Impact of Diagnosis-Related Groups (DRG) reform on cost homogeneity of treatment for patients with malignant tumours.

The cost fluctuations associated with chemotherapy, radiotherapy, and immunotherapy, as primary modalities for treating malignant tumors, are closely related to medical decision-making and impose financial burdens on patients. In response to these challenges, China has implemented the Diagnosis-Related Group (DRG) payment system to standardize costs and control expenditures. This study collected hospitalization data from patients with malignant tumors who received chemotherapy, radiotherapy, and immunotherapy at Hospital H from 2018 to 2022. The dataset was segmented into two groups: the intervention group, treated with traditional Chinese medicine (TCM) alongside standard therapies, and the control group, treated with standard therapies alone. Changes and trends in hospitalization costs under the DRG policy were analyzed using propensity-score matching (PSM), standard deviation (SD), interquartile range (IQR), and concentration index (CI). Findings showed a decreasing trend in the standard deviation of hospitalization costs across all treatment modalities. Radiotherapy exhibited the most significant decrease, with costs reducing by 2547.37 CNY in the control group and 7387.35 CNY in the intervention group. Following the DRG implementation, the concentration indexes for chemotherapy and radiotherapy increased, while those for immunotherapy did not exhibit this pattern. Costs were more concentrated in patients who did not receive TCM treatment. In summary, DRG reform positively impacted the cost homogeneity of inpatient treatments for malignant tumors, particularly in the control group not receiving TCM treatment. The effects of DRG reform varied across different treatment modalities. Although short-term fluctuations in hospitalization costs may occur, initial evidence during the study period shows the positive impact of DRG reform on cost homogeneity.

Resource allocation efficiency in 68 county-level traditional Chinese medicine hospitals in China: a data envelopment analysis.

OBJECTIVE: Analysing and evaluating how efficiently health resources are allocated to county-level Traditional Chinese Medicine (TCM) hospitals in Zhejiang Province, this study aims to provide empirical evidence for improving operational efficiency and optimising resource allocation in these hospitals. DESIGN AND SETTING: The study employed a three-stage Data Envelopment Analysis (DEA) model to assess efficiency, using data from 68 county-level TCM hospitals. Four input and five output variables related to TCM services were selected for the analysis. RESULTS: The first-stage DEA results indicated that in 2022, the technical efficiency (TE) of TCM hospitals in Zhejiang Province was 0.788, the pure technical efficiency (PTE) was 0.876 and the scale efficiency (SE) was 0.903. The classification of hospitals into four groups based on the bed size showed statistically significant differences in returns to scale (p<0.001). The Stochastic Frontier Analysis regression results were significant at the 1% level across four regressions, showing that environmental variables such as per capita GDP, population density and the number of hospitals impacted efficiency. In the third stage DEA, after adjusting the input variables, the TE, PTE and SE improved to 0.809, 0.833 and 0.917, respectively. The adjusted mean TE rankings by region were West (0.860) > East (0.844) > South (0.805) > North (0.796) > Central (0.731). CONCLUSION: There is an imbalance between the inputs and outputs of county-level TCM hospitals. Each region must consider factors such as the local economy, population and medical service levels, along with the specific development characteristics of hospitals, to reasonably determine the scale of county-level TCM hospital construction. Emphasis should be placed on improving hospital management and technical capabilities, coordinating regional development, promoting the rational allocation and efficient use of TCM resources and enhancing the efficiency of resource allocation in county-level TCM hospitals.

Spatial effects of township health centers' health resource allocation efficiency in China.

