Background Fine particulate matter (PM_2.5) monitoring stations may generate missing data for a certain period of time due to various factors. This data loss will adversely affect air quality assessment and pollution control decision-making. Objective To propose an inverse distance weighted (IDW) spatiotemporal interpolation method based on particle swarm optimization (PSO) to interpolate and fill missing PM_2.5 spatiotemporal sequence data and increase interpolation accuracy. Methods An interpolation experiment was designed into two parts. The first part used hourly PM_2.5 observational data from four moments on January 1, 2017 in the Yangtze River Delta region. The second part employed daily PM_2.5 observational data from the first 10 d of January 2017 in the Beijing-Tianjin-Hebei region. Interpolation accuracy was evaluated using four metrics: root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and mean relative error (MRE). Results IDW spatiotemporal interpolation method optimized with PSO significantly improved the accuracy of filling missing PM_2.5 spatiotemporal sequence data. In the hourly-scale experiment conducted in the Yangtze River Delta region, compared to a distance index of 2, the accuracy metrics RMSE, MAE, MAPE, and MRE generated by the proposed method improved on average by 0.17 μg·m⁻³, 0.27 μg·m⁻³, 0.17%, and 0.01%, respectively. The PM_2.5 spatial field maps generated for four moments based on this method clearly illustrated the spatiotemporal distribution characteristics of hourly PM_2.5 concentrations in the Yangtze River Delta region. In the daily-scale experiment conducted in the Beijing-Tianjin-Hebei region, the PSO-optimized distance index outperformed the traditional method, with interpolation accuracy improvements of approximately 0.215 μg·m⁻³, 0.283 μg·m⁻³, 0.174%, and 0.014%, respectively. Furthermore, the seasonal PM_2.5 spatial field maps generated by this method revealed the spatiotemporal distribution characteristics of PM_2.5 concentrations in the Beijing-Tianjin-Hebei region across different seasons, further validating the effectiveness and applicability of this method. Conclusion The IDW spatiotemporal interpolation method optimized with PSO is highly accurate and reliable for interpolating the missing data in the Yangtze River Delta region and the Beijing-Tianjin-Hebei region, providing valuable insights for air pollution control and public health protection.

Objective To investigate the setup error in patients with spinal bone metastasis who underwent radiotherapy under the guidance of kilovoltage cone-beam CT (KV-CBCT). Methods A total of 118 patients with spinal metastasis who underwent radiotherapy, including 17 cases of cervical spine, 62 cases of thoracic spine, and 39 cases of lumbar spine, were collected. KV-CBCT scans were performed using the linear accelerators from Elekta and Varian’s EDGE system. CBCT images were registered with reference CT images in the bone window mode. A total of 973 data were collected, and 3D linear errors were recorded. Results The patients with spinal bone metastasis were grouped by site, height, weight, and BMI. The P value of the patients grouped only by site was P<0.05, which was statistically significant. Conclusion When grouped by site in the 3D direction, the positioning effect of cervical spine is better than that of thoracic and lumbar spine. The positioning effect of the thoracic spine is better in the head and foot direction but worse in the left and right direction compared with that of the lumbar spine. Instead of extending or narrowing the margin according to the BMI of patients with spinal metastasis, the margin must be changed according to the site of spinal bone metastasis.

ObjectiveTemporal heterogeneity in lung cancer presents as fluctuations in the biological characteristics, genomic mutations, proliferation rates, and chemotherapeutic responses of tumor cells over time, posing a significant barrier to effective treatment. The complexity of this temporal variance, coupled with the spatial diversity of lung cancer, presents formidable challenges for research. This article will pave the way for new avenues in lung cancer research, aiding in a deeper understanding of the temporal heterogeneity of lung cancer, thereby enhancing the cure rate for lung cancer. MethodsRaman spectroscopy emerges as a powerful tool for real-time surveillance of biomolecular composition changes in lung cancer at the cellular scale, thus shedding light on the disease’s temporal heterogeneity. In our investigation, we harnessed Raman spectroscopic microscopy alongside multivariate statistical analysis to scrutinize the biomolecular alterations in human lung epithelial cells across various timeframes after benzo(a)pyrene exposure. ResultsOur findings indicated a temporal reduction in nucleic acids, lipids, proteins, and carotenoids, coinciding with a rise in glucose concentration. These patterns suggest that benzo(a)pyrene induces structural damage to the genetic material, accelerates lipid peroxidation, disrupts protein metabolism, curtails carotenoid production, and alters glucose metabolic pathways. Employing Raman spectroscopy enabled us to monitor the biomolecular dynamics within lung cancer cells in a real-time, non-invasive, and non-destructive manner, facilitating the elucidation of pivotal molecular features. ConclusionThis research enhances the comprehension of lung cancer progression and supports the development of personalized therapeutic approaches, which may improve the clinical outcomes for patients.

