Enhanced differential evolution algorithm for feature selection in tuberculous pleural effusion clinical characteristics analysis.

Tuberculous pleural effusion poses a significant threat to human health due to its potential for severe disease and mortality. Without timely treatment, it may lead to fatal consequences. Therefore, early identification and prompt treatment are crucial for preventing problems such as chronic lung disease, respiratory failure, and death. This study proposes an enhanced differential evolution algorithm based on colony predation and dispersed foraging strategies. A series of experiments conducted on the IEEE CEC 2017 competition dataset validated the global optimization capability of the method. Additionally, a binary version of the algorithm is introduced to assess the algorithm's ability to address feature selection problems. Comprehensive comparisons of the effectiveness of the proposed algorithm with 8 similar algorithms were conducted using public datasets with feature sizes ranging from 10 to 10,000. Experimental results demonstrate that the proposed method is an effective feature selection approach. Furthermore, a predictive model for tuberculous pleural effusion is established by integrating the proposed algorithm with support vector machines. The performance of the proposed model is validated using clinical records collected from 140 tuberculous pleural effusion patients, totaling 10,780 instances. Experimental results indicate that the proposed model can identify key correlated indicators such as pleural effusion adenosine deaminase, temperature, white blood cell count, and pleural effusion color, aiding in the clinical feature analysis of tuberculous pleural effusion and providing early warning for its treatment and prediction.

Optimized fuzzy K-nearest neighbor approach for accurate lung cancer prediction based on radial endobronchial ultrasonography.

Radial endobronchial ultrasonography (R-EBUS) has been a surge in the development of new ultrasonography for the diagnosis of pulmonary diseases beyond the central airway. However, it faces challenges in accurately pinpointing the location of abnormal lesions. Therefore, this study proposes an improved machine learning model aimed at distinguishing between malignant lung disease (MLD) from benign lung disease (BLD) through R-EBUS features. An enhanced manta ray foraging optimization based on elite perturbation search and cyclic mutation strategy (ECMRFO) is introduced at first. Experimental validation on 29 test functions from CEC 2017 demonstrates that ECMRFO exhibits superior optimization capabilities and robustness compared to other competing algorithms. Subsequently, it was combined with fuzzy k-nearest neighbor for the classification prediction of BLD and MLD. Experimental results indicate that the proposed modal achieves a remarkable prediction accuracy of up to 99.38%. Additionally, parameters such as R-EBUS1 Circle-dense sign, R-EBUS2 Hemi-dense sign, R-EBUS5 Onionskin sign and CCT5 mediastinum lymph node are identified as having significant clinical diagnostic value.

Multi-step influenza forecasting through singular value decomposition and kernel ridge regression with MARCOS-guided gradient-based optimization.

This research delves into the significance of influenza outbreaks in public health, particularly the importance of accurate forecasts using weekly Influenza-like illness (ILI) rates. The present work develops a novel hybrid machine-learning model by combining singular value decomposition with kernel ridge regression (SKRR). In this context, a novel hybrid model known as H-SKRR is developed by combining two robust forecasting approaches, SKRR and ridge regression, which aims to improve multi-step-ahead predictions for weekly ILI rates in Southern and Northern China. The study begins with feature selection via XGBoost in the preprocessing phase, identifying optimal precursor information guided by importance factors. It decomposes the original signal using multivariate variational mode decomposition (MVMD) to address non-stationarity and complexity. H-SKRR is implemented by incorporating significant lagged-time components across sub-components. The aggregated forecasted values from these sub-components generate ILI values for two horizons (i.e., 4-and 7-weekly ahead). Employing the gradient-based optimization (GBO) algorithm fine-tunes model parameters. Furthermore, the deep random vector functional link (dRVFL), Ridge regression, and gated recurrent unit neural network (GRU) models were employed to validate the MVMD-H-SKRR-GBO paradigm's effectiveness. The outcomes, assessed using the MARCOS (Measurement of alternatives and ranking according to compromise solution) method as a multi-criteria decision-making method, highlight the superior accuracy of the MVMD-H-SKRR-GBO model in predicting ILI rates. The results clearly highlight the exceptional performance of the MVMD-H-SKRR-GBO model, with outstanding precision demonstrated by impressive R, RMSE, IA, and U95 % values of 0.946, 0.388, 0.970, and 1.075, respectively, at t + 7.

