Diagnostic performance of PIVKA-II in identifying recurrent hepatocellular carcinoma following curative resection: a retrospective cohort study.

Protein induced by vitamin K absence or antagonist II (PIVKA-II) plays a critical role in the diagnosis of hepatocellular carcinoma (HCC), however, studies on its efficacy in diagnosing recurrent HCC were rarely found. A multicenter, retrospective, and observational study was conducted. During the overall follow-up of 5 years, HCC patients who had curative resection were monitored every 3 months in the first year post-surgery and every 6 months thereafter if no recurrence occurred. Tumor markers were collected at the diagnosis of recurrence for those with recurrence and at the last follow-up for those without recurrence. The median serum levels of PIVKA-II and AFP in the recurrence group were significantly higher than those in the non-recurrence group (PIVKA-II: 84.62 vs. 18.76 mAU/ml, p < 0.001; AFP: 4.90 vs. 3.00 ng/ml, p < 0.001) and there is a significant correlation between PIVKA-II and AFP (R = 0.901, p < 0.001). PIVKA-II showed better accuracy than AFP in the diagnosis of overall recurrent HCC (AUC: 0.883 vs. 0.672; p < 0.0001), but also in patients with negative PIVKA-II before curative resection (AUC: 0.878 vs. 0.680, p = 0.001). Clinician should pay more attention to serum PIVKA-II values when following patients after curative HCC resection to detect early recurrence.Clinical trial registration: ChiCTR2300070874.

Abnormal changes in metabolites caused by m6A methylation modification: The leading factors that induce the formation of immunosuppressive tumor microenvironment and their promising potential for clinical application.

BACKGROUND: N6-methyladenosine (m6A) RNA methylation modifications have been widely implicated in the metabolic reprogramming of various cell types within the tumor microenvironment (TME) and are essential for meeting the demands of cellular growth and maintaining tissue homeostasis, enabling cells to adapt to the specific conditions of the TME. An increasing number of research studies have focused on the role of m6A modifications in glucose, amino acid and lipid metabolism, revealing their capacity to induce aberrant changes in metabolite levels. These changes may in turn trigger oncogenic signaling pathways, leading to substantial alterations within the TME. Notably, certain metabolites, including lactate, succinate, fumarate, 2-hydroxyglutarate (2-HG), glutamate, glutamine, methionine, S-adenosylmethionine, fatty acids and cholesterol, exhibit pronounced deviations from normal levels. These deviations not only foster tumorigenesis, proliferation and angiogenesis but also give rise to an immunosuppressive TME, thereby facilitating immune evasion by the tumor. AIM OF REVIEW: The primary objective of this review is to comprehensively discuss the regulatory role of m6A modifications in the aforementioned metabolites and their potential impact on the development of an immunosuppressive TME through metabolic alterations. KEY SCIENTIFIC CONCEPTS OF REVIEW: This review aims to elaborate on the intricate networks governed by the m6A-metabolite-TME axis and underscores its pivotal role in tumor progression. Furthermore, we delve into the potential implications of the m6A-metabolite-TME axis for the development of novel and targeted therapeutic strategies in cancer research.

Demethylases in tumors and the tumor microenvironment: Key modifiers of N6-methyladenosine methylation.

RNA methylation modifications are widespread in eukaryotes and prokaryotes, with N6-methyladenosine (m6A) the most common among them. Demethylases, including Fat mass and obesity associated gene (FTO) and AlkB homolog 5 (ALKBH5), are important in maintaining the balance between RNA methylation and demethylation. Recent studies have clearly shown that demethylases affect the biological functions of tumors by regulating their m6A levels. However, their effects are complicated, and even opposite results have appeared in different articles. Here, we summarize the complex regulatory networks of demethylases, including the most important and common pathways, to clarify the role of demethylases in tumors. In addition, we describe the relationships between demethylases and the tumor microenvironment, and introduce their regulatory mechanisms. Finally, we discuss evaluation of demethylases for tumor diagnosis and prognosis, as well as the clinical application of demethylase inhibitors, providing a strong basis for their large-scale clinical application in the future.

A two-terminal fault location method for gas switch prefire in linear transformer driver.

