Optimizing SARS-CoV-2 vaccine responses in kidney transplant recipients: an urgent need.

Kidney transplant recipients (KTRs) have been identified as a population at increased risk for severe SARS-CoV-2 infection outcomes. This study focused on understanding the immune response of KTRs post-vaccination, specifically examining both serological and cellular responses to the SARS-CoV-2 vaccine. Thirteen individuals, including seven KTRs and six healthy donors, were evaluated for antibody levels and T cell responses post-vaccination. The study revealed that KTRs had significantly lower serological responses, including reduced anti-receptor binding domain (RBD) binding antibodies and neutralizing antibodies against the Wuhan, Delta, and Omicron BA.2 strains. Additionally, KTRs demonstrated weaker CD8 T cell cytotoxic responses and lower Th1 cytokine secretion, particularly IFN-γ, after stimulation with variant spike peptide pools. These findings highlight the compromised immunity in KTRs post-vaccination and underscore the need for tailored strategies to bolster immune responses in this vulnerable group. Further investigations are warranted into the mechanisms underlying reduced vaccine efficacy in KTRs and potential therapeutic interventions. IMPORTANCE: Some studies have revealed that KTRs had lower serological response against SARS-CoV-2 than healthy people. Nevertheless, limited studies investigate the cellular response against SARS-CoV-2 in KTRs receiving SARS-CoV-2 vaccines. Here, we found that KTRs have lower serological and cellular responses. Moreover, we found that KTRs had a significantly lower IFN-γ secretion than healthy individuals when their PBMCs were stimulated with SARS-CoV-2 spike peptide pools. Thus, our findings suggested that additional strategies are needed to enhance KTR immunity triggered by the vaccine.

Antibody Profiling of Dengue Severities Using Flavivirus Protein Microarrays.

Dengue is a viral disease transmitted by Aedes aegypti mosquitoes. According to the World Health Organization, about half of the world's population is at risk of dengue. There are four serotypes of the dengue virus. After infection with one serotype, it will be immune to such a serotype. However, subsequent infection with other serotypes will increase the risk of severe outcomes, e.g., dengue hemorrhagic fever, dengue shock syndrome, and even death. Since severe dengue is challenging to predict and lacks molecular markers, we aim to build a multiplexed Flavivirus protein microarray (Flaviarray) that includes all of the common Flaviviruses to profile the humoral immunity and cross-reactivity in the dengue patients with different outcomes. The Flaviarrays we fabricated contained 17 Flavivirus antigens with high reproducibility (R-square = 0.96) and low detection limits (172-214 pg). We collected serums from healthy subjects (n = 36) and dengue patients within 7 days after symptom onset (mild dengue (n = 21), hospitalized nonsevere dengue (n = 29), and severe dengue (n = 36)). After profiling the serum antibodies using Flaviarrays, we found that patients with severe dengue showed higher IgG levels against multiple Flavivirus antigens. With logistic regression, we found groups of markers with high performance in distinguishing dengue patients from healthy controls as well as hospitalized from mild cases (AUC > 0.9). We further reported some single markers that were suitable to separate dengue patients from healthy controls (AUC > 0.9) and hospitalized from mild outcomes (AUC > 0.8). Together, Flaviarray is a valuable tool to profile antibody specificities, uncover novel markers for decision-making, and shed some light on early preventions and treatments.

Multi-domain medical image translation generation for lung image classification based on generative adversarial networks.

