing the sampling time.


Due to its widespread availability, low cost, feasibility at the patient’s bedside and accessibility even in low-resource settings, chest X-ray is one of the most requested examinations in radiology departments. Whilst it provides essential information on thoracic pathology, it can be difficult to interpret and is prone to diagnostic errors, particularly in the emergency setting. The increasing availability of large chest X-ray datasets has allowed the development of reliable Artificial Intelligence (AI) tools to help radiologists in everyday clinical practice. AI integration into the diagnostic workflow would benefit patients, radiologists, and healthcare systems in terms of improved and standardized reporting accuracy, quicker diagnosis, more efficient management, and appropriateness of the therapy. This review article aims to provide an overview of the applications of AI for chest X-rays in the emergency setting, emphasizing the detection and evaluation of pneumothorax, pneumonia, heart failure, and pleural effusion. Over recent years, there has been increasing interest in the application of artificial intelligence (AI) techniques to medical imaging examinations. Chest X-ray (CXR) is one of the most frequently performed examinations, particularly in the emergency setting, due to its widespread availability, low costs, and the possibility to be performed at the patient’s bed. It provides significant information on lung parenchyma and the related pathologies, as well as on cardiovascular circulation and pleural disorders.A correct and rapid CXR report is decisive in choosing the proper treatment and improving the patients’ outcome. Although CXR reading is considered a basic radiological skill, it remains challenging and depends on the radiologist’s experience, workload, and environment. The development of robust and efficient AI algorithms could greatly facilitate CXR readout, particularly in emergency settings, where time spent on CXR interpretation and the accuracy of responses is often of vital importance.Owing to the high number of CXR examinations performed daily in hospitals around the world, there is a large amount of data available for developing robust AI algorithms. AI-based tools have been shown to facilitate the detection of numerous health-threatening conditions, as well as to prioritize the reporting of patients with critical findings ,2. In thArtificial intelligence (AI) can be defined as technology that mimics human cognitive processes, such as learning, reasoning, and problem-solving. As, in conventional radiology, diagnosis is primarily qualitative, AI-powered assessment could make a significant contribution in this field, reducing the variability in image interpretation and improving diagnostic accuracy .AI applications in radiology are driven by the idea that medical images are a set of data that can be computed by a machine to extract useful information . TherefoMachine learning (ML) is a branch of AI that applies concepts and tools from other disciplines, primarily statistics and programming, to build algorithms aimed at the automated detection of meaningful patterns in data, a field closely related to data mining . In radiThe process of developing ML-based tools embodies several phases, among which the main ones are the training and validation phase . In geneIn supervised learning, the simplest form of ML, the system simulates the human cognitive process of “learning by examples”. This type of ML is suitable for very general classification tasks in which new elements need to be labeled according to some predefined categories . LabeledWhile radiologists mainly evaluate qualitative features, such as increased or reduced radiopacity, comparing them to a subjective reference standard, ML features are low-level properties, or metrics, of a data point that can be computed or measured easily. There are hand-crafted features  and automatically extracted features . The problem of choosing which features to select to build more accurate models is one of the most challenging parts in the radiomic workflow. Even if reproducible features are not necessarily clinically informative, successful AI models must be built upon reproducible and robust features .In unsupervised learning, the system analyzes and extracts significant features from unlabeled data by forming groups or identifying relationships between subgroups . This tyAt the basis of each ML approach are models that can be trained and tested in data analysis . A modelArtificial neural networks are a particular learning paradigm inspired by the biological network of the human brain . Their oComplex artificial neural networks, called Convolutional Neural Networks (CNNs), have been developed and found to be particularly suitable for image analysis and recognition tasks. Deep learning (DL) is a domain of AI that takes advantage of complex artificial neural networks such as CNNs to discover intricate patterns in data. DL networks feature many intermediate layers, where each layer represents increasing levels of abstraction, to the extent that it is unclear exactly how processing the intermediate layers contributes to the overall result . This isDL models are built to capture the full image context and learn the correlations between the local features, resulting in a superior performance in various radiological tasks, such as interpreting radiographic exams.ML and DL algorithms are trained with datasets and are dependent on the number and quality of the training data. There is a constantly increasing number of publicly available CXR datasets that can be used for image classification and retrieval tasks. Some of the biggest and most commonly used open datasets are listed in The automatic notification of critical findings is one of the most interesting AI applications in emergency radiology. With the increased demand for imaging studies, delaying the communication of key data to the treating physician can delay critical care and compromise therapeutic efficacy, particularly in urgent scenarios .The priority assigned by the emergency doctor who first examines the patient determines the sequence in which the imaging exams are reported; unfortunately, the precedence is not always consistent with the abnormalities observed. AI-based models may detect and prioritize emergency CXR findings in real time, reduce the report response times for key findings, and optimize therapeutic pathways.A notification system developed by GE Medical System and Zebra Medical Vision for the evaluation of pneumothorax on CXR demonstrated a significant reduction in the time required for the diagnosis. Three experienced radiologists evaluated 588 CXR with the HealthPNX prioritization software with an average diagnosis time of 8.05 min versus 68.98 min without the software. The time needed to assess the radiograph and send a notification was only 22.1 s [Annarumma et al. created and tested a CNN-based tool to simulate an automatic triage for adult CXRs according to the urgency of the imaging findings. The use of the algorithms resulted in a theoretical reduction in the reporting delay for critical studies, from 11.2 to 2.7 days . AnotherIn the setting of the COVID-19 pandemic, Tricarico et al.  developePneumothorax is a pathological condition in which the pleural cavity fills with air, impairing oxygenation and ventilation. It occurs spontaneously or as a complication of trauma, medical interventions, and infections. Due to the sheer variety of the underlying etiologies and clinical scenarios, pneumothorax represents an important morbidity and mortality factor. Some forms present with a severe progressive hemodynamic compromise, with eventual cardiovascular collapse and respiratory failure if left untreated.CXR allows for the timely diagnosis and objective quantifying of pneumothorax, which is crucial for the selection of the optimal management strategy. AI could potentially increase the sensitivity for pneumothorax identification and provide quantification through volume segmentation, particularly in low-resource settings where experienced radiologists might be lacking. Automated pneumothorax detection represents a challenging technical task due to the variability of its appearances on CXR. However, a variety of AI