Large-scale linear transformer drivers (LTDs) are composed of numerous high-power gas switches, and switch prefire is a frequent operational fault. To detect and locate the faulty switch accurately and efficiently, a two-terminal location method is proposed. A B-dot sensor is integrated on the gas switch's shell to collect the discharging signal. All the B-dot sensors are connected in parallel through cables of equal length. The fault position can be determined by the time delay of the signals at the two terminals. A diode is inserted between the B-dot sensor's coil and the cable core to ensure low-loss transmission of the signal. Two methods are applied in fault location, including time-of-arrival (TOA) and time reversal (TR). For the TOA method, an energy criterion and a phase criterion are applied and compared. The accuracy of the energy criterion is greatly influenced by the signal-to-noise ratio, while the phase criterion requires a reasonable estimate of the actual delay to account for the impact of phase periodicity. The TR method based on a precise simulation model is established, which demonstrates high precision in location. The TR method has been tested and validated on a single stage LTD module. Moreover, the location method for double switches prefire is discussed theoretically. The method proposed in this paper will be helpful to improve the efficiency of the commissioning, operation, and maintenance of the large-scale LTD devices.

End-to-end prognostication in colorectal cancer by deep learning: a retrospective, multicentre study.

BACKGROUND: Precise prognosis prediction in patients with colorectal cancer (ie, forecasting survival) is pivotal for individualised treatment and care. Histopathological tissue slides of colorectal cancer specimens contain rich prognostically relevant information. However, existing studies do not have multicentre external validation with real-world sample processing protocols, and algorithms are not yet widely used in clinical routine. METHODS: In this retrospective, multicentre study, we collected tissue samples from four groups of patients with resected colorectal cancer from Australia, Germany, and the USA. We developed and externally validated a deep learning-based prognostic-stratification system for automatic prediction of overall and cancer-specific survival in patients with resected colorectal cancer. We used the model-predicted risk scores to stratify patients into different risk groups and compared survival outcomes between these groups. Additionally, we evaluated the prognostic value of these risk groups after adjusting for established prognostic variables. FINDINGS: We trained and validated our model on a total of 4428 patients. We found that patients could be divided into high-risk and low-risk groups on the basis of the deep learning-based risk score. On the internal test set, the group with a high-risk score had a worse prognosis than the group with a low-risk score, as reflected by a hazard ratio (HR) of 4·50 (95% CI 3·33-6·09) for overall survival and 8·35 (5·06-13·78) for disease-specific survival (DSS). We found consistent performance across three large external test sets. In a test set of 1395 patients, the high-risk group had a lower DSS than the low-risk group, with an HR of 3·08 (2·44-3·89). In two additional test sets, the HRs for DSS were 2·23 (1·23-4·04) and 3·07 (1·78-5·3). We showed that the prognostic value of the deep learning-based risk score is independent of established clinical risk factors. INTERPRETATION: Our findings indicate that attention-based self-supervised deep learning can robustly offer a prognosis on clinical outcomes in patients with colorectal cancer, generalising across different populations and serving as a potentially new prognostic tool in clinical decision making for colorectal cancer management. We release all source codes and trained models under an open-source licence, allowing other researchers to reuse and build upon our work. FUNDING: The German Federal Ministry of Health, the Max-Eder-Programme of German Cancer Aid, the German Federal Ministry of Education and Research, the German Academic Exchange Service, and the EU.

Trigeminal neuralgia secondary to osteoma and vascular compression: illustrative case.

BACKGROUND: Trigeminal neuralgia (TN) is a common neurosurgical issue that has a detrimental impact on patients' quality of life. Osteoma at the petrous apex is a rare etiology of TN. Here, the authors present a case involving the co-occurrence of petrous osteoma and a vascular loop around the trigeminal nerve. Both exerted pressure or compression on the exit of the trigeminal nerve. OBSERVATIONS: A 46-year-old male presented with a 3-year history of persistent severe pain in the right side of his face. Magnetic resonance tomographic angiography of the trigeminal nerve revealed an abnormal signal in the right prepontine cistern, along with a vascular loop accompanying the right trigeminal nerve. A computed tomography scan of the skull indicated a nodular calcified density. The combined anterior transpetrosal approach for petrous osteoma and microvascular decompression (MVD) for the offending vessel were successfully performed. The patient was discharged without any complications or facial pain. LESSONS: Although extremely rare, TN simultaneously secondary to petrous osteoma and offending vessels should be considered in the diagnosis. In this case, the combined surgical removal of petrous osteoma and MVD for the offending vessels proved to be an effective treatment for TN secondary to osteoma and vascular compression.

