Hasil untuk "Medical technology"

Shibo Liao, Dong Zhao, Longhuan Zeng et al.

Kuan-Pin Su

Zhe Du, Pujing Zhang, Xiaodong Huang et al.

Yi Lin, Dong Zhang, Xiao Fang et al.

Medical image segmentation is a pivotal task within the realms of medical image analysis and computer vision. While current methods have shown promise in accurately segmenting major regions of interest, the precise segmentation of boundary areas remains challenging. In this study, we propose a novel network architecture named CTO, which combines Convolutional Neural Networks (CNNs), Vision Transformer (ViT) models, and explicit edge detection operators to tackle this challenge. CTO surpasses existing methods in terms of segmentation accuracy and strikes a better balance between accuracy and efficiency, without the need for additional data inputs or label injections. Specifically, CTO adheres to the canonical encoder-decoder network paradigm, with a dual-stream encoder network comprising a mainstream CNN stream for capturing local features and an auxiliary StitchViT stream for integrating long-range dependencies. Furthermore, to enhance the model's ability to learn boundary areas, we introduce a boundary-guided decoder network that employs binary boundary masks generated by dedicated edge detection operators to provide explicit guidance during the decoding process. We validate the performance of CTO through extensive experiments conducted on seven challenging medical image segmentation datasets, namely ISIC 2016, PH2, ISIC 2018, CoNIC, LiTS17, and BTCV. Our experimental results unequivocally demonstrate that CTO achieves state-of-the-art accuracy on these datasets while maintaining competitive model complexity. The codes have been released at: https://github.com/xiaofang007/CTO.

Huipeng Meng PhD, Yanlong Zhang PhD, Jinghao Duan PhD et al.

Objective To systematically investigate the impact of adjusting the relative weight of the built-in Stereotactic Radiosurgery Normal Tissue Objective (SRS-NTO) on dosimetric quality, plan complexity, and delivery efficiency in HyperArc™ stereotactic radiosurgery (SRS) for brain oligometastases. Methods In this retrospective planning study, a cohort of 20 patients with 1-3 brain oligometastases was analyzed. For each case, six distinct HyperArc plans were designed and optimized using the Varian Eclipse™ Treatment Planning System. To precisely isolate its impact, the relative weight of the SRS-NTO to the PTV objective was systematically varied across six levels—50%, 75%, 100% (default), 125%, 150%, and 200%—while all other planning parameters were held constant. A comprehensive comparative evaluation was then performed to assess the plans across four key domains: (i) dosimetric quality, evaluated by metrics including the Paddick Conformity Index (CI), Gradient Index (GI), and dose to Organs at Risk (OARs); (ii) plan complexity, characterized by various modulation and aperture-based indices; (iii) delivery efficiency, primarily quantified by the total Monitor Units (MUs); and (iv) physical deliverability, verified via Gamma analysis. Results Increasing NTO weight did not significantly alter dosimetric quality; key metrics for CI, GI, and OAR sparing remained statistically equivalent (p > .05). Conversely, higher NTO weights prompted a significant reduction in total MUs (p < .001) that reached an optimum at the 150% setting, and enhanced plan deliverability, evidenced by significantly higher Gamma passing rates under stricter verification criteria. An inflection point was observed beyond the 150% setting, with higher weights leading to degraded plan complexity and efficiency. Strategies within the 125% to 150% range demonstrated a superior balance, achieving optimal dosimetric trends while maximizing gains in efficiency and precision. Conclusion In HyperArc SRS for brain oligometastases, moderately increasing the SRS-NTO weight from the default 100% into the 125% to 150% range is a superior clinical strategy. This adjustment significantly enhances treatment efficiency and delivery precision by reducing plan complexity, without compromising dosimetric quality, thereby achieving a superior overall performance.

Tianwei Zhang, Dong Wei, Mengmeng Zhu et al.

