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Developing photovoltaic (PV) and wind power is one of the most efficient approaches to reduce carbon emissions. Accumulating the PV and wind energy resources at different geographical locations can minimize total power output variance as injected into the power systems. To some extent, a low degree of the variance amplitude of the renewable resources can reduce the requirement of in-depth regulation and dispatch for the fossil fuel-based thermal power plants. Such an issue can alternatively reduce carbon emissions. Thus, the correlation problem by minimizing the variance of total PV and wind power plays a vital role in power system planning and operation. However, the synergistic effect of power output correlation is mainly considered on the generation side, and it is often neglected for the correlation relationship between the power grid components. To address this problem, this paper proposes a correlation coefficient analysis method for the power grid, which can quantify the relationship between energy storage and the probabilistic power flow (PPF) of the grid. Subsequently, to accelerate the mapping efficiency of power correlation coefficients, a novel deep neural network (DNN) optimized by multi-task learning and attention mechanism (MA-DNN) is developed to predict power flow fluctuations. Finally, the simulation results show that in IEEE 9-bus and IEEE14-bus systems, the strong correlation grouping percentage between the power correlation coefficients and power flow fluctuations reached 92% and 51%, respectively. The percentages of groups indicating weak correlation are 4% and 38%. In the modified IEEE 23-bus system, the computational accuracy of MA-DNN is improved by 37.35% compared to the PPF based on Latin hypercube sampling. Additionally, the MA-DNN regression prediction model exhibits a substantial improvement in assessing power flow fluctuations in the power grid, achieving a speed enhancement of 758.85 times compared to the conventional probability power flow algorithms. These findings provide the rapid selection of the grid access point with the minimum power flow fluctuations.

PurposeThis mini-review delves into the realm of Langerhans cell histiocytosis (LCH) in children, focusing on its skeletal involvement. By synthesizing pertinent literature, we sought to provide a comprehensive understanding of LCH’s clinical and radiographic spectrum. Our study then demonstrates the diagnostic prowess of whole-body 99mTc-methyl diphosphonate (MDP) scintigraphy in LCH cases, underscoring its value in tandem with existing knowledge.MethodsOur approach involved an extensive literature review that contextualized LCH within the current medical landscape. Subsequently, we presented a case series featuring five pediatric instances of skeletal LCH, one accompanied by soft tissue infiltration. The principal aim was to illuminate the diagnostic and staging potential of whole-body 99mTc-MDP scintigraphy, augmenting existing insights.ResultsThrough meticulous literature synthesis, we highlighted pediatric LCH’s protean clinical manifestations and radiological variability. Aligning with this spectrum, our case series underscored the role of 99mTc-MDP scintigraphy in diagnosing and staging LCH. Among the five pediatric cases, one demonstrated concurrent soft tissue involvement. This aligns with the multifaceted nature of LCH presentations.ConclusionPediatric LCH can present with a wide range of clinical and radiologic features. By amalgamating our cases with extant literature, we stress the necessity of a multimodal strategy. 99mTc-MDP scintigraphy emerged as an indispensable tool for accurate staging and soft tissue detection. Our findings collectively advocate for a holistic approach to managing LCH, ensuring informed therapeutic decisions for optimal patient outcomes.

Abstract Anaplastic thyroid cancer (ATC) is a rare but highly aggressive kind of thyroid cancer. Various therapeutic methods have been considered for the treatment of ATC, but its prognosis remains poor. With the advent of the nanomedicine era, the use of nanotechnology has been introduced in the treatment of various cancers and has shown great potential and broad prospects in ATC treatment. The current review meticulously describes and summarizes the research progress of various nanomedicine-based therapeutic methods of ATC, including chemotherapy, differentiation therapy, radioiodine therapy, gene therapy, targeted therapy, photothermal therapy, and combination therapy. Furthermore, potential future challenges and opportunities for the currently developed nanomedicines for ATC treatment are discussed. As far as we know, there are few reviews focusing on the nanomedicine of ATC therapy, and it is believed that this review will generate widespread interest from researchers in a variety of fields to further expedite preclinical research and clinical translation of ATC nanomedicines.

