Extensive experiments on the Plant Village dataset demonstrated the best classification performance of our DBCLNet method compared to the state-of-the-art methods for the identification of 38 categories of crop diseases. Besides, the Accuracy, Precision, Recall, and F-score of our DBCLNet for the identification of 38 categories of crop diseases are 99.89%, 99.97%, 99.67%, and 99.79%, respectively. 811Crop diseases seriously affect the quality, yield, and food security of crops. redBesides, traditional manual monitoring methods can no longer meet intelligent agriculture’s efficiency and accuracy requirements. Recently, deep learning methods have been rapidly developed in computer vision. To cope with these issues, we propose a dual-branch collaborative learning network for crop disease identification, called DBCLNet. Concretely, we propose a dual-branch collaborative module using convolutional kernels of different scales to extract global and local features of images, which can effectively utilize both global and local features. Meanwhile, we embed a channel attention mechanism in each branch module to refine the global and local features. Whereafter, we cascade multiple dual-branch collaborative modules to design a feature cascade module, which further learns features at more abstract levels  During To cope with the aforementioned issues, many methods have been presented for crop disease identification . SpecifiCurrently, most CNN-based methods use small-scale convolutional kernels, and the specialized design utilizes a large number of small-scale convolutional kernels instead of large-scale convolutional kernels to reduce the Flops of the network model to some extent . UnfortuWe propose a dual-branch collaborative module (DBCM), which employs convolutional kernels of different scales to design a dual-branch learning strategy to extract coarse-grained and fine-grained features from crop disease images. Meanwhile, we integrate dual-branch features by drawing on collaborative learning strategies to make our module take advantage of both coarse-grained and fine-grained features.We propose a feature cascaded module (FCM) that implements a stacking cascade process by stacking multiple dual-branch collaborative modules, which uses cascading features to enable better utilization of features at a more abstract level and thus improve the discriminatory performance of the DBCLNet model.We introduce a focal loss function to address the category imbalance of the samples. Specifically, this loss function decreases the weights of the loss function for categories with a large number of samples. Conversely, the weight of the loss function is increased for the category with a small number of samples. In brief, this strategy effectively reduces the misclassification problem for categories with small samples.The rest of this paper is organized in detail below. Section 2 provides an overview of work related to crop disease identification methods. Section 3 presents step-by-step details of our proposed DBCLNet model. In Section 4, we present the experimental results and analysis. Section 5 further summarizes the research work and the outlook for future work.2Currently, various identification methods are gradually applied to crop disease image identification . We cate2.1via preprocessing, hand-made features, feature extraction, and classification  represents the Swish activation function of the convolution operation, and thn feature map of the output. Swish = x · Sigmoid(βx), β represents a constant or trainable parameter. In addition, the Swish activation function is upper bound-free and lower bound-free, smooth, and non-monotonic. Meanwhile, the Swish outperforms ReLU on deep models. Subsequently, we redefine the integration of shallow feature information as where 3.2.2In depth feature extraction stage, we propose extracting coarse-grained and fine-grained features using convolutional kernels of different scales for the input features, in which the coarse-grained mainly includes thetexture and global feature information of the images, and the fine-grained feature mainly consists of the detail and local feature information of the images. Subsequently, we define the dual-branch convolution process as:3×3 and CNN7×7 denote the 3×3 and 7×7 convolution operation in the upper and lower branches of the deep feature extraction stage, B1 and B2 represent the bias vales in the upper and lower branches of the deep feature extraction stage, and where CNNwhere 3.2.3We introduce the channel attention module to exploit the features of different channel levels further. Meanwhile, we introduce the maximum pooling channel attention for the upper branch and average pooling channel attentionfor the lower branch. thc feature map. p,q) in the rectangular region Ω of the thc feature map. For the lower branch, we utilize average pooling for retaining as much background feature information of the image as possible. Mathematically, the average pooling can be defined as:where thc feature map. where r.where where 3.2.4To fully take into account the complementary advantages of the features of our DBCM integration of the upper and lower branches. The upper branch focuses on capturing fine-grained feature information, and the branch focuses on capturing coarse-grained feature information. Therefore, the process of integrating coarse-grained and fine-grained features is called collaborative learning. To fully exploit the low-level features, we integrate the input features into the coarse-grained and fine-grained feature levels in the feature integration process. Eventually, the process of collaborative learning of these features is defined as:via Eqs. (2), (4), (6), (8), and (10). via Eqs. (3), (5), (7), (9), and (11). via the DBCM. Thanks to our design, we usedeep convolution to capture deep feature information in the feature extraction process, and use different scales and different strategies of convolution to capture features with different advantages. In addition, we added the channel attention mechanism to DBCM , where DBCM is the dual-branch collaborative module, FFCM is output feature, FDBCM is input feature with h and w are the hight and width of the feature map, and c is the number of channels. Subsequently, an FCM can be represented by a series of DBCM combinations, and the stack-cascaded process is defined as:redInspired by the MBConv network [60], we cascade multiple DBCMs for extracting features at a more abstract level, called the feature cascaded module. Meanwhile, the DBCM mentioned above is the basic unit module that constitutes the FCM. An DBCM unit can be defined as a function of i times in stage i. In our FCM, we designed to repeatedly stack 6 DBCM. Specifically, each DBCM was repeated 2,3,4,4,3 and 2 times, that is to say, the repetition times of DBCM on both sides were reduced, and the repetition times of DBCM in the middle were more. The unique design makes it difficult for our network to lose key feature information in deep feature extraction. Then, the feature map obtained by FCM is reduced by 1×1 convolution. Finally, we obtain the final discrimination result through maximum pooling, and full connection layer.where 3.4Most current classification studies focus on the cross-entropy loss function in traditional classification tasks . Most cuty represents the true value, py represents the predicted value, ty = 1 denotes the predicted result is a positive sample, and ty = 0 denotes the predicted results is a negative sample. py is the result of the activation function output in the range . Note that the more positive sample with higher output probability, the smaller the loss. In contrast, the more negative samples with a smaller output probability, the smaller the loss. In general, the effectiveness of the cross-entropy loss function for the multiclassification discrimination problem appears unsatisfactory.where Since the Plant village dataset is faced with category imbalance, that is to say, the number of samples varies significantly between different crop images. These issues can also bring challenges to crop disease identification. For example, similar features are repeatedly extracted for the same crop during feature extraction, resulting in higher classification accuracy for categories with a more significant number of samples and lower classification accuracy for categories with fewer samples. Therefore, we employ a focal loss function superior to the cross-entropy loss function . It weakty and py are defined as shown in Eq. (14). α is the equalization factor, which is used to equalize the number of samples from different categories. γ is the adjustment factor, which is utilized to adjust the decayrate of the different category sample weights. In a real classification task, this function decreases the weight of loss for samples with higher prediction probability and increases the weight of loss for samples with lower prediction probability. This strategy makes our discriminative model more focused on the sample imbalance problem. As shown in γ decreases to zero as the confidence level of the correct category increases. Extensive statistical results show that our model has the best discriminatory performance when α = 2 and γ = 0.25.where 3.5Our DBCLNet with 12 compared methods is tested on the PlantVillage data . Specifi4In this section, we mainly introduce the experimental settings, evaluation matrices, identification evaluation, and ablation study. To fairly and comprehensively evaluate the discriminatory performance of our method, our DBCLNet is compared with twelve deep learning methods, including traditional network models: AlexNet , VGGNet 4.1We method run on a Windows 10 PC with AMD Ryzen 5 3600X Central Processing Unit (CPU) at 3.80 GHz, 32-GB memory, NVIDIA GeForce GTX 1080Ti GPU, and Pytorch deep learning framework.β1 = 0.9 and β1 = 0.999 optimizer decay rates. Meanwhile, the wight decay is 0.05. Our DBCLNet are trained with 50 iterations, with the base learning rate is set as 10-3. Additionally, the learning rate schedule is cosine decay, and the label smooth is 0.1. According to our DBCLNet input requirements all crop disease images are set to a size of 224×224×3. The number of samples for the original category imbalance was increased to 87867 data sets with the number of balanced samples by data augmentation. Subsequently, we build samples according to the ratio of 8:1:1 for training set, validation set and test set. The training set is used to train and optimize our DBCLNet model, the validation set is used to verify the validity of our model, and the test set is used to test the discrimination performance of our model.In our NBCLNet, we set the batch size is 16, the optimizer is AdamW with 4.2AAcc), precision (APre), recall (ARec), and F1 score (AFscore) as evaluation metrics for agricultural disease image identification. For each classification result, theymay be categorized into four cases: true positive (TP), false negative (FN), false positive (FP), and true negative (TN). AAcc indicates the ratio of the total number of correctly predicted samples to the total number of tested samples, and the higher accuracy indicates the better discrimination performance of the proposed method. APre represents the proportion of true samples among all predicted positive samples, while a higher value indicates a better discriminative performance of themethod. ARec indicates that accurate prediction is true in the proportion of all true, and the higher the value, the better the discrimination performance of this method. AFscore is the combined average of accuracy AAcc and recall ARec, and its higher value indicates the better identification performance of the method. In addition, In evaluation matrices, we selected accuracy AAcc, prec4.3To demonstrate the effectiveness and generalization performance of our DBCLNet using the PlantVillage data comparing 12 deep learning methods. Meanwhile, our DBCLNet and the compared method are configured according to the same training, test, and validation set. We chose the traditional network models , low-weiFor traditional network models, AlexNet (AAcc, precision APre, recall ARec, and F1 score AFscore. Because of its simple and shallow network structure, AlexNet ( AlexNet  obtained AlexNet  has poor AlexNet  and Goog AlexNet  increase AlexNet  in featuFor low-weight network models, MobileNet  introducFor deep network models, ResNet50 (ResNet50  introducResNet50  is betteResNet50  introducResNet50  to make ResNet50 . DenseNeResNet50  exploresResNet50  significResNet50 .In generFor attention network models, EfficientNet (cientNet  employs cientNet  proposescientNet  uses a tcientNet  effectivcientNet  achievescientNet  for cropcientNet , EfficiecientNet , and CoAcientNet  achievedAs From 4.4To explore the positive impact of each module in our DBCLNet on discriminatory performance, we performed the following ablation study on the augmented PlantVillage data , includiAAcc, APrm, ARec, and AFscore scores. Overall, our DBCLNet can obtain optimal discrimination performance thanks to the special design of each module.As shown in 5This paper presented a dual-branch collaborative learning network for crop disease identification. We first provide a comprehensive overview of the current research in the crop disease image identification field. Meanwhile, we also summarize the advantages and disadvantages of various methods and the wide application of deep learning methods in this field. Subsequently, we explained the proposed DBCLNet in detail. Our DBCLNet comprises a single-branch module, a dual-branch collaborative module, and a cascaded feature module. The SBM extracts basic features of crop disease images, and the DBCM focuses on extracting coarse-grained. Fine-grained features from crop disease images and the FCM mainly extract crop disease image features at a more abstract level. Extensive experiments on the augmented PlantVillage data demonstrate that my DBCLNet has good discrimination ability for 38 types of crop disease images.Despite the satisfactory results of my DBCLNet for the crop disease image identification issue, our method has some limitations. On the one hand, our method is inferior to other crops in disease identification of corn and potato because the disease characteristics of corn and potato are challenging to extract. However, our method outperforms other comparative methods for identifying these two crops. On the other hand, our method uses a deep network structure to extract coarse-grained, fine-grained, and more abstract features, improving discrimination performance at algorithm complexity’s cost. Compared with the low-weight network model, our method has a more complex network structure and a more significant number of parameters. We future will focus our research on two issues: extracting fine features and optimizing network models.The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.WeiZ and XS conceived and designed the experiments. WeiZ and LZ performed most of experiments. LZ and XX analyzed the data. WeiZ, XS, and WenZ wrote the manuscript. ZL and PZ provided the technical support. All author contributed to the article and approved the submitted version.


