A meta-analysis of randomized controlled studies examining the effects of sodium-glucose co-transporter-2 inhibitors on peripheral artery disease and risk of amputations.

AIM: Sodium-glucose co-transporter-2 inhibitors (SGLT-2is) are used to maintain glycaemic control as well as for their beneficial cardiovascular and renal effects in diabetes patients. However, increased risk of amputation and peripheral artery disease (PAD) have been observed with the use of some SGLT-2is. A meta-analysis was conducted to understand the effect of SGLT-2is on amputation and PAD events using data from randomized controlled trials (RCT). MATERIALS AND METHODS: A systematic literature review was conducted using Medline and Central databases for RCTs that involved the administration of SGLT-2is versus placebo/active comparators to diabetic patients. The primary outcome was amputation events and PAD. A random-effects model was used to calculate the pooled odds ratio, and subgroup analyses was performed. RESULTS: A total of 51 RCTs were included in the meta-analysis with data from 97 589 patients. Meta-analysis of the data showed that there was a significant increase in PAD risk (p = 0.04) but no significant increase in amputation risk with SGLT-2i use versus placebo/active comparators (p = 0.43). Subgroup analyses demonstrated no significant difference between SGLT-2i type, duration of treatment or patient risk factors on amputation or PAD incidence. However, length of drug treatment (> 100 weeks) was associated with a significant increase in both PAD and amputation risks in the SGLT-2i treatment groups. CONCLUSIONS: The results of the meta-analysis showed no significant association between SGLT-2i use and PAD and amputation risks in diabetic patients when used for shorter treatment durations.

Chemotherapy combined with lenvatinib and PD-1 may be a potential better alternative option for advanced unresectable intrahepatic cholangiocarcinoma: a retrospective real-world study.

Background: Currently, the prognosis of advanced intrahepatic cholangiocarcinoma (ICC) is poor, and the current treatment methods are not effective. Objective: The aim of this study was to evaluate the anticancer efficacy of chemotherapy combined with PD-1 inhibitors and tyrosine kinase inhibitors (TKIs) in patients with ICC. Methods: We retrospectively screened patients with advanced intrahepatic cholangiocarcinoma (ICC) who received chemotherapy combined with lenvatinib and PD-1. We evaluated overall survival (OS), progression-free survival (PFS), the objective response rate (ORR), the disease control rate (DCR), the tumor shrinkage rate, and safety. Results: We enrolled 95 patients with ICC and divided them into three groups with a median follow-up duration of 15.1 months. The chemotherapy group (chemo-regimen group), chemotherapy combined with immune checkpoint inhibitors (dual-regimen group), and chemotherapy combined with lenvatinib (triple-regimen group) had median OS times of 13.1 months, 20.8 months, and 39.6 months, respectively. Notably, the triple-regimen group had a significantly longer OS than did the chemo-regimen and dual-regimen groups. The chemo-regimen group, dual-regimen group, and triple-regimen group reported median PFS durations of 4.8 months, 11.9 months, and 23.4 months, respectively. Both combination groups exhibited significantly longer PFS than the chemotherapy-only group (P<0.05). The ORRs of the chemo-regimen, dual-regimen, and triple-regimen groups were 18.2%, 55.5%, and 54.7%, respectively. The DCRs were 72.7%, 90%, and 96.2%, respectively, indicating significantly better outcomes in the combination therapy groups. Conclusion: The combination of chemotherapy with PD-1 inhibitors and lenvatinib demonstrates considerable efficacy and tolerability as a treatment strategy for patients with advanced ICC.

Unraveling the core and bridge menopausal symptoms of perimenopausal women: a network analysis.

OBJECTIVE: Perimenopause is the period from the early menopausal transition to 12 months after the final menstrual period. The clustering of menopausal symptoms poses a challenge for perimenopausal management. Core symptoms are targets for interventions that can alleviate other related symptoms. Bridge symptoms are connectors that link related symptom clusters and can improve the effectiveness of interventions. This study aims to construct a network structure of menopausal symptoms and to identify core and bridge symptoms as a reference for future management. METHODS: Two hundred forty-two Chinese perimenopausal women were included in the survey. The structure and associations of the menopausal symptoms assessed by the Greene Climacteric Scale were analyzed using a network analysis. We generated the network structure graph using R software and checked its accuracy and stability. RESULTS: In the menopausal transition, the most prevalent symptoms were feeling tired or lacking in energy, excitability, and irritability. Sexual dysfunction was common among early postmenopausal women. Irritability (S = 7.16, C = 0.0167, B = 8) was a core symptom of the network. The depressive symptom cluster was a core symptom cluster, most of which have high centrality indices. Excitability (B = 6) was a bridge symptom connecting the anxiety and depressive symptom clusters. CONCLUSIONS: Our study has highlighted the crucial significance of irritability and excitability in perimenopausal management. Overcoming the challenges of perimenopausal management requires the public to ameliorate the prejudice and stigma associated with emotional symptoms.