Self-supervised learning has emerged as a powerful tool for pretraining deep networks on unlabeled data, prior to transfer learning of target tasks with limited annotation. The relevance between the pretraining pretext and target tasks is crucial to the success of transfer learning. Various pretext tasks have been proposed to utilize properties of medical image data (e.g., three dimensionality), which are more relevant to medical image analysis than generic ones for natural images. However, previous work rarely paid attention to data with anatomy-oriented imaging planes, e.g., standard cardiac magnetic resonance imaging views. As these imaging planes are defined according to the anatomy of the imaged organ, pretext tasks effectively exploiting this information can pretrain the networks to gain knowledge on the organ of interest. In this work, we propose two complementary pretext tasks for this group of medical image data based on the spatial relationship of the imaging planes. The first is to learn the relative orientation between the imaging planes and implemented as regressing their intersecting lines. The second exploits parallel imaging planes to regress their relative slice locations within a stack. Both pretext tasks are conceptually straightforward and easy to implement, and can be combined in multitask learning for better representation learning. Thorough experiments on two anatomical structures (heart and knee) and representative target tasks (semantic segmentation and classification) demonstrate that the proposed pretext tasks are effective in pretraining deep networks for remarkably boosted performance on the target tasks, and superior to other recent approaches.

Yinchi Zhou, Tianqi Chen, Jun Hou et al.

Image-to-image translation is a vital component in medical imaging processing, with many uses in a wide range of imaging modalities and clinical scenarios. Previous methods include Generative Adversarial Networks (GANs) and Diffusion Models (DMs), which offer realism but suffer from instability and lack uncertainty estimation. Even though both GAN and DM methods have individually exhibited their capability in medical image translation tasks, the potential of combining a GAN and DM to further improve translation performance and to enable uncertainty estimation remains largely unexplored. In this work, we address these challenges by proposing a Cascade Multi-path Shortcut Diffusion Model (CMDM) for high-quality medical image translation and uncertainty estimation. To reduce the required number of iterations and ensure robust performance, our method first obtains a conditional GAN-generated prior image that will be used for the efficient reverse translation with a DM in the subsequent step. Additionally, a multi-path shortcut diffusion strategy is employed to refine translation results and estimate uncertainty. A cascaded pipeline further enhances translation quality, incorporating residual averaging between cascades. We collected three different medical image datasets with two sub-tasks for each dataset to test the generalizability of our approach. Our experimental results found that CMDM can produce high-quality translations comparable to state-of-the-art methods while providing reasonable uncertainty estimations that correlate well with the translation error.

Yijian Fan, Zhenbang Yang, Rui Liu et al.

Medical report generation is a critical task in healthcare that involves the automatic creation of detailed and accurate descriptions from medical images. Traditionally, this task has been approached as a sequence generation problem, relying on vision-and-language techniques to generate coherent and contextually relevant reports. However, in this paper, we propose a novel perspective: rethinking medical report generation as a multi-label classification problem. By framing the task this way, we leverage the radiology nodes from the commonly used knowledge graph, which can be better captured through classification techniques. To verify our argument, we introduce a novel report generation framework based on BLIP integrated with classified key nodes, which allows for effective report generation with accurate classification of multiple key aspects within the medical images. This approach not only simplifies the report generation process but also significantly enhances performance metrics. Our extensive experiments demonstrate that leveraging key nodes can achieve state-of-the-art (SOTA) performance, surpassing existing approaches across two benchmark datasets. The results underscore the potential of re-envisioning traditional tasks with innovative methodologies, paving the way for more efficient and accurate medical report generation.

Xinyi Wei, Yanding Su, Qian Cheng et al.

Objective: A remarkably sensitive, accurate, and efficient ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) approach was developed as a facile and expeditious method for measuring cilofexor concentration in beagle dogs, the herb-drug interactions between silybinin and cilofexor was explored based on pharmacokinetics.Methods: The plasma sample protein of the beagles were rapidly sedimented with acetonitrile, and cilofexor and tropifexor (internal standard, ISTD) were separated by gradient elution using a 0.1% formic acid aqueous solution and acetonitrile as the mobile phase. The concentrations were detected using positive ion multiple reaction monitoring (MRM) mode. Mass transfer pairs were m/z 587.91→267.91 for cilofexor and m/z 604.08→228.03 for ISTD, respectively. A two-period self-controlled experimental design was adopted for the HDIs experiment. In the first period (Group A), six beagle dogs were orally administered cilofexor at a dose of 1 mg/kg. In the second period (Group B), silybinin (3 mg/kg) was orally administered to the six beagle dogs twice a day for seven consecutive days, after which cilofexor was orally administered. The cilofexor concentration in beagle dogs was determined, and HDIs were evaluated based on their pharmacokinetics.Results: The accuracy and precision of cilofexor were both less than 15%, and the recoveries, matrix effects, and stability met the relevant requirements. The Cmax of cilofexor in group B was 49.62% higher than that in group A, whereas the AUC(0-t) and AUC(0−∞) of cilofexor in group B were 47.85% and 48.52% higher, respectively, than those in group A. Meanwhile, the t1/2 extended from 7.84 h to 9.45 h, CL and Vz decreased in Group B.Conclusion: A novel UPLC-MS/MS approach was successfully applied for the measurement of cilofexor in beagle dog plasma. Silybinin can alter the pharmacokinetics of cilofexor in beagle dogs, thereby increasing plasma exposure to cilofexor.