The identification of novel prognostic biomarkers might enhance individualized management strategies in patients with idiopathic pulmonary fibrosis (IPF). Although several patient characteristics are currently used to predict outcomes, the prognostic significance of the body mass index (BMI), a surrogate measure of excess fat mass, has not been specifically investigated until recently. We systematically searched PubMed, Web of Science, and Scopus, from inception to July 2022, for studies investigating associations between the BMI and clinical endpoints in IPF. The Joanna Briggs Institute Critical Appraisal Checklist was used to assess the risk of bias. The PRISMA 2020 statement on the reporting of systematic reviews was followed. Thirty-six studies were identified , of which 26 were published over the last five years. Significant associations between lower BMI values and adverse outcomes were reported in 10 out of 21 studies on mortality, four out of six studies on disease progression or hospitalization, and two out of three studies on nintedanib tolerability. In contrast, 10 out of 11 studies did not report any significant association between the BMI and disease exacerbation. Our systematic review suggests that the BMI might be useful to predict mortality, disease progression, hospitalization, and treatment-related toxicity in IPF (PROSPERO registration number: CRD42022353363). LCO) percentage predicted at baseline and during follow-up, severe dyspnea, supplemental oxygen requirement, lower distance walked during the six-minute walk test (6MWT), and greater fibrotic burden on high resolution computed tomography (HRCT) are currently used as prognostic markers in IPF. Their use is typically combined in validated clinical prediction models, such as the gender-age-physiology (GAP) model, the longitudinal GAP model, and the composite physiologic model . The middle tertile (n = 85) had a median survival of 3.8 years . The upper tertile (n = 66) had a median survival of 5.8 years ; 3-year survival rate, 69% (95% CI 58 to 81%)]. Proportional hazards regression showed a significant, independent, and negative association between the baseline BMI and mortality [2 and increased 15-year mortality in 67 IPF patients [n = 131). The quartile with the greatest temporal BMI reduction (>0.68%/month) was independently associated with a higher risk of transplant or death. The association with mortality was maintained after excluding patients undergoing transplant . In further analysis, patients with temporal BMI reduction >0.68%/month in the year preceding the transplant also had a greater risk of mortality following surgery . The same authors confirmed the presence of an independent association between temporal BMI reduction >0.68%/month and risk of transplant or death in a validation cohort (n = 148) [2) and obese (BMI ≥30 kg/m2). The usual interstitial pneumonia pattern was less prevalent in the obese group . Significantly lower mortality was observed in this group. A similar trend was also observed when BMI values were analyzed as tertiles  [2 [LCO significantly increased the AUC to 0.859  [A significant association between the BMI and mortality was reported in 10 studies, including nine retrospective studies and nine with low risk of bias ,53,55,56ortality . Kim et patients . Kishabapatients . Kulkarnn = 148) . Sangani< 0.001) . Two coh.001) [2 . Zinellu.001) [2 ,58,59,60γ-1b, and the monoclonal antibody lebrikizumab, did not report a formal statistical analysis of the association between the BMI and one-year mortality [In contrast, 11 studies, including eight retrospective studies and six with low risk of bias, failed to report a significant association between the BMI and mortality ,40,45,54Only one study reported significant associations between the baseline BMI and risk of disease exacerbation. Kondoh et al. observed an independent and positive association between the baseline BMI and risk of three-year exacerbations in 64 IPF patients . In cont2 [2, no significant BMI-related differences were reported with temporal changes in FVC (% predicted) and St. George’s Respiratory Questionnaire [Two studies reported a significant impact of the BMI on IPF progression. Fang et al. reported that patients exhibiting disease progression at one year had significantly lower baseline BMI values than those with stable disease. A significant association was also observed with the Kaplan-Meyer log-rank test using a cut-off of ≥25 kg/m2 . Similar2 . In cont2 ,47. In oionnaire . In a fuionnaire .2 was independently associated with a tenfold increase in the risk of developing nausea and a threefold increase in the risk of developing diarrhea during nintedanib treatment [Two Japanese studies investigated the potential influence of the BMI on the risk of early discontinuation of treatment with the antifibrotic drug nintedanib, with contrasting results. Ikeda et al. observed that lower baseline BMI values were significantly and independently associated with increased risk of discontinuation in 72 IPF patients . In contreatment .Two studies reported a negative association between the BMI at baseline and the risk of hospitalization. Kim et al. observed that a lower BMI was significantly and independently associated with a higher rate of respiratory-related hospitalizations within two years in 1002 IPF patients . SimilarIn our systematic review, we identified 36 studies assessing the prognostic role of baseline and temporal changes in BMI values in IPF patients receiving a range of immunosuppressive and antifibrotic therapies. Whilst there is currently no evidence of a link between the BMI and a diagnosis of IPF, the available evidence suggests that this routinely assessed surrogate marker of body fatness is a promising predictor of mortality, disease progression, hospitalization, tolerability to specific antifibrotic treatments, and specific complications, i.e., pneumothorax, in this group. In particular, relatively low BMI values at baseline and/or greater temporal declines in BMI are associated with adverse clinical outcomes, barring the risk of disease exacerbation.2), normal weight (≥18.5 and <25.0 kg/m2), overweight (≥25.0 and <30.0 kg/m2), and obese (≥30.0 kg/m2). Although several experts have questioned the physiological significance of the BMI as a reliable indicator of adiposity and excess fat, its use has significantly contributed to the stratification of short- and long-term risks associated with key disease states, e.g., cardiovascular disease, diabetes, and several types of cancer, and to promote lifestyle interventions aimed at reducing this risk both individually and at the population level [2) does not take into consideration whether changes in body weight are secondary to changes of specific body composition compartments, e.g., fat mass vs. fat-free mass, and/or their distribution, e.g., visceral vs. subcutaneous adiposity [The BMI was first described by Quetelet, a Belgian scientist, as an anthropometric index in the nineteenth century under the denomination “social physics” . Followion level ,17,18,63on level ,65. Thison level ,67. One diposity ,69. Furtdiposity ,71,72. Tdiposity ,74. Thisdiposity ,76,77. Fdiposity . In anotdiposity . Anotherdiposity . Howeverdiposity ,49,52,53It is important to emphasize that several studies failed to report significant associations between the BMI and mortality ,40,45,54Another intriguing observation is the possible reduced tolerance to the antifibrotic agent nintedanib in IPF patients with lower BMI reported in two of three studies ,36,51. TLCO significantly increased the AUC for predicting four-year mortality [In order to establish the prognostic significance of the BMI in IPF, larger and appropriately designed prospective studies are warranted to confirm the findings of our review. Such trials should investigate the predictive capacity of the BMI, singly or in combination with other clinical characteristics and lung function parameters, in IPF patients with a wide range of clinical severity, comorbid status, sarcopenia, and immunosuppressive and antifibrotic treatments. The potential utility of combining the BMI with other parameters in prediction models was recently reported by Zinellu et al. in a study where the incorporation of BMI with 6MWD, FVC, and Dortality .The strengths of our systematic review include the assessment of a wide range of clinical endpoints as well as the association between the BMI and other relevant patient characteristics, including parameters of lung function and functional capacity. Furthermore, the selected studies investigated Asian, European, and North American patient populations, ensuring some degree of generalization of the findings, and the risk of bias was considered low in the majority of studies (20 out of 36). The limitations of our review include the lack of meta-analytical evaluation given the between study differences in the assessment of the BMI as a continuous variable or cut-off value, baseline variable vs. temporal changes, type of endpoint assessed, and the paucity of details regarding specific comorbidities, markers of muscle mass, and sarcopenia in most studies.Our systematic review has shown that the BMI has the potential to be used as an easily measured and inexpensive predictive marker in IPF, particularly for mortality, disease progression, risk of hospitalization, and tolerability to specific therapies. However, prospective, accurately designed studies are warranted to convincingly demonstrate the prognostic utility of this anthropometric parameter and justify its widespread use in the routine management of patients with IPF.