Trade-offs of reproductive growth and Cd remobilization regulated Cd accumulation in wheat grains (Triticum aestivum L.).

Minimization of cadmium (Cd) accumulation in wheat grain (Triticum aestivum L.) is an important way to prevent Cd hazards to humans. However, little is known about the mechanisms of varietal variation of Cd accumulation in wheat grain. This study explores the physiological mechanisms of Cd bioaccumulation through field and hydroponic experiments on two wheat varieties of low-Cd-accumulating variety (L-6331) and high-Cd-accumulating variety (H-6049). Field study showed that average Cd accumulative rates in spikes of H-6049 were 1.57-fold of L-6331 after flowering, ultimately grain-Cd of H-6049 was 1.70-fold of L-6331 in Cd-contaminated farmland. The hydroponic experiment further confirmed that more vegetative tissues of L-6331 were involved in the remobilization of Cd, which jointly mitigated the process of Cd loaded to grains when leaf-cutting conducted after Cd stress. Additionally, the L1 and N1 of L-6331 play an especially important role in regulating Cd remobilization, and the larger EVB areas in N1 have the morphological feature that facilitates the transfer of Cd to L1. Overall results implied that low-Cd-accumulating variety initiated more trade-offs of reproductive growth and Cd remobilizatoin under Cd-stress after flowering compared with high-Cd-accumulating variety, and provided new insights into the processes of Cd loaded into wheat grains among different varieties.

The common pathogenesis of nodular goiter in both sexes: An exploration into gene expression and signaling pathways.

The past few years have witnessed an increasing incidence of nodular goiter (NG), with a well-documented higher prevalence in females than males. This gender disparity has led research to focus primarily on female subjects, potentially overlooking common pathogenic mechanisms in both sexes. In this study, we investigated the shared pathogenesis of NG in males and females. Utilizing a rat model and RNA sequencing, we identified differentially expressed genes associated with the disease. We further validated these findings in normal human thyroid cells and human papillary thyroid cancer cells. A randomized experiment was conducted with equal numbers of male and female rats divided into control and NG model groups. The NG model was established using propylthiouracil and various assessments such as thyroid ultrasonography, thyroid index, thyroid function, and thyroid histology were performed. Transcriptome analysis revealed numerous upregulated and downregulated genes in both male and female model groups. Key genes like KDR, FLT1, PDGFB, and CAV1, and pathways including PI3K-Akt, MAPK, Ras, fluid shear stress and atherosclerosis, calcium signaling, and Rap1 signaling pathways were linked with the disease. Western blot and immunofluorescence analysis confirmed these findings, which were further supported by cell-based experiments. In conclusion, our findings suggest that abnormal expression of specific genes and pathways leading to irregular cell growth, blood vessel formation, and inflammation may be common factors in the pathogenesis of NG in both males and females.

DNA Logical Computing on a Transistor for Cancer Molecular Diagnosis.

Given the high degree of variability and complexity of cancer, precise monitoring and logical analysis of different nucleic acid markers are crucial for improving diagnostic precision and patient survival rates. However, existing molecular diagnostic methods normally suffer from high cost, cumbersome procedures, dependence on specialized equipment and the requirement of in-depth expertise in data analysis, failing to analyze multiple cancer-associated nucleic acid markers and provide immediate results in a point-of-care manner. Herein, we demonstrate a transistor-based DNA molecular computing (TDMC) platform that enables simultaneous detection and logical analysis of multiple microRNA (miRNA) markers on a single transistor. TDMC can perform not only basic logical operations such as "AND" and "OR", but also complex cascading computing, opening up new dimensions for multi-index logical analysis. Owing to the high efficiency, sensing and computations of multi-analytes can be operated on a transistor at a concentration as low as 2×10-16 M, reaching the lowest concentration for DNA molecular computing. Thus, TDMC achieves an accuracy of 98.4% in the diagnosis of hepatocellular carcinoma from 62 serum samples. As a convenient and accurate platform, TDMC holds promise for applications in "one-stop" personalized medicine.