Cheng-Hong Yang, Chih-Hsien Wu, Kuei-Hau Luo et al.

Air pollution has become a major global threat to human health. Urbanization and industrialization over the past few decades have increased the air pollution. Plausible connections have been made between air pollutants and dementia. This study used machine learning algorithms (k-nearest neighbors, random forest, gradient-boosted decision trees, eXtreme gradient boosting, and CatBoost) to investigate the association between cognitive impairment and air pollution. Data from the Taiwan Biobank and 75 air-pollution-monitoring stations in Taiwan were analyzed to determine individual levels of exposure to air pollutants. The pollutants examined were particulate matter with a diameter of ≤ 2.5 μm (PM2.5), nitrogen dioxide, nitric oxide, carbon monoxide, and ozone. The results revealed that the most strongly correlated with cognitive impairment were ozone, PM2.5, and carbon monoxide levels with adjustment of educational level, age, and household income. The model based on these factors achieved accuracy as high as 0.97 for detecting cognitive impairment, indicating a positive association between air pollutions and cognitive impairment.

Mengqing Ma, Rui Hu, Qi Huang et al.

Abstract Hepatocellular carcinoma (HCC) is recognized as a highly malignant tumor. Targeted combination immunotherapy, the initially approved regimen, is compromised by adverse side effects and low response rates during clinical treatment. Traditional Chinese medicine and its derived natural compounds, known for their anticancer effects, offer advantages of low toxicity and cost. In this study, we performed high-throughput phenotypic screening in vitro to identify promising anti-HCC drugs. Among 1,444 bioactive compounds, digoxigenin (DIG) was found to significantly impede HCC cell progression. We validated DIG’s therapeutic effects through assays such as cell counting by CCK8, lactate dehydrogenase, and colony formation. Analyses including transmission electron microscopy, western blotting, and immunofluorescence demonstrated that DIG inhibits HCC cell proliferation via autophagy. Network pharmacology and molecular docking studies suggest that DIG targets the PI3K/AKT/mTOR signaling pathway. Comparative treatments of Hep3B and Huh7 cells with DIG or mTOR inhibitors revealed similar inhibitory impacts, indicating that DIG induces autophagy by inhibiting the PI3K/AKT/mTOR pathway. In vivo studies confirmed that DIG halts the growth of subcutaneous xenograft tumors. In conclusion, DIG represents a potential HCC treatment by modulating the PI3K/AKT/mTOR pathway to induce autophagy. This research, via phenotypic screening, accelerates drug discovery and the development of novel therapies targeting the underlying mechanisms of liver cancer.

Feng Zhang, Long Cheng

AimsThis research investigated menopausal women older than 50 years to find whether there were any independent relationships between the duration of sleep they got and their prevalence of depression.MethodsNational Health and Nutrition Examination Survey (NHANES) datasets from 2011-2020 were utilized in a cross-sectional study. Using multivariate linear regression models, the linear relationship between sleep duration and depression in menopausal women was investigated. Fitted smoothing curves and thresholds impact evaluation were used to investigate the nonlinear relationship. Then, subgroup analyses were performed according to smoking, drinking alcohol, diabetes, hypertension, heart disease, and moderate activities.ResultsThis population-based study included a total of 3,897 menopausal women (mean age 65.47 ± 9.06 years) aged≥50 years; 3,159 had a depression score &lt;10, and 738 had a depression score≥10. After controlling for all covariates, the prevalence of depression was 17% higher among participants with short sleep duration [OR=1.17, 95%CI=(0.65, 1.70), P&lt;0.0001] and 86% [OR=1.86, 95%CI=(1.05, 2.66), P&lt;0.0001] compared to participants with normal sleep duration. In subgroup analyses stratified by smoking and diabetes, the sleep duration and depression scores of non-smokers [β=-0.18, 95%CI= (-0.33, -0.02), P=0.0241] and diabetics were independently negatively correlated [β=-0.32, 95%CI= (-0.63, -0.01), P=0.0416]. Using a two-segment linear regression model, we discovered a U-shaped relationship between sleep duration and depression scores with an inflection point of 7.5 hours. Less than 7.5 hours of sleep was associated with an increased risk of developing depression [β=-0.81, 95%CI= (-1.05, -0.57), P&lt;0.001]. However, sleeping more than 7.5 hours per night increased the risk of depression considerably [β=0.80, 95%CI= (0.51, 1.08), P&lt;0.001].ConclusionsDepression is associated with sleep duration in menopausal women. Insufficient or excessive sleep may increase the risk of depression in menopausal women.