Objective To explore the application of Plan-Do-Check-Act(PDCA)cycle management to continuously im-prove the service quality of outpatient pharmacy and enhance patient satisfaction.Methods To address the problem of long wait-ing time for patients in outpatient pharmacy,we applied PDCA cycle to investigate the factors affecting patients'waiting time in the process of medicine collection,analyze the current situation,determine the expected goals,formulate the service quality im-provement plan of outpatient pharmacy,implement the improvement plan,follow up and supervise,and summarize and analyse the problems regularly until it was solved.Results After implementing the PDCA cycle in the management,the service quality of outpatient pharmacy was improved,the waiting time was significantly shortened and the satisfaction of medical treatment was in-creased.Conclusion The application of PDCA cycle method is effective in improving the service quality of outpatient pharmacy.Therefore,it is recommended for broader implementation.

Interventions for endometriosis(EMs)include surgical excision of lesions and hormonal therapy,which usually have limited efficacy and adverse drug reactions.Traditional Chinese medicine(TCM)has the multi-component and multi-target characteristics,which can help patients achieve good clinical benefits by intervening in different parts of the disease.In this paper,we briefly discuss the modern pharmacology of Sanlang and Curcuma longa,and deeply summarize the possible mechanisms of action of TCM monomer and classical compound extracts and their active ingredients through signal pathways in inflammation,immune system,angiogenesis,hormone regulation,etc.,so as to provide theoretical bases for the clinical use of TCM monomers,drug-to-drug groups and compounds in the treatment of EMs.

Background::In vitro fertilization (IVF) has emerged as a transformative solution for infertility. However, achieving favorable live-birth outcomes remains challenging. Current clinical IVF practices in IVF involve the collection of heterogeneous embryo data through diverse methods, including static images and temporal videos. However, traditional embryo selection methods, primarily reliant on visual inspection of morphology, exhibit variability and are contingent on the experience of practitioners. Therefore, an automated system that can evaluate heterogeneous embryo data to predict the final outcomes of live births is highly desirable. Methods::We employed artificial intelligence (AI) for embryo morphological grading, blastocyst embryo selection, aneuploidy prediction, and final live-birth outcome prediction. We developed and validated the AI models using multitask learning for embryo morphological assessment, including pronucleus type on day 1 and the number of blastomeres, asymmetry, and fragmentation of blastomeres on day 3, using 19,201 embryo photographs from 8271 patients. A neural network was trained on embryo and clinical metadata to identify good-quality embryos for implantation on day 3 or day 5, and predict live-birth outcomes. Additionally, a 3D convolutional neural network was trained on 418 time-lapse videos of preimplantation genetic testing (PGT)-based ploidy outcomes for the prediction of aneuploidy and consequent live-birth outcomes.Results::These two approaches enabled us to automatically assess the implantation potential. By combining embryo and maternal metrics in an ensemble AI model, we evaluated live-birth outcomes in a prospective cohort that achieved higher accuracy than experienced embryologists (46.1% vs. 30.7% on day 3, 55.0% vs. 40.7% on day 5). Our results demonstrate the potential for AI-based selection of embryos based on characteristics beyond the observational abilities of human clinicians (area under the curve: 0.769, 95% confidence interval: 0.709–0.820). These findings could potentially provide a noninvasive, high-throughput, and low-cost screening tool to facilitate embryo selection and achieve better outcomes. Conclusions::Our study underscores the AI model’s ability to provide interpretable evidence for clinicians in assisted reproduction, highlighting its potential as a noninvasive, efficient, and cost-effective tool for improved embryo selection and enhanced IVF outcomes. The convergence of cutting-edge technology and reproductive medicine has opened new avenues for addressing infertility challenges and optimizing IVF success rates.

Objective To analyze the nucleic acid positive rate and genotype characteristics of the pa-tients with viral hepatitis C in Yongchuan District of Chongqing city during 2004-2022.Methods All the hepatitis C patients whose current address was in Yongchuan District of Chongqing City and audited for man-agement entering in the database of the Infectious disease surveillance System of China Disease Prevention and Control Information from 2004 to 2022 were selected as the study subjects.The questionnaire survey,nucleic acid and genotype detection were conducted.The nucleic acid positive rate and genotype characteristics were analyzed.Results Among 489 cases of viral hepatitis C,there were 286 cases of hepatitis C viral nucleic acid(HCV-RNA)positive(58.49％),the positive rate of males was 64.63％,which was high than 49.23％in fe-males,and the differences was statistically significant(P＜0.05).The HCV-RNA positive rate had statistical difference among different professions,cultural levels and medical insurance types(P＜0.05).But the HCV-RNA positive rates had no statistically difference among different ages,marital status,incomes and permanent residences(P＞0.05).A total of 285 cases of single infection subtype and 1 case of 1b and 6a mixed subtype were detected out.The single infection subtypes were mainly the 1b type(56.45％),3b type(12.89％)and 6a type(13.24％).Conclusion The positive rate of HCV-RNA among hepatitis C patients in Yongchuan Dis-trict during 2004-2022 was 58.49％,more than half of the previous cases are still the active infected persons requiring the antiviral treatment.The HCV genotype is mainly the 1b type,followed by 3a,3b and 6a types.It is necessary to further mobilize the treatment of previous patients with hepatitis C and improve the treatment rate and clinical cure rate.