The construction of machine learning-based predictive models for high-quality embryo formation in poor ovarian response patients with progestin-primed ovarian stimulation.

OBJECTIVE: To explore the optimal models for predicting the formation of high-quality embryos in Poor Ovarian Response (POR) Patients with Progestin-Primed Ovarian Stimulation (PPOS) using machine learning algorithms. METHODS: A retrospective analysis was conducted on the clinical data of 4,216 POR cycles who underwent in vitro fertilization (IVF) / intracytoplasmic sperm injection (ICSI) at Sichuan Jinxin Xinan Women and Children's Hospital from January 2015 to December 2021. Based on the presence of high-quality cleavage embryos 72 h post-fertilization, the samples were divided into the high-quality cleavage embryo group (N = 1950) and the non-high-quality cleavage embryo group (N = 2266). Additionally, based on whether high-quality blastocysts were observed following full blastocyst culture, the samples were categorized into the high-quality blastocyst group (N = 124) and the non-high-quality blastocyst group (N = 1800). The factors influencing the formation of high-quality embryos were analyzed using logistic regression. The predictive models based on machine learning methods were constructed and evaluated accordingly. RESULTS: Differential analysis revealed that there are statistically significant differences in 14 factors between high-quality and non-high-quality cleavage embryos. Logistic regression analysis identified 14 factors as influential in forming high-quality cleavage embryos. In models excluding three variables (retrieved oocytes, MII oocytes, and 2PN fertilized oocytes), the XGBoost model performed slightly better (AUC = 0.672, 95% CI = 0.636-0.708). Conversely, in models including these three variables, the Random Forest model exhibited the best performance (AUC = 0.788, 95% CI = 0.759-0.818). In the analysis of high-quality blastocysts, significant differences were found in 17 factors. Logistic regression analysis indicated that 13 factors influence the formation of high-quality blastocysts. Including these variables in the predictive model, the XGBoost model showed the highest performance (AUC = 0.813, 95% CI = 0.741-0.884). CONCLUSION: We developed a predictive model for the formation of high-quality embryos using machine learning methods for patients with POR undergoing treatment with the PPOS protocol. This model can help infertility patients better understand the likelihood of forming high-quality embryos following treatment and help clinicians better understand and predict treatment outcomes, thus facilitating more targeted and effective interventions.

Chronic illness trajectory-based nursing intervention improves the self-care abilities of patients with inflammatory bowel disease.

OBJECTIVE: To explore the effects of chronic illness trajectory model (CITM)-based nursing interventions on anxiety, depression, quality of life, medication adherence, and dietary compliance among patients with inflammatory bowel disease (IBD). METHODS: A retrospective analysis was performed on 112 IBD patients admitted to Shandong Provincial Hospital Affiliated to Shandong First Medical University from January to December 2023. Patients were divided into two groups: a control group (n=62) receiving routine nursing care, and an observation group (n=50) receiving CITM-based nursing care. Assessments of anxiety, depression, self-care ability, daily living ability, and symptom severity were conducted before and after the intervention. RESULTS: Post-intervention, the observation group demonstrated significantly higher quality of life scores at 1 and 3 months compared to the control group (both P<0.05). Additionally, the observation group showed improved medication adherence and lower symptom scores, with significant differences (both P<0.05). Anxiety and depression levels were also significantly reduced in the observation group compared to the control group (both P<0.05). CONCLUSION: CITM-based nursing intervention significantly enhances self-care abilities, quality of life, and compliance with medication and dietary regimens in IBD patients. Furthermore, it effectively alleviates anxiety and depression, supporting comprehensive management of this chronic disease.

Can Machine Learning Models Based on Computed Tomography Radiomics and Clinical Characteristics Provide Diagnostic Value for Epstein-Barr Virus-Associated Gastric Cancer?