Introduction: China is a large agricultural nation with the majority of the population residing in rural areas. The allocation of health resources in rural areas significantly affects the basic rights to life and health for rural residents. Despite the progress made by the Chinese government in improving rural healthcare, there is still room for improvement. This study aims to assess the spatial spillover effects of rural health resource allocation efficiency in China, particularly focusing on township health centers (THCs), and examine the factors influencing this efficiency to provide recommendations to optimize the allocation of health resources in rural China. Methods: This study analyzed health resource allocation efficiency in Chinese rural areas from 2012 to 2021 by using the super-efficiency SBM model and the global Malmquist model. Additionally, the spatial auto-correlation of THC health resource allocation efficiency was verified through Moran test, and three spatial econometric models were constructed to further analyze the factors influencing efficiency. Results: The key findings are: firstly, the average efficiency of health resource allocation in THCs was 0.676, suggesting a generally inefficient allocation of health resources over the decade. Secondly, the average Malmquist productivity index of THCs was 0.968, indicating a downward trend in efficiency with both non-scale and non-technical efficient features. Thirdly, Moran's Index analysis revealed that efficiency has a significant spatial auto-correlation and most provinces' values are located in the spatial agglomeration quadrant. Fourthly, the SDM model identified several factors that impact THC health resource allocation efficiency to varying degrees, including the efficiency of total health resource allocation, population density, PGDP, urban unemployment rate, per capita disposable income, per capita healthcare expenditure ratio, public health budget, and passenger traffic volume. Discussion: To enhance the efficiency of THC healthcare resource allocation in China, the government should not only manage the investment of health resources to align with the actual demand for health services but also make use of the spatial spillover effect of efficiency. This involves focusing on factors such as total healthcare resource allocation efficiency, population density, etc. to effectively enhance the efficiency of health resource allocation and ensure the health of rural residents.

Ficus hirta Vahl. alleviate LPS induced apoptosis via down-regulating of miR-411 in orange-spotted grouper (Epinephelus coioides) spleen cell.

Ficus hirta Vahl. (FhV) has been shown to have antimicrobial and antiviral efficacy. To further ascertain the pharmacological properties of FhV., and to search for alternatives to antibiotics. An in vitro experiment was carried out to evaluate what influence FhV. would have on LPS-induced apoptosis. In this study, Fas, an apoptosis receptor, was cloned, which included a 5'-UTR of 39 bp, an ORF of 951 bp, a protein of 316 amino acids, and a 3'-UTR of 845 bp. EcFas was most strongly expressed in the spleen tissue of orange-spotted groupers. In addition, the apoptosis of fish spleen cells induced by LPS was concentration-dependent. Interestingly, appropriate concentrations of FhV. alleviated LPS-induced apoptosis. Inhibition of miR-411 further decreased the inhibitory effect of Fas on apoptosis, which reduced Bcl-2 expression and mitochondrial membrane potential, enhanced the protein expression of Bax and Fas. More importantly, the FhV. could activate miR-411 to improve this effect. In addition, luciferase reporter assays showed that miR-411 binds to Fas 3'-UTR to inhibit Fas expression. These findings provide evidence that FhV. alleviates LPS-induced apoptosis by activating miR-411 to inhibit Fas expression and, therefore, provided possible strategies for bacterial infections in fish.

Evaluation of the impact of the polymer end groups and molecular weight on in vitro and in vivo performances of PLGA based in situ forming implants for ketoprofen.

In situ forming implants are appealing long-acting dosage forms for both preclinical and clinical applications due to their simple manufacturing process and easy delivery. This study aims to develop extended-release in situ forming solid implants for subcutaneous administration using two types of commercially available triblock poly (lactic-co-glycolic acid)-poly (ethylene glycol)-poly (lactic-co-glycolic acid) (PLGA-PEG-PLGA) polymers, with either an acid or ester end group. Both types of polymers instantly form in situ implants when injected directly into an aqueous medium. The performance of these implants, containing a model compound ketoprofen, was evaluated by comparing the in vitro drug release profiles with the in vivo performance following subcutaneous administration in rats. Analytical characterizations of two representative in situ implants were conducted to understand their structural impact on polymer degradation and drug release. All tested in situ forming implants demonstrated prolonged drug release profiles both in vitro and in vivo. This study illustrates the successful preparation of sustained-release in situ forming implant formulations for ketoprofen using commercially available polymers, with the molecular weight and the end group of the polymers affecting their degradation and the drug release from the in situ formed implants.