Abstract The kidney is a vital organ responsible for maintaining homeostasis in the human body. However, renal cell carcinoma (RCC) is a common malignancy of the urinary system and represents a serious threat to human health. Although the overall survival of RCC has improved substantially with the development of cancer diagnosis and management, there are various reasons for treatment failure. Firstly, without any readily available biomarkers, timely diagnosis has been greatly hampered. Secondly, the imaging appearance also varies greatly, and its early detection often remains difficult. Thirdly, chemotherapy has been validated as unavailable for treating renal cancer in the clinic due to its intrinsic drug resistance. Concomitant with the progress of nanotechnological methods in pharmaceuticals, the management of kidney cancer has undergone a transformation in the recent decade. Nanotechnology has shown many advantages over widely used traditional methods, leading to broad biomedical applications ranging from drug delivery, prevention, diagnosis to treatment. This review focuses on nanotechnologies in RCC management and further discusses their biomedical translation with the aim of identifying the most promising nanomedicines for clinical needs. As our understanding of nanotechnologies continues to grow, more opportunities to improve the management of renal cancer are expected to emerge.

Abstract Purpose Lung metastasis (LM) is one of the most frequent distant metastases of thyroid cancer (TC). This study aimed to develop a machine learning algorithm model to predict lung metastasis of thyroid cancer for providing relative information in clinical decision‐making. Methods Data comprising of demographic and clinicopathological characteristics of patients with thyroid cancer were extracted from the National Institutes of Health (NIH)’s Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015, which is employed to develop six machine learning algorithm models support vector machine (SVM), logistic regression (LR), eXtreme gradient boosting (XGBoost), decision tree (DT), random forest (RF), and k‐nearest neighbor (KNN). Compared and evaluated models by the following indicators: accuracy, precision, recall rate, F1‐score, the area under the ROC curve (AUC) value and Brier score, and interpreted the association between clinicopathological characteristics and target variables based on the best model. Results Nine thousand nine hundred and fifty patients were selected, which including 212 patients (2.1%) with lung metastasis, and 9738 patients without lung metastasis (97.9%). Multivariate logistic regression showed that age, T stage, N stage, and histological type were independent factors in TC with LM. Evaluation indicators of the best model‐ RF were as following: accuracy (0.99), recall rate (0.88), precision (0.61), F1‐score (0.72), AUC value (0.99), and the Brier score (0.016). Conclusion RF learning model performed better and can be applied to forecast lung metastasis of thyroid cancer, and offer valuable and significant reference for clinicians' decision‐making in advance.

IntroductionThis study aimed to investigate the feasibility of predicting progression-free survival (PFS) in breast cancer patients using pretreatment 18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) radiomics signature and clinical parameters.MethodsBreast cancer patients who underwent 18F-FDG PET/CT imaging before treatment from January 2012 to December 2020 were eligible for study inclusion. Eighty-seven patients were randomly divided into training (n = 61) and internal test sets (n = 26) and an additional 25 patients were used as the external validation set. Clinical parameters, including age, tumor size, molecularsubtype, clinical TNM stage, and laboratory findings were collected. Radiomics features were extracted from preoperative PET/CT images. Least absolute shrinkage and selection operators were applied to shrink feature size and build a predictive radiomics signature. Univariate and multivariate Cox proportional hazards models and Kaplan-Meier analysis were used to assess the association of rad-score and clinical parameter with PFS. Nomograms were constructed to visualize survival prediction. C-index and calibration curve were used to evaluate nomogram performance.ResultsEleven radiomics features were selected to generate rad-score. The clinical model comprised three parameters: clinical M stage, CA125, and pathological N stage. Rad-score and clinical-model were significantly associated with PFS in the training set (P< 0.01) but not the test set. The integrated clinical-radiomics (ICR) model was significantly associated with PFS in both the training and test sets (P< 0.01). The ICR model nomogram had a significantly higher C-index than the clinical model and rad-score in the training and test sets. The C-index of the ICR model in the external validation set was 0.754 (95% confidence interval, 0.726–0.812). PFS significantly differed between the low- and high-risk groups stratified by the nomogram (P = 0.009). The calibration curve indicated the ICR model provided the greatest clinical benefit.ConclusionThe ICR model, which combined clinical parameters and preoperative 18F-FDG PET/CT imaging, was able to independently predict PFS in breast cancer patients and was superior to the clinical model alone and rad-score alone.

