Ultrasound Image Temperature Monitoring Based on a Temporal-Informed Neural Network.

Real-time and accurate temperature monitoring during microwave hyperthermia (MH) remains a critical challenge for ensuring treatment efficacy and patient safety. This study presents a novel approach to simulate real MH and precisely determine the temperature of the target region within biological tissues using a temporal-informed neural network. We conducted MH experiments on 30 sets of phantoms and 10 sets of ex vivo pork tissues. We proposed a novel perspective: the evolving tissue responses to continuous electromagnetic radiation stimulation are a joint evolution in temporal and spatial dimensions. Our model leverages TimesNet to extract periodic features and Cloblock to capture global information relevance in two-dimensional periodic vectors from ultrasound images. By assimilating more ultrasound temporal data, our model improves temperature-estimation accuracy. In the temperature range 25-65 °C, our neural network achieved temperature-estimation root mean squared errors of approximately 0.886 °C and 0.419 °C for fresh ex vivo pork tissue and phantoms, respectively. The proposed temporal-informed neural network has a modest parameter count, rendering it suitable for deployment on ultrasound mobile devices. Furthermore, it achieves temperature accuracy close to that prescribed by clinical standards, making it effective for non-destructive temperature monitoring during MH of biological tissues.

ITGA3 acts as a purity-independent biomarker of both immunotherapy and chemotherapy resistance in pancreatic cancer: bioinformatics and experimental analysis.

Contribution of integrin superfamily genes to treatment resistance remains uncertain. Genome patterns of thirty integrin superfamily genes were analyzed of using bulk and single-cell RNA sequencing, mutation, copy number, methylation, clinical information, immune cell infiltration, and drug sensitivity data. To select the integrins that are most strongly associated with treatment resistance in pancreatic cancer, a purity-independent RNA regulation network including integrins were constructed using machine learning. The integrin superfamily genes exhibit extensive dysregulated expression, genome alterations, epigenetic modifications, immune cell infiltration, and drug sensitivity, as evidenced by multi-omics data. However, their heterogeneity varies among different cancers. After constructing a three-gene (TMEM80, EIF4EBP1, and ITGA3) purity-independent Cox regression model using machine learning, ITGA3 was identified as a critical integrin subunit gene in pancreatic cancer. ITGA3 is involved in the molecular transformation from the classical to the basal subtype in pancreatic cancer. Elevated ITGA3 expression correlated with a malignant phenotype characterized by higher PD-L1 expression and reduced CD8+ T cell infiltration, resulting in unfavorable outcomes in patients receiving either chemotherapy or immunotherapy. Our findings suggest that ITGA3 is an important integrin in pancreatic cancer, contributing to chemotherapy resistance and immune checkpoint blockade therapy resistance.

Temperature Monitoring During Microwave Hyperthermia Based on BP-Nakagami Distribution.

OBJECTIVE: The purpose of this study is to accurately monitor temperature during microwave hyperthermia. We propose a temperature estimation model BP-Nakagami based on neural network for Nakagami distribution. METHODS: In this work, we designed the microwave hyperthermia experiment of fresh ex vivo pork tissue and phantom, collected ultrasonic backscatter data at different temperatures, modeled these data using Nakagami distribution, and calculated Nakagami distribution parameter m. A neural network model was built to train the relationship between Nakagami distribution parameter m and temperature, and a BP-Nakagami temperature model with good fitting was obtained. The temperature model is used to draw the two-dimensional temperature distribution map of biological tissues in microwave hyperthermia. Finally, the temperature estimated by the model is compared with the temperature measured by thermocouples. RESULTS: The error between the temperature estimated by the temperature model and the temperature measured by the thermocouple is within 1°C in the range of 25°C-50°C for ex vivo pork tissue, and the error between the temperature estimated by the temperature model and the temperature measured by the thermocouple is within 0.5°C in the range of 25°C-50°C for phantom. CONCLUSIONS: The results show that the temperature estimation model proposed by us is an effective model for monitoring the internal temperature change of biological tissues.

Prognostic Value, Immune Signature, and Molecular Mechanisms of the PHLDA Family in Pancreatic Adenocarcinoma.

