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Electroencephalography (EEG) signals are crucial for investigating brain function and cognitive processes. This study aims to address the challenges of efficiently recording and analyzing high-dimensional EEG signals while listening to music to recognize emotional states. We propose a method combining Bidirectional Long Short-Term Memory (Bi-LSTM) networks with attention mechanisms for EEG signal processing. Using wearable EEG devices, we collected brain activity data from participants listening to music. The data was preprocessed, segmented, and Differential Entropy (DE) features were extracted. We then constructed and trained a Bi-LSTM model to enhance key feature extraction and improve emotion recognition accuracy. Experiments were conducted on the SEED and DEAP datasets. The Bi-LSTM-AttGW model achieved 98.28% accuracy on the SEED dataset and 92.46% on the DEAP dataset in multi-class emotion recognition tasks, significantly outperforming traditional models such as SVM and EEG-Net. This study demonstrates the effectiveness of combining Bi-LSTM with attention mechanisms, providing robust technical support for applications in brain–computer interfaces (BCI) and affective computing. Future work will focus on improving device design, incorporating multimodal data, and further enhancing emotion recognition accuracy, aiming to achieve practical applications in real-world scenarios.

Abstract Background Conventional single-energy CT can only provide a raw estimation of electron density (ED) for dose calculation by developing a calibration curve that simply maps the HU values to ED values through their correlations. Spectral CT, also known as dual-energy CT (DECT) or multi-energy CT, can generate a series of quantitative maps, such as ED maps. Using spectral CT for radiotherapy simulations can directly acquire ED information without developing specific calibration curves. The purpose of this study is to assess the feasibility of utilizing electron density (ED) maps generated by a novel dual-layer detector spectral CT simulator for dose calculation in radiotherapy treatment plans. Methods 30 patients from head&neck, chest, and pelvic treatment sites were selected retrospectively, and all of them underwent spectral CT simulation. Treatment plans based on conventional CT images were transplanted to ED maps with the same structure set, including planning target volume (PTV) and organs at risk (OARs), and the dose distributions were then recalculated. The differences in dose and volume histogram (DVH) parameters of the PTV and OARs between the two types of plans were analyzed and compared. Besides, gamma analysis between these plans was performed by using MEPHYSTO Navigator software. Results In terms of PTV, the homogeneity index (HI), gradient index (GI), D2%, D98%, and Dmean showed no significant difference between conventional plans and ED plans. For OARs, statistically significant differences were observed in parotids D50%, brainstem in head&neck plans, spinal cord in chest plans and rectum D50% in pelvic plans, whereas the variance remained minor. For the rest, the DVH parameters exhibited no significant difference between conventional plans and ED plans. All of the mean gamma passing rates (GPRs) of gamma analysis were higher than 90%. Conclusion Compared to conventional treatment plans relying on CT images, plans utilizing ED maps demonstrated similar dosimetric quality. However, the latter approach enables direct utilization in dose calculation without the requirements of establishing and selecting a specific Hounsfield unit (HU) to ED calibration curve, providing an advantage in clinical applications.

Recently, research on wearable devices for physiological exercise monitoring has garnered significant attention. Here, we propose a dual wave-structured triboelectric nanogenerator (DW-TENG) integrated with a cotton cloth, developed for smart running applications. The DW-TENG sensor leverages a flexible wave triboelectric layer composed of polydimethylsiloxane and silicone, with a copper electrode layer between them for sensing. This structure allows for customizable pressure sensitivity by adjusting the silicone hardness. Experimental results show that the DW-TENG achieves a sensitivity of 0.4 V kPa−1, with response and recovery times of 75 and 90 ms, respectively. The sensor effectively measures heart rate changes during various physical activities, including walking, running, and jumping. Electrical performance tests reveal that the DW-TENG’s output is significantly influenced by the silicone hardness, operational frequency, and microstructure height. The DW-TENG sensor demonstrates high durability and stability, maintaining consistent voltage output over 40 000 cycles. This research highlights the potential of the DW-TENG in multifunctional physiological and physical activity monitoring, providing real-time data on respiratory patterns, heart rate, and movement dynamics, thus enhancing athletic training, performance assessment, and health monitoring.

Abstract Background Tumor regression and organ movements indicate that a large margin is used to ensure target volume coverage during radiotherapy. This study aimed to quantify inter-fractional movements of the uterus and cervix in patients with cervical cancer undergoing radiotherapy and to evaluate the clinical target volume (CTV) coverage. Methods This study analyzed 303 iterative cone beam computed tomography (iCBCT) scans from 15 cervical cancer patients undergoing external beam radiotherapy. CTVs of the uterus (CTV-U) and cervix (CTV-C) contours were delineated based on each iCBCT image. CTV-U encompassed the uterus, while CTV-C included the cervix, vagina, and adjacent parametrial regions. Compared with the planning CTV, the movement of CTV-U and CTV-C in the anterior-posterior, superior-inferior, and lateral directions between iCBCT scans was measured. Uniform expansions were applied to the planning CTV to assess target coverage. Results The motion (mean ± standard deviation) in the CTV-U position was 8.3 ± 4.1 mm in the left, 9.8 ± 4.4 mm in the right, 12.6 ± 4.0 mm in the anterior, 8.8 ± 5.1 mm in the posterior, 5.7 ± 5.4 mm in the superior, and 3.0 ± 3.2 mm in the inferior direction. The mean CTV-C displacement was 7.3 ± 3.2 mm in the left, 8.6 ± 3.8 mm in the right, 9.0 ± 6.1 mm in the anterior, 8.4 ± 3.6 mm in the posterior, 5.0 ± 5.0 mm in the superior, and 3.0 ± 2.5 mm in the inferior direction. Compared with the other tumor (T) stages, CTV-U and CTV-C motion in stage T1 was larger. A uniform CTV planning treatment volume margin of 15 mm failed to encompass the CTV-U and CTV-C in 11.1% and 2.2% of all fractions, respectively. The mean volume change of CTV-U and CTV-C were 150% and 51%, respectively, compared with the planning CTV. Conclusions Movements of the uterine corpus are larger than those of the cervix. The likelihood of missing the CTV is significantly increased due to inter-fractional motion when utilizing traditional planning margins. Early T stage may require larger margins. Personal radiotherapy margining is needed to improve treatment accuracy.