solutions have been proposed in recent years, powered by the rapid development of DL and the availability of large CXR datasets.In 2017, Wang et al. published a large ChestX-ray8 database with image-level labels for eight chest conditions, including pneumothorax. A multilabel deep CNN model was performed on the dataset and demonstrated an accuracy of 0.0816 for pneumothorax detection, with an average false positive rate of only 0.2317 . SmallerA model involving CNNs on frontal CXR demonstrated a sensitivity of 0.55, a specificity of 0.90, and an area under the curve (AUC) of 0.82 for the assessment of large and moderate pneumothorax on internal testing, although the performance was lower on external testing (AUC = 0.75) .p = 0.11). For patients with >21.6% of pneumothorax, the model predicted the need for thoracostomy [In a pneumothorax segmentation competition organized in 2019 by the Society for Imaging Informatics in Medicine, the winning team achieved a Dice score of 0.8679 by using a deep neural network ensemble and extensive data pre-processing and augmentation . Wang etacostomy .In another study, using a fully convolutional network algorithm trained on a large dataset with pixel-level labels, the authors reached 93.45% diagnostic accuracy and high segmentation accuracy with a mean pixel-wise accuracy (MPA) of 0.93 ± 0.13 and dice similarity coefficient of 0.92 ± 0.14 .Several authors have proposed pneumothorax detection algorithms based on ResNet artificial neural networks. Gooßen et al. demonstrated an AUC of 0.96 for a ResNet-50 model .A Deep ResNet-50 model successfully detected pneumothorax with a combined Dice score of 0.82 and allowed for the segmentation of pneumothorax lesions, with a Dice score ranging between 0.72 and 0.79 . In a reAnother CNN-based model correctly recognized pneumothorax and tension pneumothorax cases with an AUC of 0.979 and 0.987, respectively .p < 0.01)). The deep CNN identified 9.7% of the pneumothoraxes missed by at least one of the residents [Yi et al. compared the performance of an algorithm based on ResNet-152 deep CNN with that of first-year radiology residents. Although the model performed faster than the first-year radiology residents, with 1980 and 2 images assessed per minute, respectively, its AUC was significantly lower  on a large CXR dataset and demonstrated the superior performance of DenseNet201, which detected pneumonia with an accuracy of 0.98 and differentiated between viral and bacterial etiologies with an accuracy of 0.95 . In a stA systematic review and meta-analysis by Li et al. demonstrated pooled AUC of 0.99 (95% CI: 0.98–100) across 15 studies assessing the performance of DL algorithms for pneumonia detection on CXR. The pooled sensitivity and specificity were 0.98 and 0.94, respectively. Moreover, it showed a pooled AUC of 0.95 for differentiating between bacterial and viral pneumonia on CXR. However, it was noted that the included studies lacked performance in comparison with healthcare professionals .p = 0.001), highlighting the potential role of AI in directing management strategies as well as refining diagnosis [In a recent study by Kwon et al., an ensemble CNN model demonstrated an AUC of 0.983 for detecting pneumonia on CXR and showed a predictive value in differentiating cases that were improving and those that worsened over seven days of follow-up , respectively [Nillmani et al. propose 16 types of segmentation-based classification deep learning-based systems for the automatic detection of COVID-19. The best performing segmentation-based classification model was UNet+Xception, which exhibited the accuracy, precision, recall, F1-score, and AUC of 97.45%, 97.46%, 97.45%, 97.43%, and 0.998 . The ChestX-ray8 dataset, which comprises 1125 X-ray images, was used to evaluate the performance of the suggested approach. In the two-class  and multi-class  classification tasks, they respectively achieved accuracies of 98.54% and 91.11% .Ezzoddin et al. proposed a similar approach using DenseNet169 to extract the features of the patients’ CXR images and LightGBM algorithm in order to classify them. The evaluation of the ChestX-ray8 dataset reached accuracies of 99.20% and 94.22% in the two-class  and multi-class  .In two other studies, Nasiri et al. employed an approach using the DenseNet169 Deep Neural Network (DNN) to extract the features of X-ray images taken from the patients’ chests. In one of the studies, the features were chosen by a feature selection method, i.e., analysis of variance (ANOVA), to reduce the computation and time complexity while overcoming the curse of dimensionality to improve the accuracy. Finally, the extracted features were given as input to the Extreme Gradient Boosting (XGBoost) algorithm in order to perform the classification task. The experiments showed an accuracy of 98.72% and 98.23% for two-class classification  and 89.7% to 92% accuracy for multiclass classification  ,61.Other studies have investigated the possibility of determining and classifying images according to the estimated degree of disease severity .Cohen et al. used a DenseNet model to generate a severity score based on CXR imaging. Images from a public COVID-19 database were scored retrospectively in terms of the extent of lung involvement and the degree of opacity. A neural network model that was pre-trained on large (non-COVID-19) CXR datasets was used to construct features for COVID-19 images. As a result, training a regression model on a subset of the outputs from this pre-trained model predicted the geographic extent score with a 1.14 and the lung opacity score with a 0.78 mean absolute error, respectively. The model allowed us to assess the severity of COVID-19 lung infections, which can be used for tracking the effectiveness of the treatment and for escalating or de-escalating care . Jiao etKhan et al. developed a new CNN architecture, STM-RENet, to interpret radiographic patterns from X-ray images. They proposed a new convolutional block STM that implements the region and edge-based operations both separately and jointly. The learning capacity of STM-RENet was further enhanced by developing a new CB-STM-RENet that exploited channel boosting and learned textural variations to effectively screen the X-ray images. The suggested model performed significantly better than typical CNNs on three datasets, particularly the CoV-NonCoV-15k dataset, with a high detection rate (97%), and accuracy (96.53%) .It is also worthwhile to mention some of the real-world applications of AI systems: Mustaq et al.  comparedBy assisting in difficult decision-making, AI has been proven to have the potential to play a pivotal role in the fight against COVID-19. AI algorithms can be trained to automatically detect and classify various features of the disease on CXRs with high accuracy. This could potentially save time and resources in the demanding pandemic setting. There are still some challenges that need to be addressed before AI can be widely used for COVID-19 diagnosis. The principal studies are summarized in In the context of an increasingly aged patient population, heart failure is one of the major causes of admission to the emergency department. Once again, CXR is one of the first-line tools in the assessment of patients in this setting .Cardiopathic patients may be not compliant and are often bedridden. As a result, CXR is often limited to anterior-posterior projection with the consequent projective enlargement of the heart. In addition, their thorax may present multiple devices, such as electrocardiogram electrodes, pacemakers, or implantable cardioverter-defibrillator, which overlap the lung fields and increase the possibility of erroneous findings . In thisA recent clinical evaluation has been performed to compare four DL models  to find which one would show the highest percentage of automatic results of the cardiac size accepted by users with a measurement variation within ±1.8% of the human-operating range. VGG-16 gave the highest-grade result (68.9%), but the combined AlbuNet + VGG-11 model yielded excellent grades in 77.8% of the images in the evaluation dataset, a coefficient of variation of 1.55%, and reduced the CTR measurement time by almost ten-fold (1.07 ± 2.62 s vs. 10.6 ± 1.5 s) compared with manual operation . AlveolaA reliable AI algorithm should be able to distinguish the most common causes of acute respiratory failure in an emergency, mainly congestive edema and pneumonia .Considering that ML is more efficient in the distinction between localized lesions and lesions with global symmetrical patterns , unilateIf CNN models could suit CXR feature recognition, the current scientific literature contains a wide selection of encouraging applications that include more features, including regarding heart failure. For example, Cicero et al. investigated five radiological signs—cardiomegaly, pleural effusion, pulmonary edema, pneumothorax, and consolidation—using the GoogLeNet CNN, trained on a total of 35038 images and tested on a set of 2443 radiographs.The sensitivity, specificity, and AUC, respectively, were 91%, 91%, and 0.962 for pleural effusion, 82%, 82%, and 0.868 for pulmonary edema, 81%, 80%, and 0.875 for cardiomegaly.The best results were achieved in classifying a study as normal with an overall sensitivity and specificity of 91% and an AUC of 0.964 .Moreover, another CNN, CheXNeXt, was developed a few years ago to detect the presence of 14 different pathologies in chest radiographs, including cardiomegaly and pleural effusion. In 2018, the model was compared with nine expert radiologists and three senior radiology residents. CheXNeXt did not achieve radiologist-level performance on three pathologies , it performed better than radiologists in detecting atelectasis and there were no statistically significant differences in AUCs for the other ten pathologies, including pleural effusion .Pleural effusion is a medical condition characterized by the pathological accumulation of fluid between the two pleural leaflets. Usually, it is used as a generic term to describe any abnormal accumulation of fluid in the pleural cavity, also because most effusions are diagnosed by CXR, which cannot distinguish between different types of fluids.Recently, and with increasing interest, the world of diagnostic imaging has embraced the use of artificial intelligence, testing its applications in a wide variety of contexts .A few papers have been published to validate AI tools in the diagnosis and/or quantification of pleural effusion via CXR. Zhou et al.  developeHuang et al.  developeNiehues et al.  created AI applications for thoracic disorders have shown promising outcomes in terms of enhancing the existing clinical systems and prognostic prediction reasoning. According to a recent study that compared the opinions of thoracic radiologists and computer scientists, 15.6% of computer scientists thought that the radiologist’s position will be obsolete in 10–20 years ; despiteThe use of open datasets had a significant impact on the development of AI algorithms, but the absence of standardized and well-defined criteria also led to the creation of flawed datasets. This needs to be addressed to increase the performance of AI models through high-quality training. As a result, it is critical to understand the limitations of the datasets for their effective use; the key limitations are summarized in Another key aspect would be the integration of AI education into the post-graduation programs for radiology residents. The possibility of increasing performance through personalized learning is the primary motivator for AI-augmented radiological precision education . AccordiImaging research is seeing an increase in the number of articles offering novel diagnostic, classification, and prediction tools based on ML that outperform the previous techniques. However, few studies have been conducted to assess the applicability of these models and the tangible advantages they offer to clinical practice in the real-world setting . When suLast but not the least, the ethical implications of AI integration should be considered before they can see widespread integration into clinical practice. When it comes to AI applications that attempt to enhance patient outcomes, the issue of responsibility becomes considerably more important, particularly when problems and errors occur. At present, it is unclear who should bear responsibility if the system fails. One of the possible solutions includes a joint effort by governments and industries to define the criteria of transparency and accountability, which aspects of the incoming data are influencing the outputs, and utilizing that to deduce what is happening within the “black box” . As descWe have provided an overview of AI applications for CXR in the emergency setting.We believe that AI will transform the healthcare system, notably, in the imaging sector. Radiologists should know the AI technologies that are currently available and use them when appropriate to improve their diagnostic accuracy and treatment planning in the emergency setting.


Intracranial germ cell tumors (IGCTs) are clinically rare. They are more common in children and adolescents and the incidence in Asia is higher than in Western countries. Histologically, IGCTs are divided into germinoma and non-germinomatous germ cell tumor (NGGCT). Germinoma is sensitive to radiotherapy and chemotherapy and therefore, patients with germinoma have a good prognosis. However, NGGCTs, especially those with malignant components, are not sensitive to radiotherapy and chemoradiotherapy, leading to a poor prognosis. The pathogenesis of IGCTs is not fully understood. By summarizing previous literature, we found that the occurrence of IGCTs may be related to the following factors: chromosomal instability, MAPK and/or PI3K pathway changes, and DNA hypomethylation in pure germ cell tumors. Intracranial germ cell tumors (IGCTs) mainly occur in the midline region, with the pineal gland being the most common site (50%), followed by the suprasellar region (about 20%–30%). Tumors outside the midline are often referred to as ectopic germ cell tumors and are mainly found in the basal ganglia and thalamus.IGCTs are more common in children and adolescents, and the ratio of the incidence among males to the incidence among females is about 3-4:1 . The difHistologically, IGCTs can be classified into eight types according to 2021 WHO guidelines , namely,et al.  . During et al. ,Rickert et al.  and Schnet al.  found chet al.  reportedet al.  found noet al.  examinedAlthough chemoradiotherapy is effective for most IGCTs, the side effects will degrade the long-term quality of life of patients and lead to a high recurrence rate. Therefore, it is extremely important to understand the pathogenesis of IGCTs and find new therapeutic targets.From the perspective of molecular genetics, chromosome instability is common in IGCTs , especiaActivation of the KIT/RAS (MAPK) pathway and AKT/mTOR (PI3K) pathway by somatic point mutation is the genetic driver of most IGCTs. Changes in the MAPK pathway play a central role in the occurrence and development of IGCTs, and the mutation frequency of this pathway is higher in germinoma , 17. TheKIT gene. KIT  but not common in NGGCTs  and mTOR complex 2 (mTORC2)  .BCORL1, TP53, SPTA1, KDM2A and LAMA4  and immune response, while NGGCTs are characterized by the expression of genes involved in neuronal differentiation, WNT/β-catenin pathway, invasion, and epithelial-mesenchymal transformation (including SNAI2 (SLUG) and TWIST2) . In addiResearchers also regarded that the mutation of PGCs is the common ancestor of IGCTs. Mutated PGCs may directly develop into pure germinoma or differentiate into other types of IGCTs through epigenetic reprogramming.This hypothesis is verified by the finding that PGCs dedifferentiate into pluripotent stem cells and develop into teratoma in mice . AdditioSuch mononuclear macrophage abundance was also reported in an independent IGCT cohort, and monocyte abundance in preoperative CSF was significantly associated with NGGCTs . This imKIT gene, selective tyrosine kinase inhibitors targeting KIT are expected to be effective molecular targeting agents. Erk1/2 inhibitors  is more than 90% . Howeverhibitors  can inhit manner . LAE002,Conceptualization- SL and LR. Writing- original draft preparation SL. Writing- review and editing LR, GW, and SL. Supervision- GW. Funding acquisition- GW. All authors read and approved the final version for submission.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.


