Insights into the Aggressiveness of the Emerging North American Population 3 (NA3) of Fusarium graminearum.

In the United States and Canada, Fusarium graminearum (Fg) is the predominant etiological agent of Fusarium head blight (FHB), an economically devastating fungal disease of wheat and other small grains. Besides yield losses, FHB leads to grain contamination with trichothecene mycotoxins that are harmful to plant, human, and livestock health. Three genetic North American populations of Fg, differing in their predominant trichothecene chemotype (i.e., NA1/15ADON, NA2/3ADON, and NA3/NX-2), have been identified. To improve our understanding of the newly discovered population NA3 and how population-level diversity influences FHB outcomes, we inoculated heads of the moderately resistant wheat cultivar Alsen with 15 representative strains from each population and evaluated disease progression, mycotoxin accumulation, and mycotoxin production per unit Fg biomass. Additionally, we evaluated population-specific differences in induced host defense responses. The NA3 population was significantly less aggressive than the NA1 and NA2 populations but posed a similar mycotoxigenic potential. Multiomics analyses revealed patterns in mycotoxin production per unit Fg biomass, expression of Fg aggressiveness-associated genes, and host defense responses that did not always correlate with the NA3-specific severity difference. Our comparative disease assay of NA3/NX-2 and admixed NA1/NX-2 strains indicated that the reduced NA3 aggressiveness is not due solely to the NX-2 chemotype. Notably, the NA1 and NA2 populations did not show a significant advantage over NA3 in perithecia production, a fitness-related trait. Together, our data highlight that the disease outcomes were not due to mycotoxin production or host defense alone, indicating that other virulence factors and/or host defense mechanisms are likely involved.

COVID-Net USPro: An Explainable Few-Shot Deep Prototypical Network for COVID-19 Screening Using Point-of-Care Ultrasound.

As the Coronavirus Disease 2019 (COVID-19) continues to impact many aspects of life and the global healthcare systems, the adoption of rapid and effective screening methods to prevent the further spread of the virus and lessen the burden on healthcare providers is a necessity. As a cheap and widely accessible medical image modality, point-of-care ultrasound (POCUS) imaging allows radiologists to identify symptoms and assess severity through visual inspection of the chest ultrasound images. Combined with the recent advancements in computer science, applications of deep learning techniques in medical image analysis have shown promising results, demonstrating that artificial intelligence-based solutions can accelerate the diagnosis of COVID-19 and lower the burden on healthcare professionals. However, the lack of large, well annotated datasets poses a challenge in developing effective deep neural networks, especially in the case of rare diseases and new pandemics. To address this issue, we present COVID-Net USPro, an explainable few-shot deep prototypical network that is designed to detect COVID-19 cases from very few ultrasound images. Through intensive quantitative and qualitative assessments, the network not only demonstrates high performance in identifying COVID-19 positive cases, using an explainability component, but it is also shown that the network makes decisions based on the actual representative patterns of the disease. Specifically, COVID-Net USPro achieves 99.55% overall accuracy, 99.93% recall, and 99.83% precision for COVID-19-positive cases when trained with only five shots. In addition to the quantitative performance assessment, our contributing clinician with extensive experience in POCUS interpretation verified the analytic pipeline and results, ensuring that the network's decisions are based on clinically relevant image patterns integral to COVID-19 diagnosis. We believe that network explainability and clinical validation are integral components for the successful adoption of deep learning in the medical field. As part of the COVID-Net initiative, and to promote reproducibility and foster further innovation, the network is open-sourced and available to the public.

Towards Building a Trustworthy Deep Learning Framework for Medical Image Analysis.