Online video conferencing services connection by multipoint control units leads to communication bottleneck and high latency. Due to the dynamic traffic in routing connection requests to video conferences, the existing routing algorithms face problems such as communication bottlenecks, high latency, congestion, path redundancy, and local optimum. In recent years, the implementation of video conferencing systems using Software-Defined Networking (SDN) provides new opportunities to improve Quality of Service (QoS) with low latency. In this paper, an Intelligent Fuzzy reinforcement learning-based Routing Algorithm is proposed, which simultaneously Guarantees Latency and Bandwidth for online video conferencing services in SDN (named IFRA-GLB). Fuzzy logic mainly performs online routing between an ingress–egress pair. Meanwhile, through ongoing training, reinforcement learning lowers the average number of hops on the path determined by fuzzy model. Enhancements to the convergence capability of IFRA and reduced reliance on network topology are achieved by adjusting link weights using a weighted shortest path algorithm focused on critical nodes. When the network encounters congestion, a deferral module is applied to assign higher priority to requests with lower resource demands. Experimental results demonstrate that IFRA-GLB significantly improves performance and convergence compared to existing solutions, enhancing the QoS for video conferencing services. Specifically, IFRA-GLB increases the average admission rate by 1.96% for MIRA topology and 2.71% for ANSNET topology with 3.5% lower latency.

During outdoor work in April 2022, a 48-year-old man was stabbed by a tree branch and underwent intraocular foreign body extraction and repair of the scleral wound with sutures and amniotic membrane graft at a local hospital. Steroid therapy with prednisone was prescribed after a diagnosis of uveitis. Vitrectomy was performed in June 2023; a fungal culture was positive, and ITS sequencing identified the organism as Paradictyoarthrinium diffractum. Empiric antifungal therapy did not have an effect, and, because of deterioration of the condition, the left eye was enucleated in October 2023. P. diffractum is a mangrove host-specific saprophytic fungus that has not been reported in humans.

In recent years, the rise of machine learning (ML) has prompted researchers to expand the datasets required for optimizing and designing thermal systems. Also, the development and widespread use of electric vehicles (EVs) have surged significantly. However, one of the major challenges associated with EVs is the efficient cooling of Lithium-ion batteries (LIBs). Therefore, the exploration of innovative cooling methods can contribute greatly to the rapidly growing electric vehicle industry. This study focused on investigating the impact of embedding a nickel porous medium around a single 38,120 LiFeO4 cell. To conduct the study, the LIB, along with the nickel porous medium, was placed inside a duct that received a flow of water and Nano-encapsulated phase change materials (NEPCMs). The results obtained from the study indicate that embedding nickel porous media around the LIB led to a significant decrease in the maximum temperature of LIB, more than 40 C, and a remarkable increase in pressure drop more than 100 times. Additionally, it was observed that the decrease in porosity from 1 to 0.97 had a more pronounced effect on pressure drop and the maximum temperature of the LIB's surface, compared to the decrease from 0.97 to 0.95.

Purpose: This study evaluated the first clinical implementation of daily iterative cone beam computed tomography (iCBCT)-guided online adaptive radiation therapy (oART) in the postoperative treatment of endometrial and cervical cancer. Methods and Materials: Seventeen consecutive patients treated with daily iCBCT-guided oART were enrolled in this prospective study, with a reduced uniform 3-dimensional PTV margin of 5 mm. Treatment plans were designed to deliver 45 or 50.4 Gy in 1.8 Gy daily fractions to PTV. Pre- and posttreatment ultrasound and iCBCT scans were performed to record intrafractional bladder and rectal volume changes. The accuracy of contouring, oART procedure time, dosimetric outcomes, and acute toxicity were evaluated. Results: The average time from first iCBCT acquisition to completion of treatment was 22 minutes and 26 seconds. During this period, bladder volume increased by 44 cm3 using iCBCT contouring, whereas rectal volume remained stable (62.9 cm3 pretreatment vs 61.9 cm3 posttreatment). A total of 91.6% of influencers and 88.1% of CTVs required no or minor edits. The adapted plan was selected in all (434) fractions and significantly improved the dosimetry coverage for CTV and PTV, especially the vaginal PTV coverage by nearly 7% (P < .05). The adapted bladder Dmean was 104.61 cGy, and the rectum Dmean was 123.67 cGy, significantly lower than the scheduled plan of 108.24 and 128.19 cGy, respectively. The bone marrow and femur head left and right dosimetry were also improved with adaptation. Grade 2 acute gastrointestinal and genitourinary toxicities were 24% and 0, respectively. There was a grade 3 acute toxicity of decreased white blood cell count in 1 patient. Conclusions: Daily oART was associated with favorable dosimetry improvement and low acute toxicity, supporting its safety and efficacy for postoperative treatment of endometrial and cervical cancer. These results need to be validated in a larger prospective randomized controlled cohort.

