Objective The prevalence of drug-resistant Mycobacterium tuberculosis (Mtb) strains is exacerbating the global burden of tuberculosis (TB), highlighting the urgent need for new treatment strategies for TB. Methods The recombinant adenovirus vaccine expressing cyclic di-adenosine monophosphate (c-di-AMP) phosphodiesterase B (CnpB) (rAd-CnpB), was administered to normal mice via mucosal immunization, either alone or in combination with drug therapy, to treat Mtb respiratory infections in mice.Enzyme-linked immunosorbent assay (ELISA) was used to detect the levels of antibodies in serum and bronchoalveolar lavage fluid (BALF). Real-time quantitative PCR was performed to assess the transcription levels of cytokines interferon γ(IFN-γ) and interleukin 10(IL-10) in mouse lungs. Flow cytometry was used to determine the proportions of CD4⁺ and CD8⁺ T cell subsets in the lungs and spleens. ELISA was employed to measure the levels of cytokines IFN-γ, IL-2, IL-10, inflammatory factors IL-6, and tumor necrosis factor α (TNF-α) secreted by spleen cells following antigen stimulation. The bacteria loads in the lungs and spleens of Mtb-infected mice were enumerated by plate counting methods. Resluts Intranasal immunization with rAd-CnpB induced high titers of IgG in mouse serum and the production of IgG and IgA in BALF, along with alterations in T lymphocyte subsets in the lungs and spleens. Administration of rAd-CnpB, either alone or in combination with drugs, to Mtb-infected mice significantly increased serum IgG levels as well as IgA and IgG levels in BALF. rAd-CnpB immunization promoted the secretion of CnpB-specific cytokines and inflammatory factors by splenocytes in Mtb-infected mice. However, rAd-CnpB immunotherapy, either alone or combined with drugs, did not significantly affect the bacterial loads in the lungs and spleens of mice with Mtb respiratory infections. Conclusion Mucosal immunization with rAd-CnpB induced significant mucosal, humoral and cellular immune responses in mice, and significantly enhanced CnpB-specific cellular immune responses in Mtb-infected mice.
Animals ; Adenoviridae/immunology* ; Mycobacterium tuberculosis/genetics* ; Mice ; Female ; Phosphoric Diester Hydrolases/genetics* ; Tuberculosis Vaccines/administration & dosage* ; Tuberculosis/prevention & control* ; Mice, Inbred BALB C ; Cytokines ; Lung/microbiology* ; Immunization ; Bronchoalveolar Lavage Fluid/immunology* ; Immunity, Mucosal

Radiotherapy is one of the primary treatment modalities for tumors. In recent years, advancements in radiotherapy technology have enabled radiation to reach target areas with greater precision. However, radiation damage to normal tissue remains unavoidable. Traditional normal tissue complication probability (NTCP) models are widely used to predict such injuries, but they often fail to account for individual and tissue heterogeneity. As an emerging technology, radiomics can provide more comprehensive biological information and shows promise in predicting radiation-induced damage. This article reviews the application value of radiomics in predicting damage to organs at risk in the head and neck, thorax, abdomen, and pelvis.

Objective To identify novel biomarkers for predicting the risk of latent tuberculosis infection(LTBI)activation using bio-informatics and machine-learning algorithms and to establish a risk model.Methods The GSE112104 and GSE193777 datasets were obtained from the Gene Expression Omnibus.Differential gene expression and weighted gene co-expression network analyses were per-formed to identify ferroptosis-related differentially expressed genes(FRG-DEGs)associated with LTBI activation.Three machine-learning algorithms,least absolute shrinkage and selection operator,support vector machine-recursive feature elimination,and random forest,were used to identify ferroptosis-related hub genes(FRG-hubs).The reliability of these genes was validated using independent validation datasets and reverse transcription polymerase chain reaction(PCR).A risk model was established using R software.Results In the GSE 112104 dataset,296 genes were upregulated and 1 569 genes were downregulated in active tuberculosis compared to those in LTBI.Among the LTBI progressors,506 genes were upregulated and 1 132 genes were downregulated.Weighted correlation network analysis identified five gene modules,with the blue module showing the strongest correlation with LTBI activation(cor=0.62,P=0.000 04),con-taining 1 340 genes.Intersections with 728 ferroptosis-related genes resulted in eight FRG-DEGs.The machine-learning algorithms iden-tified four FRG-hubs:PLA2G6,GLS2,JUN,and AMN,whose expression decreased with LTBI activation.Reverse transcription PCR con-firmed this trend.A risk model based on these genes yielded an area under the curve of 0.98 to 1.00.Conclusion This study successfully identified novel biomarkers for predicting the risk of LTBI activation and developed an accurate predictive risk model.