Computer vision and deep learning have the potential to improve medical artificial intelligence (AI) by assisting in diagnosis, prediction, and prognosis. However, the application of deep learning to medical image analysis is challenging due to limited data availability and imbalanced data. While model performance is undoubtedly essential for medical image analysis, model trust is equally important. To address these challenges, we propose TRUDLMIA, a trustworthy deep learning framework for medical image analysis, which leverages image features learned through self-supervised learning and utilizes a novel surrogate loss function to build trustworthy models with optimal performance. The framework is validated on three benchmark data sets for detecting pneumonia, COVID-19, and melanoma, and the created models prove to be highly competitive, even outperforming those designed specifically for the tasks. Furthermore, we conduct ablation studies, cross-validation, and result visualization and demonstrate the contribution of proposed modules to both model performance (up to 21%) and model trust (up to 5%). We expect that the proposed framework will support researchers and clinicians in advancing the use of deep learning for dealing with public health crises, improving patient outcomes, increasing diagnostic accuracy, and enhancing the overall quality of healthcare delivery.

Association between cortical thickness and distinct vascular cognitive impairment and dementia in patients with white matter lesions.

NEW FINDINGS: What is the central question of this study? White matter lesions (WMLs) are a brain disease characterized by altered brain structural and functional connectivity, but findings have shown an inconsistent pattern: are there distinct cortical thickness changes in patients with WMLs subtypes? What is the main finding and its importance? Patients with WMLs with non-dementia vascular cognitive impairment and WMLs with vascular dementia showed distinct pathophysiology in cortical thickness. These neural correlates of WMLs should be considered in future treatment. ABSTRACT: The effect of cortical thickness on white matter lesions (WMLs) in patients with distinct vascular cognitive impairments is relatively unknown. This study investigated the correlation between cortical thickness and vascular cognitive manifestations. WML patients and healthy controls from Beijing Tiantan Hospital between 2014 and 2018 were included. The patients were further divided into two subgroups, namely WMLs with non-dementia vascular cognitive impairment (WML-VCIND) and WMLs with vascular dementia (WML-VaD) according to the Clinical Dementia Rating (CDR) scale and the Beijing version of the Montreal Cognitive Assessment (MoCA). Changes in cortical thickness were calculated using FreeSurfer. Pearson's correlation analysis was performed to explore the relationship between cognitive manifestations and cortical thickness in WML patients. Forty-five WML patients and 23 healthy controls were recruited. The WML group exhibited significant difference in cortical thickness compared to the control group. Significantly decreased cortical thickness in the middle and superior frontal gyri, middle temporal gyrus, angular gyrus and insula was found in the WML-VaD versus WML-VCIND subgroup. Cortical thickness deficits of the left caudal middle frontal gyrus (r = 0.451, P = 0.002), left rostral middle frontal gyrus (r = 0.514, P < 0.001), left superior frontal gyrus (r = 0.410, P = 0.006), right middle temporal gyrus (r = 0.440, P = 0.003), right pars triangularis (r = 0.462, P = 0.002), right superior frontal gyrus (r = 0.434, P = 0.004) and right insula (r = 0.499, P = 0.001) were positively correlated with the MoCA score in WML patients. The specific pattern of cortical thickness deficits in the WML-VaD subgroup revealed the pathophysiology of WMLs, which should be considered in future treatment of WMLs.

Variations in Head-and-Face Shape of Chinese Civilian Workers.

This study aims to elucidate variations in head-and-face shape among the Chinese civilian workers. Most respirator manufacturers are using outdated, Western anthropometric data to design respirators for the Chinese workers. Therefore, newly acquired anthropometric data specific to the Chinese population are needed to create more effective personal protective equipment. The three-dimensional (3D) head scans of 350 participants, who were selected from the 3000 participants in the 2006 Chinese Anthropometric Survey, were processed using geometric processing techniques. Each scan was then linked with the others, making statistical shape analysis on a dense set of 3D points possible. Furthermore, this provided for the reduction of scan noise as well as for the patching of holes. Following general scan correspondence and fine tuning, principal component analysis was used to analyze the variability in head-and-face shape of the 3D images. More than 90% of the variability among head-and-face shapes was accounted for with 26 principal components. Future study is recommended so the overall usefulness of the point cloud-based approach for the quantification of variations in facial morphology may be determined.

Surgical treatment of giant aneurysm of hepatic artery: report of three cases.

