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PubMed1989

Selected pragmatic features in Spanish-speaking preschool children.

We assessed Spanish-speaking preschool children for the development of seven language functions and three discourse features. Analyses consisted of spontaneous language samples averaging 136 utterances per child, for 18 subjects between 3:0 and 4:5 (years:months). Data for the frequency of occurrence and the percentage of appropriate usage showed that the preschoolers had established communicative competence for the functions and discourse features. Implications include establishing preliminary guidelines for the development of normal pragmatics in Hispanic preschoolers. We also discuss the 10-item taxonomy as a reliable and clinically useful tool with either English-speaking or Spanish-speaking children.

Central America↗
PubMed1990

Respiratory infections caused by Branhamella catarrhalis. Selected epidemiologic features.

PURPOSE: This work reviewed existing literature pertaining to the epidemiologic aspects of respiratory tract infections caused by Branhamella catarrhalis, examined certain epidemiologic features of B. catarrhalis infections occurring at this facility, and identified relevant areas in need of further study. PATIENTS AND METHODS: Literature dealing with the epidemiology of B. catarrhalis infections was reviewed. Records in this Veterans Administration hospital microbiology laboratory were reviewed and all B. catarrhalis isolates and pure cultures of Hemophilus influenzae and Streptococcus pneumoniae were noted for the January 1986 to June 1989 study period. RESULTS: B. catarrhalis is now recognized as a disease-causing pathogen that is particularly noted for its association with acute otitis media in children and lower respiratory tract infections in adults with underlying cardiopulmonary disease. It was recovered from 2.7 percent of all respiratory specimens submitted over a 42-month period at this Veterans hospital. When compared with H. influenzae and S. pneumoniae, B. catarrhalis was found to be the second most commonly isolated respiratory pathogen. It was frequently found in pure culture (53 percent) or in combination with H. influenzae, gram-negative bacilli, or S. pneumoniae. The seasonal recovery of B. catarrhalis was apparent for the November to May period compared with the June to October period (p less than 0.001). CONCLUSION: B. catarrhalis has emerged as a major respiratory pathogen in pediatric and adult patient populations. There is a distinct seasonal pattern associated with its recovery and reasons for this are unclear. Prevalence studies aimed at identifying colonization rates among "low" and "high" risk groups are needed. The availability of restriction endonuclease analysis as a typing system for B. catarrhalis should favorably impact upon future epidemiologic studies. Many B. catarrhalis isolates produce beta-lactamase, and therapeutic options must reflect this.

Adult↗
PubMed1975

Clinical and immunologic features of selective IgA deficiency.

Selective absence of serum and secretory IgA is probably the most common form of human immunodeficiency. High frequencies of recurrent sinusitis, otitis media, pneumonia, and atopy were noted among a group of 75 such patients, all but 4 of whom were Caucasian. Seven instances of familial absence of IgA were detected among 106 relatives of 34 of the group; in 1 family 1 member from each of 3 successive generations was affected. Two IgA-deficient children were later found to have normal amounts of serum IgA. Despite their humoral deficit, B lymphocytes bearing surface IgA were detected in 9/9 IgA-deficient patients in immunofluorescence studies of their peripheral blood lymphocytes. Although in vitro lymphocyte responses to 2 putative T-cell mitogens and to allogenic cells were normal, results of spontaneous rosette formation studies with sheep erythrocytes raise the possibility of a lymphocyte subpopulation deficit in this condition.

Absorption↗
PubMed1988

Cholera, rotavirus and ETEC diarrhoea: some clinico-epidemiological features.

This paper analyses a few selected features from the history and clinical examination of 1258 patients with acute diarrhoea and a single laboratory diagnosis of either cholera, rotavirus, or enterotoxigenic (ETEC) Escherichia coli infection. Age distribution and seasonality in Bangladesh were also studied. The duration of illness before admission was not significantly different in the 3 groups. Cholera occurred especially in the spring and early winter. Most cholera patients were between 3 and 10 years of age. Over 37% of the patients developed severe dehydration. In about 90% of cholera cases, the stools were alkaline (pH greater than 7). ETEC infections were seen mostly in April-May and September-October. Infants were frequently affected but from age 25 onwards the age distribution closely followed that of cholera. Severe dehydration occurred in 8.3% of patients and was more frequent than in rotavirus cases. Stool pH was as frequently acidic as basic. Rotavirus cases were concentrated during the winter in patients under 2 years of age. They had marked vomiting, yet severe dehydration was almost absent. Cough was present in half of them. The stools were usually acidic. In spite of considerable overlap of signs and symptoms between the 3 aetiological groups, a presumptive diagnosis of cholera could be made in patients past infancy and early childhood who showed very severe dehydration. However, age-specific prevalence was strikingly different and seasonal variations considerable.

Adolescent↗
PubMed2026

Transcriptome Analysis and Experimental Validation of Palmitoylation- Related Biomarkers in Atherosclerosis.