BACKGROUND: Increasing evidence supports the belief that the pleckstrin homology domain family A (PHLDA) family is associated with the development of a variety of cancers. However, the function of the PHLDA family members in PAAD is still unclear. METHODS: Comprehensive bioinformatic analyses using R (version 3.6.3), Cytoscape (version 3.9.1), UALCAN, etc., were performed to study the clinicopathological characteristics, prognostic value, immune features, and functional mechanisms of the PHLDA family members in PAAD. RESULTS: The PHLDA family members showed significantly elevated expression in PAAD compared with paracancerous or normal tissues. Their high expression or amplification were significantly correlated with worse clinicopathological characteristics and prognosis in PAAD patients. In addition, the role of the PHLDA family members in the immune regulation is diverse and complex. Mechanistically, TP53 mutations were significantly associated with the promoter methylation and expression levels of the PHLDA family members, which were activated in multiple oncogenic pathways, including the EMT, RAS/MAPK, and TSC/mTOR pathways. Moreover, we found that their expression levels were significantly correlated with the sensitivity of multiple traditional chemotherapeutic drugs and novel targeted MEK1/2 inhibitors. CONCLUSION: The PHLDA family members play an oncogenic role in the development of PAAD and might serve as new biomarkers or therapeutic targets.

Intraoperative radiotherapy vs concurrent chemoradiotherapy in the treatment of patients with locally advanced pancreatic cancer.

PURPOSE: The purpose of the multi-institutional retrospective study was to evaluate whether intraoperative radiotherapy (IORT) has advantages in the treatment of patients with locally advanced pancreatic cancer (LAPC) compared with concurrent chemoradiotherapy (CCRT). PATIENTS AND METHODS: A total of 103 patients with LAPC whom was treated with IORT (Arm A; n = 50) or CCRT (Arm B; n = 53) from 2015.6 to 2016.7 were retrospectively identified. Data on feasibility, toxicity, and overall survival (OS) were evaluated. RESULTS: Most factors of the two cohorts were similar. The severe adverse events (grade 3 and 4) patients in Arm B were higher than patients in Arm A (34% vs 0%). Disease progression was noted in 38 patients (76%) in Arm A and 37 patients (69.8%) in Arm B. The median survival of patients in Arm A and B were 15.3 months (95% CI, 13.0-17.6 months) and 13.8 months (95% CI, 11.0-16.6 months), respectively. The 1-year survival rate were 66.3% in Arm A (95% CI, 52.3%-80.2%) and 60.9% in Arm B (95% CI, 46.4%-75.4%). There was no significant difference in OS between patients treated with IORT and with CCRT (p = 0.458). CONCLUSION: Our results demonstrated that patients with LAPC treated with IORT showed fewer adverse events, less treatment time, and high feasibility compared to CCRT. Although, IORT has no advantages in survival and tumor control compared with CCRT.

Preoperative Alkaline Phosphatase-to-Cholesterol Ratio as a Predictor of Overall Survival in Pancreatic Ductal Adenocarcinoma Patients Undergoing Radical Pancreaticoduodenectomy.

BACKGROUND The value of alkaline phosphatase and cholesterol for predicting overall survival (OS) in cancer patients has been previously studied. However, the predictive value of these variables in patients with pancreatic ductal adenocarcinoma (PDAC) was limited. Hence, we conducted this study to investigate the prognostic value of the alkaline phosphatase-to-cholesterol ratio (ACR) in patients undergoing radical pancreaticoduodenectomy (PD) for PDAC. MATERIAL AND METHODS A total of 102 PDAC patients undergoing radical PD at the Cancer Hospital Chinese Academy of Medical Sciences were retrospectively enrolled based on medical records from June 2009 to June 2019. R programming language was used for the optimal cutoff value of biological markers such as preoperative ACR. Kaplan-Meier method and log-rank test were used for univariate survival analysis, and a Cox regression model was used for multivariate survival analysis. RESULTS The optimal cutoff value of preoperative ACR was 32.988. Patients with higher preoperative ACR values had worse OS (P<0.001). Higher preoperative ACR was significantly correlated with the degree of tumor differentiation (P<0.018); levels of alanine aminotransferase (P<0.001), aspartate aminotransferase (P<0.001), total bilirubin (P<0.001), and carbohydrate antigen 19-9 (P=0.016); and clinical symptoms (P=0.001). Multivariate analysis showed that tumor differentiation (P<0.001), ACR value (hazard ratio [HR]: 2.225, 95% confidence interval [CI]: 1.33-3.724, P=0.002), and sex (HR, 1.725, 95% CI: 1.1-2.704, P=0.018) were independent factors associated with the prognosis of PDAC patients undergoing radical PD. CONCLUSIONS The preoperative ACR was correlated with OS in pancreatic cancer patients undergoing radical pancreaticoduodenectomy. Elevated ACR was correlated with poor OS.