BACKGROUND/AIMS: To explore the surgical way of treating giant hepatic artery aneurysm(HAA). METHODOLOGY: Three hepatic artery aneurysm patients who were performed aneurysm resection without revascularization of the hepatic artery were reviewed. After surgery, the values of liver function and enhanced CT scan of the patients were followed. RESULTS: All the three patients were recovered well postoperatively and only several values of biochemistry marks of liver function as ALT, AST, TBIL and DB in one case with liver cirrhosis were elevated and decreased to normal ranges in a few days postoperatively. The values of biochemistry marks of liver function in other two cases were within normal limits. The enhanced CT scan also showed arteries in the liver after hepatic artery aneurysm resection. CONCLUSIONS: Giant HAA may be safely removed without revascularization of the hepatic artery.

Biomimetic Peripheral Nerve Stimulation Promotes the Rat Hindlimb Motion Modulation in Stepping: An Experimental Analysis.

Peripheral nerve stimulation is an effective neuromodulation method in patients with lower extremity movement disorders caused by stroke, spinal cord injury, or other diseases. However, most current studies on rehabilitation using sciatic nerve stimulation focus solely on ankle motor regulation through stimulation of common peroneal and tibial nerves. Using the electrical nerve stimulation method, we here achieved muscle control via different sciatic nerve branches to facilitate the regulation of lower limb movements during stepping and standing. A map of relationships between muscles and nerve segments was established to artificially activate specific nerve fibers with the biomimetic stimulation waveform. Then, characteristic curves depicting the relationship between neural electrical stimulation intensity and joint control were established. Finally, by testing the selected stimulation parameters in anesthetized rats, we confirmed that single-cathode extraneural electrical stimulation could activate combined movements to promote lower limb movements. Thus, this method is effective and reliable for use in treatment for improving and rehabilitating lower limb motor dysfunction.

Classifying Routine Clinical Electroencephalograms With Multivariate Iterative Filtering and Convolutional Neural Networks.

Electroencephalogram (EEG) is widely used in basic and clinical neuroscience to explore neural states in various populations, and classifying these EEG recordings is a fundamental challenge. While machine learning shows promising results in classifying long multivariate time series, optimal prediction models and feature extraction methods for EEG classification remain elusive. Our study addressed the problem of EEG classification under the framework of brain age prediction, applying a deep learning model on EEG time series. We hypothesized that decomposing EEG signals into oscillatory modes would yield more accurate age predictions than using raw or canonically frequency-filtered EEG. Specifically, we employed multivariate intrinsic mode functions (MIMFs), an empirical mode decomposition (EMD) variant based on multivariate iterative filtering (MIF), with a convolutional neural network (CNN) model. Testing a large dataset of routine clinical EEG scans (n = 6540) from patients aged 1 to 103 years, we found that an ad-hoc CNN model without fine-tuning could reasonably predict brain age from EEGs. Crucially, MIMF decomposition significantly improved performance compared to canonical brain rhythms (from delta to lower gamma oscillations). Our approach achieved a mean absolute error (MAE) of 13.76 ± 0.33 and a correlation coefficient of 0.64 ± 0.01 in brain age prediction over the entire lifespan. Our findings indicate that CNN models applied to EEGs, preserving their original temporal structure, remains a promising framework for EEG classification, wherein the adaptive signal decompositions such as the MIF can enhance CNN models' performance in this task.

Byte-Pair Encoding for classifying routine clinical electroencephalograms in adults over the lifespan.