Objective: Papillary thyroid carcinoma (PTC) accounts for 80% of thyroid malignancy, and the occurrence of PTC is increasing rapidly. The present study was conducted with the purpose of identifying novel and important gene panels and developing an early diagnostic model for PTC by combining artificial neural network (ANN) and random forest (RF).Methods and results: Samples were searched from the Gene Expression Omnibus (GEO) database, and gene expression datasets (GSE27155, GSE60542, and GSE33630) were collected and processed. GSE27155 and GSE60542 were merged into the training set, and GSE33630 was defined as the validation set. Differentially expressed genes (DEGs) in the training set were obtained by “limma” of R software. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis as well as immune cell infiltration analysis were conducted based on DEGs. Important genes were identified from the DEGs by random forest. Finally, an artificial neural network was used to develop a diagnostic model. Also, the diagnostic model was validated by the validation set, and the area under the receiver operating characteristic curve (AUC) value was satisfactory.Conclusion: A diagnostic model was established by a joint of random forest and artificial neural network based on a novel gene panel. The AUC showed that the diagnostic model had significantly excellent performance.

Travel distance is a significant indicator for evaluating the mobility of rock avalanches and is usually used to identify the approximate delineation of potentially endangered regions. The deflection-type rock avalanche is a typical laterally confined rock avalanche and is characterized by obvious changes in the travel path. In this study, we selected deflection-type rock avalanches that occurred in the Wenchuan earthquake area as the research object and statistically analyzed 54 rock avalanches collected from the literature. Multiple linear regression of the logarithm of the ratio of slope height to travel distance (h/L) versus the logarithm of other parameters was developed to obtain a best-fit empirical model for the travel distance prediction of deflection-type rock avalanches. The validity of the proposed empirical model was verified by the satisfactory agreement between observations and predictions. Moreover, the sensitivity of local topographic parameters on the mobility of deflection-type rock avalanches is also discussed using regression analysis.

Cutaneous and subcutaneous soft tissue metastases are rare in lung adenocarcinoma and suggest poor prognosis. We report a patient with lung adenocarcinoma who initially presented with cutaneous and subcutaneous metastases to the abdomen that were initially presumed to be herpes zoster and an occult subcutaneous soft tissue mass. Because the lesions progressed over 3 weeks despite routine herpes zoster treatment, magnetic resonance imaging was performed and showed a presumed sarcoma; however, 18F-fluourodeoxyglucose positron emission tomography/computed tomography demonstrated pulmonary lesions. Biopsy of the abdominal lesion confirmed poorly differentiated lung adenocarcinoma. Early diagnosis of soft tissue metastasis can be difficult. Clinicians should suspect internal organ malignancy when a progressive cutaneous or subcutaneous soft tissue lesion is encountered.

Ovarian cancer has the highest mortality rate of gynecologic malignancy. 18F-FDG positron emission tomography (PET) adds an important superiority over traditional anatomic imaging modalities in oncological imaging but has drawbacks including false negative results at the early stage of ovarian cancer, and false positives when inflammatory comorbidities are present. Aminopeptidase N (APN, also known as CD13) and integrin αvβ3 are two important targets overexpressed on tumor neo-vessels and frequently on ovarian cancerous cells. In this study, we used subcutaneous and metastatic models of ovarian cancer and muscular inflammation models to identify 68Ga-NGR-RGD, a heterodimeric tracer consisting of NGR and RGD peptides targeting CD13 and integrin αvβ3, respectively, and compared it with 18F-FDG. We found that 68Ga-NGR-RGD showed greater contrast in SKOV3 and ES-2 tumors than 18F-FDG. Low accumulation of 68Ga-NGR-RGD but avid uptake of 18F-FDG were observed in inflammatory muscle. In abdominal metastasis models, PET imaging with 68Ga-NGR-RGD allowed for rapid and clear delineation of both peritoneal and liver metastases (3-6 mm), whereas, 18F-FDG could not distinguish the metastasis lesions due to the relatively low metabolic activity in tumors and the interference of intestinal physiological 18F-FDG uptake. Due to the high tumor-targeting efficacy, low inflammatory uptake, and higher tumor-to-background ratios compared to that of 18F-FDG, 68Ga-NGR-RGD presents a promising imaging agent for diagnosis, staging, and follow-up of ovarian tumors.