Breast cancer (BC) is the most common malignancy in the world and has a high cancer-related mortality rate. Basement membranes (BMs) guide cell polarity, differentiation, migration and survival, and their functions are closely related to tumor diseases. However, few studies have focused on the association of basement membrane-related genes (BMRGs) with BC. This study aimed to explore the prognostic features of BMRGs in BC and provide new directions for the prevention and treatment of BC.We collected transcriptomic and clinical data of BC patients from TCGA and GEO datasets and constructed a predictive signature for BMRGs by using univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis. The reliability of the model was further evaluated and validated by Kaplan-Meier survival curves and receiver operating characteristic curves (ROC). Column line plots and corresponding calibration curves were constructed. Possible biological pathways were investigated by enrichment analysis. Afterward, we assessed the mutation status by tumor mutational burden (TMB) analysis and compared different subtypes using cluster analysis. Finally, we examined drug treatment sensitivity and immunological correlation to lay the groundwork for more in-depth studies in this area.The prognostic risk model consisted of 7 genes . After validation, we found that the model was highly reliable and could accurately predict the prognosis of BC patients. Cluster analysis showed that patients with cluster 1 had more sensitive drugs and had better chances of better clinical outcomes. In addition, TMB, immune checkpoint, immune status, and semi-inhibitory concentrations were significantly different between high and low-risk groups, with lower-risk patients having the better anti-cancer ability.The basement membrane-related gene signature that we established can be applied as an independent prognostic factor for BC and can provide a reference for individualized treatment of BC patients. Breast cancer (BC), whose number of global new cases is estimated to be 2,261,419 in 2020, is the most common malignant tumor in the world (Basement membrane (BM), a sheet-like structure, is a cell-adherent extracellular matrix that is widely distributed in metazoan tissues, lies beneath epithelial cells, and surrounds most tissues. As core structural components of BMs, collagen type IV, laminins, nidogens, and heparan sulfate proteoglycans direct cell polarity, differentiation, migration, and survival . For exahttps://portal.gdc.cancer.gov/) database and Gene Expression Omnibus  database. The TCGA dataset contains 1222 human breast tissue samples, including 1109 BRCA (breast invasive carcinoma) and 113 normal samples. The patients in the TCGA database served as the training set for the prognostic model in this study, from which we extracted basic information, survival data and pathological staging of the patients. The GEO data were selected from the GSE20685 dataset, which contained 327 breast cancer samples with survival information. We obtained a total of 224 basement membrane related genes (BMRGs) for identification through extensive literature reading. The gene transfer format (GTF) was obtained from Ensembl (http://asia.ensembl.org), which was used to distinguish lncRNAs from genes about the samples in the TCGA database.We obtained RNA transcriptomic and clinical data required in this study from The Cancer Genome Atlas . The training set (TCGA dataset) and validation set (GEO dataset) together constitute the prognostic cohort, and the prognostic genes co-expressed by TCGA and GEO are screened out. Next, using LASSO regression analysis with the “glmnet” package to further exclude overfitting BMRGs (We used R software (version: 4.1.2) to analyze the data that had been downloaded and organized. The “Limma” package was used to compare BMRG expression in tumor and normal patients in the TCGA and GEO datasets and to filter for differentially expressed genes . The filng BMRGs , we screCoef(k) represents the regression coefficient of BMRGs, and E(k) represents the expression level of BMRGs. We used the median risk score as the cut-off for patients in the high-risk and low-risk groups.After establishing the prognostic model, it is necessary to validate the resulting model for the purpose of demonstrating its reliability in predicting prognosis. We compared the overall survival (OS) between the two groups based on the Kaplan-Meier log-rank test to see if there was a significant difference. The “pheatmap” package was used to plot risk curves, survival status maps, and risk heat maps for the TCGA and GEO datasets to visualize the effect of BMRGs on the prognosis of BC patients for better observation and comparison. In addition, we performed an independent prognostic analysis using univariate and multivariate COX regression analysis to determine whether the established model was independent of other clinicopathological characteristics as an independent prognosis factor. In this process, we excluded 51 patients with overall survival of fewer than 30 days and 183 patients with missing staging data, including 24 patients without stage, 150 patients without M stage, and 9 patients without N stage. The receiver operating characteristic (ROC) curve was used to reflect the relationship between sensitivity  and specificity  at different thresholds, and the area under the curve is called the AUC (Area Under Curve) . The larThe Nomogram is based on multivariate COX regression analysis, integrating multiple predictors to express the interrelationship between variables in the prognostic model, and is widely used in oncology . We usedp< 0.05 and FDR< 0.05 as the analysis criteria. All biological pathways were obtained from the Kyoto Encyclopedia of Genes and Genomes (KEGG).We screened differentially expressed genes in different risk groups with |log2 fold change (FC) > 0.5| and with false discovery rate (FDR)< 0.05. The Gene ontology (GO)  enrichmeTMB  is the tIt was the Wilcoxon rank sum test that was used for analyzing the differences in StromalScore, ImmuneScore, and ESTIMATEScore between high and low-risk groups. We analyzed the immune microenvironment of the tumor from these three aspects. Simultaneously, we calculated the proportion of immune cell infiltration using various algorithms, including XCELL , TIMER , QUANTIShttps://www.cancerrxgene.org/). p< 0.001 was considered significant.Based on the screened prognostic model of the 7 BCRGs, we performed cluster analysis using BC samples from the TCGA cohort. In the light of “ConensusClusterPlus” R package, we divided tumor samples into different subgroups. The clustering excluded groups with small sample sizes while strengthening intra-group correlations and reducing inter-group correlations. In the meantime, the cumulative distribution function curve was steadily increasing. Next, we plotted survival curves using the “survival” and “survminer” packages to analyze the survival differences of the clustered samples. We also generated Sankey plots using the “dplyr”, “ggplot2”, and “ggalluvial” packages to analyze the relationship between clustering and risk relationship. We used t-distributed Stochastic Neighborhood Embedding (tSNE) to analyze different clusters  and highp< 0.001 considered significant.BC’s research cannot be separated from the development of new drugs. Based on the IC50 of GDSC, we assessed the drug sensitivity of drugs in the dataset using the “pRRophetic” package , and theWe extracted 1039 samples based on the TCGA database, after excluding cases with missing or less than 30 days of survival time. By differential analysis of the expression of 224 BMRGs, we identified 113 BMRGs that were differentially expressed in normal and