Discriminating MGMT promoter methylation status in patients with glioblastoma employing amide proton transfer-weighted MRI metrics.

OBJECTIVES: To explore the feasibility of using amide proton transfer-weighted (APTw) MRI metrics as surrogate biomarkers to identify the O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status in glioblastoma (GBM). METHODS: Eighteen newly diagnosed GBM patients, who were previously scanned at 3T and had a confirmed MGMT methylation status, were retrospectively analysed. For each case, a histogram analysis in the tumour mass was performed to evaluate several quantitative APTw MRI metrics. The Mann-Whitney test was used to evaluate the difference in APTw parameters between MGMT methylated and unmethylated GBMs, and the receiver-operator-characteristic analysis was further used to assess diagnostic performance. RESULTS: Ten GBMs were found to harbour a methylated MGMT promoter, and eight GBMs were unmethylated. The mean, variance, 50th percentile, 90th percentile and Width10-90 APTw values were significantly higher in the MGMT unmethylated GBMs than in the MGMT methylated GBMs, with areas under the receiver-operator-characteristic curves of 0.825, 0.837, 0.850, 0856 and 0.763, respectively, for the discrimination of MGMT promoter methylation status. CONCLUSIONS: APTw signal metrics have the potential to serve as valuable imaging biomarkers for identifying MGMT methylation status in the GBM population. KEY POINTS: • APTw-MRI is applied to predict MGMT promoter methylation status in GBMs. • GBMs with unmethylated MGMT promoter present higher APTw-MRI than methylated GBMs. • Multiple APTw histogram metrics can identify MGMT methylation status. • Mean APTw values showed the highest diagnostic accuracy (AUC = 0.825).

Predicting IDH mutation status in grade II gliomas using amide proton transfer-weighted (APTw) MRI.

PURPOSE: To assess the amide proton transfer-weighted (APTw) MRI features of isocitrate dehydrogenase (IDH)-wildtype and IDH-mutant grade II gliomas and to test the hypothesis that the APTw signal is a surrogate imaging marker for identifying IDH mutation status preoperatively. METHODS: Twenty-seven patients with pathologically confirmed low-grade glioma, who were previously scanned at 3T, were retrospectively analyzed. The Mann-Whitney test was used to evaluate relationships between APTw intensities for IDH-mutant and IDH-wildtype groups, and receiver operator characteristic (ROC) analysis was used to assess the diagnostic performance of APTw. RESULTS: Based on histopathology and molecular analysis, seven cases were diagnosed as IDH-wildtype grade II gliomas and 20 cases as IDH-mutant grade II gliomas. The maximum and minimum APTw values, based on multiple regions of interest, as well as the whole-tumor histogram-based mean and 50th percentile APTw values, were significantly higher in the IDH-wildtype gliomas than in the IDH-mutant groups. This corresponded to the areas under the ROC curves of 0.89, 0.76, 0.75, and 0.75, respectively, for the prediction of the IDH mutation status. CONCLUSION: IDH-wildtype lesions typically were associated with relatively high APTw signal intensities as compared with IDH-mutant lesions. The APTw signal could be a valuable imaging biomarker by which to identify IDH1 mutation status in grade II gliomas. Magn Reson Med 78:1100-1109, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

Applying protein-based amide proton transfer MR imaging to distinguish solitary brain metastases from glioblastoma.