OBJECTIVE: The aim of this study was to explore whether machine learning model based on computed tomography (CT) radiomics and clinical characteristics can differentiate Epstein-Barr virus-associated gastric cancer (EBVaGC) from non-EBVaGC. METHODS: Contrast-enhanced CT images were collected from 158 patients with GC (46 EBV-positive, 112 EBV-negative) between April 2018 and February 2023. Radiomics features were extracted from the volumes of interest. A radiomics signature was built based on radiomics features by the least absolute shrinkage and selection operator logistic regression algorithm. Multivariate analyses were used to identify significant clinicoradiological variables. We developed 6 ML models for EBVaGC, including logistic regression, Extreme Gradient Boosting, random forest (RF), support vector machine, Gaussian Naive Bayes, and K-nearest neighbor algorithm. The area under the receiver operating characteristic curve (AUC), the area under the precision-recall curves (AP), calibration plots, and decision curve analysis were applied to assess the effectiveness of each model. RESULTS: Six ML models achieved AUC of 0.706-0.854 and AP of 0.480-0.793 for predicting EBV status in GC. With an AUC of 0.854 and an AP of 0.793, the RF model performed the best. The forest plot of the AUC score revealed that the RF model had the most stable performance, with a standard deviation of 0.003 for AUC score. RF also performed well in the testing dataset, with an AUC of 0.832 (95% confidence interval: 0.679-0.951), accuracy of 0.833, sensitivity of 0.857, and specificity of 0.824, respectively. CONCLUSIONS: The RF model based on clinical variables and Rad_score can serve as a noninvasive tool to evaluate the EBV status of gastric cancer.

Complement Factor C1q Mediates Vascular Endothelial Dysfunction in STZ-Induced Diabetic Mice.

Diabetes is a significant global public health issue with implications for vascular endothelial cells (ECs) dysfunction and the subsequent development and advancement of diabetes complications. This study aims to compare the cellular and molecular properties of the aorta in normal and streptozotocin (STZ)-induced diabetic mice, with a focus on elucidating potential mechanism underlying EC dysfunction. Here, we performed a single-cell RNA sequencing survey of 32,573 cells from the aorta of normal and STZ-induced diabetic mice. We found a compendium of 10 distinct cell types, mainly ECs, smooth muscle cells, fibroblast, pericyte, immune cells, and stromal cells. As the diabetes condition progressed, we observed a subpopulation of aortic ECs that exhibited significantly elevated expression of complement (C) molecule C1qa compared with their healthy counterparts. This increased expression of C1qa was found to induce reactive oxygen species (ROS) production, facilitate EC migration and increased permeability, and impair the vasodilation within the aortic segment of mice. Furthermore, AAV-Tie2-shRNA-C1qa was administered into diabetic mice by tail vein injection, showing that inhibition of C1qa in the endothelium led to a reduction in ROS production, decreased vascular permeability, and improved vasodilation. Collectively, these findings highlight the crucial involvement of C1qa in endothelial dysfunction associated with diabetes.

Development and validation of a risk prediction model for physical frailty in older adults who are disabled.

Physical frailty is highly prevalent among the older adults who are disabled. The aim of this study was to explore the risk factors for physical frailty in older adults who are disabled and construct a nomogram prediction model. The data source was the China Health and Retirement Longitudinal Study (CHARLS). The prediction model was validated with a cohort of 1183 older adults who are disabled. The results showed that sleep quality, depression, fatigue, and chronic disease were the best predictive factors. These factors were used to construct the nomogram model, which showed good concordance and accuracy. The prediction model yielded an Area under the curve (AUC) value of 0.760. Calibration curves showed significant agreement between the nomogram model and actual observations. Receiver operating characteristic (ROC) and Decision curve analysis (DCA) showed that the nomogram had good predictive performance. The nomogram is contributed to the screening of specific populations by clinicians.

Electrically Oriented Antibodies on Transistor for Monitoring Several Copies of Methylated DNA.

An antibody transistor is a promising biosensing platform for the diagnosis and monitoring of various diseases. Nevertheless, the low concentration and short half-life of biomarkers require biodetection at the trace-molecule level, which remains a challenge for existing antibody transistors. Herein, we demonstrate a graphene field-effect transistor (gFET) with electrically oriented antibody probes (EOA-gFET) for monitoring several copies of methylated DNA. The electric field confines the orientation of antibody probes on graphene and diminishes the distance between graphene and methylated DNAs captured by antibodies, generating more induced charges on graphene and amplifying the electric signal. EOA-gFET realizes a limit of detection (LoD) of ∼0.12 copy µL-1, reaching the lowest LoD reported before. EOA-gFET shows a distinguishable signal for liver cancer clinical serum samples within ∼6 min, which proves its potential as a powerful tool for disease screening and diagnosis.