Radiotherapy is one of the primary treatment modalities for tumors. In recent years, advancements in radiotherapy technology have enabled radiation to reach target areas with greater precision. However, radiation damage to normal tissue remains unavoidable. Traditional normal tissue complication probability (NTCP) models are widely used to predict such injuries, but they often fail to account for individual and tissue heterogeneity. As an emerging technology, radiomics can provide more comprehensive biological information and shows promise in predicting radiation-induced damage. This article reviews the application value of radiomics in predicting damage to organs at risk in the head and neck, thorax, abdomen, and pelvis.

Objective To identify novel biomarkers for predicting the risk of latent tuberculosis infection(LTBI)activation using bio-informatics and machine-learning algorithms and to establish a risk model.Methods The GSE112104 and GSE193777 datasets were obtained from the Gene Expression Omnibus.Differential gene expression and weighted gene co-expression network analyses were per-formed to identify ferroptosis-related differentially expressed genes(FRG-DEGs)associated with LTBI activation.Three machine-learning algorithms,least absolute shrinkage and selection operator,support vector machine-recursive feature elimination,and random forest,were used to identify ferroptosis-related hub genes(FRG-hubs).The reliability of these genes was validated using independent validation datasets and reverse transcription polymerase chain reaction(PCR).A risk model was established using R software.Results In the GSE 112104 dataset,296 genes were upregulated and 1 569 genes were downregulated in active tuberculosis compared to those in LTBI.Among the LTBI progressors,506 genes were upregulated and 1 132 genes were downregulated.Weighted correlation network analysis identified five gene modules,with the blue module showing the strongest correlation with LTBI activation(cor=0.62,P=0.000 04),con-taining 1 340 genes.Intersections with 728 ferroptosis-related genes resulted in eight FRG-DEGs.The machine-learning algorithms iden-tified four FRG-hubs:PLA2G6,GLS2,JUN,and AMN,whose expression decreased with LTBI activation.Reverse transcription PCR con-firmed this trend.A risk model based on these genes yielded an area under the curve of 0.98 to 1.00.Conclusion This study successfully identified novel biomarkers for predicting the risk of LTBI activation and developed an accurate predictive risk model.

The standard system refers to the scientific organic whole formed by the internal connections of stand-ards within a certain range.The completeness of the drug standard system plays a crucial role in ensuring drug safety.Pharmacopoeia is the core of the drug standard system.This article compared the architecture of the Chi-nese Pharmacopoeia,the United States Pharmacopoeia,the European Pharmacopoeia,and the Japanese Pharma-copoeia on the aspects of overall architecture,monographs architecture,general notice architecture,general tech-nical requirements architecture,and other standard architecture,as well as the implementation of various types of standards,aiming to provide reference for the optimization and improvement of the standard system of the Chinese Pharmacopoeia.

Objective To provide reference for the optimization and improvement of interoperability between the standard system of the Chinese Pharmacopoeia and other standards.Methods The interoperability of various pharmacopoeia standard systems was compared by searching for citations from the Chinese Pharmacopoeia,the United States Pharmacopoeia-National Formulary,the European Pharmacopoeia,the Japanese Pharmacopoeia,and other standards,including references to domestic regulations and guidelines,standards of the International Organization for Standardization,guidelines from the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use,documents of the World Health Organization,and standards from other countries and international organizations.Results In recent years,pharmacopoeias in the world had continuously increased the citation of non pharmacopoeial standards.The types,quantities,and fields of the United States Pharmacopoeia-National Formulary referencing other standards far exceed those of other pharmacopoeias.The Chinese Pharmacopoeia cites the least number of other standards.Conclusion It is suggested that the Chinese Pharmacopoeia should enhance the interoperability with other standard systems in the standards of various professional fields,enhance the openness,harmonization and advantages,and form a more complete standard system.