OBJECTIVES: To determine the utility of amide proton transfer-weighted (APTw) MR imaging in distinguishing solitary brain metastases (SBMs) from glioblastomas (GBMs). METHODS: Forty-five patients with SBMs and 43 patients with GBMs underwent conventional and APT-weighted sequences before clinical intervention. The APTw parameters and relative APTw (rAPTw) parameters in the tumour core and the peritumoral brain zone (PBZ) were obtained and compared between SBMs and GBMs. The receiver-operating characteristic (ROC) curve was used to assess the best parameter for distinguishing between the two groups. RESULTS: The APTwmax, APTwmin, APTwmean, rAPTwmax, rAPTwmin or rAPTwmean values in the tumour core were not significantly different between the SBM and GBM groups (P = 0.141, 0.361, 0.221, 0.305, 0.578 and 0.448, respectively). However, the APTwmax, APTwmin, APTwmean, rAPTwmax, rAPTwmin or rAPTwmean values in the PBZ were significantly lower in the SBM group than in the GBM group (P < 0.001). The APTwmin values had the highest area under the ROC curve 0.905 and accuracy 85.2% in discriminating between the two neoplasms. CONCLUSION: As a noninvasive imaging method, APT-weighted MR imaging can be used to distinguish SBMs from GBMs. KEY POINTS: • APTw values in the tumour core were not different between SBMs and GBMs. • APTw values in peritumoral brain zone were lower in SBMs than in GBMs. • The APTw min was the best parameter to distinguish SBMs from GBMs.

Advances in understanding the molecular mechanism of pancreatic cancer metastasis.

BACKGROUND: Pancreatic cancer (PC) is usually diagnosed at the late-stage and therefore, has widespread metastasis and a very high mortality rate. The mechanisms underlying PC metastasis are not well understood. Recent advances in genomic sequencing have identified groups of gene mutations that affect PC metastasis, but studies elucidating their roles are lacking. The present review was to investigate the molecular mechanisms of PC metastasis. DATA SOURCES: Relevant articles on PC metastasis were searched in MEDLINE via PubMed prior to April 2015. The search was limited in English publications. RESULTS: PC metastatic cascades are multi-factorial events including both intrinsic and extrinsic elements. This review highlights the most important genetic alterations and other mechanisms that account for PC invasion and metastasis, with particular regard to epithelial-mesenchymal transition, inflammation, stress response, and circulating tumor cells. CONCLUSIONS: Analyses of relevant gene functions and signaling pathways are needed to establish the gene regulatory network and to define the pivotal modulators. Another promising area of study is the genotyping and phenotyping of circulating tumor cells, which could lead to a new era of personalized therapy by identifying specific markers and targets.

Intraoperative assessment of anastomotic blood supply using indocyanine green fluorescence imaging following esophagojejunostomy or esophagogastrostomy for gastric cancer.

Objective: This retrospective study aimed to evaluate the feasibility and safety of intraoperative assessment of anastomotic blood supply in patients undergoing esophagojejunostomy or esophagogastrostomy for gastric cancer using Indocyanine Green Fluorescence Imaging (IGFI). Materials and methods: From January 2019 to October 2021, we conducted a retrospective analysis of patients who had undergone laparoscopic gastrectomy for the treatment of gastric cancer. The patients were consecutively enrolled and categorized into two study groups: the Indocyanine Green Fluorescence Imaging (IGFI) group consisting of 86 patients, and the control group comprising 92 patients. In the IGFI group, intravenous administration of Indocyanine Green (ICG) was performed, and we utilized a fluorescence camera system to assess anastomotic blood supply both before and after the anastomosis. Results: The demographic characteristics of patients in both groups were found to be comparable. In the IGFI group, the mean time to observe perfusion fluorescence was 26.3 ± 12.0 seconds post-ICG injection, and six patients needed to select a more proximal resection point due to insufficient fluorescence at their initial site of choice. Notably, the IGFI group exhibited a lower incidence of postoperative anastomotic leakage, with no significant disparities observed in terms of pathological outcomes, postoperative recovery, or other postoperative complication rates when compared to the control group (p > 0.05). Conclusion: This study underscores the potential of IGFI as a dependable and pragmatic tool for the assessment of anastomotic blood supply following esophagojejunostomy or esophagogastrostomy for gastric cancer. The use of IGFI may potentially reduce the occurrence of postoperative anastomotic leakage.

Extracellular vesicle-mediated pre-metastatic niche formation via altering host microenvironments.