There are few data on the clinical outcomes of patients with coronavirus disease 2019 (COVID-19) in cities over 1000 m above sea level (masl).To describe the clinical characteristics and mortality of patients with COVID-19 treated at a high complexity hospital in Bogotá, Colombia, at 2640 masl.This was an observational study of a cohort including 5161 patients with confirmed COVID-19 infection from 19 March 2020 to 30 April 2021. Demographic data, laboratory values, comorbidities, oxygenation indices, and clinical outcomes were collected. Data were compared between survivors and nonsurvivors. An independent predictive model was performed for mortality and invasive mechanical ventilation (IMV) using classification and regression trees (CART).2) to fraction inspired oxygen (FiO2), or the P/F ratio, upon ICU admission was 105 (IQR 77–146) and 137 (IQR 91–199) in the deceased and survivors, respectively. The CART model showed that the need for IMV, age greater than 79 years, ratio of oxygen saturation (SaO2) to FiO2, or the S/F ratio, less than 259, and lactate dehydrogenase (LDH) greater than 617 U/L at admission were associated with a greater probability of death.The median cohort age was 66 years (interquartile range (IQR) 53–77), with 1305 patients dying (25%) and 3856 surviving (75%). The intensive care unit (ICU) received 1223 patients (24%). Of 898 patients who received IMV, 613 (68%) of them perished. The ratio of partial pressure arterial oxygen (PaOAmong more than 5000 patients with COVID-19 treated in our hospital, mortality at hospital discharge was 25%. Older age, low S/F ratio, and high LDH at admission were predictors of mortality. Epidemiological data on risk factors and clinical outcomes related to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and the associated clinical entity (i.e. COVID-19) have emerged from all over the world; however, there are limited data on the clinical outcomes of patients with COVID-19 living in cities over 1000 masl.2 remains constant (20.93%) at any altitude. The pressure of inspired oxygen (PiO2) is positively correlated with BP. At sea level (BP = 760 mmHg), the PiO2 is 159.1 mmHg [BP = 560 mmHg), it is 117.2 mmHg. Lower PiO2 leads to lower alveolar oxygen pressure and PaO2, causing tissue hypoxia [2 in Bogotá is 67 mmHg [According to the Wilderness Medicine Society, regions are classified according to altitude as high altitude when they are located at more than 2500 masl . More thtant 20.9% at any  hypoxia . The aveel BP = 70 mmHg, tHigh altitude has effects on the burdens of chronic diseases. Convincing data from population studies show that living above 2000 masl reduces mortality from coronary heart disease and stroke , in addiIt is known that high altitude has varying effects on other respiratory conditions, such as influenza pneumonia and tuberculosis. Influenza pneumonia at high altitude is associated with higher mortality, stating that hypobaric hypoxaemia directly increases the risk of serious disease and requires early treatment with oxygen , whereasArias-Reyes et al.  raised tHowever, information is contradictory. Zhang et al. and Dang et al. found a decrease in ACE-2 in cellular and animal models with hypoxaemia , 22; howThe current evidence does not allow us to ascertain whether hypobaric hypoxaemia can prevent, treat, or aggravate COVID-19 infection .https://saludata.saludcapital.gov.co/osb/index.php/datos-de-salud/enfermedades-trasmisibles/covid19/.This study was carried out in the city of Bogotá, capital of Colombia, located 2640 masl, with 8,848,588 inhabitants. By June 2021, more than 1,382,000 COVID-19 cases and 25,000 deaths had been reported The aim of this study was to describe the clinical characteristics, laboratory findings, and mortality of patients with COVID-19 treated at the Hospital Universitario Mayor Méderi located in Bogotá, Colombia.This was an observational, single-centre, cohort study that included all patients with COVID-19 admitted to the Hospital Universitario Mayor Méderi in Bogotá, Colombia, conducted between 19 March 2020 and 30 April 2021. This is a high complexity university hospital located in the centre of the city of Bogotá, at 2640 masl. It has 99 intensive care beds, which during the pandemic were expanded to 131 beds, with 94 exclusively for the care of patients with COVID-19. It also has 790 general hospital beds, of which 295 are used for the care of patients with COVID-19. SARS-CoV-2 infection was confirmed by real-time reverse transcription-polymerase chain reaction (RT-PCR) in throat swab samples. We excluded individuals with incomplete medical records and patients under 16 years of age.We reviewed clinical electronic medical records and laboratory findings for all patients with laboratory-confirmed SARS-CoV-2 infection. On admission, we collected the following data: age, sex, past medical history, days of hospital stay, days of ICU stay, body mass index (BMI), management site , laboratory values on admission , haematocrit, haemoglobin, LDH, D-dimer, procalcitonin, ferritin), and oxygenation indices . As is routine, electronic medical data were archived onto a local server, from which we retrieved these data.Outpatients: those who consulted and were discharged in the first 24 h and were not readmitted in the following 3 weeks.Inpatients: those who were admitted to the hospitalization service, with a stay > 24 h and did not transfer to the ICU at any time during their hospital stay.Intensive care unit (ICU): those who were admitted to the ICU at any time during their hospital stay.Emergency department (ED): those who received care only in the ED.The clinical outcomes assessed were discharge or death, need for IMV, and site management. For this study, 4 management sites were defined as follows:U-tests for all continuous variables (assuming heterogeneous variance) and X2 tests for categorical variables. We fitted a classification and regression tree (CART) using all the collected variables for comparison purposes. Selection of the covariates included for CART modelling was based on their biological and clinical relevance, on what was previously reported in the literature, and their statistical significance in the bivariate analysis. The CART algorithm quantified each variable’s weight and built risk profiles. This methodology contrasts with classical regression models in that the CART algorithm can discover effective modifiers and complex interactions between variables, whereas in regression models, the researcher must provide those interactions based on a prior hypothesis. Statistical analysis was performed using R software version 4.1.0 (Free Software Foundation’s GNU Public Licence), and the CART were modelled using the package RPART (recursive partition and regression trees).Qualitative variables were reported as frequencies and percentages, while quantitative variables were reported as medians and interquartile ranges. Clinical, sociodemographic, and laboratory characteristics were compared in relation to mortality, mechanical ventilation requirements, and management site  using nonparametric Mann-Whitney The cohort included 5161 adult patients treated at the Hospital Universitario Mayor Méderi. Patients who were still hospitalized by 30 April 2021 and 1 patient in whom the review of the electronic medical record was not possible were excluded from the final analysis. In total, 321 patients were excluded.Of the 5161 patients, 2340 (45%) of them were women. A total of 1305 patients died (25%), and 3856 survived to hospital discharge (75%). The median age was 66 years (IQR 53–77). All patients were identified as Latino.The most frequent comorbidity was arterial hypertension (23%), followed by hypothyroidism (7%), chronic obstructive pulmonary disease (COPD) (5.6%), and diabetes mellitus (5%).Table A total of 114 patients (2%) were managed in the ED, with 114 deaths (100%). In all, 39 of these patients (34%) were ventilated and died before being transferred to the ICU, and 75 patients (66%) were admitted for conservative management but remained in the emergency expansion area of the ED until their deaths due to high hospital occupancy. This high mortality can be explained given that the median age in this group was 81 years .In all, 854 patients (17%) received outpatient management without reporting any mortality in the national registry system the month after discharge; these patients were the youngest in the cohort, with a median age of 47 years .A total to 2970 (58%) participants were inpatients, with 511 deaths (17%). The median age in this group was 68 years . The high number of patients who died while hospitalized without being admitted to the ICU is explained by the elderly characteristics of the group, as the median age was 83 years . Out of these 511 patients, 8 (0.02%) were ventilated and died before being transferred to the ICU, and the 503 remaining inpatients, together with the family and the guidelines of the institutional ethics committee, decided to limit invasive manoeuvres, such as orotracheal intubation or transfer to the ICU.A total of 1223 participants (24%) were admitted to the ICU, with 680 deaths (56%). Of the 1223 ICU admissions, 851 of them were ventilated (70%). Out of the ventilated group, 566 of them died (67%) vs. 114 (31%) in the nonventilated group. These patients were admitted to the ICU but did not want orotracheal intubation, and this intervention was not performed. It is clarified that some patients were admitted to the ICU to receive noninvasive mechanical ventilation and haemodynamic support without orotracheal intubation, which was possible only at moments without the pandemic peaking. In Bogotá, for the date of the performance of this analysis, May 2021, there were 2 peaks with high hospital occupancy, the first in July and August 2020 and the second in December 2020 and January 2021, at which time only patients were admitted to the ICU with requirements of IMV.Forty-seven patients received IMV and did not enter the ICU, along with 39 patients in the emergency room and 8 patients in hospitalization; these patients died before being transferred to the ICU because they were so sick. A total of 898 patients (17%) received IMV in this cohort, of whom 613 died (68%).Table p < 0.05, where p = 0.05 is the limitation for statistical significance , and the median P/F ratio was 233 . Among the patients who remained in the hospital for 48 h, the S/F ratio decreased by a median of 325 . The median S/F ratio at ICU admission was 168 , and the median P/F ratio was 118 . In the ICU patients who died, the median P/F ratio was 105 , and in those who survived, it was 137 . The median P/F ratio at ICU admission in patients who underwent IMV was 112  and 154  in those who did not. It is clearly observed that as oxygenation indices fall, the possibility of dying, requiring IMV, and entering the ICU increases , which was one of the oldest reported –33, and In all, 25% of the patients in this study died. If we excluded patients who were managed as outpatients and lost to follow-up, the mortality of patients requiring hospitalization was 30%, which is similar to other series reported in Italy (25%), the United States (US) (21%), Iran (24%), and Germany (27%) , 38, 39 In this cohort, 1223 patients (24%) required ICU management, which was a larger proportion than the 7% found by Yang et al. in China  but simiOut of the 898 patients who received IMV, 613 of them died (68%), which is a figure lower than the