OBJECTIVE: Lung image classification-assisted diagnosis has a large application market. Aiming at the problems of poor attention to existing translation models, the insufficient ability of key transfer and generation, insufficient quality of generated images, and lack of detailed features, this paper conducts research on lung medical image translation and lung image classification based on generative adversarial networks. METHODS: This paper proposes a medical image multi-domain translation algorithm MI-GAN based on the key migration branch. After the actual analysis of the imbalanced medical image data, the key target domain images are selected, the key migration branch is established, and a single generator is used to complete the medical image multi-domain translation. The conversion between domains ensures the attention performance of the medical image multi-domain translation model and the quality of the synthesized images. At the same time, a lung image classification model based on synthetic image data augmentation is proposed. The synthetic lung CT medical images and the original real medical images are used as the training set together to study the performance of the auxiliary diagnosis model in the classification of normal healthy subjects, and also of the mild and severe COVID-19 patients. RESULTS: Based on the chest CT image dataset, MI-GAN has completed the mutual conversion and generation of normal lung images without disease, viral pneumonia and Mild COVID-19 images. The synthetic images GAN-test and GAN-train indicators reached, respectively 92.188% and 85.069%, compared with other generative models in terms of authenticity and diversity, there is a considerable improvement. The accuracy rate of pneumonia diagnosis of the lung image classification model is 93.85%, which is 3.1% higher than that of the diagnosis model trained only with real images; the sensitivity of disease diagnosis is 96.69%, a relative improvement of 7.1%. 1%, the specificity was 89.70%; the area under the ROC curve (AUC) increased from 94.00% to 96.17%. CONCLUSION: In this paper, a multi-domain translation model of medical images based on the key transfer branch is proposed, which enables the translation network to have key transfer and attention performance. It is verified on lung CT images and achieved good results. The required medical images are synthesized by the above medical image translation model, and the effectiveness of the synthesized images on the lung image classification network is verified experimentally.

Classification of lungs infected COVID-19 images based on inception-ResNet.

OBJECTIVE: Nowadays, COVID-19 is spreading rapidly worldwide, and seriously threatening lives . From the perspective of security and economy, the effective control of COVID-19 has a profound impact on the entire society. An effective strategy is to diagnose earlier to prevent the spread of the disease and prompt treatment of severe cases to improve the chance of survival. METHODS: The method of this paper is as follows: Firstly, the collected data set is processed by chest film image processing, and the bone removal process is carried out in the rib subtraction module. Then, the set preprocessing method performed histogram equalization, sharpening, and other preprocessing operations on the chest film. Finally, shallow and high-level feature mapping through the backbone network extracts the processed chest radiographs. We implement the self-attention mechanism in Inception-Resnet, perform the standard classification, and identify chest radiograph diseases through the classifier to realize the auxiliary COVID-19 diagnosis process at the medical level, all in an effort to further enhance the classification performance of the convolutional neural network. Numerous computer simulations demonstrate that the Inception-Resnet convolutional neural network performs CT image categorization and enhancement with greater efficiency and flexibility than conventional segmentation techniques. RESULTS: The experimental COVID-19 CT dataset obtained in this paper is the new data for CT scans and medical imaging of normal, early COVID-19 patients and severe COVID-19 patients from Jinyintan hospital. The experiment plots the relationship between model accuracy, model loss and epoch, using ACC, TPR, SPE, F1 score and G-mean to measure the image maps of patients with and without the disease. Statistical measurement values are obtained by Inception-Resnet are 88.23%, 83.45%, 89.72%, 95.53% and 88.74%. The experimental results show that Inception-Resnet plays a more effective role than other image classification methods in evaluation indicators, and the method has higher robustness, accuracy and intuitiveness. CONCLUSION: With CT images in the clinical diagnosis of COVID-19 images being widely used and the number of applied samples continuously increasing, the method in this paper is expected to become an additional diagnostic tool that can effectively improve the diagnostic accuracy of clinical COVID-19 images.

Can NT-proBNP Levels Be an Early Biomarker of Reduced Left Ventricular Ejection Fraction in Preterm Infants?

Background/Objective: N-terminal pro-B-type natriuretic peptide (NT-proBNP) is a cardiac natriuretic hormone that cardiomyocytes release in response to ventricular stretch. It helps with the diagnosis of heart failure in adults, but this application in preterm infants has rarely been reported. This study aimed to evaluate whether NT-proBNP could be used for the early detection of reduced cardiac ejection fraction in preterm infants and the optimal timing for NT-proBNP assessment. Design/Methods: This prospective, single-center, observational study enrolled all preterm infants with NT-proBNP measurements from October 2014 to February 2022. They underwent echocardiographic examinations within 48 h of the NT-proBNP measurements. Reduced left ventricular ejection fraction was defined as below 60%. Receiver operator characteristic (ROC) curves were generated to assess the optimal NT-proBNP cutoff point for the early prediction of reduced cardiac ejection fraction. Results: A total of 68 preterm infants were enrolled, with a total of 134 NT-proBNP measurements being available for analysis. Reduced left ventricular ejection fraction was present in seven infants (10.3%) due to various underlying diseases. The NT-proBNP cutoff level for detecting reduced left ventricular ejection fraction was 9248 pg/mL, with 71.4% sensitivity and 60.8% specificity; the area under the curve was 0.623 (95% CI: 0.487~0.760). The threshold for the optimal postnatal age for applying NT-proBNP to detect reduced left ventricular ejection fraction was >2 days of life (AUC: 0.682; 95% CI: 0.518~0.845), with 70% sensitivity and 67.1% specificity. Conclusions: Although the NT-proBNP levels declined dramatically after birth, a NT-proBNP serum level of 9248 pg/mL might be helpful for the early detection of reduced ejection fraction in preterm infants, and the optimal age for detection was after 2 days of life.