Abstract Background To determine the optimal planning target volume (PTV) margins for adequate coverage by daily iterative cone-beam computed tomography (iCBCT)-guided online adaptive radiotherapy (oART) in postoperative treatment of endometrial and cervical cancer and the benefit of reducing PTV margins. Methods Fifteen postoperative endometrial and cervical cancer patients treated with daily iCBCT-guided oART were enrolled in this prospective phase 2 study. Pre- and posttreatment iCBCT images of 125 fractions from 5 patients were obtained as a training cohort, and clinical target volumes (CTV) were contoured separately. Uniform three-dimensional expansions were applied to the PTVpre to assess the minimum margin required to encompass the CTVpost. The dosimetric advantages of the proposed online adaptive margins were compared with conventional margin plans (7–15 mm) using an oART emulator in another cohort of 125 iCBCT scans. A CTV-to-PTV expansion was verified on a validation cohort of 253 fractions from 10 patients, and further margin reduction and acute toxicity were studied. Results The average time from pretreatment iCBCT to posttreatment iCBCT was 22 min. A uniform PTV margin of 5 mm could encompass nodal CTVpost in 100% of the fractions (175/175) and vaginal CTVpost in 98% of the fractions (172/175). The margin of 5 mm was verified in our validation cohort, and the nodal PTV margin could be further reduced to 4 mm if ≥ 95% CTV coverage was predicted to be achieved. The adapted plan with a 5 mm margin significantly improved pelvic organ-at-risk dosimetry compared with the conventional margin plan. Grade 3 toxicities were observed in only one patient with leukopenia, and no patients experienced acute urinary toxicity. Conclusion In the postoperative treatment of endometrial and cervical cancer, oART could reduce PTV margins to 5 mm, which significantly decrease the dose to critical organs at risk and potentially lead to a lower incidence of acute toxicity.

The functional architecture undergoes alterations during the preclinical phase of Alzheimer’s disease. Consequently, the primary research focus has shifted towards identifying Alzheimer’s disease and its early stages by constructing a functional connectivity network based on resting-state fMRI data. Recent investigations show that as Alzheimer’s Disease (AD) progresses, modular tissue and connections in the core brain areas of AD patients diminish. Sparse learning methods are powerful tools for understanding Functional Brain Networks (FBNs) with Regions of Interest (ROIs) and a connectivity matrix measuring functional coherence between them. However, these tools often focus exclusively on functional connectivity measures, neglecting the brain network’s modularity. Modularity orchestrates dynamic activities within the FBN to execute intricate cognitive tasks. To provide a comprehensive delineation of the FBN, we propose a local similarity-constrained low-rank sparse representation (LSLRSR) method that encodes modularity information under a manifold-regularized network learning framework and further formulate it as a low-rank sparse graph learning problem, which can be solved by an efficient optimization algorithm. Specifically, for each modularity structure, the Schatten p-norm regularizer reduces the reconstruction error and provides a better approximation of the low-rank constraint. Furthermore, we adopt a manifold-regularized local similarity prior to infer the intricate relationship between subnetwork similarity and modularity, guiding the modeling of FBN. Additionally, the proximal average method approximates the joint solution’s proximal map, and the resulting nonconvex optimization problems are solved using the alternating direction multiplier method (ADMM). Compared to state-of-the-art methods for constructing FBNs, our algorithm generates a more modular FBN. This lays the groundwork for further research into alterations in brain network modularity resulting from diseases.

Abstract Background Training senior radiation therapists as “adapters” to manage influencers and target editing is critical in daily online adaptive radiotherapy (oART) for cervical cancer. The purpose of this study was to evaluate the accuracy and dosimetric outcomes of automatic contouring and identify the key areas for modification. Methods A total of 125 oART fractions from five postoperative cervical cancer patients and 140 oART fractions from five uterine cervical cancer patients treated with daily iCBCT-guided oART were enrolled in this prospective study. The same adaptive treatments were replanned using the Ethos automatic contours workflow without manual contouring edits. The clinical target volume (CTV) was subdivided into several separate regions, and the average surface distance dice (ASD), centroid deviation, dice similarity coefficient (DSC), and 95% Hausdorff distance (95% HD) were used to evaluate contouring for the above portions. Dosimetric results from automatic oART plans were compared to supervised oART plans to evaluate target volumes and organs at risk (OARs) dose changes. Results Overall, the paired CTV had high overlap rates, with an average DSC value greater than 0.75. The uterus had the largest consistency differences, with ASD, centroid deviation, and 95% HD being 2.67 ± 1.79 mm, 17.17 ± 12 mm, and 10.45 ± 5.68 mm, respectively. The consistency differences of the lower nodal CTVleft and nodal CTVright were relatively large, with ASD, centroid deviation, and 95% HD being 0.59 ± 0.53 mm, 3.6 ± 2.67 mm, and 5.41 ± 4.08 mm, and 0.59 ± 0.51 mm, 3.6 ± 2.54 mm, and 4.7 ± 1.57 mm, respectively. The automatic online-adapted plan met the clinical requirements of dosimetric coverage for the target volume and improved the OAR dosimetry. Conclusions The accuracy of automatic contouring from the Ethos adaptive platform is considered clinically acceptable for cervical cancer, and the uterus, upper vaginal cuff, and lower nodal CTV are the areas that need to be focused on in training.