INTRODUCTION: Protein palmitoylation contributes to membrane localisation, signal transduction, and cell-fate regulation. It is closely associated with lipid metabolic dysfunction, immune inflammation, and vascular remodelling in atherosclerosis (AS). However, key palmitoylation-related transcriptomic markers and their potential causal associations with AS remain incompletely defined. METHODS: The Gene Expression Omnibus (GEO) dataset GSE100927 was used as the training cohort, and GSE43292 was used as an external validation cohort. Differentially expressed genes were identified using limma and intersected with palmitoylation-related genes to obtain palmitoylation-related differentially expressed genes (PRDEGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were then performed using clusterProfiler. Two-sample Mendelian randomisation was used to evaluate potential causal relationships between characteristic genes and AS. Feature selection was conducted using random forest and support vector machine recursive feature elimination (SVM-RFE), and the overlapping genes selected by both methods were retained. Receiver operating characteristic (ROC) curves were used to assess diagnostic performance. A five-gene nomogram was constructed, and its clinical utility was evaluated using calibration curves and decision curve analysis (DCA). Gene set variation analysis (GSVA) was applied to compare pathway activity between high- and low-expression groups for each core gene. Single-cell analysis using Seurat and expression-based cell-cell communication analysis using CellChat were conducted with GSE159677, and upstream transcription factors were predicted using NetworkAnalyst. For in vivo validation, an AS model was established in ApoE⁸/⁸ mice fed a high-fat diet, and aortic gene and protein expression were assessed by RT-qPCR and western blotting. RESULTS: In GSE100927, 51 PRDEGs were identified. GO and KEGG enrichment analyses highlighted pathways associated with regulation of monoatomic ion transport, sarcomere and myofibril organisation, and immune inflammation. Mendelian randomisation suggested a potential protective causal association between SLC7A7 and AS. By integrating MR with random forest and SVM-RFE feature selection, we prioritised five core genes: PLCB2, GMIP, NEXN, PLN, and SLC7A7. These genes showed good diagnostic performance in GSE43292. The resulting nomogram was well calibrated and demonstrated stable net benefit in decision curve and clinical impact curve analyses. Single-gene GSVA identified consistently activated pathways across multiple genes, including innate and adaptive immune recognition, calcium signalling and myocardial contraction/cardiomyopathy, extracellular matrix-receptor interaction, cell junction pathways, autophagy-lysosome pathways, and several metabolic programmes. At the single-cell level, PLCB2 and GMIP were predominantly expressed in T cells and macrophages, NEXN and PLN were enriched in vascular smooth muscle cells, and SLC7A7 was mainly expressed in macrophages. CellChat analysis indicated increased signals for immune-related ligand-receptor interactions. In ApoE⁸/⁸ mice fed a high-fat diet, PLCB2, GMIP, and SLC7A7 were upregulated, whereas NEXN and PLN were downregulated; protein-level changes were concordant with the transcriptomic trends. DISCUSSION: These findings indicate that palmitoylation-related dysregulation in AS converges on immune inflammation, calcium signalling/contractile programmes, ECM remodelling, and autophagy-linked metabolism. The five-gene panel is supported by external validation, single-cell localisation to immune and vascular compartments, and concordant results in ApoE⁸/⁸ mice. CONCLUSION: This study identified and validated five palmitoylation-related genes associated with AS. SLC7A7 showed a potential protective causal signal in MR analysis. The enriched pathway patterns linked these genes to immune inflammation, calcium signalling-contraction coupling, ECM remodelling, cell adhesion, and autophagy- associated metabolic reprogramming. The five-gene nomogram showed potential utility for diagnostic classification and decision support, nominating candidate biomarkers and pathway targets for AS molecular subtyping, diagnosis, and mechanistic investigation.

Atherosclerosis (AS)↗
PubMed2026

A machine learning-based predictive model for radiosensitivity in nasopharyngeal carcinoma utilizing serum proteomics.

BACKGROUND: Nasopharyngeal carcinoma (NPC) remains highly sensitive to radiotherapy; however, radioresistance in a subset of patients leads to local recurrence and distant metastasis. Serum proteomics provides a minimally invasive approach to capturing dynamic physiological changes, and machine learning enables efficient construction of predictive models. This study aimed to develop and validate a serum proteomics–based machine-learning model for predicting radiotherapy sensitivity in nasopharyngeal carcinoma (NPC). METHODS: Pretreatment serum samples from newly diagnosed NPC patients were analyzed using SELDI-TOF-MS. Differentially expressed proteins between radiosensitive and radioresistant groups were identified using limma. GO and KEGG analyses were performed to explore functional enrichment. Twelve machine-learning algorithms were used to construct predictive models, and the top-performing models were optimized through feature selection. A Random Forest model with seven features was identified as the optimal model. External validation was performed using an independent cohort with ELISA-quantified protein levels. Model performance was assessed using Receiver operating characteristic curve (ROC), calibration analysis, decision curve analysis (DCA), and 10-fold cross-validation. SHapley Additive exPlanations (SHAP) analysis was applied for model interpretability, and the final model was deployed via a ShinyAPP. RESULTS: A total of 96 differentially expressed proteins were identified, which involved multiple function and signaling pathways. The Random Forest model demonstrated the best predictive performance, achieving an area under the curve (AUC) of 0.963 in the training set and 0.975 in the validation set. Cross-validation yielded an average AUC of 0.965. DCA indicated high clinical utility across a broad threshold range, and calibration curves showed good model agreement. Seven proteins (PLXND1, GSR, PGD, PTPRC, OR2T29, ACTG2, CHAD) were selected as final features. SHAP analysis provided global and individual-level interpretability. A web-based tool was developed to facilitate clinical application. CONCLUSION: This study establishes a robust serum proteomics–based machine-learning model capable of accurately predicting radiotherapy sensitivity in NPC. The model offers clinical interpretability and practical implementation, supporting personalized radiotherapy decision-making.

Humans↗