Experimental investigation and theoretical analysis of the breakdown time delay and jitter of multi-gap gas switch gaps.

The gas gap of a multi-gap gas switch can be classified as trigger and self-breakdown gaps based on the breakdown condition. A two-gap gas switch consisting of a trigger gap and a self-breakdown gap is developed to independently study the breakdown characteristics of these two types of switch gaps. Trigger experiments for the switch are conducted under various trigger voltage rise rates and different working coefficients. The experimental results indicate that the trigger gap has significantly more jitter than the self-breakdown gap, and the overall performance of the gas switch is determined primarily by the trigger gap. A novel pre-ionization structure with disks is implemented into the two-gap gas switch, considerably decreasing the breakdown delay of the trigger gap and reducing the jitter to a quarter or even less compared to that without pre-ionization. A calculation model of the breakdown time delay for the trigger gap is provided based on the foundational development of the avalanche. The probability distribution of the time required for the initial electron generation is derived in the absence of pre-ionization. The calculated breakdown time delay agrees well with the experimental results in cases with and without pre-ionization under most trigger settings. The method and principle of calculating the breakdown time delay can analyze the collapse of a gas gap with different electrode configurations (quasi-uniform or uniform electrical fields) and various gas media under a nanosecond pulse voltage.

Deep learning trained on lymph node status predicts outcome from gastric cancer histopathology: a retrospective multicentric study.

AIM: Gastric cancer (GC) is a tumour entity with highly variant outcomes. Lymph node metastasis is a prognostically adverse biomarker. We hypothesised that GC primary tissue contains information that is predictive of lymph node status and patient prognosis and that this information can be extracted using deep learning (DL). METHODS: Using three patient cohorts comprising 1146 patients, we trained and validated a DL system to predict lymph node status directly from haematoxylin and eosin-stained GC tissue sections. We investigated the concordance between the DL-based prediction from the primary tumour slides (aiN score) and the histopathological lymph node status (pN). Furthermore, we assessed the prognostic value of the aiN score alone and when combined with the pN status. RESULTS: The aiN score predicted the pN status reaching area under the receiver operating characteristic curves of 0.71 in the training cohort and 0.69 and 0.65 in the two test cohorts. In a multivariate Cox analysis, the aiN score was an independent predictor of patient survival with hazard ratios of 1.5 in the training cohort and of 1.3 and 2.2 in the two test cohorts. A combination of the aiN score and the pN status prognostically stratified patients by survival with p-values <0.05 in logrank tests. CONCLUSION: GC primary tumour tissue contains additional prognostic information that is accessible using the aiN score. In combination with the pN status, this can be used for personalised management of GC patients after prospective validation.

Self-assembly of Hyaluronic Acid-Cu-Quercetin flavonoid nanoparticles: synergistic chemotherapy to target tumors.

Background: In this study, a natural compound quercetin (Qu) was investigated for its various antitumor effects. However, due to its poor water solubility and low bioavailability, its clinical application is limited. To overcome this constraint, a modification was to Qu, which resulted in the creation of novel flavonoid self-assembling nanoparticles (HCQ NPs). Methods: HCQ NPs were synthesized by a self-assembly method and characterized using transmission electron microscopy, the Malvern Zetasizer instrument, X-ray photoelectron spectroscopy (XPS), the ultraviolet-visible spectrophotometric method (UV-vis), Fourier transform infrared (FITR) and inductively coupled plasma mass spectrometry. Extracellular, methylene blue spectrophotometric analysis was used to determine the ability of HCQ NPs to react with different concentrations of H2O2 to form hydroxyl radicals (•OH). Intracellular, DCFH-DA staining was used to detect the ability of HCQ NPs to react with H2O2 to generate reactive oxygen species. Flow cytometry was used to detect the uptake of HCQ NPs by MDA-MB-231 cells at different time points. The biocompatibility of HCQ NPs was evaluated using the Cell Counting Kit-8 (CCK-8) assay. Calcein AM/PI double staining and the CCK-8 assay were used to evaluate the synergistic antitumor effect of HCQ NPs and H2O2. Results: HCQ NPs showed uniformly sized analogous spherical shapes with a hydrodynamic diameter of 55.36 ± 0.27 nm. XPS revealed that Cu was mainly present as Cu2+ in the HCQ NPs. UV-vis absorption spectrum of the characteristic peak of HCQ NPs was located at 296 nm. Similarly, FTIR spectroscopy revealed a complex formation of Qu and Cu2+ that substantially changed the wavenumber of the 4-position C = O characteristic absorption peak. Based on the proportion of Qu and Cu2+ (1:2), the total drug loading of Qu and Cu2+ in the HCQ NPs for therapeutic purposes was calculated to be 9%. Methylene blue spectrophotometric analysis of •OH indicated that Cu can lead to the generation of •OH by triggering Fenton-like reactions. HCQ NPs rapidly accumulated in MDA-MB-231 cells with the extension of time, and the maximum accumulation concentration was reached at about 0.5 h. Calcein AM/PI double staining and CCK-8 revealed synergistic antitumor effects of HCQ NPs including the chemotherapeutic effect of Qu and chemodynamic therapy by Cu2+ in a simulated tumor microenvironment. HCQ NPs demonstrated very low toxicity in LO2 cells in the biocompatibility experiment. Conclusion: This study show cases a new method of creating self-assembled flavonoid HCQ NPs that show great for fighting cancer.