ObjectiveRecent research has highlighted the insula as a critical hub in human brain networks and the most susceptible region to subjective cognitive decline (SCD). However, the changes in functional connectivity of insular subregions in SCD patients remain poorly understood. The present study aims to clarify the altered functional connectivity patterns within insular subregions in individuals with SCD using resting-state functional magnetic resonance imaging (rs-fMRI).MethodsIn this study, we collected rs-fMRI data from 30 patients with SCD and 28 healthy controls (HCs). By defining three subregions of the insula, we mapped whole-brain resting-state functional connectivity (RSFC). We identified several distinct RSFC patterns of the insular subregions. Specifically, for positive connectivity, three cognitive-related RSFC patterns were identified within the insula, suggesting anterior-to-posterior functional subdivisions: (1) a dorsal anterior zone of the insula that shows RSFC with the executive control network (ECN); (2) a ventral anterior zone of the insula that shows functional connectivity with the salience network (SN); and (3) a posterior zone along the insula that shows functional connectivity with the sensorimotor network (SMN).ResultsCompared to the controls, patients with SCD exhibited increased positive RSFC to the sub-region of the insula, demonstrating compensatory plasticity. Furthermore, these abnormal insular subregion RSFCs are closely correlated with cognitive performance in the SCD patients.ConclusionOur findings suggest that different insular subregions exhibit distinct patterns of RSFC with various functional networks, which are affected differently in patients with SCD.

Microbial keratitis (MK) is the fourth leading cause of blindness globally, imposing a substantial burden on the healthcare system. This study aims to determine the cost composition of MK patients and explore factors influencing these expenses. We analyzed the demographics, clinical features, and costs of 602 MK patients treated at Beijing Tongren Hospital from June 2021 to October 2023. The analysis revealed the average total cost of treating MK was USD 1646.8, with a median of USD 550.3 (IQR: 333.3–1239.1). Patients with Acanthamoeba keratitis (AK) incurred the highest median total costs at USD 706.2 (IQR: 399.2–3370.2). Additionally, AK patients faced the highest costs for ophthalmic exams and laboratory tests (both p < 0.001), while patients with fungal keratitis (FK) and viral keratitis (VK) experienced higher medication costs. Costs varied significantly with the severity of MK, especially for outpatients at severity level 4, which was markedly higher than levels 1–3 (USD 1520.1 vs. USD 401.0, p < 0.001). Delayed presentation also resulted in increased costs (USD 385.2 vs. USD 600.3, p < 0.001). Our study highlights the financial burden associated with MK treatment and underscores the importance of timely and accurate diagnosis and intervention.

ObjectiveTo explore the value of multiparametric magnetic resonance imaging(MRI) radiomics in the preoperative prediction of isocitrate dehydrogenase (IDH) genotype for gliomasMethodsThe preoperative routine MRI sequences of 114 patients with pathologically confirmed grade II-IV gliomas were retrospectively analysed. All patients were randomly divided into training cohort(n=79) and validation cohort(n=35) in the ratio of 7:3. After feature extraction, we eliminated covariance by calculating the linear correlation coefficients between features, and then identified the best features using the F-test. The Logistic regression was used to build the radiomics model and the clinical model, and to build the combined model. Assessment of these models by subject operating characteristic (ROC) curves, area under the curve (AUC), sensitivity and specificity.ResultsThe multiparametric radiomics model was built by eight selected radiomics features and yielded AUC values of 0.974 and 0.872 in the training and validation cohorts, which outperformed the conventional models. After incorporating the clinical model, the combined model outperformed the radiomics model, with AUCs of 0.963 and 0.892 for the training and validation cohorts.ConclusionRadiomic models based on multiparametric MRI sequences could help to predict glioma IDH genotype before surgery.

ObjectiveThe aim of this study is to investigate the value of ultrasound combined with computed tomography (CT) in identifying early low-grade appendiceal mucinous neoplasm and appendicitis.MethodsPatients with early low-grade appendiceal mucinous neoplasm and appendicitis from September 2017 to September 2021, including 40 patients with low-grade appendiceal mucinous neoplasm and 40 patients with appendicitis, were collected in this study. Clinical data as well as ultrasound and CT findings of all patients were retrospectively analyzed. Univariate and multivariate logistic regression analyses were applied to establish the ultrasound model, the CT model, and the combined model.ResultsThe nomogram showed that specific characteristics of CT were dilated appendiceal diameter and clear surrounding fat space in the low-grade appendiceal mucinous neoplasm and that specific characteristics of ultrasound were thin or clear layer appendix wall and flocculent echo in the appendix cavity. These four features were used to construct a nomogram for predicting early low-grade appendiceal mucinous neoplasm, and the area under the curve value was 0.839.ConclusionUltrasound combined with CT for diagnosis of early low-grade appendiceal mucinous neoplasm has a significant value; when found significantly dilated appendix in the lower right abdomen, with thin wall, wall calcification, clear surrounding fat space, and progressive enhancement, especially non-specific symptoms similar to appendicitis, the physician should timely consider the possibility of low-grade appendiceal mucinous neoplasm.