tumor tissues  and hazard ratio (HR) of 11 BMRGs between cancer and normal tissues. We next penalized the 11 BMRGs by LASSO regression analysis  and hazard ratio (HR) are presented in p<0.001). And multivariate COX regression analysis showed that age and model were independent prognostic factors for BC patients (p<0.001). Univariate COX regression based on the GEO dataset showed that T, M, and N stages could be independent prognostic indicators for BC patients (p<0.001). Multivariate COX showed that N stage and model could be used as independent prognostic factors for BC patients (p<0.005). Finally, we judged the reliability of the prognostic model by plotting the ROC curve and calculating the area under the curve (AUC). The AUC value of the risk score was 0.649, which had better predictive performance than the gender, M stage and N stage . The correlation curve from p = 3.2e-16). The TMB survival curve in p = 0.00098). Among all groups in Based on the data from the TCGA dataset, we obtained theb mutations in BC patients using the R package “maftools”, which included a total of 926 BC samples. The mutation rate in the high-risk group was 83.08% (p = 1.3e-15), ImmuneScore (p< 2.22e-16) and ESTIMATEScore (p< 2.22e-16) were significantly lower in the high-risk group than in the low-risk group. We tested the proportion of immune infiltration using a combination of algorithms. According to As can be clearly seen from the boxplots of p<0.001). As seen in p = 7.4e-0.6) in StromalScore (p = 0.018) and in ESTIMATEScore (p = 0.00046). Cluster 2 had significantly lower StromalScore, ImmuneScore and ESTIMATEScore than cluster 1. The immune response heat map was used to visualize the immune cell infiltration between the two clusters, indicating that cluster 2 had the most immune cell infiltration .The promising research related to drug treatment of BC is a hot topic of current research and has received widespread attention. We analyzed the relationship between risk scores and drug resistance by calculating the IC50 of drugs against BC. The 20 most significantly different drugs are listed in BC is one of the most common malignancies in the world and the leading cause of most cancer-related deaths . DespiteThis study screened the 7 best BMRGs  to construct a predictive model. Previous studies have shown that basement membrane-associated genomes are associated with other BC-related pathophysiology. ITGB2-AS1 has a facilitative effect on BC cell migration and invasion through the upregulation of ITGB2 . SDC1 isWe assessed the prognostic value of BMRGs by training and validating survival curves, risk score plots, survival status plots, and heat maps. The scatter plots of the training and validation sets showed that the survival status of BC patients was related to the risk score, and the mortality of patients increased with the risk score. Combining the seven prognostic BMRGs, we plotted heat maps to compare the expression levels of genes in the training and validation sets in the high-risk and low-risk groups, and it could be seen that the expression trends of LAMC3, MMP1, SDC1, UNC5A and ITGB2 were consistent. In contrast, the expression trends of EVA1B and FBLN5 were inconsistent. We speculated that there might be two reasons for the different gene expression trends. The training and validation sets were from two databases (TCGA and GEO). On the one hand, the sample sizes screened by the two databases are different, and the different sample sizes might impact the percentage of their gene expressions. On the other hand, the two databases were from diverse populations. The TCGA data were from Europe and the US, while the GEO data were from Taiwan. The number, ethnicity, tissues taken, and age group might influence the percentage of gene expression. Our findings have clinical and pathological implications. Univariate and multivariate analyses showed that risk score could be an independent prognostic factor for BC patients (p<0.001). The AUC values of 0.649 for prognostic features and 0.649, 0.666, and 0.658 for 1-year, 3-year, and 5-year predicted survival, respectively, showed not only better predictive performance compared with other clinicopathological features , but also reflected the reliability and accuracy of prognostic features.Through GO enrichment analysis, we noted that leukocyte-mediated immunity, humoral immune response, and B cell activation play critical roles in the biological pathways associated with BMRGs. Through GSEA enrichment analysis, we found that high-risk patients have abundant biosynthetic and metabolic pathways, such as terpenoid backbone biosynthesis, citrate cycle, and protein export. Undoubtedly, tumor development is dependent on high levels of biosynthesis and metabolism. In contrast, the hematopoietic cell lineage pathway is more active in low-risk patients. The establishment of hematopoietic and immune capacity inhibits the pro-carcinogenic effect. erk1/1-mediated CD44 expression increases stromal production and migration, leading to stromal expansion . cd44 anTMB indicates the number of mutations per megabase (Mut/Mb) in DNA sequenced in cancers . Studiesin vivo  and TrkA inhibitor. one of the principles of the antitumor activity of ATRA is that it is a promising agent for the prevention/treatment of BC by reducing the number of mitochondria leading to an inadequate respiratory/energy balance in BC cells . The compathways . Functiopathways . For thoOncology therapy is an essential area of interest. Through IC50 screening analysis of potential drugs, we found that high-risk patients may be sensitive to PF.4708671, the first reported selective inhibitor of S6K1 . Choi HNin vivo and in vitro experiments are still needed to reveal the role of BMRGs in the onset and progression of BC. Third, we still need more clinical data and prospective studies to validate the clinical value of the BMRGs prognostic signature.Beyond all doubt, our study has the following limitations. First, although we have validated our prognostic risk scores in the TCGA and GEO datasets, validation of differentially trending expressed genes in an expanded sample size may further increase the confidence of the risk scores. Second, we confirmed that the prognostic features of BMRGs have good predictive value, but We constructed and validated a model for the prognostic characteristics of BMRGs, which includes FBLN5, ITGB2, LAMC3, MMP1, EVA1B, SDC1, UNC5A. The signature can be used as an independent prognostic factor for BC and can provide a new direction for individualized treatment of BC patients.The original contributions presented in the study are included in the article/JC, XZ, and AL conceived and designed the study. WX and ZL involved themself in data collection, collation, and statistical analysis. WL ran the R language and drew and analyzed the graphs. XZ, WX, and AL participated in the literature search and revised the manuscript for important content. JC, ZL and AL analyzed, interpreted the results, and edited a draft version of the manuscript. All authors contributed to the article and approved the submitted version.This work was supported by the Special Fund Project of Guangdong Science and Technology (210728156901524), Shantou Medical Science and Technology Planning Project , Administration of Traditional Chinese Medicine of Guangdong Province project (202205092315428030), the Scientific Research Fund of Hunan Provincial Education Department (No. 20A213 and No. 19B236) and Natural Science Foundation of Hunan Province (No. 2020JJ5221).This is a short text to acknowledge the contributions of specific colleagues, institutions, or agencies that aided the efforts of the authors.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.