A stochastic block Ising model for multi-layer networks with inter-layer dependence.

Community detection has attracted tremendous interests in network analysis, which aims at finding group of nodes with similar characteristics. Various detection methods have been developed to detect homogeneous communities in multi-layer networks, where inter-layer dependence is a widely acknowledged but severely under-investigated issue. In this paper, we propose a novel stochastic block Ising model (SBIM) to incorporate the inter-layer dependence to help with community detection in multi-layer networks. The community structure is modeled by the stochastic block model (SBM) and the inter-layer dependence is incorporated via the popular Ising model. Furthermore, we develop an efficient variational EM algorithm to tackle the resultant optimization task and establish the asymptotic consistency of the proposed method. Extensive simulated examples and a real example on gene co-expression multi-layer network data are also provided to demonstrate the advantage of the proposed method.

Comprehensive analysis of cuproptosis-related genes in immune infiltration and prognosis in lung adenocarcinoma.

Copper-dependent cell death, called cuproptosis, is connected to tumor development, prognosis, and the immune response. Nevertheless, the function of cuproptosis-related genes (CRGs) in the tumor microenvironment (TME) of lung adenocarcinoma (LUAD) remains unknown. This work used R software packages to classify the raw data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases of LUAD patients. Afterward, the connections of the various subgroups, clinical pathological traits, and immune infiltration (IMIF) features with the TME mutation status were explored. Ultimately, a nomogram and calibration curve were developed, aiming at enhancing the clinical application of CRG scores and estimating the survival probability of patients. Moreover, the relationships between cuproptosis and the molecular traits, immune cell infiltration of tumor tissue, prognosis, and clinical treatment of patients were investigated in this work. Subsequently, the CRG score was established to predict overall survival (OS), and its credible predictive ability in LUAD patients was identified. Afterward, a highly credible nomogram was created to contribute to the clinical viability of the CRG score. Furthermore, as demonstrated, gene signatures could be applied in assessing tumor immune cell infiltration, clinical traits, and prognosis. In addition, high tumor mutation burden, immunological activity, and significant survival probability were characterized by low CRG scores, and high CRG scores were related to immunosuppression and stromal pathway activation. The current work also discovered a predictive CRG-related signature for LUAD patients, probably contributing to TME trait clarification and more potent immunotherapy strategy exploration.

DGRUnit: Dual graph reasoning unit for brain tumor segmentation.

Many fully automatic segmentation models have been created to solve the difficulty of brain tumor segmentation, thanks to the rapid growth of deep learning. However, few approaches focus on the long-range relationships and contextual interdependence in multimodal Magnetic Resonance (MR) images. In this paper, we propose a novel approach for brain tumor segmentation called the dual graph reasoning unit (DGRUnit). Two parallel graph reasoning modules are included in our proposed method: a spatial reasoning module and a channel reasoning module. The spatial reasoning module models the long-range spatial dependencies between distinct regions in an image using a graph convolutional network (GCN). The channel reasoning module uses a graph attention network (GAT) to model the rich contextual interdependencies between different channels with similar semantic representations. Our experimental results clearly demonstrate the superior performance of the proposed DGRUnit. The ablation study shows the flexibility and generalizability of our model, which can be easily integrated into a wide range of neural networks and further improve them. When compared to several state-of-the-art methods, experimental results show that the proposed approach significantly improves both visual inspection and quantitative metrics for brain tumor segmentation tasks.

Influenza virus NS1 interacts with 14-3-3ε to antagonize the production of RIG-I-mediated type I interferons.

For influenza A viruses (IAVs), non-structural protein 1 (NS1) protein was recognized to be the key factor to enhance virulence by antagonizing host innate anti-viral responses. However, for the pathways allowing NS1 to regulate the type I interferon (IFN) response, the identification of the substrates was still incomplete. Here a recombinant IAV encoding a NS1 containing an affinity tag (NS1-Strep) was generated to capture the NS1-interactome in the lungs of infected mice. Several scaffold proteins of the 14-3-3 family were distinguished as the most potent candidates. Based on the conserved motif RxxTxxT of NS1, the interaction between NS1 and 14-3-3ε was enabled, which competed for the binding of RIG-I to 14-3-3ε and prevented RIG-I translocation to the adaptor MAVS, consequently inhibiting IFN-ß expression. A recombinant mutant IAV deficient in 14-3-3ε binding elicited a markable innate immune responses and showed impaired growth kinetics.