Endodontic diseases are a kind of chronic infectious oral disease.Common endodontic treatment concepts are based on the removal of inflamed or necrotic pulp tissue and the replacement by gutta-percha.However,it is very essential for endodontic treatment to debride the root canal system and prevent the root canal system from bacterial reinfection after root canal therapy(RCT).Recent research,encompassing bacterial etiology and advanced imaging techniques,contributes to our understanding of the root canal system's anatomy intricacies and the technique sensitivity of RCT.Success in RCT hinges on factors like patients,infection severity,root canal anatomy,and treatment techniques.Therefore,improving disease management is a key issue to combat endodontic diseases and cure periapical lesions.The clinical difficulty assessment system of RCT is established based on patient conditions,tooth conditions,root canal configuration,and root canal needing retreatment,and emphasizes pre-treatment risk assessment for optimal outcomes.The findings suggest that the presence of risk factors may correlate with the challenge of achieving the high standard required for RCT.These insights contribute not only to improve education but also aid practitioners in treatment planning and referral decision-making within the field of endodontics.

Objective:To investigate the status of medical care seeking delay and diagnosis delay of pulmonary tuberculosis (PTB) patients in Tongzhou District and Changping District of Beijing, analyze the related factors and put forward suggestions for early detection and scientific management of PTB patients.Methods:A retrospective epidemiological survey was conducted to collect the incidence data of PTB registered in Tongzhou and Changping from January 1 to December 31, 2021 by using the Chinese Tuberculosis Information Management System, and telephone interview were used for information supplement. Multivariate logistic regression model and decision tree model were used to analyze the influencing factors of medical care seeking delay and diagnosis delay of PTB patients.Results:In 2021, the medical care seeking delay time M( Q1, Q3) in the PTB patients in Tongzhou and Changping was 11 (5, 26) days, with a delay rate of 41.71%. Results from multivariate logistic regression model analysis revealed that factors influencing the medical care seeking delay included regular health check-up ( OR=0.033, 95% CI: 0.008-0.147), coughing for less than 2 weeks or showing any symptom of PTB before medical care seeking ( OR=0.378, 95% CI: 0.215-0.665), showing other symptoms before medical care seeking( OR=2.791, 95% CI: 1.710-4.555), no work or school in medical care seeking ( OR=2.990, 95% CI: 1.419-6.298). The diagnosis delay time M( Q1, Q3) in the PTB patients was 8 (0, 18) days, with a delay rate of 35.20%. Multivariate logistic regression model analysis revealed that the factors influencing the diagnosis delay of PTB included being diagnosed at a specialized tuberculosis hospital ( OR=0.426, 95% CI: 0.236-0.767) or a tuberculosis prevention and control institution ( OR=1.843, 95% CI: 1.061-3.202) and being traced as a source of infection ( OR=2.632, 95% CI: 1.062-6.521). The overall performance of the multivariate logistic regression model was comparable to that of the decision tree model, with the decision tree model exhibiting higher sensitivity but lower specificity. Conclusions:The medical care seeking delay rate and diagnosis delay rate of tuberculosis in Tongzhou and Changping were at low levels in 2021. However, it is still necessary to strengthen the health education and active screening, improve the public awareness of PTB prevention and control, and further improve the level of medical services and medical access to reduce the medical care seeking delay and diagnosis delay of PTB patients.

Drug resistance presents a significant challenge to achieving positive clinical outcomes in anti-tumor therapy.Prior research has illuminated reasons behind drug resistance,including increased drug efflux,alterations in drug targets,and abnormal activation of oncogenic pathways.However,there's a need for deeper investigation into the impact of drug-resistant cells on parental tumor cells and intricate crosstalk between tumor cells and the malignant tumor microenvironment(TME).Recent studies on extracellular vesicles(EVs)have provided valuable insights.EVs are membrane-bound particles secreted by all cells,mediating cell-to-cell communication.They contain functional cargoes like DNA,RNA,lipids,proteins,and metabolites from mother cells,delivered to other cells.Notably,EVs are increasingly recognized as regulators in the resistance to anti-cancer drugs.This review aims to summarize the mechanisms of EV-mediated anti-tumor drug resistance,covering therapeutic approaches like chemo-therapy,targeted therapy,immunotherapy and even radiotherapy.Detecting EV-based biomarkers to predict drug resistance assists in bypassing anti-tumor drug resistance.Additionally,targeted inhibition of EV biogenesis and secretion emerges as a promising approach to counter drug resistance.We highlight the importance of conducting in-depth mechanistic research on EVs,their cargoes,and functional ap-proaches specifically focusing on EV subpopulations.These efforts will significantly advance the devel-opment of strategies to overcome drug resistance in anti-tumor therapy.