Routine clinical EEG is a standard test used for the neurological evaluation of patients. A trained specialist interprets EEG recordings and classifies them into clinical categories. Given time demands and high inter-reader variability, there is an opportunity to facilitate the evaluation process by providing decision support tools that can classify EEG recordings automatically. Classifying clinical EEG is associated with several challenges: classification models are expected to be interpretable; EEGs vary in duration and EEGs are recorded by multiple technicians operating various devices. Our study aimed to test and validate a framework for EEG classification which satisfies these requirements by transforming EEG into unstructured text. We considered a highly heterogeneous and extensive sample of routine clinical EEGs (n = 5785), with a wide range of participants aged between 15 and 99 years. EEG scans were recorded at a public hospital, according to 10/20 electrode positioning with 20 electrodes. The proposed framework was based on symbolizing EEG signals and adapting a previously proposed method from natural language processing (NLP) to break symbols into words. Specifically, we symbolized the multichannel EEG time series and applied a byte-pair encoding (BPE) algorithm to extract a dictionary of the most frequent patterns (tokens) reflecting the variability of EEG waveforms. To demonstrate the performance of our framework, we used newly-reconstructed EEG features to predict patients' biological age with a Random Forest regression model. This age prediction model achieved a mean absolute error of 15.7 years. We also correlated tokens' occurrence frequencies with age. The highest correlations between the frequencies of tokens and age were observed at frontal and occipital EEG channels. Our findings demonstrated the feasibility of applying an NLP-based approach to classifying routine clinical EEG. Notably, the proposed algorithm could be instrumental in classifying clinical EEG with minimal preprocessing and identifying clinically-relevant short events, such as epileptic spikes.

Time-Varying Functional Connectivity of Rat Brain during Bipedal Walking on Unexpected Terrain.

The cerebral cortex plays an important role in human and other animal adaptation to unpredictable terrain changes, but little was known about the functional network among the cortical areas during this process. To address the question, we trained 6 rats with blocked vision to walk bipedally on a treadmill with a random uneven area. Whole-brain electroencephalography signals were recorded by 32-channel implanted electrodes. Afterward, we scan the signals from all rats using time windows and quantify the functional connectivity within each window using the phase-lag index. Finally, machine learning algorithms were used to verify the possibility of dynamic network analysis in detecting the locomotion state of rats. We found that the functional connectivity level was higher in the preparation phase compared to the walking phase. In addition, the cortex pays more attention to the control of hind limbs with higher requirements for muscle activity. The level of functional connectivity was lower where the terrain ahead can be predicted. Functional connectivity bursts after the rat accidentally made contact with uneven terrain, while in subsequent movement, it was significantly lower than normal walking. In addition, the classification results show that using the phase-lag index of multiple gait phases as a feature can effectively detect the locomotion states of rat during walking. These results highlight the role of the cortex in the adaptation of animals to unexpected terrain and may help advance motor control studies and the design of neuroprostheses.

COVIDx-US: An Open-Access Benchmark Dataset of Ultrasound Imaging Data for AI-Driven COVID-19 Analytics.

BACKGROUND: The Coronavirus Disease 2019 (COVID-19) pandemic continues to have a devastating effect on the health and well-being of the global population. Apart from the global health crises, the pandemic has also caused significant economic and financial difficulties and socio-physiological implications. Effective screening, triage, treatment planning, and prognostication of outcome play a key role in controlling the pandemic. Recent studies have highlighted the role of point-of-care ultrasound imaging for COVID-19 screening and prognosis, particularly given that it is non-invasive, globally available, and easy-to-sanitize. COVIDx-US Dataset: Motivated by these attributes and the promise of artificial intelligence tools to aid clinicians, we introduce COVIDx-US, an open-access benchmark dataset of COVID-19 related ultrasound imaging data. The COVIDx-US dataset was curated from multiple data sources and its current version, i.e., v1.5., consists of 173 ultrasound videos and 21,570 processed images across 147 patients with COVID-19 infection, non-COVID-19 infection, other lung diseases/conditions, as well as normal control cases. CONCLUSIONS: The COVIDx-US dataset was released as part of a large open-source initiative, the COVID-Net initiative, and will be continuously growing, as more data sources become available. To the best of the authors' knowledge, COVIDx-US is the first and largest open-access fully-curated benchmark lung ultrasound imaging dataset that contains a standardized and unified lung ultrasound score per video file, providing better interpretation while enabling other research avenues such as severity assessment. In addition, the dataset is reproducible, easy-to-use, and easy-to-scale thanks to the well-documented modular design.

Evaluation of Respiratory Sounds Using Image-Based Approaches for Health Measurement Applications.