The COVID-19 pandemic has posed a significant challenge to global public health. In response, the search for specific antiviral drugs that can effectively treat the disease caused by the SARS-CoV-2 virus has become a priority. While significant progress has been made in this regard, much work remains to address this ongoing crisis effectively. Favipiravir is an antiviral drug initially developed for the treatment of influenza and has received approval for emergency use for COVID-19 in many countries. A better understanding of the biodistribution and pharmacokinetics of Favipiravir in vivo would facilitate the development and translation of clinical antiviral drugs for COVID-19. Herein, we report the evaluation of [18F]Favipiravir in naive mice, transgenic mice models of Alzheimer’s disease, and nonhuman primates (NHP) with positron emission tomography (PET). The [18F]Favipiravir was obtained in an overall decay-corrected radiochemical yield of 29% with a molar activity of 25 GBq/µmol at the end of synthesis (EOS). PET imaging in naive mice, transgenic mice models of Alzheimer’s disease, and nonhuman primates revealed a low initial brain uptake, followed by a slow washout of [18F]Favipiravir in vivo. The [18F]Favipiravir was eliminated by a combination of hepatobiliary and urinary excretion. The low brain uptake was probably attributed to the low lipophilicity and low passive permeability of the drug. We hope this proof-of-concept study will provide a unique feature to study antiviral drugs using their corresponding isotopologues by PET.

For a new power system using high-penetration renewable energy, the traditional deterministic power flow analysis method cannot accurately represent the stochastic characteristics of each state variable. The aggregation of renewable energy with different meteorological characteristics in the AC/DC interconnected grid significantly increases the difficulty of establishing a steady-state model. Therefore, this study proposes an improved Latin hypercube sampling algorithm using the van der Waerden scores and diffusion kernel density estimation to overcome the limitations of a priori assumption on probability distributions in uncertainty modeling and to retain the correlations among random variables in the sampling data. Interconnected grids are constructed with IEEE 9-bus and IEEE 14-bus and modified with IEEE 57-bus to describe common application cases of aggregated renewable energy. On this basis, the approximation errors of the proposed probabilistic power flow algorithm to the statistical characteristics of the power parameters are evaluated by setting the Nataf algorithm and the Latin hypercube algorithm using adaptive kernel density estimation as the control group. The results show that the improved Latin hypercube sampling algorithm can exhibit high computational accuracy and strong adaptability, both in severe operating scenarios with large amplitude of load fluctuations and with nonlinear power balance equations incorporating high dimensional random variables.

Pulmonary epithelioid hemangioendothelioma (EHE) is a rare vascular malignancy that is typically low-to-intermediate grade. We report a 47-year-old man with a rapidly progressive pulmonary EHE who initially presented with asymptomatic pulmonary nodules. One nodule was mildly hypermetabolic on initial 18F-FDG PET/CT. 10 months later, the patient developed severe bone pain and night sweats. Repeat imaging revealed several lung lesions, diffuse pleural thickening, and multiple skeletal metastases with considerably increased tracer uptake. The patient underwent vertebral, pleural, and pulmonary biopsies and a diagnosis of advanced pulmonary EHE was made. His disease progressed despite four courses of antineoplastic therapy, after which he began palliative care. Pulmonary EHE can be aggressive and spread rapidly. Biopsy of hypermetabolic lung lesions using PET/CT guidance might enable early definitive diagnosis.