studies from the onset of the pandemic in China (81%)  and the CI, 39–52%); however, at the time of publication, many patients were still hospitalized. In this same study, the estimated mortality for patients aged 61 to 70 years was 77%.Roedl et al. , in a muOxygenation indices have been broadly studied around the world as predictors of mortality and ICU admission in patients with COVID-19, leading to useful clinical practices to prioritize patients at hospital and ICU admission. Within those, a lower P/F ratio at ICU admission has been found to be a predictor of mortality among these patients, but few studies have been carried out regionally where this is stated.2 at ICU admission in these patients was 90%, compared to ours, in which the median FiO2 at ICU admission was 50%.In this study, the P/F ratio at ICU admission is one of the lowest when compared to other cohorts around the world, 118 vs. 129 in the USA; 160 in Italy; 154 in France, Belgium, and Switzerland; and 172 in China , 35, 46.The P/F ratio on admission to the ICU in patients who underwent IMV was 112 vs. 154 in those who did not, thus indicating that the decision to start IMV was based not only on the oxygenation indices but also on the clinical presence of respiratory failure, and that a higher level of hypoxaemia was tolerated for the start of this therapy in relation to cities located at altitudes less than 1000 masl.In patients admitted to the ICU, the P/F ratio was also significantly lower in patients who died (105) than in those who survived (137) compared to other studies where the P/F ratios in deceased patients and survivors were 134 and 163 [There is insufficient clinical evidence to change the standard management of patients with COVID-19 based on oxygenation indices. The 2012 Berlin consensus  recommenBased on the current evidence, the oxygenation level at which it is best to start IMV is unknown. It must also be taken into account that during peaks of the pandemic, hospital occupancy was close to 100%, which interferes with the quality of care and may delay the start of IMV.There is little information about the clinical characteristics of patients in high-altitude areas. Chen et al. , in a reAbdelsalam et al.  analysedSeveral authors have documented alterations in C-reactive protein, D-dimer, and LDH that are associated with severity and mortality from COVID-19 . VariatiAn uncontrolled inflammatory response is associated with worse clinical outcomes in SARS-CoV-2-infected patients . ElevateSimilar to the data described, our findings show a higher NLR among the deceased (NLR 11.78) than among the survivors (NLR 7.58). Likewise, the NLR was higher in patients admitted to the ICU  and those who required IMV.Moreover, the NLR among surviving patients was higher than those previously reported . It remaOR 6.7, 95% CI: 2.4–18.9) [OR 4.22, 95% CI: 2.49–7.14) [LDH is the enzyme responsible for the passage from pyruvate to lactate, and its elevation has been associated with severity and death in infectious and neoplastic diseases , 62. Dif.4–18.9)  and four49–7.14) . In our Ballaz et al.  describeIn the prediction model presented by the CART methodology, oxygenation indices, LDH level (as an inflammatory marker), and COPD (as a comorbidity) increase the probability of IMV, findings similar to other studies , 44.In the prediction model for mortality, it is not surprising that the requirement for IMV is the main factor associated with death, since this indicates which patients are sicker; however, the model allows us to observe that being older than 79 years, hypoxaemia and elevated levels of LDH (as an inflammatory marker) increase the probability of death, even in nonventilated patients. These findings are similar to other studies , 44, 46.We conclude that, although the oxygenation indices are lower in the city of Bogotá due to hypobaric hypoxaemia, the need for hospitalization, admission to the ICU, and IMV are similar to other regions less than 1000 masl, except China, where most data indicate that patients with less severe disease were hospitalized.The mortality of patients with IMV in this study is high but similar to that reported in high-income countries at the beginning of the pandemic and in other Latin American countries; however, it has remained high throughout the entire pandemic, unlike high-income countries where it has decreased, suggesting hypobaric hypoxaemia may not be the main factor in mortality from COVID-19 leading to IMV, and the availability of resources is an important factor in mortality.In this study, it was also found that the levels of oxygenation indices at altitude, which is management decisions such as the start of IMV and therefore associated therapies (e.g. use of neuromuscular relaxants and prone), are lower than at sea level; however, clinical trials should be performed to determine the P/F ratio cut-off point of greatest benefit for these therapies.This study has many limitations. It is a descriptive study of a single-centre high complexity hospital in the city of Bogotá, which may not reflect the behaviour of the pandemic throughout the country. It does not describe the pharmacological therapies used in the patients; however, it is noted that, in Colombia, in the drugs with some evidence of benefit in clinical use in patients with COVID-19 for general use, only dexamethasone in patients admitted to the hospital with supplemental oxygen was available (no other drug therapies were available (e.g. remdesivir and tocilizumab). Also, the vaccination effort began at the end of March 2021, so this study does not yet reflect the impact of this measure. The use of other ventilation therapies, such as noninvasive and high-flow cannulas, is not described, since these were used outside of the ICU and in expansion areas, and the quantification of their time of use given that the records in the clinical history were not performed exactly.


Our results reveal that the effects of tourism on PM2.5, emissions vary according to the modes of tourist arrivals. At the national level, the effect of tourism on PM2.5 emissions exhibits an inverted-U shape. At the regional level, tourism exerts a U-shaped impact on PM2.5 emissions in eastern China, and tourism is nonlinearly negatively associated with PM2.5 emissions in central and western China. An important theoretical contribution of our study is the proposal and validation of the U-shaped tourism-induced EKC hypothesis.Based on the theoretical framework of the Environmental Kuznets Curve (EKC), this study investigates whether tourism development can decrease air pollution. This study applies the panel smooth transition regression approach and panel data for 2005–2019 from 283 prefecture-level cities in China to examine the nonlinear effect of tourism development on PM In recent years, the tourism industry has been regarded as one of the strategic pillar industries of the Chinese national economy, partially because it exerts an increasingly important impetus to promote economic growth and increase employment ,2. Accor2.5, PM10, CO, and SO2 emissions) is marginal in comparison with that of the manufacturing industry [Another reason for the importance of the tourism industry is that tourism was once characterized as a ‘smokeless industry’. Tourism-induced air pollution  used a semi-parametric approach to model the impacts of air pollutant PM10 in Mallorca, Spain [10 emissions. Robaina et al. (2020) studied the influence of tourism on PM10 concentrations as a representative of air quality in five European countries. The empirical results prove that tourism growth can deteriorate air quality in Austria and Italy [Although most studies find evidence supporting a significant impact of tourism development on air pollution, the direction of its influence is still under discussion. Overall, these empirical results can be classified into three strands of research. The first strand suggests that tourism has a positive effect on air pollution. Many studies find that tourism leads to an increase in air pollution ,23. Koçasselló 20 used a snd Italy .2 emissions in 27 European countries. Specifically, a 1% increase in tourism resulted in a 0.11% decrease in CO2 emissions [2 emissions in the long run in G20 economies [x, PM10, and PM2.5 emissions, while local tourism increases their emissions [The second strand considers the negative impact of tourism on air pollution. Lee and Brahmasrene (2013) found that tourism was negatively correlated with COmissions . Bojanicmissions . Tian etconomies . Ciarlan2 emissions exceed a certain threshold, the estimated coefficient of tourism on CO2 emissions begins to decrease owing to government policies, especially in developed countries [2 emissions in five Southeast Asian countries [2 emissions [The third strand shows a nonlinear relationship between tourism and air pollution. Paramati et al. 2017) concluded that, when COountries . Sherafa concludemissions .2.5 emissions exhibits an inverted U-shaped curve [In China, only a few studies have considered the effect of tourism on air pollution, and the conclusions are inconsistent. For example, Bi and Zeng (2019) explored the effects of tourism on carbon emissions in China from nonlinear and spatial perspectives and found that a significant inverse U-shaped relationship exists between them. Moreover, they found that tourism has a spatial nonlinear spillover effect on carbon emissions . Similared curve .The Environmental Kuznets Curve (EKC) hypothesis, proposed by Grossman and Krueger (1991), suggests that, when a country’s economic development is at a low level, environmental deterioration intensifies with economic growth; when economic development reaches a specific threshold, that is, after reaching a critical, or ‘inflection point’, environmental pollution continuously decreases with economic growth. This implies that environmental quality is gradually improving. It demonstrates an inverted U-shaped pattern between economic growth and pollution. Thereafter, a large number of studies have emerged to validate the EKC hypothesis, but the results have been inconsistent . There aThe tourism-induced EKC hypothesis, first proposed by Katircioglu (2014) , provideVarious theories have been used to reveal the theoretical connections between tourism development and air pollution and to explain the shape of the EKC, such as the dynamic general equilibrium theoretical model  and DutcIn contrast to the conventional EKC hypothesis, the U-shaped tourism-induced-EKC hypothesis see d suggestTo account for the potentially non-linear impact of tourism development on air pollution, this study adopts the panel smooth transition regression (PSTR) approach developed by Gonzalez et al. (2005) , which ci = 1, …, N and t = 1, …, T; N and T represent the number of cities and period of the panel, respectively. In Equation (1), ity denotes the dependent variable and itx denotes a vector of the time-varying independent variable. itz denotes the control variables and α is their estimated coefficients. m is the number of m = 1 or m = 2 that capture the nonlinearities caused by regime switching. m = 1 corresponds to a logistic PSTR model, and m = 2 represents a logistic quadratic PSTR specification [The general equation of the PSTR model with two more regimes can be defined as follows:. (2005)  found thfication , where tfication .k denotes the number of independent variables, SSR0 denotes the sum of the squared residuals under H0 , and SSR1 denotes the