Goal: The evaluation of respiratory events using audio sensing in an at-home setting can be indicative of worsening health conditions. This paper investigates the use of image-based transfer learning applied to five audio visualizations to evaluate three classification tasks (C1: wet vs. dry vs. whooping cough vs. restricted breathing; C2: wet vs. dry cough; C3: cough vs. restricted breathing). Methods: The five visualizations (linear spectrogram, logarithmic spectrogram, Mel-spectrogram, wavelet scalograms, and aggregate images) are applied to a pre-trained AlexNet image classifier for all tasks. Results: The aggregate image-based classifier achieved the highest overall performance across all tasks with C1, C2, and C3 having testing accuracies of 0.88, 0.88, and 0.91 respectively. However, the Mel-spectrogram method had the highest testing accuracy (0.94) for C2. Conclusions: The classification of respiratory events using aggregate image inputs to transfer learning approaches may help healthcare professionals by providing information that would otherwise be unavailable to them.

Effect of neuromorphic transcutaneous electrical nerve stimulation (nTENS) of cortical functional networks on tactile perceptions: an event-related electroencephalogram study.

Background.Transcutaneous electrical nerve stimulation (TENS) is generally applied for tactile feedback in the field of prosthetics. The distinct mechanisms of evoked tactile perception between stimulus patterns in conventional TENS (cTENS) and neuromorphic TENS (nTENS) are relatively unknown. This is the first study to investigate the neurobiological effect of nTENS for cortical functional mechanism in evoked tactile perception.Methods.Twenty-one healthy participants were recruited in this study. Electroencephalogram (EEG) was recorded while the participants underwent a tactile discrimination task. One cTENS pattern (square pattern) and two nTENS patterns (electromyography and single motor unit patterns) were applied to evoke tactile perception in four fingers, including the right and left index and little fingers. EEG was preprocessed and somatosensory-evoked potentials (SEPs) were determined. Then, source-level functional networks based on graph theory were evaluated, including clustering coefficient, path length, global efficiency, and local efficiency in six frequency bands.Main results.Behavioral results suggested that the single motor units (SMUs) pattern of nTENS was the most natural tactile perception. SEPs results revealed that SMU pattern exhibited significant shorter latency in P1 and N1 components than the other patterns, while nTENS patterns have significantly longer latency in P3 component than cTENS pattern. Cortical functional networks showed that the SMU pattern had the lowest short path and highest efficiency in beta and gamma bands.Conclusion.This study highlighted that distinct TENS patterns could affect brain activities. The new characteristics in tactile manifestation of nTENS would provide insights for the application of tactile perception restoration.

Understanding the temporal evolution of COVID-19 research through machine learning and natural language processing.

The outbreak of the novel coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been continuously affecting human lives and communities around the world in many ways, from cities under lockdown to new social experiences. Although in most cases COVID-19 results in mild illness, it has drawn global attention due to the extremely contagious nature of SARS-CoV-2. Governments and healthcare professionals, along with people and society as a whole, have taken any measures to break the chain of transition and flatten the epidemic curve. In this study, we used multiple data sources, i.e., PubMed and ArXiv, and built several machine learning models to characterize the landscape of current COVID-19 research by identifying the latent topics and analyzing the temporal evolution of the extracted research themes, publications similarity, and sentiments, within the time-frame of January-May 2020. Our findings confirm the types of research available in PubMed and ArXiv differ significantly, with the former exhibiting greater diversity in terms of COVID-19 related issues and the latter focusing more on intelligent systems/tools to predict/diagnose COVID-19. The special attention of the research community to the high-risk groups and people with complications was also confirmed.

Multisite Simultaneous Neural Recording of Motor Pathway in Free-Moving Rats.

Neural interfaces typically focus on one or two sites in the motoneuron system simultaneously due to the limitation of the recording technique, which restricts the scope of observation and discovery of this system. Herein, we built a system with various electrodes capable of recording a large spectrum of electrophysiological signals from the cortex, spinal cord, peripheral nerves, and muscles of freely moving animals. The system integrates adjustable microarrays, floating microarrays, and microwires to a commercial connector and cuff electrode on a wireless transmitter. To illustrate the versatility of the system, we investigated its performance for the behavior of rodents during tethered treadmill walking, untethered wheel running, and open field exploration. The results indicate that the system is stable and applicable for multiple behavior conditions and can provide data to support previously inaccessible research of neural injury, rehabilitation, brain-inspired computing, and fundamental neuroscience.