Application of individualized differential expression analysis in human cancer proteome.

Liquid chromatography-mass spectrometry-based quantitative proteomics can measure the expression of thousands of proteins from biological samples and has been increasingly applied in cancer research. Identifying differentially expressed proteins (DEPs) between tumors and normal controls is commonly used to investigate carcinogenesis mechanisms. While differential expression analysis (DEA) at an individual level is desired to identify patient-specific molecular defects for better patient stratification, most statistical DEP analysis methods only identify deregulated proteins at the population level. To date, robust individualized DEA algorithms have been proposed for ribonucleic acid data, but their performance on proteomics data is underexplored. Herein, we performed a systematic evaluation on five individualized DEA algorithms for proteins on cancer proteomic datasets from seven cancer types. Results show that the within-sample relative expression orderings (REOs) of protein pairs in normal tissues were highly stable, providing the basis for individualized DEA for proteins using REOs. Moreover, individualized DEA algorithms achieve higher precision in detecting sample-specific deregulated proteins than population-level methods. To facilitate the utilization of individualized DEA algorithms in proteomics for prognostic biomarker discovery and personalized medicine, we provide Individualized DEP Analysis IDEPAXMBD (XMBD: Xiamen Big Data, a biomedical open software initiative in the National Institute for Data Science in Health and Medicine, Xiamen University, China.) (https://github.com/xmuyulab/IDEPA-XMBD), which is a user-friendly and open-source Python toolkit that integrates individualized DEA algorithms for DEP-associated deregulation pattern recognition.

Development of SARS-CoV-2 variant protein microarray for profiling humoral immunity in vaccinated subjects.

SARS-CoV-2 is quickly evolving from wild-type to many variants and spreading around the globe. Since many people have been vaccinated with various types of vaccines, it is crucial to develop a high throughput platform for measuring the antibody responses and surrogate neutralizing activities against multiple SARS-CoV-2 variants. To meet this need, the present study developed a SARS-CoV-2 variant (CoVariant) array which consists of the extracellular domain of spike variants, e.g., wild-type, D614G, B.1.1.7, B.1.351, P.1, B.1.617, B.1.617.1, B.1.617.2, and B.1.617.3. A surrogate virus neutralization on the CoVariant array was established to quantify the bindings of antibody and host receptor ACE2 simultaneously to spike variants. By using a chimeric anti-spike antibody, we demonstrated a broad binding spectrum of antibodies while inhibiting the bindings of ACE2 to spike variants. To monitor the humoral immunities after vaccination, we collected serums from unvaccinated, partial, or fully vaccinated individuals with either mRNA-1273 or AZD1222 (ChAdOx1). The results showed partial vaccination increased the surrogate neutralization against all the mutants while full vaccination boosted the most. Although IgG, IgA, and IgM isotypes correlated with surrogate neutralizing activities, they behave differently throughout the vaccination processes. Overall, this study developed CoVariant arrays and assays for profiling the humoral responses which are useful for immune assessment, vaccine research, and drug development.

Exhausting T Cells During HIV Infection May Improve the Prognosis of Patients with COVID-19.