Haiquan Kang, Haiquan Kang, Ziling Wang et al.

Bloodstream infections (BSIs) are a critical medical concern, characterized by elevated morbidity, mortality, extended hospital stays, substantial healthcare costs, and diagnostic challenges. The clinical outcomes for patients with BSI can be markedly improved through the prompt identification of the causative pathogens and their susceptibility to antibiotics and antimicrobial agents. Traditional BSI diagnosis via blood culture is often hindered by its lengthy incubation period and its limitations in detecting pathogenic bacteria and their resistance profiles. Surface-enhanced Raman scattering (SERS) has recently gained prominence as a rapid and effective technique for identifying pathogenic bacteria and assessing drug resistance. This method offers molecular fingerprinting with benefits such as rapidity, sensitivity, and non-destructiveness. The objective of this study was to integrate deep learning (DL) with SERS for the rapid identification of common pathogens and their resistance to drugs in BSIs. To assess the feasibility of combining DL with SERS for direct detection, erythrocyte lysis and differential centrifugation were employed to isolate bacteria from blood samples with positive blood cultures. A total of 12,046 and 11,968 SERS spectra were collected from the two methods using Raman spectroscopy and subsequently analyzed using DL algorithms. The findings reveal that convolutional neural networks (CNNs) exhibit considerable potential in identifying prevalent pathogens and their drug-resistant strains. The differential centrifugation technique outperformed erythrocyte lysis in bacterial isolation from blood, achieving a detection accuracy of 98.68% for pathogenic bacteria and an impressive 99.85% accuracy in identifying carbapenem-resistant Klebsiella pneumoniae. In summary, this research successfully developed an innovative approach by combining DL with SERS for the swift identification of pathogenic bacteria and their drug resistance in BSIs. This novel method holds the promise of significantly improving patient prognoses and optimizing healthcare efficiency. Its potential impact could be profound, potentially transforming the diagnostic and therapeutic landscape of BSIs.

Xue-Geng Hong, Mei-Qi Zhang, Fang Tang et al.

Toscana virus (TOSV), a member of the <i>Phlebovirus</i> genus transmitted by sandflies, is acknowledged for its capacity to cause neurological infections and is widely distributed across Mediterranean countries. The potential geographic distribution and risk to the human population remained obscure due to its neglected nature. We searched PubMed and Web of Science for articles published between 1 January 1971 and 30 June 2023 to extract data on TOSV detection in vectors, vertebrates and humans, clinical information of human patients, as well as the occurrence of two identified sandfly vectors for TOSV. We further predicted the global distribution of the two sandfly vectors, based on which the global risk of TOSV was projected, after incorporating the environmental, ecoclimatic, biological, and socioeconomic factors. A total of 1342 unique studies were retrieved, among which 389 met the selection criteria and were included for data extraction. TOSV infections were documented in 10 sandfly species and 14 species of vertebrates, as well as causing a total of 7571 human infections. The occurrence probabilities of two sandfly vectors have demonstrated the greatest contributions to the potential distribution of TOSV infection risk. This study provides a comprehensive overview of global TOSV distribution and potential risk zones. Future surveillance and intervention programs should prioritize high-risk areas based on updated quantitative analyses.

Junling Zhang, Hezhou Li, Demin Feng et al.

