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.

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.

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 diabetic 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 (SMCs), 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 to 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.

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.

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.

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.

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.

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.

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.

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.

Analysis of the inhibitory effect of a refined human resources salary system on job transfer and transfer tendency of nurses.

To evaluate the impact of a refined human resources salary system on job transfer and transfer tendency. From January 2019 to December 2019, a refined human resources salary system reform was implemented at the Tangshan Workers' Hospital in Hebei Province, and the job transfer and transfer tendency of clinical nurses was assessed using the nurse job transfer tendency scale before and 1 year after the intervention. A total of 640 nurses completed the intervention and evaluation. The results showed that the job transfer rate following the intervention reduced to 0.22%. The total score of clinical nurse job transfer tendency was (10.80 ±â€…3.23) before the intervention and (9.66 ±â€…3.58) after 1 year of intervention, which was substantially lower (P < .001). The satisfaction scores of nurses on performance-based salary increased significantly from (67.83 ±â€…18.54) before the intervention to (80.66 ±â€…15.87) after intervention, with varying degrees of increase observed in each dimension (P < .001). The refined human resources salary system effectively reduced job transfer and transfer tendency of clinical nurses in hospital nursing management, and can be widely promoted and applied.

Study on the optimal time limit of frozen embryo transfer and the effect of a long-term frozen embryo on pregnancy outcome.

In this retrospective study conducted at Sichuan Jinxin Xinan Women and Children's Hospital spanning January 2015 to December 2021, our objective was to investigate the impact of embryo cryopreservation duration on outcomes in frozen embryo transfer. Participants, totaling 47,006 cycles, were classified into 3 groups based on cryopreservation duration: ≤1 year (Group 1), 1 to 6 years (Group 2), and ≥6 years (Group 3). Employing various statistical analyses, including 1-way ANOVA, Kruskal-Wallis test, chi-square test, and a generalized estimating equation model, we rigorously adjusted for confounding factors. Primary outcomes encompassed clinical pregnancy rate and Live Birth Rate (LBR), while secondary outcomes included biochemical pregnancy rate, multiple pregnancy rate, ectopic pregnancy rate, early and late miscarriage rates, preterm birth rate, neonatal birth weight, weeks at birth, and newborn sex. Patient distribution across cryopreservation duration groups was as follows: Group 1 (40,461 cycles), Group 2 (6337 cycles), and Group 3 (208 cycles). Postcontrolling for confounding factors, Group 1 exhibited a decreased likelihood of achieving biochemical pregnancy rate, clinical pregnancy rate, and LBR (OR < 1, aOR < 1, P < .05). Furthermore, an elevated incidence of ectopic pregnancy was observed (OR > 1, aOR > 1), notably significant after 6 years of freezing time [aOR = 4.141, 95% confidence intervals (1.013-16.921), P = .05]. Cryopreservation exceeding 1 year was associated with an increased risk of early miscarriage and preterm birth (OR > 1, aOR > 1). No statistically significant differences were observed in birth weight or sex between groups. However, male infant birth rates were consistently higher than those of female infants across all groups. In conclusion, favorable pregnancy outcomes align with embryo cryopreservation durations within 1 year, while freezing for more than 1 year may diminish clinical pregnancy and LBRs, concurrently elevating the risk of ectopic pregnancy and preterm birth.

[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.

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.

Rare esophageal carcinoma-primary adenoid cystic carcinoma of the esophagus: A case report.

BACKGROUND: Esophageal adenoid cystic carcinoma (EACC) is an exceedingly rare malignant tumor of the esophagus, posing significant challenges in the clinic. CASE SUMMARY: This report detailed the case of a 72-year-old male whose diagnosis of EACC was confirmed through postoperative histopathological examination. The patient underwent thoracoscopy-assisted radical resection of the esophageal tumor, coupled with lymph node dissection. Pathological findings revealed an adenoid cystic carcinoma infiltrating the entire layer of the muscularis propria, locally extending into the outer membrane of the esophageal fiber, involving the cardia and exhibiting no lymph node metastasis. The patient's condition was classified as primary EACC, T3N0M0, per the American Joint Committee on Cancer (2017; 8th edition). One month after surgery, the patient received postoperative adjuvant radiation therapy. CONCLUSION: In addressing the rarity and high potential for biopsy misdiagnosis of EACC, this study delved into its diagnostic methods and treatment.

Self-supervised context-aware correlation filter for robust landmark tracking in liver ultrasound sequences.

OBJECTIVES: Respiratory motion-induced displacement of internal organs poses a significant challenge in image-guided radiation therapy, particularly affecting liver landmark tracking accuracy. METHODS: Addressing this concern, we propose a self-supervised method for robust landmark tracking in long liver ultrasound sequences. Our approach leverages a Siamese-based context-aware correlation filter network, trained by using the consistency loss between forward tracking and back verification. By effectively utilizing both labeled and unlabeled liver ultrasound images, our model, Siam-CCF , mitigates the impact of speckle noise and artifacts on ultrasonic image tracking by a context-aware correlation filter. Additionally, a fusion strategy for template patch feature helps the tracker to obtain rich appearance information around the point-landmark. RESULTS: Siam-CCF achieves a mean tracking error of 0.79 ± 0.83 mm at a frame rate of 118.6 fps, exhibiting a superior speed-accuracy trade-off on the public MICCAI 2015 Challenge on Liver Ultrasound Tracking (CLUST2015) 2D dataset. This performance won the 5th place on the CLUST2015 2D point-landmark tracking task. CONCLUSIONS: Extensive experiments validate the effectiveness of our proposed approach, establishing it as one of the top-performing techniques on the CLUST2015 online leaderboard at the time of this submission.