Background. The BRAF V600E mutation is an important genetic event in papillary thyroid cancer (PTC). This study aimed to provide additional information regarding the association of the BRAF V600E mutation with PTC prognosis. Methods. A retrospective single-center study based on a Chinese population was performed to analyze the association of the BRAF V600E mutation with several clinicopathological features. Kaplan–Meier survival curves and Cox proportional hazards regression analysis were applied to implement the survival analysis. Results. The BRAF V600E mutation was present in 1102 (87.7%) of the 1257 patients and was significantly associated with older age, conventional subtype, multifocality, advanced TNM stage, and a reduced prevalence of Hashimoto’s thyroiditis. The Kaplan–Meier survival curves demonstrated that the difference between the BRAF V600E-positive and BRAF V600E-negative groups was significant with a log-rank P-value of 0.048. The Cox proportional hazards regression analysis adjusted HR was 3.731 (95% CI, 1.457 to 9.554). We further demonstrated that larger tumor size (>1 cm), extrathyroidal extension (ETE), and lateral lymph node metastasis (LNM) were associated with a higher probability of PTC recurrence in patients harboring the BRAF V600E mutation. Conclusions. The BRAF V600E mutation remains an independent risk factor for PTC recurrence and may be useful for clinical decisions when it combines with some pathological factors.

Nowadays, pancreatic cancer is still a formidable disease to diagnose. The CXC chemokine receptor 4 (CXCR4) and integrin αvβ3 play important roles in tumor development, progression, invasion, and metastasis, which are overexpressed in many types of human cancers. In this study, we developed a heterodimeric tracer 68Ga-yG5-RGD targeting both CXCR4 and integrin αvβ3, and evaluated its feasibility and utility in PET imaging of pancreatic cancer. The 68Ga-yG5-RGD could accumulate in CXCR4/integrin αvβ3 positive BxPC3 tumors in a high concentration and was much higher than that of 68Ga-yG5 (p < 0.001) and 68Ga-RGD (p < 0.001). No increased uptake of 68Ga-yG5-RGD was found in MX-1 tumors (CXCR4/integrin αvβ3, negative). In addition, the uptake of 68Ga-yG5-RGD in BxPC3 was significantly blocked by excess amounts of AMD3100 (an FDA-approved CXCR4 antagonist) and/or unlabeled RGD (p < 0.001), confirming its dual-receptor targeting properties. The ex vivo biodistribution and immunohistochemical results were consistent with the in vivo imaging results. The dual-receptor targeting strategy achieved improved tumor-targeting efficiency and prolonged tumor retention in BxPC3 tumors, suggesting 68Ga-yG5-RGD is a promising tracer for the noninvasive detection of tumors that express either CXCR4 or integrin αvβ3 or both, and therefore may have good prospects for clinical translation.

Introduction: This study aimed to investigate the feasibility of predicting progression-free survival (PFS) in breast cancer patients using pretreatment 18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) radiomics signature and clinical parameters. Methods: Breast cancer patients who underwent 18F-FDG PET/CT imaging before treatment from January 2012 to December 2020 were eligible for study inclusion. Eighty-seven patients were randomly divided into training (n = 61) and internal test sets (n = 26) and an additional 25 patients were used as the external validation set. Clinical parameters, including age, tumor size, molecular subtype, clinical TNM stage, and laboratory findings were collected. Radiomics features were extracted from preoperative PET/CT images. Least absolute shrinkage and selection operators were applied to shrink feature size and build a predictive radiomics signature. Univariate and multivariate Cox proportional hazards models and Kaplan-Meier analysis were used to assess the association of radscore and clinical parameter with PFS. Nomograms were constructed to visualize survival prediction. C-index and calibration curve were used to evaluate nomogram performance. Results: Eleven radiomics features were selected to generate rad-score. The clinical model comprised three parameters: clinical M stage, CA125, and pathological N stage. Rad-score and clinical-model were significantly associated with PFS in the training set (P< 0.01) but not the test set. The integrated clinical-radiomics (ICR) model was significantly associated with PFS in both the training and test sets (P< 0.01). The ICR model nomogram had a significantly higher C-index than the clinical model and rad-score in the training and test sets. The C-index of the ICR model in the external validation set was 0.754 (95% confidence interval, 0.726-0.812). PFS significantly differed between the low- and high-risk groups stratified by the nomogram (P = 0.009). The calibration curve indicated the ICR model provided the greatest clinical benefit. Conclusion: The ICR model, which combined clinical parameters and preoperative 18F-FDG PET/CT imaging, was able to independently predict PFS in breast cancer patients and was superior to the clinical model alone and radscore alone. [ABSTRACT FROM AUTHOR]