The disordered growth, invasion and metastasis of cancer are mainly attributed to bidirectional cell-cell interactions. Extracellular vesicles (EVs) secreted by cancer cells are involved in orchestrating the formation of pre-metastatic niches (PMNs). Tumor-derived EVs mediate bidirectional communication between tumor and stromal cells in local and distant microenvironments. EVs carrying mRNAs, small RNAs, microRNAs, DNA fragments, proteins and metabolites determine metastatic organotropism, enhance angiogenesis, modulate stroma cell phenotypes, restructure the extracellular matrix, induce immunosuppression and modify the metabolic environment of organs. Evidence indicates that EVs educate stromal cells in secondary sites to establish metastasis-supportive microenvironments for seeding tumor cells. In this review, we provide a comprehensive overview of PMN formation and the underlying mechanisms mediated by EVs. Potential approaches to inhibit cancer metastasis by inhibiting the formation of PMNs are also presented.

Delayed gastric emptying risk stratification in patients with pancreatic ductal adenocarcinoma after pancreatoduodenectomy: An international validation cohort study.

BACKGROUND: Currently, there is still a lack of an accurate predictive model for delayed gastric emptying (DGE) following pancreaticoduodenectomy (PD) in patients with pancreatic ductal adenocarcinoma (PDAC). The aim of this study was to develop a concise model that could effectively predict the risk of DGE. METHODS: This retrospective cohort study included a training cohort of 1251 consecutive PDAC patients who underwent PD from the US multicenter ACS-NSQIP database. Additionally, a validation cohort of 934 consecutive PDAC patients who underwent PD was included from the National Cancer Center in China. A total of 46 perioperative indicators were incorporated in the analysis. The DGE risk stratification (DGERS) model was then developed and validated using Lasso-logistic regression. RESULTS: After screening using Lasso-logistic regression, we identified four independent predictors that were significantly correlated with DGE: days to pancreatic drain removal (HR, 1.05; 95% CI, 1.02-1.08; p < 0.001), pancreatic fistula (HR, 2.61; 95% CI, 1.65-4.12; p < 0.001), sepsis/septic shock (HR, 2.46; 95% CI, 1.52-3.91; p < 0.001), and reoperation (HR, 4.16; 95% CI, 2.27-7.57; p < 0.001). Based on these factors, we developed a nomogram to predict postoperative DGE. The model demonstrated excellent calibration and optimal performance in the validation cohorts (AUC, 0.73; 95% CI, 0.67-0.73). In the validation cohort, the DGERS exhibited significant risk stratification ability, with AUC values of 0.7, 0.61, and 0.74 for the low-, moderate-, and high-risk groups, respectively. CONCLUSIONS: This study identified four factors that independently increased the occurrence of DGE in patients with PDAC after PD, including days to pancreatic drain removal, pancreatic fistula, sepsis/septic shock, and reoperation. Based on these findings, we developed a personalized and straightforward DGERS that enables dynamic and precise prediction of DGE risk, allowing for effective stratification of individuals based on their risk profiles.

Development and validation of a novel predictive model for postpancreatectomy hemorrhage using lasso-logistic regression: an international multicenter observational study of 9,631 pancreatectomy patients.

BACKGROUND: Hemorrhage following pancreatectomy represents a grave complication, exerting a significant impact on patient prognosis. The formulation of a precise predictive model for postpancreatectomy hemorrhage risk holds substantial importance in enhancing surgical safety and improving patient outcomes. MATERIALS AND METHODS: This study utilized the patient cohort from the American College of Surgeons National Surgical Quality Improvement Program database, who underwent pancreatectomy between 2014 and 2017 (n=5779), as the training set to establish the Lasso-logistic model. For external validation, a patient cohort (n=3852) from the Chinese National Multicenter Database of Pancreatectomy Patients, who underwent the procedure between 2014 and 2020, was employed. A predictive nomogram for postpancreatectomy hemorrhage was developed, and polynomial equations were extracted. The performance of the predictive model was assessed through the receiver operating characteristic curve, calibration curve, and decision curve analysis. RESULTS: In the training and validation cohorts, 9.0% (520/5779) and 8.5% (328/3852) of patients, respectively, experienced postpancreatectomy hemorrhage. Following selection via lasso and logistic regression, only nine predictive factors were identified as independent risk factors associated with postpancreatectomy hemorrhage. These included five preoperative indicators (BMI, ASA ≥3, preoperative obstructive jaundice, chemotherapy within 90 days before surgery, and radiotherapy within 90 days before surgery), two intraoperative indicators (total operation time, vascular resection), and two postoperative indicators (postoperative septic shock, pancreatic fistula). The new model demonstrated high predictive accuracy, with an area under the receiver operating characteristic curve of 0.87 in the external validation cohort. Its predictive performance significantly surpassed that of the previous five postpancreatectomy hemorrhage risk prediction models (P<0.001, likelihood ratio test). CONCLUSION: The Lasso-logistic predictive model we developed, constructed from nine rigorously selected variables, accurately predicts the risk of PPH. It has the potential to significantly enhance the safety of pancreatectomy surgeries and improve patient outcomes.