Foliar uptake screening: A promising strategy for identifying wheat varieties with low lead accumulation.

Lead (Pb) contamination in wheat grain is of great concern, especially in North China. Atmospheric deposition is a major contributor to Pb accumulation in wheat grain. Screening low Pb accumulating wheat varieties has been an effective method for addressing Pb contamination in wheat grain. However, identifying wheat varieties with low Pb accumulation based on foliar uptake of atmospheric Pb has been neglected. Therefore, two field trials with distinct atmospheric Pb deposition were conducted to screen for stable varieties with low Pb accumulation. It was verified that YB700 and CH58, which have high thousand-grain weights and stable low Pb accumulation in field 1 (0.19 and 0.13 mg kg-1) and field 2 (0.17 and 0.20 mg kg-1), respectively, were recommended for cultivation in atmospheric Pb contaminated farmlands in North China. Furthermore, indoor experiments were conducted to investigate Pb uptake by the roots and leaves of different wheat varieties. Our findings indicate that Pb accumulation in different wheat varieties is primarily influenced by foliar Pb uptake rather than root Pb uptake. Interestingly, there was a positive correlation (p < 0.05) between the Pb concentrations in leaves and the stomatal width and trichome length of the adaxial epidermal surface. Additionally, there is a positive correlation (p < 0.01) between the Pb concentration in the wheat grain and trichome length. In conclusion, the screening of wheat varieties with narrower stomatal widths or shorter trichomes based on foliar uptake pathways is an effective strategy for ensuring food safety in areas contaminated by atmospheric Pb.

Dyadic coping, resilience, and quality of life in young and middle-aged couples after gynecologic cancer: An actor-partner interdependence mediation model.

PURPOSE: To examine the effects of dyadic coping on quality of life (QoL) and the mediating role of resilience in these effects among young and middle-aged couples after gynecologic cancer (GC). METHODS: A cross-sectional study was conducted between July 2022 and June 2023 from one tertiary hospital in Wuhan, China. 240 pairs of young and middle-aged GC couples were recruited. The demographic and clinical characteristics questionnaire, the Dyadic Coping Inventory, the 10-item Connor-Davidson Resilience Scale, and the 12-item Short-Form Health Survey were used to collect data. The process of dyadic analysis was based on the actor-partner interdependence mediation model. RESULTS: GC patients' dyadic coping had an actor effect on both their own physical and mental QoL, while spouses' dyadic coping only exerted an actor effect on their own mental QoL. The mediating effects of resilience on the relationship between dyadic coping and QoL were identified in dyads. Moreover, spouses' dyadic coping could indirectly influence patients' QoL through their own and patients' resilience. CONCLUSION: The findings confirm the dyadic relationships between dyadic coping, resilience, and QoL among young and middle-aged couples facing GC. These results suggest that it is necessary to develop couple-based interventions to improve dyadic coping and resilience, thus enhancing the QoL of both members.

Identifying symptom clusters and temporal interconnections in patients with lung tumors after CT-guided microwave ablation: A network analysis.

PURPOSE: To explore symptom clusters and interrelationships using a network analysis approach among symptoms in patients with lung tumors who underwent computed tomography (CT)-guided microwave ablation (MWA). METHODS: A longitudinal study was conducted, and 196 lung tumor patients undergoing MWA were recruited and were measured at 24 h, 48 h, and 72 h after MWA. The Chinese version of the MD Anderson Symptom Inventory and the Revised Lung Cancer Module were used to evaluate symptoms. Network analyses were performed to explore the symptom clusters and interrelationships among symptoms. RESULTS: Four stable symptom communities were identified within the networks. Distress, weight loss, and chest tightness were the central symptoms. Distress, and weight loss were also the most key bridge symptoms, followed by cough. Three symptom networks were temporally stable in terms of symptom centrality, global connectivity, and network structure. CONCLUSION: Our findings identified the central symptoms, bridge symptoms, and the stability of symptom networks of patients with lung tumors after MWA. These network results will have important implications for future targeted symptom management intervention development. Future research should focus on developing precise interventions for targeting central symptoms and bridge symptoms to promote patients' health.

Plastome structure, phylogeny and evolution of plastid genes in Reevesia (Helicteroideae, Malvaceae).