In the field of information and communication technology, the Internet of Things is regarded as a brand-new and important technology. The introduction of new protocols in this area is caused by the presence of devices in these networks with constrained resources and relatively low computing power. One of the most well-known routing protocols for low-power devices is the RPL protocol. This algorithm cannot take into account all of the required routing goals at once. This article introduces a proposed data-oriented RPL algorithm that divides data during routing according to their content. This can decrease the amount of duplicate data transferred through the network, shorten the communication system's delay, conserve the node's limited energy, and prolong the network's lifespan. The effectiveness of RPL can be increased by selecting the best route utilizing the Binary Gray Wolf Optimization. The best parent node in the routing procedure is chosen using an objective function during the tree construction phase. This objective function is built using fuzzy logic and the Binary Gray Wolf Optimization in the suggested technique. The results of Matlab 2022a and OMNET environment tests have shown that the proposed method has increased the efficiency of energy consumption and reduced the period of instability and end-to-end delay. that the ratio of the instability period in the proposed method is much less than the other three methods, so that the ratio of the instability period is 57% for the proposed method in the ORPL and Qos RPL methods, it is equal to 80%, and in the RPL method it is equal to 89%. This problem shows that the proposed method is more stable, or, in other words, it has been active for a longer period of time with the maximum number of nodes.

Introduction Systemic lupus erythematosus (SLE) is a prototypic autoimmune disorder with a variable clinical course, ranging from mild to severe forms. It mainly occurs in women, especially those of fertile age. The aim of the study was to systematically analyze the associations of perinatal disease activity with adverse outcomes of Chinese patients with SLE and their off-spring. Material and methods Data of prenatal SLE patients and healthy pregnant woman admitted to our hospital during the period October 2001 to January 2018 were retrospectively collected, and the status of offspring of SLE patients was followed up in March 2020. Disease activity was evaluated by SLE disease activity index 2000 (SLEDAI-2k), and those with scores > 6 were defined as having active disease. Results In total, 198 deliveries of 194 SLE patients and 199 deliveries of healthy women were documented. Maternal and fetal adverse outcomes occurred in 74 (37.4%) and 90 (45.5%) deliveries of SLE patients, respectively, which were significantly higher than those of healthy subjects. Among SLE patients, the active group had higher rates of gestational hypertension (p < 0.001), reeclampsia/eclampsia (p < 0.001), low birth weight (p < 0.001), premature birth (p < 0.001) and fetal growth restriction (FGR) (p < 0.01) than the inactive group. Multivariate logistic analysis revealed that perinatal renal activity was associated with gestational hypertension (OR 4.43, p < 0.001), preeclampsia/ eclampsia (OR 9.14, p < 0.001), low birth weight (OR 2.24, p < 0.05) and premature birth (OR 4.20, p < 0.001). Compared with the general population, offspring of SLE patients had relatively high rates of eczema (50/142, 35.2%) and congenital heart disease (6/142, 4.2%), which were irrelevant to perinatal disease activity, but related to specific antibodies. Conclusions For perinatal women with SLE, renal activity is associated with a variety of adverse pregnancy outcomes. However, maternal perinatal dis�ease activity does not seem to affect the growth of their offspring

Background/ObjectiveEarly recurrence (ER) affects the long-term survival prognosis of patients with hepatocellular carcinoma (HCC). Many previous studies have utilized CT/MRI-based radiomics to predict ER after radical treatment, achieving high predictive value. However, the diagnostic performance of radiomics for the preoperative identification of ER remains uncertain. Therefore, we aimed to perform a meta-analysis to investigate the predictive performance of radiomics for ER in HCC.MethodsA systematic literature search was conducted in PubMed, Web of Science (including MEDLINE), EMBASE and the Cochrane Central Register of Controlled Trials to identify studies that utilized radiomics methods to assess ER in HCC. Data were extracted and quality assessed for retrieved studies. Statistical analyses included pooled data, tests for heterogeneity, and publication bias. Meta-regression and subgroup analyses were performed to investigate potential sources of heterogeneity.ResultsThe analysis included fifteen studies involving 3,281 patients focusing on preoperative CT/MRI-based radiomics for the prediction of ER in HCC. The combined sensitivity, specificity, and area under the curve (AUC) of the receiver operating characteristic were 75% (95% CI: 65-82), 78% (95% CI: 68-85), and 83% (95% CI: 79-86), respectively. The combined positive likelihood ratio, negative likelihood ratio, diagnostic score, and diagnostic odds ratio were 3.35 (95% CI: 2.41-4.65), 0.33 (95% CI: 0.25-0.43), 2.33 (95% CI: 1.91-2.75), and 10.29 (95% CI: 6.79-15.61), respectively. Substantial heterogeneity was observed among the studies (I²=99%; 95% CI: 99-100). Meta-regression showed imaging equipment contributed to the heterogeneity of specificity in subgroup analysis (P= 0.03).ConclusionPreoperative CT/MRI-based radiomics appears to be a promising and non-invasive predictive approach with moderate ER recognition performance.

Abstract Objective To investigate the role of diffusion‐weighted imaging (DWI) for diagnosis and posttreatment assessment of hepatic fungal infection in patients with acute leukemia. Methods Patients with acute leukemia and highly suspected hepatic fungal infection were collected in the study. All the patients underwent MRI examination, including initial and follow‐up DWI. The apparent diffusion coefficient (ADC) values of the lesions and the normal liver parenchyma were compared using Student's t‐test. The ADC values of the hepatic fungal lesions of pretreatment and posttreatment were compared using paired t‐test. Results A total of 13 patients with hepatic fungal infections have enrolled this study. Hepatic lesions were rounded or oval shaped, measured from 0.3 to 3 cm in diameter. The lesions showed significantly hyperintense signal on DWI and markedly hypointense signal on the ADC map, reflecting a marked restricted diffusion. The mean ADC values of the lesions were significantly lower than those of normal liver parenchyma (1.08 ± 0.34 × 10−3 vs. 1.98 ± 0.12 × 10−3 mm2/s, p