sum of the squared residuals under H1 (PSTR model with two regimes). Under the null hypothesis, the LM and LR statistics are χ2(k) distributions, whereas the LMF statistics are distributed with F values . If the null hypothesis (H0) does not pass the significance test, then the PSTR model is linear. If the null hypothesis (H0) is rejected, the PSTR model is nonlinear. Next, the non-remaining nonlinearity test proposed by Fouquau et al. (2008) [Before the estimation, according to Colletaz and Hurlin (2006) , a linea. (2008)  can be u. (2008) .2.5 concentrations  are used to measure the level of air pollution because PM2.5 concentrations are widely monitored and their data are continuously collected by governments worldwide, making them easily obtainable. In current tourism and environmental research, it is a common practice to use PM2.5 concentrations as a proxy for air pollution [2.5 emissions [2.5 emissions exhibits an inverted U shape [2.5 data for 2005–2019 were obtained from the measurement data of global surface PM2.5 concentration by atmospheric composition analysis group of Washington University . Following the study of Xu et al. (2020) [2.5.This study uses panel data from 283 prefecture-level cities in mainland China from 2005 to 2019. In the regression model, the annual average PMollution . Some scmissions ,52. Howe. (2020) , this stThe core explanatory variable is the level of tourism development, which is represented by the ratio of total tourist arrivals to the local inhabitants (TA) . The totFurther, the economic development level (PGDP), population density (DENS), R&D investment (TECH), fixed capital investment (INVEST), transportation development (TRAFF), and green coverage (GREEN) have significant impacts on air pollution ,53,54,55P, A, and T represent the population, affluence, and technology, respectively; a, b, and c are their estimated coefficients; α denotes the constant; μ is the error term; and I and t denote the city and year, respectively. According to Liddle (2014) [To explore the linear and nonlinear effects of tourism on air pollution, we followed the model of Stochastic impacts by regression on population, affluence, and technology (STIRPAT model) developed by York et al. (2003)  to specie (2014) , Ahmad ae (2014) , and Zene (2014) , touristIn the PSTR model, it can further be specified as follow: 2.5 concentrations and total tourist arrivals in 2005 and 2019. A darker color in the figures implies higher PM2.5 concentration and total tourist arrival values. China has suffered severe PM2.5 air pollution in the past 15 years, demonstrating a deterioration of air quality. Highly polluted cities are located in the North China Plain and Sichuan Basin. There was a slight change in the spatial distribution of PM2.5 concentrations among cities between 2005 and 2019. The annual average PM2.5 concentration for the entire area decreased from 45.79 μg/m3 in 2005 to 33.79 μg/m3. Nevertheless, it can still harm human health because the annual average is above 10 μg/m3 [10 μg/m3 .From 2005 to 2019, the tourism industry at the city level developed rapidly. According to the official statistics of China, the total number of total tourist arrivals increased from 1.2 billion in 2005 to 6 billion in 2019 (more than a 500% increase). There were significant differences in the levels of tourist arrivals among the cities. The degree of high tourist arrivals increased throughout the period, depicting a hierarchical diffusion trend from east to west. Due to unique resource endowments, location conditions, and transport accessibility, the high-value areas of total tourist flows are scattered in Beijing–Tianjin, the Yangtze River Delta, Pearl River Delta urban agglomerations , and theAs a prerequisite for empirical analysis, it is necessary to test the order of integration of these variables to avoid a spurious regression. This study utilizes four unit-root tests, including the LLC, IPS, Fisher-ADF, and Fisher-PP tests, to check sequence stationarity in their levels and first differences. Furthermore, the panel cointegration test was used to verify whether a long-term equilibrium relationship exists among the studied variables . The panm = 1 are −4.668 and −4.644, respectively, which are less than their values of −4.667 and −4.642 under m = 2, respectively. This implies that the optimum number of location parameters is 1. Therefore, the PSTR model with one transition function (r = 1) and one transition location (m = 1) was preferred and selected. After performing the stationarity test, it is essential to test whether the empirical analysis based on the PSTR model is appropriate using linearity and remaining nonlinearity tests. This study used lnTA as the transition variable to investigate the effect of tourism development on air pollution under different levels of tourism arrivals. 2.5 emissions, estimated using the panel threshold regression (PTR) model. The threshold variable TA has a single threshold of 2.045 and produces two regimes for the PTR model. When the TA is less than 2.045, the estimated coefficient of lnTA is −0.067 and is significant at a 1% statistic level, implying that a 1% increase in PM2.5 emissions may significantly lead to a 0.067% decrease in the number of tourist arrivals. When the TA exceeds 2.045, the coefficient is negative and statistically significant, with an effect size of −0.97, which is stronger than that in the first regime. The control variables lnPGDP, lnTECH, lnINVEST, and lnTRAFF have positive effects on the lnPM, showing that economic development, R&D investment, capital investment, and transportation can significantly increase PM2.5 emissions.Column 3 in Columns 4 and 5 show the estimated results of the PSTR model with one transition function and one transition location. The location parameter c was 2.294, indicating that the PSTR model transitioned to a double-regime mode. The transition parameter γ is 0.419, indicating that the transition between the two regimes is smooth. 2.5 emissions, when lnTA is less than 2.294 (TA < 9.915), the estimated coefficient of lnTA is 0.098 and is significant at 1% level, indicating that, under the low-tourism development regime, a 1% increase in the ratio of tourist arrivals to local inhabitants can induce a 0.098% increase in PM2.5 emissions. A possible reason is that, in cities with low levels of urban tourism development, many short-sighted behaviors, such as over-exploitation and disorderly competition in the tourism industry, a large number of high-energy consumption and high-pollution enterprises, and imperfect environmental regulations, lead to higher PM2.5 emissions [2.5 emissions. Therefore, there is an inverted U-shaped link between tourism and PM2.5 emissions, verifying the presence of an inverted U-shaped EKC hypothesis for tourism [In terms of the effect of tourism development on PM tourism . For eac2.5 emissions under the low-tourism development regime, but its impact is significantly negative under the high-tourism development regime. One possible reason for this is that tourism-oriented economic development can decrease air pollution in local cities. Variable transportation development (lnTRAFF) is positively correlated with PM2.5 emissions under both regimes, but its coefficient becomes nonsignificant under the high tourism development regime. Among all the control variables, this study finds that the economic development (lnPGDP) has a positive significant effect on PMOne may be concerned about the existence of a heterogeneous relationship between tourism and air pollution when considering different regions as destinations. Therefore, this study further divided the entire sample into the three sub-samples of easter, central, and west cities and examines heterogeneity across different regions. The main estimation results for the East, Central, and West cities based on the PSTR model are presented in 2.5 emissions. When the lnTA is between 2.362 and 2.796, the impact is still negative but becomes less sensitive (−0.028). When the lnTA is higher than 2.796, the coefficient is significant (0.344) at a 1% level, indicating that, under the high-tourism development regime, tourism can increase PM2.5 emissions. These empirical results support the U-shaped tourism-induced EKC hypothesis. According to the sample of the east cities, most cities with a high regime (lnTA > 2.796), including Beijing, Benxi, Huzhou, Sanya, Xiamen, and Zhoushan, also have high PM2.5 emissions.For the East cities of China, there are two location parameters, 2.362 and 2.796, which transition to a triple-regime PSTR model. Interestingly, this indicates that tourism-induced air pollution is U-shaped see a, which For the Central and West cities, both PSTR models have double regimes. In the model for the Central cities, the effects of the lnTA are negative under both regimes, but the impact becomes weaker and nonsignificant under the high tourism development regime b. In the2.5 emissions in China. The direction and magnitude of the coefficients of tourism revenue remain unchanged. Similarly, the impact of most control variables changed slightly. These results support the robustness of this study. A robustness check must be conducted to ascertain the reliability of the estimated results. We used the contribution of the total tourism revenue to the GDP (lnTR) as an important indicator of tourism development and as a substitute for the lnTA. 2.5 emissions. Considering the internal heterogeneity across the different regions, we partitioned China into three distinct regions of East, central, and West cities, and further explored the validity of the tourism-induced EKC hypothesis as well as the impacts of other variables on air pollution at both the national and regional levels. This study highlights the importance of the relationship between tourism development and air pollution owing to its implications for sustainable tourism development. Considering the main criticisms of the tourism-induced EKC hypothesis, this study focuses on whether and how tourism can reduce or contribute to air pollution. Using a dataset of 283 prefecture-level cities in mainland China for 2005–2019, this study applies the PSTR approach to investigate the nonlinear effect of tourism development on air pollution proxied by PM2.5 emissions changes from promoting to inhibiting. These findings are consistent with those of Bi and Zeng (2019) [10 concentrations [Based on the theoretical framework of the EKC, our results show that the effects of tourism development on air pollution are neither linear nor nonlinear. Specifically, at the national level, the empirical results from the PSTR model reveal a significantly inverted U-shaped relationship between tourism and air pollution, supporting the validity of the conventional tourism-induced EKC hypothesis in China. When the number of tourists reaches the high regime (lnTA > 2.271), its effect on PMg (2019)  and Zengg (2019) , who foug (2019) . Ahmad ag (2019) . Saenz-dtrations . The diftrations . 