An efficient multilevel thresholding image segmentation method based on the slime mould algorithm with bee foraging mechanism: A real case with lupus nephritis images.

To improve the diagnosis of Lupus Nephritis (LN), a multilevel LN image segmentation method is developed in this paper based on an improved slime mould algorithm. The search of the optimal threshold set is key to multilevel thresholding image segmentation (MLTIS). It is well known that swarm-based methods are more efficient than the traditional methods because of the high complexity in finding the optimal threshold, especially when performing image partitioning at high threshold levels. However, swarm-based methods tend to obtain the poor quality of the found segmentation thresholds and fall into local optima during the process of segmentation. Therefore, this paper proposes an ASMA-based MLTIS approach by combining an improved slime mould algorithm (ASMA),  where ASMA is mainly implemented by introducing the position update mechanism of the artificial bee colony (ABC) into the SMA. To prove the superiority of the ASMA-based MLTIS method, we first conducted a comparison experiment between ASMA and 11 peers using 30 test functions. The experimental results fully demonstrate that ASMA can obtain high-quality solutions and almost does not suffer from premature convergence. Moreover, using standard images and LN images, we compared the ASMA-based MLTIS method with other peers and evaluated the segmentation results using three evaluation indicators called PSNR, SSIM, and FSIM. The proposed ASMA can be an excellent swarm intelligence optimization method that can maintain a delicate balance during the segmentation process of LN images, and thus the ASMA-based MLTIS method has great potential to be used as an image segmentation method for LN images. The lastest updates for the SMA algorithm are available in https://aliasgharheidari.com/SMA.html.

Horizontal and vertical search artificial bee colony for image segmentation of COVID-19 X-ray images.

The artificial bee colony algorithm (ABC) has been successfully applied to various optimization problems, but the algorithm still suffers from slow convergence and poor quality of optimal solutions in the optimization process. Therefore, in this paper, an improved ABC (CCABC) based on a horizontal search mechanism and a vertical search mechanism is proposed to improve the algorithm's performance. In addition, this paper also presents a multilevel thresholding image segmentation (MTIS) method based on CCABC to enhance the effectiveness of the multilevel thresholding image segmentation method. To verify the performance of the proposed CCABC algorithm and the performance of the improved image segmentation method. First, this paper demonstrates the performance of the CCABC algorithm itself by comparing CCABC with 15 algorithms of the same type using 30 benchmark functions. Then, this paper uses the improved multi-threshold segmentation method for the segmentation of COVID-19 X-ray images and compares it with other similar plans in detail. Finally, this paper confirms that the incorporation of CCABC in MTIS is very effective by analyzing appropriate evaluation criteria and affirms that the new MTIS method has a strong segmentation performance.

Tuberculous pleural effusion prediction using ant colony optimizer with grade-based search assisted support vector machine.

Introduction: Although tuberculous pleural effusion (TBPE) is simply an inflammatory response of the pleura caused by tuberculosis infection, it can lead to pleural adhesions and cause sequelae of pleural thickening, which may severely affect the mobility of the chest cavity. Methods: In this study, we propose bGACO-SVM, a model with good diagnostic power, for the adjunctive diagnosis of TBPE. The model is based on an enhanced continuous ant colony optimization (ACOR) with grade-based search technique (GACO) and support vector machine (SVM) for wrapped feature selection. In GACO, grade-based search greatly improves the convergence performance of the algorithm and the ability to avoid getting trapped in local optimization, which improves the classification capability of bGACO-SVM. Results: To test the performance of GACO, this work conducts comparative experiments between GACO and nine basic algorithms and nine state-of-the-art variants as well. Although the proposed GACO does not offer much advantage in terms of time complexity, the experimental results strongly demonstrate the core advantages of GACO. The accuracy of bGACO-predictive SVM was evaluated using existing datasets from the UCI and TBPE datasets. Discussion: In the TBPE dataset trial, 147 TBPE patients were evaluated using the created bGACO-SVM model, showing that the bGACO-SVM method is an effective technique for accurately predicting TBPE.

Role of arachidonic acid lipoxygenase pathway in Asthma.