Shark IgNAR: The Next Broad Application Antibody in Clinical Diagnoses and Tumor Therapies?

Antibodies represent a relatively mature detection means and serve as therapeutic drug carriers in the clinical diagnosis and treatment of cancer-among which monoclonal antibodies (mAbs) currently occupy a dominant position. However, the emergence and development of small-molecule monodomain antibodies are inevitable due to the many limitations of mAbs, such as their large size, complex structure, and sensitivity to extreme temperature, and tumor microenvironments. Thus, since first discovered in Chondroid fish in 1995, IgNAR has become an alternative therapeutic strategy through which to replace monoclonal antibodies, thus entailing that this novel type of immunoglobulin has received wide attention with respect to clinical diagnoses and tumor therapies. The variable new antigen receptor (VNAR) of IgNAR provides an advantage for the development of new antitumor drugs due to its small size, high stability, high affinity, as well as other structural and functional characteristics. In that respect, a better understanding of the unique characteristics and therapeutic potential of IgNAR/VNAR in clinical and anti-tumor treatment is needed. This article reviews the advantages of its unique biochemical conditions and molecular structure for clinical diagnoses and novel anti-tumor drugs. At the same time, the main advantages of the existing conjugated drugs, which are based on single-domain antibodies, are introduced here, thereby providing new ideas and methods for the development of clinical diagnoses and anti-tumor therapies in the future.

A Longitudinal MRI-Based Artificial Intelligence System to Predict Pathological Complete Response After Neoadjuvant Therapy in Rectal Cancer: A Multicenter Validation Study.