2.5 emissions across different regions are heterogeneous. For the East cities, the effect of tourism on air pollution exhibits a U-shape, confirming the validity of the U-shaped tourism-induced EKC hypothesis. When the lnTA exceeds the second threshold value of 2.796, its effect on PM2.5 emissions transitions from inhibiting to promoting. For the Central and West cities, tourism exerts inhibitory effects on PM2.5 emissions in both the linear and nonlinear parts of tourist arrivals. These results imply that the tourism-induced EKC hypothesis does hold for central and western China. The findings differ slightly from those of Zhang and Gao (2016), who found that the tourism-induced EKC hypothesis is weakly supported in eastern and western China and fail to document the evidence in central China [At the regional level, the results of the nonlinear marginal analysis indicate that the effects of tourism on PMal China .Theoretically, our research contributes to the debate on the importance of the relationship between tourism development and air pollution. First, we propose a U–shaped tourism-induced EKC hypothesis which extends the theoretical connection between tourism development and air pollution under the traditional EKC framework. Our findings provide support for these two seemingly conflicting perspectives and empirically confirm that the nexus between tourism development and air pollution is nonlinear. This means that the mechanism through which tourism may have a significant impact on air pollution depends on different regimes of tourist arrivals. We have proved the nonlinear negatively shaped and inverted U-shaped relationships, which were automatically assumed in existing studies. We found empirical support for the extended tourism-induced EKC hypothesis that displays a U–shaped impact. Second, the nonlinear relationship between tourism and air pollution in our study may provide new insights into the ambiguous results of the linear model in the existing literature. Scholars should consider the nonlinear behavior of tourism-induced air pollution, not by including squared tourism specialization terms in their linear model, but by using a regime-switching model, such as the PSTR model. According to Lahouel et al. (2022) , the resThe findings of this study may provide valuable policy recommendations. First, the empirical results have documented that tourism can be responsible for air pollution reduction, implying that tourism is a cleaner industry than manufacturing and agricultural industries. However, as some scholars have suggested, tourism is an energy-intensive industry that produces a large amount of pollution; therefore, governments should not neglect the negative environmental externality of tourism and should formulate a series of stricter regulations and measures for the tourism industry to reduce pollution emissions and ensure the coordination between tourism and environmental protection. For example, the development of green tourism or eco-tourism can reduce air pollution. Second, the validity of the tourism-induced EKC is vital for considering sustainable growth and development for national and regional growth. At the national level, the early stages of tourism development should be of more concerns. Environment-friendly policies and measures should be issued by the government to maintain a balance between tourism development and environmental protection . For the2, and NO2. According to Zhang et al. (2020), different tourism sectors have different effects on air pollution; thus, their profiles can influence this relationship [Our study has some limitations. First, due to limited data availability, this study lacks discussions on the profiles of tourists  and other air pollutants such as CO, SOtionship . Future


Early intervention can delay the progress of Alzheimer’s Disease (AD), but currently, there are no effective prediction tools. The goal of this study is to generate a reliable artificial intelligence (AI) model capable of detecting the high risk of AD, based on gene expression arrays from blood samples. To that end, a novel image-formation method is proposed to transform single-dimension gene expressions into a discriminative 2-dimensional (2D) image to use convolutional neural networks (CNNs) for classification. Three publicly available datasets were pooled, and a total of 11,618 common genes’ expression values were obtained. The genes were then categorized for their discriminating power using the Fisher distance (AD vs. control (CTL)) and mapped to a 2D image by linear discriminant analysis (LDA). Then, a six-layer CNN model with 292,493 parameters were used for classification. An accuracy of 0.842 and an area under curve (AUC) of 0.875 were achieved for the AD vs. CTL classification. The proposed method obtained higher accuracy and AUC compared with other reported methods. The conversion to 2D in CNN offers a unique advantage for improving accuracy and can be easily transferred to the clinic to drastically improve AD (or any disease) early detection. Despite the intense research in recent decades, AD still lacks effective treatment options. There are two forms of the disease: early onset (before 65 years of age) [PSEN1 and PSEN2) [CD36), or ATP-binding cassette transporter subfamily A member 7 (ABCA7) [According to the World Alzheimer Report 2019, more than 50 million people were estimated to suffer from AD in 2021 (d PSEN2)  (for a rd PSEN2) ) were fod PSEN2) , with ma (ABCA7) . In addiAccumulated evidence from preclinical  and clinA genome-wide analysis (GWAS) has identified new polymorphisms that make a person susceptible to developing AD ,16. As aMachine learning algorithms have frequently been used to propose a solution to predicting the risk of AD or MCI using multiple biomarkers ,20,21 orDeep learning, which is a subfield of machine learning, reduces and, in many cases, eliminates the requirement for feature engineering . ConvoluIn this study, a novel image-formation method was proposed to transform one-dimensional gene expression into a discriminative 2D image, which makes gene expressions appropriate for image-based classifiers such as CNN. The proposed model categorizes the DEGs using Fisher distance criteria, which maximizes the distance between classes and minimizes the variance within classes .Thus, the goal of this study is to generate a reliable AI model capable of detecting the high risk of AD, based on gene expression arrays from blood samples, allowing for early risk detection. Hence, preventive interventions can be prescribed to slow or even avoid progression of the disease.Three publicly available Alzheimer’s study datasets were extracted from NCBI: GSE63060 , GSE6306First, the three datasets were normalized, then integrated by their respective groups , and normalized again with the same normalization. The min–max approach was used for normalization, which rescaled the range of values for each gene to the intervals 0 and 1. These normalized datasets obtained from GSE63060, GSE63061, and GSE140829 include 29,958, 24,900 and 15,987 probes, respectively . A totalAlthough it is possible to create a 2D image by mapping the common 11,168 genes, a majority of the genes were expected to be irrelevant for AD, and these genes fall into the least significant categories  and leadj is the class label , and i is the sample identification. The image-based representation approach was performed in two steps; first, categorize the genes for their discriminating power , and second, use their discriminating power to map them onto 2D images. For the first step  is the category assigned by the Fisher distance. An equal number of samples was assigned to each category.The gene expression dataset rst step , the genIn the next step, each gene was mapped into a 2D space using a linear discriminant analysis (LDA) in contrast with that used for tSNE/kPCA . LDA is −5, L2 = 1 × 10−4), each followed by a dropout layer (0.4) to avoid overfitting. The Relu and Sigmoid activation functions were used at the dense and output layers, respectively. During the deep learning phase, the Adam optimizer was used with a 1 × 10−4 learning rate. The total number of parameters of the model was 292,493. The CNN model was constructed with Python  using Keras  with a Tensorflow  backend, and evaluations were made on Google Colab .CNNs are a type of deep neural network that uses convolutional layers to extract features from data. A CNN model includes convolutional, pooling, and fully connected layers. Each using different characteristics, convolutional layers extract the discriminating features from the images, and pooling layers perform down-sampling to prevent overfitting, whereas fully connected layers combine the output of the features to complete the classification model. Because of their superior ability to extract features, CNNs are the most commonly used deep learning architecture for image classification, object detection, and tracking . In this−4 and 1 × 10−3 were assigned to the −3 has been defined as the best value that leads to a subset of 488 genes. As the dataset contained three groups , pairwise (two-class) and three-class classifications were performed. For classifications, the samples in the dataset were randomly divided into training (80%) and test (20%) sets, and the accuracies obtained with the test samples are presented.A total of 488 most discriminative genes (which have non-zero coefficients) were selected using the LASSO regression method to be transformed into 2D images. For the LASSO regression, various values between 1 × 10Prior to LDA mapping, the genes were divided into 7, 9, 11, 13, 15, and 17 categories, and the experiments were repeated for all. For the experimental study, a total of 11,168 common genes were mapped to an image with 13 Fisher categories D, and deSeveral studies have been performed on gene-expression classification, most of which focused on the AD vs. CTL groups ,33,38. TTo compare the proposed method for AD vs. CTL classification, the combined dataset  was also used on publicly available gene classification codes ,39,40 T.The first  and the In addition to AD vs. CTL classification, the proposed method used not only AD vs. CTL but also AD vs. MCI and MCI vs. CTL classifications, and lower AUC and Acc values were obtained, respectively . FurtherA higher classification performance was achieved when MCI samples were joined to the AD samples compared with the results obtained when the MCI samples were assigned to the CTL samples. This can be explained by the fact that MCI is a pre-state of AD. However, the best accuracy among these classifications was achieved for AD vs. CTL, indicating that not all MCI patients will evolve into AD; therefore, it is a confounding factor in the classification. Data, such as environmental information, psychological background, and eating behaviors, would be beneficial in improving the accuracy of classification when taking into account MCI patients.The proposed method was also used for three-class classification. In that case, the Fisher distance (Equation (1)) of a gene was evaluated by averaging the pairwise Fisher distances among AD vs. CTL, AD vs. MCI, and MCI vs. CTL, and an LDA-based image was created for the three-class classification. In that scenario, 97 AD, 93 MCI, and 63 CTL samples were used to test the trained CNN model. An average Acc of 0.61 was obtained . The traBlood specimens are valuable tools for the diagnosis of many diseases. In the case of the currently incurable AD, early detection prior to the manifestation of clinical symptoms would be key to effective preventive interventions. The method described herein is estimated to provide a high accuracy rate for AD prediction early in life. Thus, expression data obtained from a drop of blood can provide a numeric value for the risk of developing AD. A numeric value provided by means of AI can have drastic altering implications on lifestyle choices and/or