Objective: To investigate the value of dual-energy CT left ventricular iodinogram imaging and plasma B-type natriuretic peptide level in the early diagnosis and prediction of heart failure and severity in patients with acute myocardial infarction. Methods: AMI patients admitted to our hospital from January 2018 to December 2019 were selected to complete dual-energy coronary artery CTA and plasma BNP examination, and cardiac function grading was assessed within 1 week after admission. Myocardial iodine content was measured to evaluate the myocardial perfusion on dual-energy CT. The correlation between myocardial iodine content, left ventricular ejection fraction and BNP level was analyzed, and the differences of myocardial perfusion, LVEF and BNP levels among different Killip cardiac function grading groups in HF patients were compared. Results: A total of 115 patients with AMI were enrolled, including 42 patients in HF group and 73 patients in non-HF group. There were significant differences in myocardial iodine concentration, LVEF and BNP between the two groups. Iodine concentration and LVEF were negatively correlated with BNP (r=−0.527, −0.674). The ROC curve showed that the AUC of myocardial iodine concentration combined with BNP in predicting HF after AMI was higher than that of iodine map and BNP alone. Among the 42 HF patients after AMI, with the increase of Killip grade, serum BNP level increased, LVEF and iodine content of infarct myocardial decreased, and there were statistically significant differences in BNP, LVEF and iodine content of infarct myocardial among different grade groups. Conclusion: Dual-energy CT left ventricular iodinogram combined with BNP level has predictive value on the occurrence of heart failure after acute myocardial infarction, and has a certain correlation with the clinical severity of heart failure.

Abstract Background Our aim was to create and validate a nomogram predicting cesarean delivery after induction of labor among nulliparous women at term. Methods Data were obtained from medical records from Nanjing Drum Tower Hospital. Nulliparous women with singleton pregnancies undergoing induction of labor at term were involved. A total of 2950 patients from Jan. 2014 to Dec. 2015 were served as derivation cohort. A nomogram was constructed by multivariate logistic regression using maternal, fetal and pregnancy characteristics. The predictive accuracy and discriminative ability of the nomogram were internal validated by 1000-bootstrap resampling, followed by external validation of a new dataset from Jan. 2016 to Dec. 2016. Results Logistic regression revealed nine predictors of cesarean delivery, including maternal height, age, uterine height, abdominal circumference, estimated fetal weight, indications for induction of labor, initial cervical consistency, cervical effacement and station. Nomogram was well calibrated and had an AUC of 0.73 (95% confidence interval [CI], 0.70-0.75) after bootstrap resampling for internal validation. The AUC in external validation reached 0.67, which was significantly higher than that of three models published previously (P

Summary: Background: Fungal keratitis (FK) is a leading cause of corneal blindness in developing countries due to poor clinical recognition and laboratory identification. Here, we aimed to identify the distinct clinical signature of FK and develop a diagnostic model to differentiate FK from other types of infectious keratitis. Methods: We reviewed the electronic health records (EHRs) of all patients with suspected infectious keratitis in Beijing Tongren Hospital from January 2011 to December 2021. Twelve clinical signs of slit-lamp images were assessed by Lasso regression analysis and collinear variables were excluded. Three models based on binary logistic regression, random forest classification, and decision tree classification were trained for FK diagnosis and employed for internal validation. Independent external validation of the models was performed in a cohort of 420 patients from seven different ophthalmic centers to evaluate the accuracy, specificity, and sensitivity in real world. Findings: Three diagnostic models of FK based on binary logistic regression, random forest classification, and decision tree classification were established and internal validation were achieved with the mean AUC of 0.916, 0.920, and 0.859, respectively. The models were well-calibrated by external validation using a prospective cohort including 210 FK and 210 non-FK patients from seven eye centers across China. The diagnostic model with the binary logistic regression algorithm classified the external validation dataset with a sensitivity of 0.907 (0.774, 1.000), specificity 0.899 (0.750, 1.000), accuracy 0.905 (0.805, 1.000), and AUC 0.903 (0.808, 0.998). Interpretation: Our model enables rapid identification of FK, which will help ophthalmologists to establish a preliminary diagnosis and to improve the diagnostic accuracy in clinic. Funding: The Open Research Fund from the National Key Research and Development Program of China (2021YFC2301000) and the Open Research Fund from Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beijing Tongren Hospital, Beihang University & Capital Medical University (BHTR-KFJJ-202001) supported this study.

Background: Infectious keratitis (IK) is an ocular emergency caused by a variety of microorganisms, including bacteria, fungi, viruses, and parasites. Culture-based methods were the gold standard for diagnosing IK, but difficult biopsy, delaying report, and low positive rate limited their clinical application. Objectives: This study aims to construct a deep-learning-based auxiliary diagnostic model for early IK diagnosis. Design: A retrospective study. Methods: IK patients with pathological diagnosis were enrolled and their slit-lamp photos were collected. Image augmentation, normalization, and histogram equalization were applied, and five image classification networks were implemented and compared. Model blending technique was used to combine the advantages of single model. The performance of combined model was validated by 10-fold cross-validation, receiver operating characteristic curves (ROC), confusion matrix, Gradient-wright class activation mapping (Grad-CAM) visualization, and t-distributed Stochastic Neighbor Embedding (t-SNE). Three experienced cornea specialists were invited and competed with the combined model on making clinical decisions. Results: Overall, 4830 slit-lamp images were collected from patients diagnosed with IK between June 2010 and May 2021, including 1490 (30.8%) bacterial keratitis (BK), 1670 (34.6%) fungal keratitis (FK), 600 (12.4%) herpes simplex keratitis (HSK), and 1070 (22.2%) Acanthamoeba keratitis (AK). KeratitisNet, the combination of ResNext101_32x16d and DenseNet169, reached the highest accuracy 77.08%. The accuracy of KeratitisNet for diagnosing BK, FK, AK, and HSK was 70.27%, 77.71%, 83.81%, and 79.31%, and AUC was 0.86, 0.91, 0.96, and 0.98, respectively. KeratitisNet was mainly confused in distinguishing BK and FK. There were 20% of BK cases mispredicted into FK and 16% of FK cases mispredicted into BK. In diagnosing each type of IK, the accuracy of model was significantly higher than that of human ophthalmologists ( p