Reevesia is an eastern Asian-eastern North American disjunction genus in the family Malvaceae s.l. and comprises approximately 25 species. The relationships within the genus are not well understood. Here, 15 plastomes representing 12 Reevesia species were compared, with the aim of better understanding the species circumscription and phylogenetic relationships within the genus and among genera in the family Malvaceae s.l. The 11 newly sequenced plastomes range between 161,532 and 161, 945 bp in length. The genomes contain 114 unique genes, 18 of which are duplicated in the inverted repeats (IRs). Gene content of these plastomes is nearly identical. All the protein-coding genes are under purifying selection in the Reevesia plastomes compared. The top ten hypervariable regions, SSRs, and the long repeats identified are potential molecular markers for future population genetic and phylogenetic studies. Phylogenetic analysis based on the whole plastomes confirmed the monophyly of Reevesia and a close relationship with Durio (traditional Bombacaceae) in subfamily Helicteroideae, but not with the morphologically similar genera Pterospermum and Sterculia (both of traditional Sterculiaceae). Phylogenetic relationships within Reevesia suggested that two species, R. pubescens and R. thyrsoidea, as newly defined, are not monophyletic. Six taxa, R. membranacea, R. xuefengensis, R. botingensis, R. lofouensis, R. longipetiolata and R. pycnantha, are suggested to be recognized.

A Cross-Process Signal Integrity Analysis (CPSIA) Method and Design Optimization for Wafer-on-Wafer Stacked DRAM.

A multi-layer stacked Dynamic Random Access Memory (DRAM) platform is introduced to address the memory wall issue. This platform features high-density vertical interconnects established between DRAM units for high-capacity memory and logic units for computation, utilizing Wafer-on-Wafer (WoW) hybrid bonding and mini Through-Silicon Via (TSV) technologies. This 3DIC architecture includes commercial DRAM, logic, and 3DIC manufacturing processes. Their design documents typically come from different foundries, presenting challenges for signal integrity design and analysis. This paper establishes a lumped circuit based on 3DIC physical structure and calculates all values of the lumped elements in the circuit model with the transmission line model. A Cross-Process Signal Integrity Analysis (CPSIA) method is introduced, which integrates three different manufacturing processes by modeling vertical stacking cells and connecting DRAM and logic netlists in one simulation environment. In combination with the dedicated buffer driving method, the CPSIA method is used to analyze 3DIC impacts. Simulation results show that the timing uncertainty introduced by 3DIC crosstalk ranges from 31 ps to 62 ps. This analysis result explains the stable slight variation in the maximum frequency observed in vertically stacked memory arrays from different DRAM layers in the physical testing results, demonstrating the effectiveness of this CPSIA method.

Pixel-by-pixel correction of beam hardening artifacts by bowtie filter in fan-beam CT.

Objective. Beam hardening (BH) artifacts in computed tomography (CT) images originate from the polychromatic nature of x-ray photons. In a CT system with a bowtie filter, residual BH artifacts remain when polynomial fits are used. These artifacts lead to worse visuals, reduced contrast, and inaccurate CT numbers. This work proposes a pixel-by-pixel correction (PPC) method to reduce the residual BH artifacts caused by a bowtie filter.Approach. The energy spectrum for each pixel at the detector after the photons pass through the bowtie filter was calculated. Then, the spectrum was filtered through a series of water slabs with different thicknesses. The polychromatic projection corresponding to the thickness of the water slab for each detector pixel could be obtained. Next, we carried out a water slab experiment with a mono energyE= 69 keV to get the monochromatic projection. The polychromatic and monochromatic projections were then fitted with a 2nd-order polynomial. The proposed method was evaluated on digital phantoms in a virtual CT system and phantoms in a real CT machine.Main results. In the case of a virtual CT system, the standard deviation of the line profile was reduced by 23.8%, 37.3%, and 14.3%, respectively, in the water phantom with different shapes. The difference of the linear attenuation coefficients (LAC) in the central and peripheral areas of an image was reduced from 0.010 to 0.003cm-1and 0.007cm-1to 0 in the biological tissue phantom and human phantom, respectively. The method was also validated using CT projection data obtained from Activion16 (Canon Medical Systems, Japan). The difference in the LAC in the central and peripheral areas can be reduced by a factor of two.Significance. The proposed PPC method can successfully remove the cupping artifacts in both virtual and authentic CT images. The scanned object's shapes and materials do not affect the technique.

