Background. With the COVID‐19 epidemic breakout in China, up to 25% of diagnosed cases are considered to be severe. To effectively predict the progression of COVID‐19 via patients’ clinical features at an early stage, the prevalence of these clinical factors and their relationships with severe illness were assessed. Methods. In this study, electronic databases (PubMed, Embase, Web of Science, and Chinese database) were searched to obtain relevant studies, including information on severe patients. Publication bias analysis, sensitivity analysis, prevalence, sensitivity, specificity, likelihood ratio, diagnosis odds ratio calculation, and visualization graphics were achieved through software Review Manager 5.3, Stata 15, Meta‐DiSc 1.4, and R. Results. Data of 3.547 patients from 24 studies were included in this study. The results revealed that patients with chronic respiratory system diseases (pooled positive likelihood 6.07, 95% CI: 3.12‐11.82), chronic renal disease (4.79, 2.04‐11.25), cardiovascular disease (3.45, 2.19‐5.44), and symptoms of the onset of chest tightness (3.8, 1.44‐10.05), shortness of breath (3.18, 2.24‐4.51), and diarrhea (2.04, 1.38‐3.04) exhibited increased probability of progressing to severe illness. C‐reactive protein, ratio of neutrophils to lymphocytes, and erythrocyte sedimentation rate increased a lot in severe patients compared to nonsevere. Yet, it was found that clinical features including fever, cough, and headache, as well as some comorbidities, have little warning value. Conclusions. The clinical features and laboratory examination could be used to estimate the process of infection in COVID‐19 patients. The findings contribute to the more efficient prediction of serious illness for patients with COVID‐19 to reduce mortality.

To date, most genetic association analyses of smoking behaviors have been conducted in populations of European ancestry and many of these studies focused on the phenotype that measures smoking quantity, that is, cigarettes per day. Additional association studies in diverse populations with different linkage disequilibrium patterns and an alternate phenotype, such as total tobacco exposure which accounts for intermittent periods of smoking cessation within a larger smoking period as measured in large cardiovascular risk studies, can aid the search for variants relevant to smoking behavior. For these reasons, we undertook an association analysis by using a genotyping array that includes 2,100 genes to analyze smoking persistence in unrelated African American participants from the Atherosclerosis Risk in Communities study. A locus located approximately 4 kb downstream from the 3′-UTR of the brain-derived neurotrophic factor (BDNF) significantly influenced smoking persistence. In addition, independent variants rs12915366 and rs12914385 in the cluster of genes encoding nicotinic acetylcholine receptor subunits (CHRNA5–CHRNA3–CHRNB4) on 15q25.1 were also associated with the phenotype in this sample of African American subjects. To our knowledge, this is the first study to more extensively evaluate the genome in the African American population, as a limited number of previous studies of smoking behavior in this population included evaluations of only single genomic regions. Cancer Prev Res; 4(5); 729–34. ©2011 AACR.

Aims: We aimed to develop and validate a novel prediction model for osteoporosis based on serotonin, fat-soluble vitamins, and bone turnover markers to improve prediction accuracy of osteoporosis.Methods: Postmenopausal women aged 55 to 65 years were recruited and divided into three groups based on DXA (normal, osteopenia, and osteoporosis). A total of 109 participants were included in this study and split into healthy (39/109, 35.8%), osteopenia (35/109, 32.1%), and osteoporosis groups (35/109, 32.1%). Serum concentrations of serotonin, fat-soluble vitamins, and bone turnover markers of participants were measured. Stepwise discriminant analysis was performed to identify efficient predictors for osteoporosis. The prediction model was developed based on Bayes and Fisher’s discriminant functions, and validated via leave-one-out cross-validation. Normal and empirical volume under the receiver operating characteristic (ROC) surface (VUS) tests were used to evaluate predictive effects of variables in the prediction model.Results: Significant variables including oestrogen (E2), total procollagen type 1 amino-terminal propeptide (TP1NP), parathyroid hormone (PTH), BMI, vitamin K, serotonin, osteocalcin (OSTEOC), vitamin A, and vitamin D3 were used for the development of the prediction model. The training accuracy for normal, osteopenia, and osteoporosis is 74.4% (29/39), 80.0% (28/35), and 85.7% (30/35), respectively, while the total training accuracy is 79.8% (87/109). The internal validation showed excellent performance with 72.5% testing accuracy (72/109). Among these variables, serotonin and vitamin K exert important roles in the prediction of osteoporosis.Conclusion: We successfully developed and validated a novel prediction model for osteoporosis based on serum concentrations of serotonin, fat-soluble vitamins, and bone turnover markers. In addition, interactive communication between serotonin and fat-soluble vitamins was observed to be critical for bone health in this study.Cite this article: Bone Joint Res 2025;14(2):111–123.

Bovine spongiform encephalopathy (BSE) is a transmissible, fatal neurodegenerative disorder of cattle produced by prions. The use of excessive parallel sequencing for comparison of gene expression in bovine control and infected tissues may help to elucidate the molecular mechanisms associated with this disease. In this study, tag profiling Solexa sequencing was used for transcriptome analysis of bovine brain tissues. Replicate libraries were prepared from mRNA isolated from control and infected (challenged with 100 g of BSE-infected brain) medulla tissues 45 mo after infection. For each library, 5–6 million sequence reads were generated and approximately 67–70% of the reads were mapped against the Bovine Genome database to approximately 13,700–14,120 transcripts (each having at least one read). About 42–47% of the total reads mapped uniquely. Using the GeneSifter software package, 190 differentially expressed (DE) genes were identified (>2.0-fold change, p < .01): 73 upregulated and 117 downregulated. Seventy-nine DE genes had functions described in the Gene Ontology (GO) database and 16 DE genes were involved in 38 different pathways described in the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Digital analysis expression by tag profiling may be a powerful approach to comprehensive transcriptome analysis to identify changes associated with disease progression, leading to a better understanding of the underlying mechanism of pathogenesis of BSE.