Alzheimer’s disease (AD) is the most common form of dementia, causing progressive cognitive decline. Radiomic features obtained from structural magnetic resonance imaging (sMRI) have shown a great potential in predicting this disease. However, radiomic features based on the whole brain segmented regions have not been explored yet. In our study, we collected sMRI data that include 80 patients with AD and 80 healthy controls (HCs). For each patient, the T1 weighted image (T1WI) images were segmented into 106 subregions, and radiomic features were extracted from each subregion. Then, we analyzed the radiomic features of specific brain subregions that were most related to AD. Based on the selective radiomic features from specific brain subregions, we built an integrated model using the best machine learning algorithms, and the diagnostic accuracy was evaluated. The subregions most relevant to AD included the hippocampus, the inferior parietal lobe, the precuneus, and the lateral occipital gyrus. These subregions exhibited several important radiomic features that include shape, gray level size zone matrix (GLSZM), and gray level dependence matrix (GLDM), among others. Based on the comparison among different algorithms, we constructed the best model using the Logistic regression (LR) algorithm, which reached an accuracy of 0.962. Conclusively, we constructed an excellent model based on radiomic features from several specific AD-related subregions, which could give a potential biomarker for predicting AD.

PurposeThe aim of this study is to compare two methods for improving the image quality of the Varian Halcyon cone-beam CT (iCBCT) system through the deformed planning CT (dpCT) based on the convolutional neural network (CNN) and the synthetic CT (sCT) generation based on the cycle-consistent generative adversarial network (CycleGAN).MethodsA total of 190 paired pelvic CT and iCBCT image datasets were included in the study, out of which 150 were used for model training and the remaining 40 were used for model testing. For the registration network, we proposed a 3D multi-stage registration network (MSnet) to deform planning CT images to agree with iCBCT images, and the contours from CT images were propagated to the corresponding iCBCT images through a deformation matrix. The overlap between the deformed contours (dpCT) and the fixed contours (iCBCT) was calculated for purposes of evaluating the registration accuracy. For the sCT generation, we trained the 2D CycleGAN using the deformation-registered CT-iCBCT slicers and generated the sCT with corresponding iCBCT image data. Then, on sCT images, physicians re-delineated the contours that were compared with contours of manually delineated iCBCT images. The organs for contour comparison included the bladder, spinal cord, femoral head left, femoral head right, and bone marrow. The dice similarity coefficient (DSC) was used to evaluate the accuracy of registration and the accuracy of sCT generation.ResultsThe DSC values of the registration and sCT generation were found to be 0.769 and 0.884 for the bladder (p < 0.05), 0.765 and 0.850 for the spinal cord (p < 0.05), 0.918 and 0.923 for the femoral head left (p > 0.05), 0.916 and 0.921 for the femoral head right (p > 0.05), and 0.878 and 0.916 for the bone marrow (p < 0.05), respectively. When the bladder volume difference in planning CT and iCBCT scans was more than double, the accuracy of sCT generation was significantly better than that of registration (DSC of bladder: 0.859 vs. 0.596, p < 0.05).ConclusionThe registration and sCT generation could both improve the iCBCT image quality effectively, and the sCT generation could achieve higher accuracy when the difference in planning CT and iCBCT was large.

In order to deeply understand the specific patterns of volume, microstructure, and functional changes in Multiple System Atrophy patients with cerebellar ataxia syndrome (MSA-c), we perform the current study by simultaneously applying structural (T1-weighted imaging), Diffusion tensor imaging (DTI), functional (BOLD fMRI) and extended Network-Based Statistics (extended-NBS) analysis. Twenty-nine MSA-c type patients and twenty-seven healthy controls (HCs) were involved in this study. First, we analyzed the whole brain changes of volume, microstructure, and functional connectivity (FC) in MSA-c patients. Then, we explored the correlations between significant multimodal MRI features and the total Unified Multiple System Atrophy Rating Scale (UMSARS) scores. Finally, we searched for sensitive imaging biomarkers for the diagnosis of MSA-c using support vector machine (SVM) classifier. Results showed significant grey matter atrophy in cerebellum and white matter microstructural abnormalities in cerebellum, left fusiform gyrus, right precentral gyrus and lingual gyrus. Extended-NBS analysis found two significant different connected components, featuring altered functional connectivity related to left and right cerebellar sub-regions, respectively. Moreover, the reduced fiber bundle counts at right Cerebellum_3 (Cbe3) and decreased fractional anisotropy (FA) values at bilateral Cbe9 were negatively associated with total UMSARS scores. Finally, the significant features at left Cbe9, Cbe1, and Cbe7b were found to be useful as sensitive biomarkers to differentiate MSA-c from HCs according to the SVM analysis. These findings advanced our understanding of the neural pathophysiological mechanisms of MSA from the perspective of multimodal neuroimaging.