Unexpected Terrain Induced Changes in Cortical Activity in Bipedal-Walking Rats.

Humans and other animals can quickly respond to unexpected terrains during walking, but little is known about the cortical dynamics in this process. To study the impact of unexpected terrains on brain activity, we allowed rats with blocked vision to walk on a treadmill in a bipedal posture and then walk on an uneven area at a random position on the treadmill belt. Whole brain EEG signals and hind limb kinematics of bipedal-walking rats were recorded. After encountering unexpected terrain, the θ band power of the bilateral M1, the γ band power of the left S1, and the θ to γ band power of the RSP significantly decreased compared with normal walking. Furthermore, when the rats left uneven terrain, the ß band power of the bilateral M1 and the α band power of the right M1 decreased, while the γ band power of the left M1 significantly increased compared with normal walking. Compared with the flat terrain, the θ to low ß (3-20 Hz) band power of the bilateral S1 increased after the rats contacted the uneven terrain and then decreased in the single- or double- support phase. These results support the hypothesis that unexpected terrains induced changes in cortical activity.

Circ-SFMBT2 promotes the proliferation of gastric cancer cells through sponging miR-182-5p to enhance CREB1 expression.

BACKGROUND: Circular RNAs(circRNAs) have been reported as a diverse class of endogenous RNA that regulate gene expression in eukaryotes. Recent evidence suggested that many circular RNAs can act as oncogenes or tumor suppressors through sponging microRNAs. However, the function of circular RNAs in gastric cancer remains largely unknown. MATERIALS AND METHODS: The circRNA levels in gastric carcinoma tissues and plasmas were detected by real-time quantitative reverse transcription-polymerase chain reaction. The correlation between the expression of circRNA and clinic pathological features was analyzed. Rate of inhibiting of proliferation was measured using a CCK-8 cell proliferation assay. Clone formation ability was assessed with a clone formation inhibition test. We used the bioinformatics software to predict circRNA-miRNA and miRNA-mRNA interactions. Relative gene expression was assessed using quantitative real-time polymerase chain reaction and relative protein expression levels were determined with western blotting. CircRNA and miRNA interaction was confirmed by dual-luciferase reporter assays. RESULTS: We characterized that one circRNA named circ-SFMBT2 showed an increased expression level in gastric cancer tissues compared to adjacent non-cancerous tissues and was associated with higher tumor stages of gastric cancer. Silencing of circ-SFMBT2 inhibited the proliferation of gastric cancer cells significantly. Importantly, we demonstrated that circ-SFMBT2 could act as a sponge of miR-182-5p to regulate the expression of CREB1 mRNA, named as cAMP response element binding protein 1, and further promote the proliferation of gastric cancer cells. CONCLUSION: Our study reveals that circ-SFMBT2 participates in progression of gastric cancer by competitively sharing miR-182-5p with CREB1, providing a novel target to improve the treatment of gastric cancer. mutation-analysis-of-beta-thalassemia-in-east-western-indian-populatio-peer-reviewed-article-TACG for an example.

[Mucinous noncystic (colloid) adenocarcinoma of the pancreas].