The arachidonic acid (AA) metabolism pathways play a key role in immunological response and inflammation diseases, such as asthma, etc. AA in cell membranes can be metabolized by lipoxygenases (LOXs) to a screen of bioactive substances that include leukotrienes (LTs), lipoxins (LXs), and eicosatetraenoic acids (ETEs), which are considered closely related to the pathophysiology of respiratory allergic disease. Studies also verified that drugs regulating AA LOXs pathway have better rehabilitative intervention for asthma. This review aims to summarize the physiological and pathophysiological importance of AA LOXs metabolism pathways in asthma and to discuss its prospects of therapeutic strategies.

Tandem mass tag-based quantitative proteomics analysis reveals the new regulatory mechanism of progranulin in influenza virus infection.

Progranulin (PGRN) plays an important role in influenza virus infection. To gain insight into the potential molecular mechanisms by which PGRN regulates influenza viral replication, proteomic analyzes of whole mouse lung tissue from wild-type (WT) versus (vs) PGRN knockout (KO) mice were performed to identify proteins regulated by the absence vs. presence of PGRN. Our results revealed that PGRN regulated the differential expression of ALOX15, CD14, CD5L, and FCER1g, etc., and also affected the lysosomal activity in influenza virus infection. Collectively these findings provide a panoramic view of proteomic changes resulting from loss of PGRN and thereby shedding light on the functions of PGRN in influenza virus infection.

An Effective Machine Learning Approach for Identifying Non-Severe and Severe Coronavirus Disease 2019 Patients in a Rural Chinese Population: The Wenzhou Retrospective Study.

This paper has proposed an effective intelligent prediction model that can well discriminate and specify the severity of Coronavirus Disease 2019 (COVID-19) infection in clinical diagnosis and provide a criterion for clinicians to weigh scientific and rational medical decision-making. With indicators as the age and gender of the patients and 26 blood routine indexes, a severity prediction framework for COVID-19 is proposed based on machine learning techniques. The framework consists mainly of a random forest and a support vector machine (SVM) model optimized by a slime mould algorithm (SMA). When the random forest was used to identify the key factors, SMA was employed to train an optimal SVM model. Based on the COVID-19 data, comparative experiments were conducted between RF-SMA-SVM and several well-known machine learning algorithms performed. The results indicate that the proposed RF-SMA-SVM not only achieves better classification performance and higher stability on four metrics, but also screens out the main factors that distinguish severe COVID-19 patients from non-severe ones. Therefore, there is a conclusion that the RF-SMA-SVM model can provide an effective auxiliary diagnosis scheme for the clinical diagnosis of COVID-19 infection.

Recombinant HA1-ΔfliC enhances adherence to respiratory epithelial cells and promotes the superiorly protective immune responses against H9N2 influenza virus in chickens.

H9N2 subtype avian influenza virus (AIV) is an ongoing threat causing substantial loss to the poultry industry and thus necessitating the development of safe and effective vaccines against AIV. Given that inactivated vaccines are less effective in activating the mucosal immune system, we aimed to generate a vaccine that can actively engage the mucosal immunity which is the front line of the immune system. We generated a group of flagellin-based hemagglutinin globular head (HA1) fusion proteins and characterized their immunogenicity and efficacy. We found that Salmonella typhimurium flagellin (fliC) lacking the hypervariable domain (called herein as HA1-ΔfliC) was recognized by TLR5 and induced a moderate innate immune response compared to N-terminus of fliC (HA1-fliC) and C-terminus of fliC (fliC-HA1). The HA1-ΔfliC protein had increased adherence to the nasal cavity and trachea than HA1-fliC and fliC-HA1 and significantly increased the HA-specific sIgA titers. Our in vivo results revealed that chickens treated with HA1-ΔfliC had a significantly reduced level of viral loads in the cloaca and throat compared with chickens treated with inactivated vaccine. Overall, these results revealed that HA1-ΔfliC can protect chickens against H9N2 AIV by eliciting the efficient mucosal immune responses.

Ant colony optimization with Cauchy and greedy Levy mutations for multilevel COVID 19 X-ray image segmentation.