Introduction: Systemic lupus erythematosus (SLE) is a prototypic autoimmune disorder with a variable clinical course, ranging from mild to severe forms. It mainly occurs in women, especially those of fertile age. The aim of the study was to systematically analyze the associations of perinatal disease activity with adverse outcomes of Chinese patients with SLE and their offspring. Material and methods: Data of prenatal SLE patients and healthy pregnant woman admitted to our hospital during the period October 2001 to January 2018 were retrospectively collected, and the status of offspring of SLE patients was followed up in March 2020. Disease activity was evaluated by SLE disease activity index 2000 (SLEDAI-2k), and those with scores > 6 were defined as having active disease. Results: In total, 198 deliveries of 194 SLE patients and 199 deliveries of healthy women were documented. Maternal and fetal adverse outcomes occurred in 74 (37.4%) and 90 (45.5%) deliveries of SLE patients, respectively, which were significantly higher than those of healthy subjects. Among SLE patients, the active group had higher rates of gestational hypertension (p < 0.001), preeclampsia/eclampsia (p < 0.001), low birth weight (p < 0.001), premature birth (p < 0.001) and fetal growth restriction (FGR) (p < 0.01) than the inactive group. Multivariate logistic analysis revealed that perinatal renal activity was associated with gestational hypertension (OR = 4.43, p < 0.001), preeclampsia/eclampsia (OR = 9.14, p < 0.001), low birth weight (OR = 2.24, p < 0.05) and premature birth (OR = 4.20, p < 0.001). Compared with the general population, offspring of SLE patients had relatively high rates of eczema (50/142, 35.2%) and congenital heart disease (6/142, 4.2%), which were irrelevant to perinatal disease activity, but related to specific antibodies. Conclusions: For perinatal women with SLE, renal activity is associated with a variety of adverse pregnancy outcomes. However, maternal perinatal disease activity does not seem to affect the growth of their offspring. [ABSTRACT FROM AUTHOR]

Background: Patient-specific quality assurance (PSQA) is an indispensable and essential procedure in radiotherapy workflow, and several studies have been done to develop prediction models based on the conventional C-arm linac of single-layered multileaf collimator (MLC) with machine learning (ML) and deep learning techniques to predict PSQA results and improve efficiency. Recently, a newly designed O-ring gantry linac "Halcyon" equipped with unique jawless stacked-and-staggered dual-layered MLC was released. However, few studies have focused on developing PSQA prediction models for this novel dual-layered MLC system. Purpose: To evaluate the performance of ML to predict PSQA results of fixed field intensity-modulated radiation therapy (FF-IMRT) plans for linac equipped with dual-layered MLC. Methods and materials: A total of 213 FF-IMRT treatment plans, including 1383 beams from various treatment sites, were selected and delivered with portal dosimetry verification on Halcyon linac. Gamma analysis was performed using 1%/1, 2%/2, and 3%/2 mm criteria with a 10% threshold. The training set (TS) of ML models consisted of 1106 beams, and an independent evaluation set (ES) consisted of 277 beams. For each beam, 33 complexity metrics were extracted as input data for training models. Three ML algorithms (gradient boosting decision tree/GBDT, random forest/RF, and Poisson Lasso/PL) were utilized to build the models and predict gamma passing rates (GPRs). To improve the prediction accuracy in the rare region, a method of reweighting for TS has been performed and compared to the unweighted results. The importance of complexity metrics was studied by permuted interesting features. Results: The GBDT model had the best performance in this study. In ES, the minimal mean prediction error for unweighted results was 1.93%,1.16%,0.78% under 1%/1, 2%/2, and 3%/2 mm criteria, respectively, from GBDT model. Comparing to the unweighted results, the models after reweighting gained up to 30% improvement in the rare region, whereas the overall prediction error was slightly worse depending on the kind of models. For feature importance, 2 tree-based models (GBDT and RF) had in common the top 10 most important metrics as well as the same metric with the largest impact. Conclusion: For linac equipped with novel dual-layered MLC, the ML model based on GBDT algorithm shows a certain degree of accuracy for GPRs prediction. The specific ML model for dual-layered MLC configuration could be a useful tool for physicists detecting PSQA measurement failures. [ABSTRACT FROM AUTHOR]

Objective: To observe the clinical efficacy on improving the quality of meibum in patients suffer from dry eye disease (DED) due to meibomian gland dysfunction (MGD) with hyperactivity ofdue todeficiency pattern after being treated with Pinggan Yuyin Qingre formula (, PGYYQR)., Methods: Totally 120 patients who met the inclusion criteria were included and stratified into three levels according to the level of MGD (1-3), and patients in each level was randomly allocated into the treatment group and control group according to a 1∶1 ratio. Both groups were treated with sodium hyaluronate eye drops, and the treatment group was also given PGYYQR granules. Both groups were treated continuously for eight weeks. The score of the properties of meibomian gland (MG) secretion, the score of the palpebral margins, the average noninvasive tear breakup time (NITBUTav), lipid layer thickness (LLT), and Traditional Chinese Medicine (TCM) syndrome efficacy were compared between the two groups after treatment., Results: A total of 116 cases were included in the statistical analysis. The differences were statistically significant in the score of the properties of MG secretion, the score of the palpebral margins, and NITBUTav between the two groups after treatment, the treatment group was superior to the control group; there was no evidence of a difference in LLT. In terms of TCM syndrome efficacy, the total effective rate was 84.7% in the treatment group and 50.9% in the control group, with the statistically significant difference. None of the included cases had adverse reactions., Conclusions: PGYYQR is effective in improving the quality of meibum, and the tear film stability which thereby relieving the ocular symptoms in MGD-related DED patients with hyperactivity ofdue todeficiency pattern.