T-cell reduction is an important characteristic of coronavirus disease 2019 (COVID-19), and its immunopathology is a subject of debate. It may be due to the direct effect of the virus on T-cell exhaustion or indirectly due to T cells redistributing to the lungs. HIV/AIDS naturally served as a T-cell exhaustion disease model for recognizing how the immune system works in the course of COVID-19. In this study, we collected the clinical charts, T-lymphocyte analysis, and chest CT of HIV patients with laboratory-confirmed COVID-19 infection who were admitted to Jin Yin-tan Hospital (Wuhan, China). The median age of the 21 patients was 47 years [interquartile range (IQR) = 40-50 years] and the median CD4 T-cell count was 183 cells/µl (IQR = 96-289 cells/µl). Eleven HIV patients were in the non-AIDS stage and 10 were in the AIDS stage. Nine patients received antiretroviral treatment (ART) and 12 patients did not receive any treatment. Compared to the reported mortality rate (nearly 4%-10%) and severity rate (up to 20%-40%) among COVID-19 patients in hospital, a benign duration with 0% severity and mortality rates was shown by 21 HIV/AIDS patients. The severity rates of COVID-19 were comparable between non-AIDS (median CD4 = 287 cells/µl) and AIDS (median CD4 = 97 cells/µl) patients, despite some of the AIDS patients having baseline lung injury stimulated by HIV: 7 patients (33%) were mild (five in the non-AIDS group and two in the AIDS group) and 14 patients (67%) were moderate (six in the non-AIDS group and eight in the AIDS group). More importantly, we found that a reduction in T-cell number positively correlates with the serum levels of interleukin 6 (IL-6) and C-reactive protein (CRP), which is contrary to the reported findings on the immune response of COVID-19 patients (lower CD4 T-cell counts with higher levels of IL-6 and CRP). In HIV/AIDS, a compromised immune system with lower CD4 T-cell counts might waive the clinical symptoms and inflammatory responses, which suggests lymphocyte redistribution as an immunopathology leading to lymphopenia in COVID-19.

Emergence of macrolide-resistant Streptococcus pyogenes emm12 in southern Taiwan from 2000 to 2019.

BACKGROUND: Group A Streptococcus (GAS) is an important pathogen causing morbidity and mortality worldwide. Surveillance of resistance and emm type has important implication to provide helpful information on the changing GAS epidemiology and empirical treatment. METHODS: To study the emergence of resistant GAS in children with upper respiratory tract infection (URTI), a retrospective study was conducted from 2000 to 2019 in southern Taiwan. Microbiological studies, including antibiotic susceptibility, were performed. GAS emm types and sequences were determined by molecular methods. The population was divided into two separate decades to analyze potential changes over time. The 1st decade was 2000-2009; the 2nd decade was 2010-2019. Multivariate analyses were performed to identify independent risk factors associated with macrolide resistance between these periods. RESULTS: A total of 320 GAS from 339 children were enrolled. Most of the children (75%) were under 9 years of age. The most common diagnosis was scarlet fever (225, 66.4%), and the frequency increased from 54.8% in the 1st to 77.9% in the 2nd decade (p < 0.0001). There was a significant increase in resistance to erythromycin and azithromycin from 18.1%, 19.3% in the 1st to 58.4%, 61.0% in the 2nd decade (p < 0.0001). This was associated with clonal expansion of the GAS emm12-ST36 which carrying erm(B) and tet(M) from 3.0% in the 1st to 53.2% in the 2nd decade (p < 0.0001). CONCLUSIONS: Significant emergence of macrolide-resistant GAS emm12-ST36 in children supports the need for continuing surveillance and investigation for the clonal virulence.

Clinical characteristics of chronic liver disease with coronavirus disease 2019 (COVID-19): a cohort study in Wuhan, China.

BACKGROUND: Previous work has described acute liver injury (ALI) in coronavirus disease 2019 (COVID-19) pneumonia patients, However, there is limited analyses available investigating chronic liver disease (CLD) in COVID-19 patients. This study aimed to investigate clinical characteristics and outcomes of CLD confirmed in COVID-19 patients. RESULTS: A total of 104 cases (each group containing 52 patients) were analyzed in this study. The CLD group showed an average of 14 (10.0~21.2) length of stay (LOS) days, compared to the group without CLD that only showed an average of 12.5 (10~16) LOS days (Relative Risk [RR] = 1.34, 95% CI (1.22~1.48), P<0.001; Adjusted Relative Risk was 1.24 (95% CI: 1.12~1.39)). The CLD group contained a higher mortality rate and slight liver injury. Furthermore, COX regression model analyses suggested that the neutrophil-to-lymphocyte ratio (NLR) was an independent predictor of mortality risk (P < 0.001) in the CLD group. Additionally, a high NLR significantly correlated with a shorter overall survival (P <0.001). CONCLUSIONS: COVID-19 patients also diagnosed with CLD suffered longer LOS, slight liver injuries and a higher mortality when compared to COVID-19 patients without CLD. The NLR was an independent risk factor for in-hospital deaths. Increased expression of NLR was an indicator of poor prognosis in COVID-19 patients with CLD. Thus, COVID-19 patients diagnosed with CLD and who show a higher NLR need additional care. METHODS: A retrospective cohort study was performed at the Wuhan Jin Yin-tan Hospital from February 2, 2020 to April 2, 2020. COVID-19 patients diagnosed with CLD or not diagnosed with CLD were enrolled in this study. The clinical characteristics and outcomes of these patients were compared.