This paper focuses on the study of multilevel COVID-19 X-ray image segmentation based on swarm intelligence optimization to improve the diagnostic level of COVID-19. We present a new ant colony optimization with the Cauchy mutation and the greedy Levy mutation, termed CLACO, for continuous domains. Specifically, the Cauchy mutation is applied to the end phase of ant foraging in CLACO to enhance its searchability and to boost its convergence rate. The greedy Levy mutation is applied to the optimal ant individuals to confer an improved ability to jump out of the local optimum. Furthermore, this paper develops a novel CLACO-based multilevel image segmentation method, termed CLACO-MIS. Using 2D Kapur's entropy as the CLACO fitness function based on 2D histograms consisting of non-local mean filtered images and grayscale images, CLACO-MIS was successfully applied to the segmentation of COVID-19 X-ray images. A comparison of CLACO with some relevant variants and other excellent peers on 30 benchmark functions from IEEE CEC2014 demonstrates the superior performance of CLACO in terms of search capability, and convergence speed as well as ability to jump out of the local optimum. Moreover, CLACO-MIS was shown to have a better segmentation effect and a stronger adaptability at different threshold levels than other methods in performing segmentation experiments of COVID-19 X-ray images. Therefore, CLACO-MIS has great potential to be used for improving the diagnostic level of COVID-19. This research will host a webservice for any question at https://aliasgharheidari.com.

Multilevel threshold image segmentation with diffusion association slime mould algorithm and Renyi's entropy for chronic obstructive pulmonary disease.

Image segmentation is an essential pre-processing step and is an indispensable part of image analysis. This paper proposes Renyi's entropy multi-threshold image segmentation based on an improved Slime Mould Algorithm (DASMA). First, we introduce the diffusion mechanism (DM) into the original SMA to increase the population's diversity so that the variants can better avoid falling into local optima. The association strategy (AS) is then added to help the algorithm find the optimal solution faster. Finally, the proposed algorithm is applied to Renyi's entropy multilevel threshold image segmentation based on non-local means 2D histogram. The proposed method's effectiveness is demonstrated on the Berkeley segmentation dataset and benchmark (BSD) by comparing it with some well-known algorithms. The DASMA-based multilevel threshold segmentation technique is also successfully applied to the CT image segmentation of chronic obstructive pulmonary disease (COPD). The experimental results are evaluated by image quality metrics, which show the proposed algorithm's extraordinary performance. This means that it can help doctors analyze the lesion tissue qualitatively and quantitatively, improve its diagnostic accuracy and make the right treatment plan. The supplementary material and info about this article will be available at https://aliasgharheidari.com.

INSL-3 protein expression in normal and cryptorchid testes of Ziwuling black goats.

Ziwuling black goats are typically found in loess plateaus regions and the Ziwuling Nature Reserve. Cryptorchidism is a common disease in this inbred goat, and its pathogenesis has been linked with the expression of insulin-like factor 3 (INSL-3). Therefore, this study aimed to investigate anatomical alterations caused by cryptorchism and the expression and distribution of INSL-3 in normal and cryptorchid testicular tissues. The testicular tissues of 6-month-old Ziwuling black goats were collected for microscopic analyses using histochemical, immunohistochemical, immunofluorescence and biometrical methods, as well as Western blotting to compare the expression and distribution of INSL-3. A lower expression of INSL-3 was observed in cryptorchid compared with normal testicular tissues (p < .01). Cryptorchidism caused a significant reduction in layers of spermatogenic epithelium and tubule areas in Ziwuling black goat (p < .01). The interstitial to seminiferous tubule area ratio was larger in cryptorchid than in normal group. Periodic Acid-Schiff (PAS) staining revealed pronounced positive bands in the interstitial tissue, while positive Alcian blue (AB) staining was not clear, and AB-PAS staining revealed a positive red band in the basement membrane of cryptorchid group. Immunofluorescence revealed a strong signal of INSL-3 expression in Sertoli and peritubular myoid cells, and moderate signal in Leydig and spermatogenic cells in the normal group. However, in cryptorchid testicular tissues, the signal of INSL-3 expression was strong in primary spermatocytes, occasional in Sertoli cells, limited in Leydig cells and absent in peritubular myoid cells. Furthermore, immunohistochemistry showed that INSL-3 expression was higher in normal testes compared with cryptorchid testicular tissues (p < .05), especially in primary spermatocytes and Sertoli cells. Collectively, our results indicate that cryptorchidism is closely related to the disorder of acid glycoprotein metabolism and the reduction in release of INSL-3 from Leydig cells. Moreover, Sertoli and peritubular myoid cells are crucial for INSL signalling and could underpin further research on the mechanism of cryptorchidism in animal.