Implant dynamics, inner structure, and their impact on drug release of in situ forming implants uncovered through CT imaging.

In situ forming implants (ISFIs) composed of biodegradable polymers and biocompatible solvents are generally designed for sustained drug release. In this study, a non-invasive computed tomography (CT) imaging approach is used to achieve real time imaging of ISFIs in vivo and in vitro using leuprolide acetate in situ forming implant as a model drug product. The process of implant formation, inner structure change and their impact on drug release were elucidated. Real-time drug distribution was unveiled by the CT contrast agent, iohexol, where it shows a core-shell structure of the deposition. The incorporation of leuprolide acetate (LA) led to a reduced extent of burst release, prolongated release profile, and extended implant size expansion. LA was found to interact with the solvent and slowed down the polymer phase inversion, thus significantly changed the drug distribution in the implant and reduced the drug release. The implant inner structure identified through SEM, implant size change, and polymer degradation along with the CT real time imaging all consistently support the implant formation differences and their implant on the drug release. Similar patterns of implant size expansion and iohexol distribution in the implants were observed both in vitro and in vivo for the implants with and without LA. The comprehensive understanding of the impact of implant formation on drug release through real time CT imaging facilitates the ISFI product development and evaluation.

Applications of Nanoparticle-Antibody Conjugates in Immunoassays and Tumor Imaging.

Modern diagnostic technologies rely on both in vitro and in vivo modalities to provide a complete understanding of the clinical state of a patient. Nanoparticle-antibody conjugates have emerged as promising systems to confer increased sensitivity and accuracy for in vitro diagnostics (e.g., immunoassays). Meanwhile, in vivo applications have benefited from the targeting ability of nanoparticle-antibody conjugates, as well as payload flexibility and tailored biodistribution. This review provides an encompassing overview of nanoparticle-antibody conjugates, from chemistry to applications in medical immunoassays and tumor imaging, highlighting the underlying principles and unique features of relevant preclinical applications employing commonly used imaging modalities (e.g., optical/photoacoustics, positron-emission tomography, magnetic resonance imaging, X-ray computed tomography).

AdipoRs- a potential therapeutic target for fibrotic disorders.

Introduction: Fibrotic disorders are a leading cause of morbidity and mortality; hence effective treatments are still vigorously sought. AdipoRs (AdipoR1 and Adipo2) are responsible for the antifibrotic effects of adiponectin (APN). APN exerts antifibrotic effects by binding to its receptors. APN concentration and AdipoR expression are closely associated with fibrotic disorders. Decreased AdipoR expression may reduce APN-AdipoR signaling, while the upregulation of AdipoR expression may restore the anti-fibrotic effects of APN. Loss of APN signaling exacerbates fibrosis in vivo and in vitro. Areas covered: We assess the relationship between APN and fibrotic disorders, the structure of receptors for APN and the pathways accounting for APN or its analogs blocking fibrotic disorders. This article also discusses designed APN products and their therapeutic prospects for fibrotic disorders. Expert opinion: AdipoRs have a critical role in blocking fibrosis. The development of small-molecule agonists toward this target represents a valid drug development pathway.

Discovery of CDK4 inhibitors by convolutional neural networks.

Aim: Descriptors of molecules are important in the discovery of lead compounds. Most of these descriptors are used to represent molecular structures, although structural formulas are the most intuitive representation. Convolutional neural networks (ConvNets) are effective for managing intuitive information. Results/methodology: Convolutional neural networks (ConvNets) based on two-dimensional structural formulas were used for the preliminary screening of CDK4 inhibitors. After supervised learning of our homemade dataset, our models screened out ten approved drugs, including indocyanine green and candesartan cilexetil, with IC50 values of 2.0 and 5.2 µM, respectively. Conclusion: Depending only on intuitive information, the developed method was shown to be feasible, thus providing a new method of lead compound discovery.