Sensory features in autism spectrum disorder (ASD) have received increasing interest in clinical work and research during the recent years. With the Sensory Perception Quotient (SPQ), Tavasolli and colleagues have produced a self-rating scale for adults with ASD that measures sensory hyper-sensitivity in different sensory modalities, without also tapping cognitive or motivational aspects that precede or follow autistic sensory experiences. Here, we present the results of a translation of the SPQ to German and its short version as well as their validation in samples of autistic or neuro-typical participants. We, furthermore, present the psychometric properties and validities of Tavasolli's original SPQ-short version as well as an alternative short version based on different psychometric item-selection criteria. We can show here that our alternative SPQ-short version, overlapping with the original short-version in 61% of its items, exhibits superior reliabilities, reasonable concurrent validities with other related measures. It, furthermore, exhibits excellent differentiation between autistic and non-autistic samples, underscoring its utility as a screening instrument in research and a clinical instrument to supplement the ASD diagnostic process. Qualitative impairments in social interaction and communication as well as restricted interests and repetitive behavior are the core symptoms of autism spectrum disorder (ASD) and have guided much of the ASD research so far. Since the publication of the Diagnostic and Statistical Manual—Version 5 (DSM-5), however, sensory features in persons with ASD have gained in research interest and are defined as “hyper- or hypo-reactivity to sensory stimulation as well as “unusual interest in sensory aspects of the environment” by the American Psychiatric Association . RecogniSensory abnormalities may contribute to higher-order functions such as social cognition or may be even mistaken for them if, for instance, an autistic child does not respond when his/her name is called or reacts aversively to touch. They may also impact on non-social autistic features when, for example, repetitive behavior is a result of unusual seeking or avoidance of sensory stimulation. Repetitive behavior, distress as well as melt- or shutdown reactions in ASD can be associated with sensory hyper-reactivity. Understanding sensory abnormalities in autistic individuals may therefore deepen the understanding of the core symptoms of autism and its clinical heterogeneity .Given that autism shows a male preponderance of diagnoses and autistic females can exhibit divergent core symptoms , anotherSensory features of autistic individuals are not always observable by others. Therefore, questionnaires have been designed to assess sensory features in adolescents or adults with autism, including the “Sensory Experience Questionnaire”  or the With the “Sensory Perception Quotient” (SPQ) by Tavassoli et al. , 21, a sBased on a translation of the original SPQ , 21 to GOur study was approved by the ethics committee of the University of Freiburg (EK 85/16) and all study participants gave their informed written consent beforehand.N = 188 adult participants took part in the study, including N=85 autistic individuals with an autism spectrum diagnosis  and N = 103 healthy control participants . These groups did not differ significantly regarding sex [p = 0.151], age  and IQ . The autistic participants were current or former patients treated in the Department of Psychiatry (N = 70) or the Department of Child and Adolescent Psychiatry (N = 17). Controls were recruited with flyers posted at public places within the city. General inclusion criteria were: age > 18.0 years, German as mother language, IQ > 70. For the autistic individuals, an autism spectrum diagnosis  was required; for controls, the absence of a psychiatric or neurological diagnosis. The diagnostic procedures included the ADOS-2 and, for the Department of Child and Adolescent Psychiatry, also the ADI-R  are recoded. The SPQ total score varies between 0 and 276, with a low score indicating hyper-sensitivity. In addition to the total score, sum scores for specific sensory modalities can be computed.As described in Tavassoli et al. , 21, theThe guidelines for trans-cultural research  guided tp < 0.05) and indicated greater sensitivity  in the ASD compared to the TD group. About 50% of these items showed moderate (Cohen's d ≥ 0.5) or large (Cohen's d ≥ 0.8) effect sizes in separating the groups, the other ones small effect sizes (Cohen's d ≥ 0.2). To determine (concurrent) validity of the SPQ, its sum score was correlated with those of the AQ and EQ. Any outcome of such correlations will help understand how the SPQ sum scores may fit into a nomological network of inter-dependencies of constructs central to ASD.In order to derive a short version of the German SPQ that separates our participants groups statistically at least as well as the full version, those 33 items were picked that differed between groups at least at the 5% level  with GROUP  and SEX  as between-subject factors, In contrast to the procedures applied by Tavassoli et al. , 21, selincreased sensory sensitivity. This difference corresponds to a medium effect size of eta2 = 0.118 (corresponding to d = 0.732 ≥ 0.115), less to taste, and not to smell (0.018 ≤ eta2 ≤ .044). Among the 20 items of each of the modalities hearing, touch and vision, 11-12 items show significant group differences; this holds only for 3 or 1 item(s) of the modalities taste and smell, respectively (not further detailed here). Overall, the inclusion of items from the modalities taste and smell reduce the diagnostic sensitivity of the overall scale.As can be seen in 1,184 = 4.472, p = 0.036, eta2 = 0.024) as well as the subscale scores for touch , taste  and, as a trend, vision  alongside the descriptive statistics shown in 1,184 = 1.37, p = 0.24, eta2 = 0.007) and smell , sex differences do not differ between the autistic and non-autistic groups.While the overall sex differences are largely non-significant see , signifi2 = 0.164) and even more so for the 33-item short-version derived in the present study (eta2 = 0.409). Both short-versions also confirm greater sensory sensitivity in female as compared to male individuals with autism, with the original short-version showing a slightly smaller  and the present short-version a clearly larger  GROUP x SEX interaction than the SPQ total score  and three modalities that reveal group-specific sex differences at the statistical trend level or better . Despite these differences in item composition between the original and the present short-version of the SPQ it is noteworthy that 61% of the 33 items of the present version are also included in Tavassoli's short-version  Consistency: The auditory modality is suggested as a marker scale of sensory hypersensitivity for both groups; (4) Correlates: The sensory experiences tapped by the SPQ are unrelated to intelligence but increase somewhat with age and show group specific correlations with autistic traits (AQ) and, less so, with empathy (EQ); (5) Sex differences: Sex differences were found exclusively for the autistic group, suggesting that female autistic individuals are more sensitive than male in the sensory modalities touch, vision and taste, but not hearing and smell.The present study translated the English “Sensory Perception Quotient” SPQ; , 21] to  to 21)] Hypersensitivity: With our German translation and adaptation of the SPQ it was possible to find sensory hypersensitivity in adult autistic individuals. This finding replicates previous reports on sensory abnormalities in autism using other questionnaires, including the Sensory Overresponsivity Scale  as R Scale; ]. SensorR Scale;  who haveAd (2)—New short version: Regarding the differentiation of autistic and non-autistic individuals, all short-versions performed better than the long version of the scale. In the present study, this holds both for Tavassoli et al.  only, respectively. This clear under-representation of smell in particular also replicates previous findings, as items from this modality did not differentiate groups in the studies of Tavassoli et al.  version of the SPQ (with subsets of items that do or do not differentiate groups), Tavassoli and colleagues short-version , and our short-version (based on item selection by group differentiation). Overall, this finding is complemented by factor analytical dimensional reduction, extracting consistently the auditory modality both within and across the diagnostic groups. More specifically: The results of the PCAs for both groups showed that inter-individual differences in the auditory domain explain more than 70% of the entire inter-individual variance in sensory experiences across all five modalities in the present samples. It has been suggested that autistic individuals are particularly sensitive in the auditory modality (Ad (3)—modality , 10. FinCorrelates: Our short-version and the one developed by Tavassoli et al. (Ad (4)—i et al. , 21 perfi et al. , 21, r =i et al.  or τ = −i et al. , the Geri et al. , 21. Stii et al.  in Dutchi et al. , 38, auti et al. , 21, 24 i et al. . No suchi et al. . Regardip = 0.95] and indicating that at least in the upper IQ range , sensory experience and cognitive abilities are unrelated features. This result is complemented by studies showing sensory abnormalities in low-functioning autistic individuals —d et al.  for theid et al. , may nevOverall, here we have presented an alternative short-version of Tavassoli and colleagues' SPQ that performs better than Tavassoli's short-version regarding the differentiation of autistic and non-autistic groups and in a modality-specific manner the sexes within the autistic sample, performs similarly well regarding different validity criteria, and performs slight worse in terms of internal consistency.The sample size of this study is only modest, which impacts the generalisability of our findings. Nevertheless, most effects or associations reported here were moderate to large in size, underlining the general robustness of our findings. Related to the first limitation, the set of additional measures used for concurrent validity analyses is rather limited and includes IQ, age, AQ and EQ. Positioning sensory features in a broader net(work) of inter-dependencies would be desirable but would require large sample sizes for appropriate modelling . The final short version presented here consists exclusively of items measuring perceptual hyper-sensitivity and this confined to the modalities touch, hearing and seeing. Future studies on this topic should therefore invest in building an item pool for the modalities smell and taste as well. In order to assess the utility of the scale for differential diagnostic purposes, it would be important to administer it to clinical populations other than the autistic that may also show sensory features, such as schizophrenia or ADHD.per se. Some of these differences have been interpreted in the sense of sample and / or cultural differences and lead to the conclusion that more diverse samples should be recruited and administered the SPQ or already collected data sets should be concatenated to analyse sample and / or cultural differences meta-analytically. Other differences can be explained by differences in the methods of item selection for constructing a short version. This holds in particular for the item selection by statistical group separation (here) as opposed to factor analysis (Tavassoli and others). While the original short version of the SPQ   the SPQ , 21 sepa the SPQ , the preOverall, our study has added to the field of autism research another translation of the SPQ by Tavassoli et al. , 21, a nThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the Ethics Committee of the University of Freiburg (EK 85/16). Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.AR, MB, and CK planned the study. TM, MK, and HR collected and analyzed data. CK analyzed data and wrote manuscript. AR, MB, LT, CF, TJ, UT, and NB contributed to writing the paper. 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.


This study aimed to identify the potential risk factors for small airway dysfunction (SAD) in non-smokers with chronic cough.Non-smokers with chronic cough who underwent lung function tests at Xiangya Hospital from May 2019 to May 2020 were enrolled, and divided into the derivation and validation cohorts based on their hospital admission time. SAD was determined based on the presence of at least two of the following three indicators of lung function being less than 65% of predicted: maximal mid-expiratory flow, forced expiratory flow at 50% of forced vital capacity (FVC), and forced expiratory flow at 75% of FVC. Clinical data of these patients were collected. Risk factors for SAD were identified by logistic regression analysis in the derivation cohort and further confirmed in the validation cohort.P < 0.001), was more commonly exposed to second-hand smoke (SHS) , and tended to be older . The median FVC, forced expiratory volume in one second (FEV1) % pred, FEV1/FVC ratio, and peak expiratory flow (PEF) % pred were slightly lower in the SAD group. Multivariable logistic analysis showed that exposure to SHS was an independent risk factor  for SAD in non-smokers with chronic cough after adjusting for related variables. In the validation cohort (n = 146), patients with SHS exposure had a relative risk of 1.976  for SAD compared to those without SHS exposure. Multivariable logistic analysis consistently confirmed that exposure to SHS was an independent risk factor  for SAD in non-smokers.In total, 316 patients (152 in the non-SAD group and 164 in the SAD group) were included in the derivation cohort. Compared with the non-SAD group, the SAD group had a higher proportion of female patients (82.3 vs. 59.2%, Exposure to SHS is independently associated with a higher risk of SAD in non-smokers with chronic cough. Reduction in SHS exposure may ameliorate lung function, thus lowering the risk of irreversible airway obstruction. Chronic cough is the most common symptom in outpatients with respiratory disorders. In China, patients with chronic cough account for more than one-third of respiratory outpatients, most of whom are never-smokers. Persistent cough symptoms seriously impair the quality of life of these patients and pose a huge burden on public health resources –3. ApproSmall airways are defined as those with a lumen diameter <2 mm . AccordiPrevious studies have identified multiple risk factors for SAD, including smoking status, age, gender, urbanization, and education level , 10. How1 and FEV1/FVC ratio; and (4) age ≥ 18 years old. Patients who met any of the following criteria were excluded: (1) age <18 years; (2) current or former smokers; (3) patients who were diagnosed with asthma, COPD, pulmonary