Discovery of a novel GLP-1/GIP dual receptor agonist CY-5 as long-acting hypoglycemic, anti-obesity agent.

Glucagon-like peptide-1 (GLP-1) receptor agonists as an effective approach for type 2 diabetes mellitus (T2DM) has been explored extensively, multi agonists based on GLP-1 may have better clinical benefits on obesity, Nonalcoholic steatohepatitis (NASH) and other metabolic diseases. To get multi agonists based on GLP-1, 15 conjugates were designed, synthesized, and tested for biological activity. GLP-1/glucagon dual receptor agonist E1 showed moderate long-acting hypoglycemic effect, CY-5 and CY-16 with GLP-1/GIP dual receptor agonistic activity exhibited longer duration of continuous blood glucose stabilization. The long-acting hypoglycemic effect was equal to that of semaglutide. Although they have lost the agonistic activity on glucagon receptor, chronic in vivo studies on T2DM mice and diet-induced obesity (DIO) mice showed that CY-5 can effectively reduce food intake, inhibit body weight gain, repair islets damage and improve the glucose tolerance. One month treatment on NASH mice showed that CY-5 can significantly lower the TG, TC, AST, ALT and LDL-C and increase the HDL-C. CY-5 can also improve the liver vacuolation, reduce fat accumulation and delay the process of the fibrosis. The liver protection effect is better than that of semaglutide. In summary, CY-5 is a promising candidate for the treatment of metabolic diseases and worthy for further development.

Efficacy and safety of vascular intervention combined with intravenous thrombolysis in treatment of acute intracranial arterial occlusion.

Efficacy and safety of vascular intervention combined with intravenous thrombolysis (IVT) was investigated in the treatment of acute intracranial arterial occlusion (AIAO). Ninety-two patients with AIAO treated in People's Hospital of Tongchuan from January 2014 to February 2016 were enrolled in this retrospective study. Forty-two patients were treated with vascular intervention (control group), while another 50 patients were treated with vascular intervention combined with IVT (study group). They were observed in terms of the improvement of clinical efficacy after treatment, the comparison of complications after treatment, the National Institute of Health Stroke Scale (NIHSS) score after treatment, the modified Rankin Scale (mRS) score at 3 months after treatment, and the Mini-Mental State Examination (MMSE) score at 3 months after treatment. Compared with those in the control group, patients in the study group had statistically significantly higher marked effectiveness and statistically significantly lower ineffectiveness (P=0.018), and a statistically significantly higher overall effective rate (P=0.042). The NIHSS score in the study group was statistically significantly lower than that in the control group after treatment (P=0.001). There was a statistically significant difference between the two groups in the mRS score at 3 months after treatment (Z=8.764, P>0.05). Compared with those in the control group, patients in the study group had a statistically significantly higher MMSE score after treatment, and a statistically significantly lower total incidence of postoperative complications (P=0.001). Vascular intervention combined with IVT has good efficacy and high safety in the treatment of AIAO, and the combination can statistically significantly improve patients' quality of life, so it has a good clinical application value.

Evasion mechanisms of the type I interferons responses by influenza A virus.

The type I interferons (IFNs) represent the first line of host defense against influenza virus infection, and the precisely control of the type I IFNs responses is a central event of the immune defense against influenza viral infection. Influenza viruses are one of the leading causes of respiratory tract infections in human and are responsible for seasonal epidemics and occasional pandemics, leading to a serious threat to global human health due to their antigenic variation and interspecies transmission. Although the host cells have evolved sophisticated antiviral mechanisms based on sensing influenza viral products and triggering of signalling cascades resulting in secretion of the type I IFNs (IFN-α/ß), influenza viruses have developed many strategies to counteract this mechanism and circumvent the type I IFNs responses, for example, by inducing host shut-off, or by regulating the polyubiquitination of viral and host proteins. This review will summarise the current knowledge of how the host cells recognise influenza viruses to induce the type I IFNs responses and the strategies that influenza viruses exploited to evade the type I IFNs signalling pathways, which will be helpful for the development of antivirals and vaccines.