Abstract Background To develop an ultrasound scoring system for placenta accreta spectrum (PAS), evaluate its diagnostic value, and provide a practical approach to prenatal diagnosis of PAS. Methods A total of 532 pregnant women (n = 184 no PAS, n = 120 placenta accreta, n = 189 placenta increta, n = 39 placenta percreta) at high-risk for placenta accreta who delivered in the Third Affiliated Hospital of Zhengzhou University between January 2021 and December 2022 underwent prenatal ultrasound to evaluate placental invasion. An ultrasound scoring system that included placental and cervical morphology and history of cesarean section was created. Each feature was assigned a score of 0 ~ 2, according to severity. Thresholds for the total ultrasound score that discriminated between no PAS, placenta accreta, placenta increta, and placenta percreta were calculated. Results Univariate and multivariate regression analysis identified seven indicators of PAS that were included in the ultrasound scoring system, including placental location, placental thickness, presence/absence of the retroplacental space, thickness of the retroplacental myometrium, presence/absence of placental lacunae, retroplacental myometrial blood flow and history of cesarean section. Using the final ultrasound scoring system, no PAS is diagnosed at a total score < 5, placenta accreta or placenta increta is diagnosed at a total score 5–10, and placenta percreta is diagnosed at a total score ≥ 10. Conclusions This study identified seven indicators of PAS and included them in an ultrasound scoring system that has good diagnostic efficacy and clinical utility. Trial registration ChiCTR2300069261 (retrospectively registered on 10/03/2023).

Mélanie Gaillochet, Christian Desrosiers, Hervé Lombaert

The performance of learning-based algorithms improves with the amount of labelled data used for training. Yet, manually annotating data is particularly difficult for medical image segmentation tasks because of the limited expert availability and intensive manual effort required. To reduce manual labelling, active learning (AL) targets the most informative samples from the unlabelled set to annotate and add to the labelled training set. On the one hand, most active learning works have focused on the classification or limited segmentation of natural images, despite active learning being highly desirable in the difficult task of medical image segmentation. On the other hand, uncertainty-based AL approaches notoriously offer sub-optimal batch-query strategies, while diversity-based methods tend to be computationally expensive. Over and above methodological hurdles, random sampling has proven an extremely difficult baseline to outperform when varying learning and sampling conditions. This work aims to take advantage of the diversity and speed offered by random sampling to improve the selection of uncertainty-based AL methods for segmenting medical images. More specifically, we propose to compute uncertainty at the level of batches instead of samples through an original use of stochastic batches (SB) during sampling in AL. Stochastic batch querying is a simple and effective add-on that can be used on top of any uncertainty-based metric. Extensive experiments on two medical image segmentation datasets show that our strategy consistently improves conventional uncertainty-based sampling methods. Our method can hence act as a strong baseline for medical image segmentation. The code is available on: https://github.com/Minimel/StochasticBatchAL.git.

Yutong Xie, Bing Yang, Qingbiao Guan et al.

Medical image segmentation plays an important role in computer-aided diagnosis. Attention mechanisms that distinguish important parts from irrelevant parts have been widely used in medical image segmentation tasks. This paper systematically reviews the basic principles of attention mechanisms and their applications in medical image segmentation. First, we review the basic concepts of attention mechanism and formulation. Second, we surveyed over 300 articles related to medical image segmentation, and divided them into two groups based on their attention mechanisms, non-Transformer attention and Transformer attention. In each group, we deeply analyze the attention mechanisms from three aspects based on the current literature work, i.e., the principle of the mechanism (what to use), implementation methods (how to use), and application tasks (where to use). We also thoroughly analyzed the advantages and limitations of their applications to different tasks. Finally, we summarize the current state of research and shortcomings in the field, and discuss the potential challenges in the future, including task specificity, robustness, standard evaluation, etc. We hope that this review can showcase the overall research context of traditional and Transformer attention methods, provide a clear reference for subsequent research, and inspire more advanced attention research, not only in medical image segmentation, but also in other image analysis scenarios.