fibrosis and/or active pulmonary infectious diseases; (4) patients who were pregnant or breastfeeding; and (5) incomplete clinical data. Enrolled patients were divided into the non-SAD and SAD groups according to the presence or absence of SAD. Eligible patients admitted to our hospital because of chronic cough between May 2019 and December 2019 were included in the derivation cohort to explore the potential risk factors for SAD. Eligible patients admitted to our hospital between January 2020 and May 2020 were included in the validation cohort to further validate the risk factors found in the derivation cohort.A flowchart of patient enrollment is shown in 1 were reproducible within 150 mL. All spirometry data were reviewed by an expert panel based on the criteria of the American Thoracic Society and European Respiratory Society . Each participant was required to perform up to eight forced expiratory maneuvers until the FVC and FEV Society . A normapulation .1/FVC ratio, FEV1, peak expiratory flow (PEF), fractional exhaled nitric oxide (FeNO).Demographics, history of respiratory diseases, exposure to second-hand smoke (SHS), laboratory findings, and lung function parameters of each patient were obtained from medical records. Demographics included age, gender, and body mass index (BMI) of patients. Exposure to SHS was defined as exposure to tobacco smoke as a result of having one or more active smokers who regularly smoked cigarettes at home and in the workplace and/or entertainment venues . Informawww.r-project.org) was used for data analysis. Continuous variables with normal distribution are presented as mean ± standard deviation and compared using Student's t-test, while continuous variables with non-normal distribution are expressed as median (interquartile ranges) and compared using the Mann–Whitney U test. Categorical variables are presented as counts and percentages and compared using the chi-square test or Fisher's exact test. Wilcoxon rank-sum and signed-rank tests were used to compare lung function in the non-SAD and SAD groups. Univariable logistic regression analysis was used to identify potential risk factors for SAD in non-smokers with chronic cough, and variables that showed statistical significance were selected for inclusion in a multivariable logistic regression analysis to further define independent risk factors after adjusting for potential confounders. The interaction effect between two different variables was assessed using the lrm function in the R package rms to fit the logistic regression model with maximum likelihood estimation. The Wald test was used to test the interaction effect. Relative risk was calculated by dividing the probability of SAD in group with exposure to SHS by the probability of SAD in group without exposure to SHS. P-values <0.05 were considered statistically significant.R software  were female, and 91 (28.8%) were male. History of respiratory diseases included chronic bronchitis (8.5%), tuberculosis (17.7%), bronchiectasis (3.5%), and interstitial lung disease (1.9%). Compared with the non-SAD group, the proportion of females was higher  and patients tended to be older  in the SAD group. No significant differences were found in laboratory findings between the two groups, including peripheral white blood cell counts, hemoglobin, liver function, renal function, serum IgE, and allergen skin tests.In total, 316 patients were enrolled in the derivation cohort. Of these patients, 152 and 164 were divided into the non-SAD and SAD groups based on the absence or presence of SAD, respectively. Demographic characteristics, history of respiratory diseases, and laboratory findings of all patients are shown in P < 0.001), FEF50% pred , and FEF75% pred , compared with those in the non-SAD group (P < 0.001), FEV1% pred , FEV1/FVC ratio , and PEF% pred  were mildly lower in the SAD group (P = 0.520).Lung function parameters of all patients were analyzed. As expected, patients in the SAD group showed impaired small airway function parameters, including lower MMEF% pred  in the SAD group than in the non-SAD group, as shown in P = 0.002).The proportion of patients who were exposed to SHS was significantly higher  and after adjusting for age, female gender, BMI, and history of respiratory diseases . Female gender was independently associated with a higher risk of SAD after adjusting for age and exposure to SHS , which was not observed after additionally adjusting for BMI and history of respiratory diseases . No significant interaction effect was found between SHS exposure and female gender (P = 0.766) in logistic regression.To identify potential risk factors for SAD in non-smokers with chronic cough, a univariable logistic regression analysis was conducted. As shown in P = 0.004) for the outcome of SAD, which supports and validates the results obtained in the derivation cohort. In the multivariable logistic regression analysis (P = 0.002), or after additionally adjusting for age, female gender, BMI and history of respiratory diseases .The association between SHS exposure and SAD was further confirmed in patients of the validation cohort, whose demographic and clinical characteristics were summarized in analysis , our datHerein, we conducted a retrospective study to analyze the clinical characteristics of non-smoking patients presenting with chronic cough, and explore the risk factors for SAD in this subpopulation. This study is the first to demonstrate that SHS exposure is an independent risk factor for SAD in non-smokers with chronic cough.Small airways, known as the “silent zone” of the lung disease, have a large surface area and strong reserve capacity. SAD is recognized as a precursor to chronic airway diseases such as asthma and COPD , 15. FroOne of the most interesting findings of the present study is that SHS exposure emerges as an risk factor for SAD in non-smokers with chronic cough. SHS is defined as tobacco smoke produced by an active smoker, both from the exhalation of smoked tobacco and the burning end of the cigarette, that is inhaled by nonsmokers , 27. PreExisting evidence on the correlation between gender and SAD is lacking so far. Our data suggest that female gender might be associated with SAD in non-smokers with chronic cough. It has been reported that lung function is influenced by female sex hormones , and and1% pred, FEV1/FVC ratio, and PEF% pred than those in the non-SAD group. This suggests that the non-smokers with chronic cough combined with SAD may present with a trend of airflow obstruction, which in turn supports that SAD is an early stage of chronic airway diseases. Previous studies have demonstrated that FeNO levels negatively correlate with small airway indexes in non-smokers, rather than smokers . Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.JJ and BZ: study concept and design and critical revision of the manuscript. BZ, LB, RW, YW, LQ, QX, PP, and CH: acquisition of data. RW and LB: statistical analysis. JJ, BZ, and LB: analysis and interpretation of data. JJ and LB: drafting of the manuscript. All authors contributed to the article and approved the submitted version.This work was supported by grants from the National Key Technology R&D Program of China (2015BAI12B10), National Natural Science Foundation of China (82100099 and 82170041), and the Innovative Research Platform of Hunan Development and Reform Commission (2021-212).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.