BACKGROUND: Accurate prediction of response to neoadjuvant chemoradiotherapy is critical for subsequent treatment decisions for patients with locally advanced rectal cancer. OBJECTIVE: To develop and validate a deep learning model based on the comparison of paired MRI before and after neoadjuvant chemoradiotherapy to predict pathological complete response. DESIGN: By capturing the changes from MRI before and after neoadjuvant chemoradiotherapy in 638 patients, we trained a multitask deep learning model for response prediction (DeepRP-RC) that also allowed simultaneous segmentation. Its performance was independently tested in an internal and 3 external validation sets, and its prognostic value was also evaluated. SETTINGS: Multicenter study. PATIENTS: We retrospectively enrolled 1201 patients diagnosed with locally advanced rectal cancer who underwent neoadjuvant chemoradiotherapy before total mesorectal excision. Patients had been treated at 1 of 4 hospitals in China between January 2013 and December 2020. MAIN OUTCOME MEASURES: The main outcome was the accuracy of predicting pathological complete response, measured as the area under receiver operating curve for the training and validation data sets. RESULTS: DeepRP-RC achieved high performance in predicting pathological complete response after neoadjuvant chemoradiotherapy, with area under the curve values of 0.969 (0.942-0.996), 0.946 (0.915-0.977), 0.943 (0.888-0.998), and 0.919 (0.840-0.997) for the internal and 3 external validation sets, respectively. DeepRP-RC performed similarly well in the subgroups defined by receipt of radiotherapy, tumor location, T/N stages before and after neoadjuvant chemoradiotherapy, and age. Compared with experienced radiologists, the model showed substantially higher performance in pathological complete response prediction. The model was also highly accurate in identifying the patients with poor response. Furthermore, the model was significantly associated with disease-free survival independent of clinicopathological variables. LIMITATIONS: This study was limited by its retrospective design and absence of multiethnic data. CONCLUSIONS: DeepRP-RC could be an accurate preoperative tool for pathological complete response prediction in rectal cancer after neoadjuvant chemoradiotherapy. UN SISTEMA DE IA BASADO EN RESONANCIA MAGNTICA LONGITUDINAL PARA PREDECIR LA RESPUESTA PATOLGICA COMPLETA DESPUS DE LA TERAPIA NEOADYUVANTE EN EL CNCER DE RECTO UN ESTUDIO DE VALIDACIN MULTICNTRICO: ANTECEDENTES:La predicción precisa de la respuesta a la quimiorradioterapia neoadyuvante es fundamental para las decisiones de tratamiento posteriores para los pacientes con cáncer de recto localmente avanzado.OBJETIVO:Desarrollar y validar un modelo de aprendizaje profundo basado en la comparación de resonancias magnéticas pareadas antes y después de la quimiorradioterapia neoadyuvante para predecir la respuesta patológica completa.DISEÑO:Al capturar los cambios de las imágenes de resonancia magnética antes y después de la quimiorradioterapia neoadyuvante en 638 pacientes, entrenamos un modelo de aprendizaje profundo multitarea para la predicción de respuesta (DeepRP-RC) que también permitió la segmentación simultánea. Su rendimiento se probó de forma independiente en un conjunto de validación interna y tres externas, y también se evaluó su valor pronóstico.ESCENARIO:Estudio multicéntrico.PACIENTES:Volvimos a incluir retrospectivamente a 1201 pacientes diagnosticados con cáncer de recto localmente avanzado y sometidos a quimiorradioterapia neoadyuvante antes de la escisión total del mesorrecto. Eran de cuatro hospitales en China en el período entre enero de 2013 y diciembre de 2020.PRINCIPALES MEDIDAS DE RESULTADO:Los principales resultados fueron la precisión de la predicción de la respuesta patológica completa, medida como el área bajo la curva operativa del receptor para los conjuntos de datos de entrenamiento y validación.RESULTADOS:DeepRP-RC logró un alto rendimiento en la predicción de la respuesta patológica completa después de la quimiorradioterapia neoadyuvante, con valores de área bajo la curva de 0,969 (0,942-0,996), 0,946 (0,915-0,977), 0,943 (0,888-0,998), y 0,919 (0,840-0,997) para los conjuntos de validación interna y las tres externas, respectivamente. DeepRP-RC se desempeñó de manera similar en los subgrupos definidos por la recepción de radioterapia, la ubicación del tumor, los estadios T/N antes y después de la quimiorradioterapia neoadyuvante y la edad. En comparación con los radiólogos experimentados, el modelo mostró un rendimiento sustancialmente mayor en la predicción de la respuesta patológica completa. El modelo también fue muy preciso en la identificación de los pacientes con mala respuesta. Además, el modelo se asoció significativamente con la supervivencia libre de enfermedad independientemente de las variables clinicopatológicas.LIMITACIONES:Este estudio estuvo limitado por el diseño retrospectivo y la ausencia de datos multiétnicos.CONCLUSIONES:DeepRP-RC podría servir como una herramienta preoperatoria precisa para la predicción de la respuesta patológica completa en el cáncer de recto después de la quimiorradioterapia neoadyuvante. (Traducción-Dr. Felipe Bellolio ).

Primary hepatic Burkitt lymphoma in a child and review of literature.

BACKGROUND: Primary hepatic Burkitt lymphoma (PHBL) in children is an extremely rare hepatic malignancy with a dismal prognosis, unless it is detected and treated promptly. An 11-year-old child with abdominal pain was admitted to our hospital. No notable abnormalities were found during his physical examination or laboratory workup, but the abdominal computed tomography and magnetic resonance imaging both indicated a malignant hepatic mass measuring 9.2 × 7.1 × 7.5 cm in size. His postoperative pathology revealed an unexpected primary hepatic Burkitt lymphoma following a laparoscopic liver lobectomy. He then received rituximab and intense multi-agent chemotherapy as treatment. Despite post-chemotherapy bone marrow suppression, the patient eventually made a full recovery and had a good overall state. CONCLUSION: In this study, we describe a rare case of pediatric primary hepatic Burkitt lymphoma and review the literature on clinical features, diagnosis, and treatment for primary hepatic Burkitt lymphoma in children. We stress that this diagnosis should be taken into account in the absence of other single hepatic lesions or primary tumors of hematological disorders, particularly when there is a normal AFP level.