Deep learning-based target tracking with X-ray images for radiotherapy: a narrative review.

Background and Objective: As one of the main treatment modalities, radiotherapy (RT) (also known as radiation therapy) plays an increasingly important role in the treatment of cancer. RT could benefit greatly from the accurate localization of the gross tumor volume and circumambient organs at risk (OARs). Modern linear accelerators (LINACs) are typically equipped with either gantry-mounted or room-mounted X-ray imaging systems, which provide possibilities for marker-less tracking with two-dimensional (2D) kV X-ray images. However, due to organ overlapping and poor soft tissue contrast, it is challenging to track the target directly and precisely with 2D kV X-ray images. With the flourishing development of deep learning in the field of image processing, it is possible to achieve real-time marker-less tracking of targets with 2D kV X-ray images in RT using advanced deep-learning frameworks. This article sought to review the current development of deep learning-based target tracking with 2D kV X-ray images and discuss the existing limitations and potential solutions. Finally, it also discusses some common challenges and potential future developments. Methods: Manual searches of the Web of Science, and PubMed, and Google Scholar were carried out to retrieve English-language articles. The keywords used in the searches included "radiotherapy, radiation therapy, motion tracking, target tracking, motion estimation, motion monitoring, X-ray images, digitally reconstructed radiographs, deep learning, convolutional neural network, and deep neural network". Only articles that met the predetermined eligibility criteria were included in the review. Ultimately, 23 articles published between March 2019 and December 2023 were included in the review. Key Content and Findings: In this article, we narratively reviewed deep learning-based target tracking with 2D kV X-ray images in RT. The existing limitations, common challenges, possible solutions, and future directions of deep learning-based target tracking were also discussed. The use of deep learning-based methods has been shown to be feasible in marker-less target tracking and real-time motion management. However, it is still quite challenging to directly locate tumor and OARs in real-time with 2D kV X-ray images, and more technical and clinical efforts are needed. Conclusions: Deep learning-based target tracking with 2D kV X-ray images is a promising method in motion management during RT. It has the potential to track the target in real time, recognize motion, reduce the extended margin, and better spare the normal tissue. However, it still has many issues that demand prompt attention, and further development before it can be put into clinical practice.

[The experience on the construction of the cluster prevention and control system for COVID-19 infection in designated hospitals during the period of "Category B infectious disease treated as Category A"].

The COVID-19 epidemic has spread to the whole world for three years and has had a serious impact on human life, health and economic activities. China's epidemic prevention and control has gone through the following stages: emergency unconventional stage, emergency normalization stage, and the transitional stage from the emergency normalization to the "Category B infectious disease treated as Category B" normalization, and achieved a major and decisive victory. The designated hospitals for prevention and control of COVID-19 epidemic in Tianjin has successfully completed its tasks in all stages of epidemic prevention and control, and has accumulated valuable experience. This article summarizes the experience of constructing a hospital infection prevention and control system during the "Category B infectious disease treated as Category A" period in designated hospital. The experience is summarized as the "Cluster" hospital infection prevention and control system, namely "three rings" outside, middle and inside, "three districts" of green, orange and red, "three things" before, during and after the event, "two-day pre-purification" and "two-director system", and "one zone" management. In emergency situations, we adopt a simplified version of the cluster hospital infection prevention and control system. In emergency situations, a simplified version of the "Cluster" hospital infection prevention and control system can be adopted. This system has the following characteristics: firstly, the system emphasizes the characteristics of "cluster" and the overall management of key measures to avoid any shortcomings. The second, it emphasizes the transformation of infection control concepts to maximize the safety of medical services through infection control. The third, it emphasizes the optimization of the process. The prevention and control measures should be comprehensive and focused, while also preventing excessive use. The measures emphasize the use of the least resources to achieve the best infection control effect. The fourth, it emphasizes the quality control work of infection control, pays attention to the importance of the process, and advocates the concept of "system slimming, process fattening". Fifthly, it emphasizes that the future development depends on artificial intelligence, in order to improve the quality and efficiency of prevention and control to the greatest extent. Sixth, hospitals need to strengthen continuous training and retraining. We utilize diverse training methods, including artificial intelligence, to ensure that infection control policies and procedures are simple. We have established an evaluation and feedback mechanism to ensure that medical personnel are in an emergency state at all times.