Facile Synthesis of Optically Active and Magnetic Nanoparticles Carrying Helical Poly(phenyl isocyanide) Arms and Their Application in Enantioselective Crystallization.

In this paper, the facile synthesis of hybrid Fe3 O4 magnetic nanoparticles carrying helical poly(phenyl isocyanide) (PPI) arms via both "grafting from" and "grafting onto" strategies is reported. First, alkyne-Pd(II) catalysts are anchored onto the surface of the Fe3 O4 magnetic nanoparticle, which promote the polymerization of enantiopure phenyl isocyanide, affording the expected hybrid magnetic nanoparticle with Fe3 O4 in core and helical PPI as arms. The nanoparticle also exhibits highly optical activity due to the excess of one-handed helicity of the PPI arms. Moreover, the hybrid magnetic nanoparticle can be alternatively synthesized via "grafting onto" strategy. A triethoxysilanyl-terminated single handed helical PPI bearing l-alanine ester pendants is prepared and grafted onto the surface of Fe3 O4 nanoparticle. The generated hybrid magnetic nanoparticles show both magnetic character and optical activity. Taking advantage of these properties, they can be used in enantioselective crystallization of racemic threonine. The enantiomeric excess (ee) of the induced crystals is up to 93%. Moreover, the nanoparticles can be facilely recovered and recycle used for at least four times in enantioselective crystallization without significantly loss of its enantioselectivity.

Mycoplasma pneumoniae-associated encephalitis complicated by cerebral salt wasting syndrome.

Cerebral salt wasting syndrome can occur in children with encephalitis. Clinicians should be aware of hyponatremia in patients who develop polyuria with the signs of dehydration and deteriorated consciousness. Furthermore, patients who present with status epilepticus or who are suspected to have high intracranial pressure may have an increased risk of cerebral salt wasting syndrome.

TRIg: a robust alignment pipeline for non-regular T-cell receptor and immunoglobulin sequences.

BACKGROUND: T cells and B cells are essential in the adaptive immunity via expressing T cell receptors and immunoglogulins respectively for recognizing antigens. To recognize a wide variety of antigens, a highly diverse repertoire of receptors is generated via complex recombination of the receptor genes. Reasonably, frequencies of the recombination events have been shown to predict immune diseases and provide insights into the development of immunity. The field is further boosted by high-throughput sequencing and several computational tools have been released to analyze the recombined sequences. However, all current tools assume regular recombination of the receptor genes, which is not always valid in data prepared using a RACE approach. Compared to the traditional multiplex PCR approach, RACE is free of primer bias, therefore can provide accurate estimation of recombination frequencies. To handle the non-regular recombination events, a new computational program is needed. RESULTS: We propose TRIg to handle non-regular T cell receptor and immunoglobulin sequences. Unlike all current programs, TRIg does alignments to the whole receptor gene instead of only to the coding regions. This brings new computational challenges, e.g., ambiguous alignments due to multiple hits to repetitive regions. To reduce ambiguity, TRIg applies a heuristic strategy and incorporates gene annotation to identify authentic alignments. On our own and public RACE datasets, TRIg correctly identified non-regularly recombined sequences, which could not be achieved by current programs. TRIg also works well for regularly recombined sequences. CONCLUSIONS: TRIg takes into account non-regular recombination of T cell receptor and immunoglobulin genes, therefore is suitable for analyzing RACE data. Such analysis will provide accurate estimation of recombination events, which will benefit various immune studies directly. In addition, TRIg is suitable for studying aberrant recombination in immune diseases. TRIg is freely available at https://github.com/TLlab/trig .

Obtaining long 16S rDNA sequences using multiple primers and its application on dioxin-containing samples.