An MRI Deep Learning Model Predicts Outcome in Rectal Cancer.

Background Deep learning (DL) models can potentially improve prognostication of rectal cancer but have not been systematically assessed. Purpose To develop and validate an MRI DL model for predicting survival in patients with rectal cancer based on segmented tumor volumes from pretreatment T2-weighted MRI scans. Materials and Methods DL models were trained and validated on retrospectively collected MRI scans of patients with rectal cancer diagnosed between August 2003 and April 2021 at two centers. Patients were excluded from the study if there were concurrent malignant neoplasms, prior anticancer treatment, incomplete course of neoadjuvant therapy, or no radical surgery performed. The Harrell C-index was used to determine the best model, which was applied to internal and external test sets. Patients were stratified into high- and low-risk groups based on a fixed cutoff calculated in the training set. A multimodal model was also assessed, which used DL model-computed risk score and pretreatment carcinoembryonic antigen level as input. Results The training set included 507 patients (median age, 56 years [IQR, 46-64 years]; 355 men). In the validation set (n = 218; median age, 55 years [IQR, 47-63 years]; 144 men), the best algorithm reached a C-index of 0.82 for overall survival. The best model reached hazard ratios of 3.0 (95% CI: 1.0, 9.0) in the high-risk group in the internal test set (n = 112; median age, 60 years [IQR, 52-70 years]; 76 men) and 2.3 (95% CI: 1.0, 5.4) in the external test set (n = 58; median age, 57 years [IQR, 50-67 years]; 38 men). The multimodal model further improved the performance, with a C-index of 0.86 and 0.67 for the validation and external test set, respectively. Conclusion A DL model based on preoperative MRI was able to predict survival of patients with rectal cancer. The model could be used as a preoperative risk stratification tool. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Langs in this issue.

Epidemiological characteristics and risk factors for cystic and alveolar echinococcosis in China: an analysis of a national population-based field survey.

BACKGROUND: Human cystic and alveolar echinococcosis are neglected tropical diseases that WHO has prioritized for control in recent years. Both diseases impose substantial burdens on public health and the socio-economy in China. In this study, which is based on the national echinococcosis survey from 2012 to 2016, we aim to describe the spatial prevalence and demographic characteristics of cystic and alveolar echinococcosis infections in humans and assess the impact of environmental, biological and social factors on both types of the disease. METHODS: We computed the sex-, age group-, occupation- and education level-specific prevalences of cystic and alveolar echinococcosis at national and sub-national levels. We mapped the geographical distribution of echinococcosis prevalence at the province, city and county levels. Finally, by analyzing the county-level echinococcosis cases combined with a range of associated environmental, biological and social factors, we identified and quantified the potential risk factors for echinococcosis using a generalized linear model. RESULTS: A total of 1,150,723 residents were selected and included in the national echinococcosis survey between 2012 and 2016, of whom 4161 and 1055 tested positive for cystic and alveolar echinococcosis, respectively. Female gender, older age, occupation at herdsman, occupation as religious worker and illiteracy were identified as risk factors for both types of echinococcosis. The prevalence of echinococcosis was found to vary geographically, with areas of high endemicity observed in the Tibetan Plateau region. Cystic echinococcosis prevalence was positively correlated with cattle density, cattle prevalence, dog density, dog prevalence, number of livestock slaughtered, elevation and grass area, and negatively associated with temperature and gross domestic product (GDP). Alveolar echinococcosis prevalence was positively correlated with precipitation, level of awareness, elevation, rodent density and rodent prevalence, and negatively correlated with forest area, temperature and GDP. Our results also implied that drinking water sources are significantly associated with both diseases. CONCLUSIONS: The results of this study provide a comprehensive understanding of geographical patterns, demographic characteristics and risk factors of cystic and alveolar echinococcosis in China. This important information will contribute towards developing targeted prevention measures and controlling diseases from the public health perspective.

Nomogram for predicting the unfavourable outcomes of percutaneous endoscopic transforaminal discectomy for lumbar disc herniation: a retrospective study.