OBJECTIVE: To determine the clinicopathologic characteristics and the relationship between related gene expression and pathobiologic behavior of pancreatic mucinous noncystic adenocarcinoma. METHODS: Among the 249 pancreatic carcinoma cases from the department files, 6 tumors were identified to meet the pathologic criteria of colloid carcinoma. Envision immunohistochemical staining technique was used to detect expression of p21(ras), p21(WAF1), p16, p33(ING1), p53, ATM, MDM2, PCNA, Cyclins (D1, D3, A, B and E). Intra- and extra- cellular mucin production were determined by AB-PAS staining. Clinically, all of 6 cases were followed to June, 2003. RESULTS: In all 6 cases, the tumors were located in the head of the pancreas and all displayed similar microscopic findings. Duodenal invasion was seen in 4 cases and perineural invasion was seen in 1 case. Tumor metastasis in the liver was seen in 2 cases and in the regional lymph nodes in 2 cases. Positive immunostaining was seen in 5 cases with p21(ras), 3 cases with p21(WAF1), 1 case with p16, 4 cases with p33(ING1), 2 cases with p53, 3 cases with ATM, 3 cases with MDM2, 6 cases with PCNA, 3 cases with cyclinA, 3 cases with cyclinD1, 4 cases with cyclinD3, 4 cases with cyclinB and 6 cases with cyclinE. Both extracellular and intracellular mucin was strongly positive for AB-PAS staining. Clinical follow-up found that 2 patients died of their tumors at 14 and 20 months. Three patients were alive after 28, 49 and 87 months of follow-up. One case were lost contact. CONCLUSIONS: Pancreatic mucinous noncystic adenocarcinoma has distinct morphologic features and biologic behavior. Multiple gene products including many cyclins may be involved in the pathogenesis of pancreatic colloid carcinoma. The tumor has an aggressive behavior with a high frequency of invasion and metastases, though the prognosis could be better than that of ordinary ductal adenocarcinoma of pancreas.

Clinical analysis of uncinate process carcinoma of the pancreas.

OBJECTIVE: To analyse the clinical features of uncinate process carcinoma of the pancreas and the diagnosis and treatment of this malignancy. METHOD: Fifty-nine patients with pancreas uncinate process carcinoma treated from January 1998 to September 2002 at our hospital were analysed retrospectively. RESULTS: Major symptoms of these patients were upper abdominal pain accompanied with lumbar pain, body weight loss and jaundice. Thirty-seven patients received regional pancreaticoduodenectomy (RP), 16 partial resection of the superior mesenteric vein-portal vein (SMV-PV) or superior mesenteric artery (SMA) and reconstruction, 1 anhydrous alcohol injection in the celiac nerve plexus, regional chemotherapy via a chemotherapy pump, and liver biopsy, and 5 no operation. The survival of the patients after operation was 2-46 months (median 12.1 months). Eleven patients are still alive with a longest survival of 46 months. The 1- and 3-year survival rates were 37.7% and 5.6%. CONCLUSIONS: Pancreas uncinate process carcinoma invading the adjacent SMV/SMA-PV causes difficulty in early diagnosis and poor prognosis, which are related to its location, not tumor's aggressive nature. This carcinoma has a high resection rate of 89.8%.

[Uncinate process carcinoma of the pancreas: clinical features and diagnosis and treatment].

OBJECTIVE: To summarize the clinical features and diagnosis and treatment of uncinate process carcinoma of the pancreas. METHODS: From January 1998 to December 2000, 41 patients of pancreas uncinate process carcinoma were retrospectly analysed. RESULTS: Upper abdominal pain accompanied with back pain,weight loss and jaundice were the main symptoms. Thirty-six patients were subjected to regional pancreaticoduodenectomy (RP), 11 to SMV-PV or SMA lateral wall partial resection or partial vascular resection and reconstruction, of which PV reconstruction with PTEE grafts was performed in 2 patients. Two cholecystojejunostomy Roux-en-Y. Alcohol injection was made in the nerve plexus of the trunks of both celic axes and the superior mesenteric artery and regional chemotherapy via chemotherapy pump and liver biopsy in one case. Abdominal exploration was performed in 1 case and no-operation in another. None of the cases died perioperatively. Postoperation survival 2 - 44 months and was 1 the median survival was 11.2 months. Four patients are still alive, the longest surviving have been 44 months. The 1-year and 3-year survival rate was 37.0% and 3.7%. CONCLUSIONS: Pancreas uncinate process carcinoma has a tendency to invade adjacent SMV/SMA-PV and is difficult to diagnose early. Since those are related to its location and not to its invading behaviors, the tumon is highly resecable (87.8%).