therapeutic interventions that can effectively slow down the progression to disease.This paper introduces a novel method of transforming gene expression data into an image with rich discriminative spatial content for image-based classifications. The method developed was implemented on an AD dataset obtained by the combination of three different publicly available datasets. After selecting the subset of DEGs, the method developed located these DEG onto an 2D image using LDA. The images obtained were further classified by our newly developed CNN model.The number of categories where the genes were labeled according to their Fisher distances before LDA-based 2D mapping affects the performance of the feature extraction steps of CNN, since a higher category number creates a more compact image compared with a lower category number. Hence, we observed that the best Acc and AUC results were obtained when the genes were grouped into 13 categories.The method developed was implemented on pairwise classes  and the results obtained for AD vs. CTL classification were compared with three methods from previously reported studies, including the (i) Multiple Feature Selection + SVM , (ii) LAThe pairwise classification was also performed for AD vs. MCI, and MCI vs. CTL but with lower results than the AD vs. CTL classification. In addition, pairwise classifications were performed by pooling MCI samples with AD and by comparing with CTL ((AD and MCI) vs. CTL), and by pooling MCI samples with CTL and by comparing with AD (AD vs. (MCI and CTL)). From these analyses, the best results were obtained for (AD and MCI) vs. CTL, suggesting that the MCI samples are closer to AD samples than to CTL samples. Therefore, the developed method is a promising tool not only for AD detection but also for MCI detection, which may be important in preventing further progression of the disease. However, the Acc and AUC values of (AD and MCI) vs. CTL were poorer than those of AD vs. CTL, implying that not all MCIs progress into AD. Further studies are warranted to delimit the differences between MCI and AD, and even other dementias associated with aging.The proposed method is also applicable for multi-class classification where AD, MCI, and CTL detection is performed in a unique CNN model. In this three-class classification, 71% of the AD samples and 76% of MCI samples were correctly classified, where only 10% of AD samples and 15% of the MCI samples were classified as CTL. However, a higher misclassification was observed upon detecting the CTL samples when three-class classification was performed.The proposed method has advantages over previously reported methods in terms of accuracy during the early detection of AD risk, which provides the possibility of wide implementation, helping in disease prevention. Since different gene expression database can also be transformed into 2D image, the proposed method can also be applied for other diseases beyond AD.


Germanium (Ge) chalcogenides are characterized by unique properties which make these materials interesting for a very wide range of applications, from phase change memories to ovonic threshold switches, from photonics to thermoelectric and photovoltaic devices. In many cases, physical properties can be finely tuned by doping or by changing the Ge amount, which can thus play a key role in determining the applications, performance, and even the reliability of the devices. In this Special Issue, we include 11 articles, mainly focusing on applications of Ge chalcogenides for nonvolatile memories.Most of the papers were produced with funding received from the European Union’s Horizon 2020 Research and Innovation program under grant agreement n. 824957 for the project “BeforeHand: Boosting Performance of phase change Devices by Hetero- and Nanostructure Material Design”.2Sb2Te5 compound, which is a widely studied composition and is already integrated in many devices such as optical and electronic memories. In [Two contributions ,2 are reries. In , M. BertIn , M. A. LAnother approach to improve the thermal stability of the amorphous phase is presented in , where sGe-rich GeSbTe (GST) alloys are currently explored for embedded memory applications, with the aim to increase the crystallization temperature, therefore improving the amorphous phase stability. However, deposited homogenous alloys are thermodynamically unstable and undergo phase separation upon annealing.Five articles of this Special Issue focus on Ge-rich GST alloys, exploring their electronic and electrical properties ,5,6,7 asIn , S. Cecc29Sb20Te28 via physical vapour deposition (PVD). They study the electronic properties and phase formation and report the electrical characterization of a single memory cell, showing the possibility to enhance the thermal stability up to 230 °C while maintaining a fair alignment of electrical parameters with the current state of the art of conventional GST alloys.In , A. DiazThe contribution of D. Tadesse Yimam et al.  investigThe alloy compositions and the observed phase separation pathways reported in ,6 agree 2Te3 core shells are extensively characterized with several techniques to analyze the surface morphology, crystalline structure, vibrational properties and elemental composition.In , A. KumaOther tree contributions ,10,11 arIn , V. Brag2Te3 and on Ge2Sb2Te5. Information on interdiffusion and on the evolution of the composition across the interface was obtained; it was found that, in both heterostructures, the final composition was GST212, which is a thermodynamically favorable off-stoichiometry alloy in the Sb-GeTe pseudo-binary line.As another important case in which interfaces play a significant role, article  by C. Ch2Te3 shell over GeTe and a Ge-rich GST core is studied in [The interdiffusion at the interface of core–shell nanowires with a Sbudied in  by Kumar


Hemangioendotheliomas are highly vascularized lesions, and their intracranial presentation is extremely rare. We present the case of a 65-year-old female patient who was evaluated for cranial deformity, headache, and left hemiplegia. Two bone lesions that were destroying and expanding the bone diploe with intracranial extension were identified in the fronto-temporal and parietal regions. Both lesions were multilobed and showed heterogeneous behavior. Mixed hemangioendotheliomas were identified after the successful resection of both tumors in two separate surgical procedures. The prognosis of this type of tumor with an intracranial location is not well-defined because there are too few reported cases. Hemangioendotheliomas (HE) were first reported in 1982 and were characterized as tumors with a vascular component and an epithelioid appearance that usually occur in soft tissues . IntracrMedical history and physical examinationWe present a 65-year-old woman who has had systemic arterial hypertension for 20 years. She first presented a head lump in the right frontal zone 12 years earlier without receiving any medical treatment, then two other lumps, in the parietal and temporal zones on the same side, appeared after 11 years. Then she started suffering from mild headaches, gait disturbances, left hemiparesis, sensitive loss on the left side of the body, urinary incontinence, and left hemineglect for a year, for which she self-prescribed pain medication without improvement until she was referred to our hospital by her family physician. Physical examination revealed frontal, parietal, and temporal palpable masses greater than 7 cm in diameter, left lower visual field defects, left central facial palsy, and a paretic left gait. There was no evidence of meningism or impaired higher mental functioning.Diagnostic testsMRI scanned two bone lesions that were destroying and expanding the right skull bone with intracranial extension. Both were multilobulated, with cystic and solid components. They showed a hyperintense signal on T2 and FLAIR (fluid-attenuated inversion recovery), consistent with necrosis areas, and a hyperintense signal on T1 and SWI (susceptibility-weighted imaging), compatible with chronic hemorrhage. Enhancement of the solid components was observed after the administration of intravenous contrast media. The largest lesion in the frontal and temporal regions had two lobes and measured 8 cm × 7.7 cm × 7.8 cm, and it was compressing the frontal lobe and the lateral ventricle on the same side. The image also showed surrounding edema with a displacement of the midline brain structures up to 2 cm to the left. The parietal lesion measured 6.8 cm × 6.5 cm × 7.3 cm, and it was displacing the basal nuclei and the ipsilateral mesencephalic peduncle. The orbits, eyeballs, and maxillary sinuses were not affected  34, CD31, factor VIII (FVIII), vascular endothelial growth factor (VEGF), and platelet-derived growth factor (PDGF); they were negative for cytokeratins, epithelial membrane antigen (EMA), vimentin, and P53. The Ki67 was 1% (Figure Follow-upFollow-up was carried out by the neurosurgery, radiosurgery, and neuro-oncology services two months later. Thalidomide was prescribed as an adjuvant treatment, along with radiosurgery. The patient presented with significant improvement in left-side strength and no signs of cranial nerve involvement. She reported no more fluctuations in mental functions. The controlled imaging studies did not show tumor progression.Clinically, HE shows malignant behaviors, such as invasion, recurrence, and metastasis. This tumor is considered a borderline brain tumor with a proliferative histopathological appearance and intermediate biological behavior between a hemangioma and an angiosarcoma ,8,9.HE has been described in adults ranging in age from 20 to 74 years. In children, the age reported is between 2 months and 15 years. The ratio between men and women is about 1.2:1 in adults and 3.5:1 in children [The symptoms associated with these tumors depend mainly on their location. They can be related to headaches, seizures, and limb weakness. Within the extracranial location, it is more frequently found in the liver, lungs, or skin. It is important to scan the chest and abdomen because of the possibility of metastasis -12.MRI data in T1 and T2 sequences is consistent with isointense, hyperintense, or heterogeneous signal lesions. In half of the cases, they present perilesional edema . Other cWithin the treatment modalities, surgery is included as the first choice because it is associated with a favorable prognosis without the need for adjuvant therapy. However, when it is required, it is recommended to continue with radiotherapy and chemotherapy ,9,23,24.The prognosis for this type of tumor and its associated intracranial locations is not well established due to its rare presentation and the limited series of cases that exist. The recurrence rate corresponds to 20-24%, and regarding the rate of metastasis, it is estimated that it can occur in 15-20% of all patients ,26. The Adequate identification and approach selection are important since, although HE is a rare tumor with low proliferative activity, it is extremely invasive with a high risk of complications associated with its treatment. This is why a more extensive study must be carried out prior to surgery to rule out a metastatic process and to give a better follow-up due to the possibility of recurrence. Therefore, specific criteria must be established for its maximum resection with embolization support and, if necessary, subsequent adjuvant management with radiosurgery and chemotherapy.