Objective: To investigate and integrate multiple independent risk factors to establish a nomogram for predicting the unfavourable outcomes of percutaneous endoscopic transforaminal discectomy (PETD) for lumbar disc herniation (LDH). Methods: From January 2018 to December 2019, a total of 425 patients with LDH undergoing PETD were included in this retrospective study. All patients were divided into the development and validation cohort at a ratio of 4:1. Univariate and multivariate logistic regression analyses were used to investigate the independent risk factors associated with the clinical outcomes of PETD for LDH in the development cohort, and a prediction model (nomogram) was established to predict the unfavourable outcomes of PETD for LDH. In the validation cohort, the nomogram was validated by the concordance index (C-index), calibration curve, and decision curve analysis (DCA). Results: 29 of 340 patients showed unfavourable outcomes in the development cohort, and 7 of 85 patients showed unfavourable outcomes in the validation cohort. Body mass index (BMI), course of disease (COD), protrusion calcification (PC), and preoperative lumbar epidural steroid injection (LI) were independent risk factors associated with the unfavourable outcomes of PETD for LDH and were identified as predictors for the nomogram. The nomogram was validated by the validation cohort and showed high consistency (C-index = 0.674), good calibration and high clinical value. Conclusions: The nomogram based on patients' preoperative clinical characteristics, including BMI, COD, LI and PC, can be used to accurately predict the unfavourable outcomes of PETD for LDH.

Screening and anti-glioma activity of Chiloscyllium plagiosum anti-human IL-13Rα2 single-domain antibody.

Glioblastoma is a common and fatal malignant tumour of the central nervous system, with high invasiveness. Conventional treatments for this disease, including comprehensive treatment of surgical resection combined with chemoradiotherapy, are ineffective, with low survival rate and extremely poor prognosis. Targeted therapy is promising in overcoming the difficulties in brain tumour treatment and IL-13Rα2 is a widely watched target. The development of new therapies for glioma, however, is challenged by factors, such as the unique location and immune microenvironment of gliomas. The unique advantages of single-domain antibodies (sdAbs) may provide a novel potential treatment for brain tumours. In this study, Chiloscyllium plagiosum was immunized with recombinant IL-13Rα2 protein to produce sdAb and sdAb sequences were screened by multi-omics. The targeted sdAb genes obtained were efficiently expressed in the Escherichia coli prokaryotic expression system, showing a significant binding capacity to IL-13Rα2 in vitro. The cell proliferation and migration inhibitory effects of recombinant variable domain of the new antigen receptor (VNAR) on glioma cells were detected by CCK-8 and cell scratch assays. The sdAb obtained in this study showed high in vitro activity and favourable cell proliferation inhibitory effect on glioma cells, with potential clinical application value. The present study also provides a new direction and experimental basis for the development of targeted therapies for glioma.

Study on discharge characteristics of six-gap gas switch with corona assisted triggering.

To improve the triggering characteristics of the gas switch used for linear transformer driver, a method of corona assisted triggering based on the pre-ionization in switch gaps is proposed and applied to a six-gap gas switch. The principle is demonstrated by electrostatic field analysis and verified by the experimental study on the discharge characteristics of the gas switch. The results indicate that when the gas pressure is 0.3 MPa, the self-breakdown voltage remains about ±80 kV, and its dispersivity is lower than 3%. The effect of corona assisted triggering on the triggering characteristics increases with the higher permittivity of the inner shield. The positive trigger voltage of the switch with the proposed method can be reduced from 110 to 30 kV at a charging voltage of ±80 kV when the jitter is equal to that of the original switch. There are no pre-fire or late-fire when the switch operates continuously for 2000 shots.

Different biopsy techniques for different pancreas graft locations after homolateral simultaneous pancreas-kidney transplantation.

Background: Biopsy of a transplanted pancreas is sometimes necessary in patients who have undergone simultaneous pancreas-kidney (SPK) transplantation and have elevated serum lipase and amylase concentrations. However, the risks associated with pancreatic graft biopsy are high, and the best biopsy technique for different location of pancreatic graft remains unclear. Methods: Depending on the anatomical location of the transplanted pancreas, percutaneous computed tomography (CT) combined with color Doppler-guided puncture biopsy or laparoscopic biopsy was used to obtain samples of transplanted pancreatic tissue that were shallow and deep, respectively. Results: After SPK transplantation, 4 patients developed abnormal serum lipase and amylase concentrations and underwent pancreas graft biopsy, 1 patient underwent percutaneous CT combined with color Doppler-guided puncture biopsy, 2 patients underwent laparoscopic wedge biopsy, and 1 patient underwent laparoscopic and puncture biopsy. All biopsies were performed successfully, with no intra- or postoperative complications (e.g., bleeding, pancreatic leakage, intestinal leakage). Biopsy sampling was effective in 3 patients, including 1 case of acute pancreatic rejection, 1 case of pancreatitis, and 1 case of pancreatic plasmablastic lymphoma. Biopsy failed to retrieve samples in 1 patient with a deep pancreatic graft who underwent laparoscopic wedge biopsy. Conclusions: Pancreas graft biopsy is safe and feasible after SPK transplantation. In addition to the two biopsy methods mentioned, other methods can also be used. Different biopsy strategies should be formulated according to the anatomical location of the transplanted pancreas.