R. Keikha, S.M. Hashemi-Shahri, A. Jebali

Introduction: The expression of specific miRNAs and their mRNA targets are changed in infectious disease. The aim of this study was to analyze the expression of pro-neuroinflammatory miRNAs, anti-neuroinflammatory miRNAs, and their mRNA targets in the serum of COVID-19 patients with different grades. Methods: COVID-19 patients with different grades were enrolled in this study and the expression of pro-neuroinflammatory miRNAs, anti-neuroinflammatory miRNAs, and their target mRNAs was analyzed by q-PCR. Results: The relative expression of anti- neuroinflammatory miRNAs (mir-21, mir-124, and mir-146a) was decreased and the relative expression of their target mRNAs (IL-12p53, Stat3, and TRAF6) was increased. Also, the relative expression of pro-neuroinflammatory miRNAs (mir-326, mir-155, and mir-27b) was increased and the relative expression of their target mRNA (PPARS, SOCS1, and CEBPA) was decreased in COVID-19 patients with increase of disease grade. A negative significant correlation was seen between mir-21 and IL-12p53 mRNA, mir-124 and Stat3 mRNA, mir-146a and TRAF6 mRNA, mir-27b and PPARS mRNA, mir-155 and SOCS1 mRNA, and between mir-326 and CEBPA mRNA in COVID-19 patients (P < 0.05). Conclusions: This study showed that the relative expression of anti- neuroinflammatory miRNAs was decreased and the relative expression of their targeted mRNAs was increased in COVID-19 patients from asymptomatic to critical illness. Also, this study showed that the relative expression of pro-neuroinflammatory miRNAs was increased and the relative expression of their targeted mRNA was decreased in COVID-19 patients from asymptomatic to critical illness. Resumen: Introducción: La expresión de miARN específicos y sus dianas de ARNm se modifican en las enfermedades infecciosas. El objetivo de este estudio fue analizar la expresión de miARN pro-neuroinflamatorios, miARN anti-neuroinflamatorios y sus ARNm dianas en el suero de pacientes con COVID-19 de diferentes grados. Métodos: Se incluyeron en este estudio pacientes con COVID-19 de diferentes grados y se analizó la expresión de miARN pro-neuroinflamatorios, miARN anti-neuroinflamatorios y sus ARNm diana mediante q-PCR. Resultados: La expresión relativa de miARN anti-neuroinflamatorios (mir-21, mir-124 y mir-146a) disminuyó y la expresión relativa de sus ARNm diana (IL-12p53, Stat3 y TRAF6) aumentó. Además, la expresión relativa de miARN pro-neuroinflamatorios (mir-326, mir-155 y mir-27b) aumentó y la expresión relativa de su ARNm diana (PPARS, SOCS1 y CEBPA) disminuyó en pacientes con COVID-19 con aumento del grado de enfermedad. Se observó una correlación negativa significativa entre ARNm de mir-21 e IL-12p53, ARNm de mir-124 y Stat3, ARNm de mir-146a y TRAF6, ARNm de mir-27b y PPARS, ARNm de mir-155 y SOCS1, y entre mir-326 y ARNm de CEBPA en pacientes con COVID-19 (p < 0,05). Conclusiones: Este estudio mostró que la expresión relativa de miARN anti-neuroinflamatorios disminuyó y la expresión relativa de sus ARNm diana se incrementó en pacientes con COVID-19 de enfermedad asintomática a crítica. Además, este estudio mostró que la expresión relativa de miARN pro-neuroinflamatorios aumentó y la expresión relativa de su ARNm diana disminuyó en pacientes con COVID-19 de enfermedad asintomática a crítica.

Hu CY, Cui WS, Lei Y et al.