Effects of surgical management for gastrointestinal stromal tumor patients with liver metastasis on survival outcomes.

Purpose: To investigate the effect of surgical resection on survival in gastrointestinal stromal tumors synchronous liver metastasis (GIST-SLM) and to develop clinically usable predictive models for overall survival (OS) and cancer-specific survival (CSS) in patients. Methods: We identified patients in the SEER database diagnosed with GISTs from 2010 to 2019. We used propensity score matching (PSM) to balance the bias between the Surgery and No surgery groups. Kaplan-Meier(K-M) analysis was used to detect differences in OS and CSS between the two groups. The nomogram to predict 1, 3, and 5-year OS and CSS were developed and evaluated. Results: After PSM, 228 patients were included in this study. There were significant differences in 1, 3, and 5-year OS and CSS between the two groups (OS: 93.5% vs. 84.4%, 73.2% vs. 55.3%, 60.9% vs. 36.9%, P=0.014; CSS: 3.5% vs.86.2%,75.3% vs.57.9%, 62.6% vs. 42.9%, P=0.02). We also found that patients who received surgery combined with targeted therapy had better OS and CSS at 1, 3, and 5 years than those who received surgery only (OS: 96.6% vs.90.9%, 74.9% vs. 56.8%, 61.7% vs. 35.5%, P=0.022; CSS: 96.6% vs. 92.1%, 77.4% vs.59.2%,63.8% vs. 42.0%, P=0.023). The area under the curve (AUC) was 0.774, 0.737, and 0.741 for 1, 3, and 5-year OS, respectively, with 0.782 and 0.742 for 1, 3, and 5-year CSS. In the model, C-index was 0.703 for OS and 0.705 for CSS and showed good consistency. Conclusion: Surgical treatment can improve the OS and CSS of patients with GIST-SLM. In addition, the combination with chemotherapy may be more favorable for the long-term survival of patients. Meanwhile, we constructed the nomograms for predicting OS and CSS at 1, 3, and 5-year, and validated them internally. Our model can contribute to clinical management and treatment strategy optimization.

Spatial transcriptomics in pancreatic cancer: Advances, prospects and challenges.

Pancreatic cancer remains one of the deadliest malignancies with an overall 5-year survival rate of 13 %. This dismal fact can be partly attributed to currently limited understanding of tumor heterogeneity and immune microenvironment. Traditional bulk-sequencing techniques overlook the diversity of tumor cells, while single-cell sequencing disorganizes the position localizing of cells in tumor microenvironment. The advent of spatial transcriptomics (ST) presents a novel solution by integrating location and whole transcript expression information. This technology allows for detailed observation of spatio-temporal changes across various cell subtypes within the pancreatic tumor microenvironment, providing insights into their potential functions. This review offers an overview of recent studies implementing ST in pancreatic cancer research, highlighting its instrumental role in investigating the heterogeneity and functions of tumor cells, stromal cells, and immune cells. On the basis, we also prospected and summarized the clinical application scenarios, technical limitations and challenges of ST technology in pancreatic cancer.

Magnetic Bimetallic Heterointerface Nanomissiles with Enhanced Microwave Absorption for Microwave Thermal/Dynamics Therapy of Breast Cancer.