Association between postoperative thrombocytopenia and outcomes after traumatic brain injury surgery: A cohort study.

BACKGROUND: It is well known that thrombocytopenia occurs in patients with traumatic brain injury (TBI), and its incidence increases with the severity of injury. We aimed to determine whether postoperative thrombocytopenia in patients with TBI is associated with poor clinical outcomes. METHODS: This was a retrospective cohort study of a large international database called the Medical Information Mart for Intensive Care III (MIMIC-III), which included 1093 patients who underwent TBI surgery. Hospital mortality was the primary endpoint of this study. RESULTS: Multivariate logistic regression analysis revealed non-thrombocytopenia was significantly associated with a decreased hospital mortality (adjusted odds ratio [OR] 0.49; 95% confidence interval [CI] 0.33-0.75; p = .01). In addition, platelet counts increased over time in both survivors and non-survivors, according to generalized additive mixed model (GAMM). However, the platelet count increased more noticeably in the survivors than in the non-survivors and the difference in platelet count between the two groups showed a trend toward increasing within 7 days after surgery. This difference increased by 7.97 per day on average. CONCLUSIONS: Patients with TBI who experienced postoperative thrombocytopenia were more likely to have a poor short-term prognosis. In addition, we found that the rate of platelet growth over time varied significantly between the survival and non-survival groups. Patients with TBI who experienced a greater early increase in platelet count had a lower mortality rate.

The role of ferroptosis-related genes in airway epithelial cells of asthmatic patients based on bioinformatics.

It has been reported that airway epithelial cells and ferroptosis have certain effect on asthma. However, the action mechanism of ferroptosis-related genes in airway epithelial cells of asthmatic patients is still unclear. Firstly, the study downloaded the GSE43696 training set, GSE63142 validation set and GSE164119 (miRNA) dataset from the gene expression omnibus database. 342 ferroptosis-related genes were downloaded from the ferroptosis database. Moreover, differentially expressed genes (DEGs) between asthma and control samples in the GSE43696 dataset were screened by differential analysis. Consensus clustering analysis was performed on asthma patients to classify clusters, and differential analysis was performed on clusters to obtain inter-cluster DEGs. Asthma-related module was screened by weighted gene co-expression network analysis. Then, DEGs between asthma and control samples, inter-cluster DEGs and asthma-related module were subjected to venn analysis for obtaining candidate genes. The last absolute shrinkage and selection operator and support vector machines were respectively applied to the candidate genes to screen for feature genes, and functional enrichment analysis was performed. Finally, a competition endogenetic RNA network was constructed and drug sensitivity analysis was conducted. There were 438 DEGs (183 up-regulated and 255 down-regulated) between asthma and control samples. 359 inter-cluster DEGs (158 up-regulated and 201 down-regulated) were obtained by screening. Then, the black module was significantly and strongly correlated with asthma. The venn analysis yielded 88 candidate genes. 9 feature genes (NAV3, ITGA10, SYT4, NOX1, SNTG2, RNF182, UPK1B, POSTN, SHISA2) were screened and they were involved in proteasome, dopaminergic synapse etc. Besides, 4 mRNAs, 5 miRNAs, and 2 lncRNAs collectively formed competition endogenetic RNA regulatory network, which included RNF182-hsa-miR-455-3p-LINC00319 and so on. The predicted therapeutic drug network map contained NAV3-bisphenol A and other relationship pairs. The study investigated the potential molecular mechanisms of NAV3, ITGA10, SYT4, NOX1, SNTG2, RNF182, UPK1B, POSTN, SHISA2 in airway epithelial cells of asthmatic patients through bioinformatics analysis, providing a reference for the research of asthma and ferroptosis.