NPC2 gene. Our patient faced a range of difficulties, including prolonged mechanical ventilation and diagnostic and therapeutic challenges. Conclusion: Niemann–Pick disease type C2 is a progressive and lethal condition that requires a high index of suspicion to pinpoint the diagnosis. Gene study remains the method of choice to confirm the diagnosis. There are limited choices of therapeutic interventions; therefore, genetic counseling and the prevention of recurrence should be the ultimate goal for affected families.Niemann–Pick disease type C (NPC) is an autosomal recessive lipid storage disorder. There are two types, NPC1, which is the predominant form (95%), and the rare NPC2, which represents less than 5% of the reported cases. Niemann–Pick disease type C2 usually presents with respiratory symptoms, cholestasis, neurological impairment, and hepatosplenomegaly. Case report: Here, we report a 3-year-old boy who presented to our hospital with exacerbation of chronic lung disease requiring invasive ventilatory support. He was previously diagnosed with interstitial lung disease. His parents used to instill olive oil in his nose  to treat frequent nasal bleeding. A detailed history revealed prolonged neonatal jaundice for four months, with hepatosplenomegaly. In his second year, generalized hypotonia and delayed psychomotor development were observed. Upon presentation to our institute, chest CT showed evidence of intraparenchymal fat; therefore, lipoid pneumonia and lipid storage disease were suspected. The bronchoalveolar lavage results suggested pulmonary alveolar proteinosis (PAP). Whole-exome sequencing (WES) revealed a class one homozygous pathogenic variant in the  SMPD1, which causes an enzymatic deficiency of sphingomyelinase (SMA) [NPC1 or NPC2 gene. Defects in the NPC1 gene account for approximately 95% of patients, but NPC2 is the cause in approximately 5% of reported cases [Niemann–Pick disease (NPD) is a heterogeneous group of lysosomal storage disorders inherited in an autosomal recessive manner . Niemannse (SMA) . Niemanned cases ,4,5.Niemann–Pick disease is caused by the impaired trafficking of unesterified cholesterol and glycolipids in lysosomes and late endosomes, leading to the disturbed transport of various intracellular lipids, the reduced degradation of glucosylceramide, and the lysosomal storage of cholesterol and glycosphingolipids . Lipid aA 3-year-old Pakistani boy was admitted to our institute with an exacerbation of chronic lung disease. He was born at full term by spontaneous vaginal delivery with a weight of 3.0 kg, and there were no antenatal concerns. The parents are first cousins with no family history of infant death, developmental delay, or genetic disorder. The neonatal period was marked by jaundice, requiring a 4-day course of phototherapy. Later, in the neonatal period, he developed prolonged jaundice with cholestasis characterized by mixed hyperbilirubinemia, elevated liver enzymes, and hepatomegaly.In the first year of life, he had frequent epistaxis, for which he used to receive olive oil in the nostrils, aiming to control the bleeding and nasal dryness. In the second year, the patient had a mild course of COVID-19 infection that did not require hospital admission. During his second year (a year ago), he also developed frequent respiratory symptoms that required admission to the ward on many occasions and responded to antibiotics, inhaled steroids, and bronchodilators. Six months ago, he presented with cough, dyspnoea, and desaturation, requiring admission to the pediatric intensive care unit (PICU) and ventilation for 2 days. Computerized tomography (CT) of the chest  showed dIn his recent admission to our institute, at the age of three, he had another respiratory exacerbation requiring 5 L/m oxygen. On examination, he was in respiratory distress with subcostal and intercostal recessions, and his respiratory rate was 50 breaths per minute. His oxygen saturation was 70% in room air. He required 5–6 L/m oxygen through the nasal cannula to maintain saturation above 90%. He was febrile, had a heart rate of 140 beats per minute, and had blood pressure of 110/75 mmHg. His weight, length, and head circumference were between the 25th and 50th percentiles. He had grade three clubbing in both hands. Respiratory examination revealed markedly reduced breath sounds at the lung bases with fine inspiratory crackles and absent bronchial breath sounds. His splenic tip was palpable, and his liver was palpable at 4 cm below the costal margin. Neurological assessment revealed global developmental delay with central hypotonia and normal reflexes. Fundoscopy was normal.There were homogenous ground glass opacities involving the entire right lung and the left upper and middle zone with mesocardium due to mild rotation to the right side; there was some retrocardiac collapse and secondary hyperinflation of the left lower lobe but there was no attenuation of the blood vessels to suspect pulmonary emphysema as a possible sign of NPC2 . 2 49 mmHg, P02: 35.8%, O2 saturation 69.9%, HCO3 27.3 mmol/L, base excess 4.6, and lactate 2.3 mmol/L. Abdominal ultrasound revealed moderate hepatosplenomegaly. Whole-exome sequencing (WES) confirmed a homozygous nonsense pathogenic variant in NPC2 (NM_006432.3: c.141C>A), p. (Cys47*), which was previously prescribed as disease-causing by Chikh et al., 2005 (PMID: 15937921). Additionally, the biomarker lyso-SM509 measured by liquid chromatography—mass spectrometry was significantly elevated: 1.6 ng/mL (≤0.9 ng/mL), in the variant coordinates. The blood work, including normal complete blood count, liver, and renal function, and coagulation profile, was benign. On 2 L nasal cannula oxygen, the venous blood gas, consistent with type 2 respiratory failure, showed pH 7.42, PCOThe bronchoalveolar lavage  was turbUpon admission to our hospital, the working diagnosis was the exacerbation of the interstitial lung disease of childhood (ChILD) with possible lipoid pneumonia based on the clear history of the instillation of olive oil in the nostrils for several months. However, the severity of the lung involvement together with the presence of neonatal jaundice and hepatosplenomegaly raised the possibility of lipid storage disease (Niemann–Pick or Gaucher disease). Awaiting the result of the WES, the patient received supportive therapy, including noninvasive ventilation, a course of intravenous Tazocin, proton pump inhibitor, and hydration therapy with salbutamol, hypertonic saline, and frequent chest physiotherapy. No clinical improvement was observed, and he remained markedly hypoxic despite two weeks of supportive therapy. Thereafter, therapeutic lung lavage was performed. Unfortunately, this procedure was not tolerated well, with a worsening of his respiratory status that required high-frequency oscillatory ventilation (HFOV) for ten more days and then he was switched to conventional mechanical ventilation and remained on this ventilatory mode. He received low-fat feed with a medium-chain triglyceride (MCT) supplement given through a nasogastric tube. As the therapeutic options were limited and the prognosis was poor, the family received genetic counseling and a do not resuscitate code was discussed, unfortunately the patient passed away.Niemann–Pick disease type C2 is a rare lipid storage disorder characterized by a slowly progressive course leading to death in infancy. Its main clinical features include cholestasis, hepatosplenomegaly, and pulmonary and neurological insult . Similar to our patient who presented with cholestasis, Vanier et al. described a child with NPC2 who presented with prolonged jaundice and cholestasis, which was misdiagnosed as galactosemia, and other cases in the literature were misdiagnosed with neonatal haemochromatosis . In our Niemann–Pick disease type C2 is predominantly respiratory, in contrast to NPC1 ,8. ThereTherapeutic lung lavage is the main supportive therapy in both PAP and lipoid pneumonia, and overlap between the two conditions is common. However, the coexistence of the two conditions has not been reported in the literature to the best of our knowledge. In our experience, gross inspection of the lavage container may help in differentiating between these two conditions. The proteinous material of PAP is likely to sink to the bottom of the container; the color of the lavage is usually pink with a frothy floating substance, as the lipid part will float on the top of the container due to being less dense than normal saline . No curative therapy is available for NPC2. Multidisciplinary team input would help to support the patient and the family 9, 10). Miglustat is the first and the only approved medication for NPC with neurological manifestations. It is a glucosylceramide synthase inhibitor and an essential enzyme for the synthesis of glycosphingolipids. It has been shown to increase the survival of patients with NPD by five years from the date of diagnosis or approximately ten years from the onset of neurologic manifestations , 10. Mig,19. TherNiemann–Pick disease type C2 is a lethal, progressive, and chronically debilitating disease with multisystem involvement. It starts with neonatal jaundice, with variable degrees of visceromegaly and psychomotor retardation and pulmonary involvement. In this case, NPC2 was misdiagnosed as ChILD, resulting in unnecessary treatment with steroids and hydroxychloroquine, thus highlighting the importance of a detailed history and a vigilant investigation of past illnesses; in this case, discovering the neonatal hepatic manifestations, combined with respiratory and neurological involvement suggested the correct diagnosis. Lipoid pneumonia should be considered as an important differential diagnosis, especially in higher incidence areas such as Saudi Arabia. Lavage is very beneficial to differentiate alveolar proteinosis from lipoid pneumonia. Whole-exome sequencing is the preferred confirmatory diagnostic method. Currently, there is no cure for NPC2.