Microwave thermotherapy (MWT) has shown great potential in cancer treatment due to its deep tissue penetration and minimally invasive nature. However, the poor microwave absorption (MA) properties of the microwave thermal sensitizer in the medical frequency band significantly limit the thermal effect of MWT and then weaken the therapeutic efficacy. In this paper, a Ni-based multilayer heterointerface nanomissile of MOFs-Ni-Ru@COFs (MNRC) with improved MA performance in the desired frequency band via introducing magnetic loss and dielectric loss is developed for MWT-based treatment. The loading of the Ni nanoparticle in MNRC mediates the magnetic loss, introducing the MA in the medical frequency band. The heterointerface formed in the MNRC by nanoengineering induces significant interfacial polarization, increasing the dielectric loss and then enhancing the generated MA performance. Moreover, MNRC with the strong MA performance in the desired frequency range not only enhances the MW thermal effect of MWT but also facilitates the electron and energy transfer, generating reactive oxygen species (ROS) at tumor sites to mediate microwave dynamic therapy (MDT). The strategy of strengthening the MA performance of the sensitizer in the medical frequency band to improve MWT-MDT provides a direction for expanding the clinical application of MWT in tumor treatment.

Expression, prognostic value and mechanism of SP100 family in pancreatic adenocarcinoma.

BACKGROUND: Pancreatic adenocarcinoma (PAAD) is one of the most aggressive malignancies with a very poor prognosis. Exploring more therapeutic targets and prognostic biomarkers is of great significance to improve the prognosis of PAAD patients. Increasing evidence supports that the speckled protein (SP) 100 family is associated with human cancer and immune disorders. However, the function of the SP100 family members in PAAD is still unclear. METHODS: R, Cytoscape, cBioPortal, and other software and online databases were used to comprehensively analyze the expression characteristics, prognostic value, and oncogenic mechanism of the SP100 family in PAAD. RESULTS: The high expression of SP100 family members in PAAD was significantly correlated with poor clinicopathological features and poor prognosis of PAAD patients. Mechanistically, TP53 mutations were significantly associated with the expression levels of the SP100 family members, which were significantly coexpressed with M6A methylation regulators and were activated in multiple oncogenic pathways, including the EMT pathways. Moreover, we found that their expression levels were significantly correlated with the sensitivity of multiple traditional chemotherapeutic drugs. CONCLUSION: The SP100 family is closely related to the occurrence and development of PAAD and can be used as a new biomarker and therapeutic target for patients with PAAD.

Development and validation of a novel nomogram to predict postoperative pancreatic fistula after pancreatoduodenectomy using lasso-logistic regression: an international multi-institutional observational study.

BACKGROUND: Existing prediction models for clinically relevant postoperative pancreatic fistula (POPF) after pancreatoduodenectomy (PD) lack discriminatory power or are too complex. This study aimed to develop a simple nomogram that could accurately predict clinically relevant POPF after PD. METHODS: A high-volume, multicenter cohort of patients who underwent PD from the American College of Surgeons-National Surgical Quality Improvement Program database in the United States during 2014-2017 was used as the model training cohort ( n =3609), and patients who underwent PD from the Pancreatic Center of the National Cancer Center Hospital in China during 2014-2019 were used as the external validation cohort ( n =1347). The study used lasso penalized regression to screen large-scale variables, then logistic regression was performed to screen the variables and build a model. Finally, a prediction nomogram for clinically relevant POPF was established based on the logistic model, and polynomial equations were extracted. The performance of the nomogram was evaluated by receiver operating characteristic curve, calibration curve, and decision curve analysis. RESULTS: In the training and validation cohorts, there were 16.7% (601/3609) and 16.6% (224/1347) of patients who developed clinically relevant POPF, respectively. After screening using lasso and logistic regression, only six predictors were independently associated with clinically relevant POPF, including two preoperative indicators (weight and pancreatic duct size), one intraoperative indicator (pancreatic texture), and three postoperative indicators (deep surgical site infection, delayed gastric emptying, and pathology). The prediction of the new nomogram was accurate, with an area under the curve of 0.855 (95% CI: 0.702-0.853) in the external validation cohort, and the predictive performance was superior to three previously proposed POPF risk score models (all P <0.001, likelihood ratio test). CONCLUSIONS: A reliable lasso-logistic method was applied to establish a novel nomogram based on six readily available indicators, achieving a sustained, dynamic, and precise POPF prediction for PD patients. With a limited number of variables and easy clinical application, this new model will enable surgeons to proactively predict, identify, and manage pancreatic fistulas to obtain better outcomes from this daunting postoperative complication.