BACKGROUND: Next-generation sequencing (NGS) technology has transformed metagenomics because the high-throughput data allow an in-depth exploration of a complex microbial community. However, accurate species identification with NGS data is challenging because NGS sequences are relatively short. Assembling 16S rDNA segments into longer sequences has been proposed for improving species identification. Current approaches, however, either suffer from amplification bias due to one single primer or insufficient 16S rDNA reads in whole genome sequencing data. RESULTS: Multiple primers were used to amplify different 16S rDNA segments for 454 sequencing, followed by 454 read classification and assembly. This permitted targeted sequencing while reducing primer bias. For test samples containing four known bacteria, accurate and near full-length 16S rDNAs of three known bacteria were obtained. For real soil and sediment samples containing dioxins in various concentrations, 16S rDNA sequences were lengthened by 50% for about half of the non-rare microbes, and 16S rDNAs of several microbes reached more than 1000 bp. In addition, reduced primer bias using multiple primers was illustrated. CONCLUSIONS: A new experimental and computational pipeline for obtaining long 16S rDNA sequences was proposed. The capability of the pipeline was validated on test samples and illustrated on real samples. For dioxin-containing samples, the pipeline revealed several microbes suitable for future studies of dioxin chemistry.

Verification of wild-type EGFR status in non-small cell lung carcinomas using a mutant-enriched PCR on selected cases.

EGFR genotyping is required for targeted therapy of lung adenocarcinoma. Because a false-negative result might prevent a patient from receiving appropriate targeted therapies, it is desirable to recheck equivocal results of EGFR genotyping. A cohort of 346 lung cancers was tested with a commercial kit for EGFR mutations; nine of the cases had upward real-time amplification curves at late cycles. They were also investigated using mutant-enriched PCR with peptide nucleic acid-locked nucleic acid (PNA-sequencing). Six of the nine equivocal cases harbored EGFR mutations. These cases likely had a small amount of mutant DNA near the detection limit of the commercial kit. Twenty nonequivocal, wild-type cases were reconfirmed using PNA-sequencing. We noticed a College of American Pathologists proficiency test material that showed a suspicious upward curve and eventually proved to have an H773_V774insPH in exon 20, for which a specific primer was not designed in the commercial kit. Further study using cloned DNA fragments showed that the upward curve most likely resulted from cross-reaction between similar, but nonidentical, sequences. It is desirable to keep the number of false-negative results as low as possible, but rechecking all wild-type cases is impractical. The late upward curves we observed helped identify suspicious cases for rechecking. A second method, such as PNA-sequencing, is recommended to verify wild-type cases.

Changing epidemiology of Streptococcus pyogenes emm types and associated invasive and noninvasive infections in Southern Taiwan.

A total of 242 isolates were recovered from 76 patients with invasive diseases, 89 with scarlet fever, and 77 with pharyngitis. The most frequent emm types were types 12 (43.4%), 4 (18.2%), and 1 (16.9%). emm12 reemerged in 2005 and peaked in 2007. emm11 was recovered only from patients with invasive disease.

The severity of Streptococcus pyogenes infections in children is significantly associated with plasma levels of inflammatory cytokines.

Cytokines are intimately involved with the innate and adaptive immune response to bacterial infections. This study was designed to determine the expression of inflammatory cytokines in children by the severity of Streptococcus pyogenes (group A Streptococcus [GAS]) infections. The study population consisted of 16 invasive, 20 noninvasive, and 24 pharyngeal colonization, and 21 healthy controls. All children underwent the laboratory tests and cytokine measurement. GAS isolates were analyzed for emm gene typing. Patients with invasive GAS diseases had significantly higher interferon (IFN)-gamma, interleukin (IL)-1beta, IL-6, IL-8, IL-10, and IL-18 than those with noninvasive diseases, colonization, and healthy controls. There was no difference in tumor necrosis factor (TNF)-alpha, IL-12, and IL-2 levels among the groups. Elevated white blood cell counts and levels of C-reactive protein and C3 were detected only in patients with invasive diseases. emm1 and emm12 predominated in invasive disease and colonization. Children with invasive GAS infections exhibited significant up-regulation of plasma levels of IFN-gamma, IL-1beta, IL-6, IL-8, IL-10, and IL-18, and suppression of TNF-alpha and IL-12 during the acute phase of their illness. An exuberant cytokine response was associated with the severity of illness.