Cheng-Yi Hu,1 Wen-Shuai Cui,1 Yi Lei,1 Yu-Wen Tang,1 Yan-Yan Zhang,1 Qi-Min Su,1 Fang Peng,2 Yun-Fei Zeng,1 Jia-Lin Song,1 Cheng-Na Luo,1 Yan Zhou,1 Xin-Yan Li,1 Zhu-Xiang Zhao1 1Department of Infectious Diseases, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, People’s Republic of China; 2Department of Critical Care Medicine, the Third Affiliated Hospital of Guang Zhou Medical University, Guangzhou, Guangdong, People’s Republic of ChinaCorrespondence: Zhu-Xiang Zhao, Department of Infectious Diseases, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, 510515, People’s Republic of China, Email zhaozhuxiang@126.comPurpose: To compare the effectiveness of azvudine and nirmatrelvir/ritonavir for the treatment of coronavirus disease (COVID-19).Patients and Methods: We conducted a retrospective analysis of data from 576 patients with COVID-19, comprising 195 patients without antiviral therapy, 226 patients treated with azvudine, 114 patients treated with nirmatrelvir/ritonavir, and 41 patients were treated with azvudine and nirmatrelvir/ritonavir concurrently. We compared their symptoms, mortality rates, and the length and cost of hospitalization.Results: The incidence of symptoms was similar in patients treated with azvudine and in those treated with nirmatrelvir/ritonavir. However, among patients experiencing weakness, the duration of weakness was significantly shorter in the azvudine group than in the nirmatrelvir/ritonavir group (P=0.029). Mortality did not differ significantly between the azvudine group and the nirmatrelvir/ritonavir group (18.14% vs.10.53%, P=0.068). Among “severe patients”, the mortality rate was markedly lower in patients treated with nirmatrelvir/ritonavir than in patients treated with azvudine (16.92% vs.32.17%, P=0.026). In patients with hepatic insufficiency, those treated with nirmatrelvir/ritonavir had substantially lower mortality than those treated with azvudine (15.09% vs.34.25%, P=0.016). In addition, patients treated with nirmatrelvir/ritonavir had longer hospital stays (P=0.002) and higher hospital costs (P< 0.001) than those receiving azvudine. Compared with patients treated with nirmatrelvir/ritonavir or azvudine alone, patients taking nirmatrelvir/ritonavir and azvudine concurrently had no significant improvement in survival (P> 0.05), length of stay (P> 0.05), or hospital costs (P> 0.05).Conclusion: Azvudine is recommended for patients with non-severe COVID-19 with weakness. Nirmatrelvir/ritonavir is recommended for patients with severe COVID-19, to reduce mortality, and it could be the best choice for patients with hepatic insufficiency. The concurrent use of nirmatrelvir/ritonavir and azvudine in patients with COVID-19 could be not recommended.Keywords: azvudine, nirmatrelvir/ritonavir, COVID-19, SARS-CoV-2

Lingyan Ye, Guifei Yao, Shuangxiang Lin et al.

Background. Novel coronavirus disease 2019 (COVID-19) was discovered in December 2019 and has infected more than 80 million people worldwide, and more than 50 million people have achieved a clinical cure. In this study, the pulmonary function results of patients after clinical medicine for three months were reported. Objective. To investigate the effect of COVID-19 on lung function in patients. Methods. A retrospective analysis was performed on 56 COVID-19-infected patients who were cured after the clinical treatment at Taizhou Public Health Medical Center in Zhejiang Province from January 31, 2020, to March 10, 2020. At discharge and three months after discharge, lung function was measured, including inspiratory vital capacity (IVC), forced vital capacity (FVC), forced expiratory volume in first second (FEV1), forced expiratory volume in first second to inspiratory vital capacity (FEV1/IVC), maximum mid-expiratory flow rate (MEF), peak expiratory flow rate (PEF), and carbon monoxide dispersion (DLCO). Results. At discharge, there were 37 patients (66.1%) with pulmonary dysfunction, 22 patients (39.3%) with ventilation dysfunction, 31 cases (55.4%) with small airway dysfunction, and 16 cases (28.6%) with restricted ventilation dysfunction combined with small airway dysfunction. At 3 months after discharge, 24 of the 56 patients still had pulmonary dysfunction and all of them had small airway dysfunction, of which 10 patients (17.9%) were restricted ventilation dysfunction combined with small airway dysfunction. DLCO was measured three months after discharge. Twenty-nine patients (51.8%) had mild to moderate diffuse dysfunction. All pulmonary function indexes of 56 patients recovered gradually after 3 months after release, except FEV1/IVC, and the difference was statistically significant ( P  < 0.05). There were 41 patients of normal type (73.2%) and 15 patients of severe type (26.8%). Among the 15 severe patients, 8 patients (53.3%) had ventilation dysfunction at discharge, 9 patients (60%) had small airway dysfunction, 4 patients (26.7%) still had ventilation dysfunction 3 months after discharge, 7 patients (46.7%) had small airway dysfunction, and 10 patients (66.7%) had diffuse dysfunction. Among the 41 common type patients, 14 patients (34.1%) had ventilation dysfunction at discharge, 22 patients (53.7%) had small airway dysfunction, 6 patients (14.6%) still had ventilation dysfunction 3 months after discharge, 17 patients (41.5%) had small airway dysfunction, and 19 patients (46.3%) had diffuse dysfunction. Patients with severe COVID-19 had more pulmonary impairment and improved pulmonary function than normal patients. Conclusion. COVID-19 infection can cause lung function impairment, manifested as restricted ventilation dysfunction, small airway dysfunction, and diffuse dysfunction. The pulmonary function of most patients was improved 3 months after clinical cure and discharge, and some patients remained with mild to moderate diffuse dysfunction and small airway dysfunction.