Tryptophan intake and pancreatic cancer: findings from a case-control study.

BACKGROUND: Pancreatic cancer is a leading cause of cancer-related death worldwide. Tryptophan plays a vital role in cell growth and maintenance as a building block of protein and coordination of organismal responses to environmental and dietary cues. Animal model study showed that dietary tryptophan improved treatment response in those who received chemotherapy or immune checkpoint inhibitors. Limited data are available assessing the association between tryptophan intake and risk of pancreatic cancer. We aimed to evaluate this association in a case-control study in Vietnam. METHODS: We analyzed data from a case-control study, including 3759 cancer cases and 2995 control subjects of whom 37 with pancreatic cancer cases. Tryptophan intake was derived from food frequency questionnaire. Unconditional logistic regression was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs) for different levels of tryptophan intake with pancreatic cancer risk. RESULTS: Overall, tryptophan intake was inversely associated with pancreatic cancer risk in a dose-dependent manner. The ORs and 95% CIs of pancreatic cancer were 0.51 (0.29-0.92) for continuous scale, 0.27 (0.10-0.73) for tertile 2 and 0.34 (0.11-1.06) for tertile 3, compared with tertile 1 (the lowest intake) (Ptrend = 0.02). In stratified analysis, this inverse association pattern was present among those with BMI < 23 kg/m2 and ever drinkers. CONCLUSION: A diet with a higher intake of tryptophan was significantly associated with a lower incidence of pancreatic cancer among Vietnamese population. These suggest that dietary modification may be an effective strategy for primary prevention of pancreatic cancer development.

Pathogenic/likely pathogenic mutations identified in Vietnamese children diagnosed with autism spectrum disorder using high-resolution SNP genotyping platform.

Among the most prevalent neurodevelopmental disorders, Autism Spectrum Disorder (ASD) is highly diverse showing a broad phenotypic spectrum. ASD also couples with a broad range of mutations, both de novo and inherited. In this study, we used a proprietary SNP genotyping chip to analyze the genomic DNA of 250 Vietnamese children diagnosed with ASD. Our Single Nucleotide Polymorphism (SNP) genotyping chip directly targets more than 800 thousand SNPs in the genome. Our primary focus was to identify pathogenic/likely pathogenic mutations that are potentially linked to more severe symptoms of autism. We identified and validated 23 pathogenic/likely pathogenic mutations in this initial study. The data shows that these mutations were detected in several cases spanning multiple biological pathways. Among the confirmed SNPs, mutations were identified in genes previously known to be strongly associated with ASD such as SLCO1B1, ACADSB, TCF4, HCP5, MOCOS, SRD5A2, MCCC2, DCC, and PRKN while several other mutations are known to associate with autistic traits or other neurodevelopmental disorders. Some mutations were found in multiple patients and some patients carried multiple pathogenic/likely pathogenic mutations. These findings contribute to the identification of potential targets for therapeutic solutions in what is considered a genetically heterogeneous neurodevelopmental disorder.

Beyond linear regression: Modeling COVID-19 clinical cases with wastewater surveillance of SARS-CoV-2 for the city of Athens and Ohio University campus.

Wastewater-based surveillance has emerged as a detection tool for population-wide infectious diseases, including coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Infected individuals shed the virus, which can be detected in wastewater using molecular techniques such as reverse transcription-digital polymerase chain reaction (RT-dPCR). This study examined the association between the number of clinical cases and the concentration of SARS-CoV-2 in wastewater beyond linear regression and for various normalizations of viral loads. Viral loads were measured in a total of 446 wastewater samples during the period from August 2021 to April 2022. These samples were collected from nine different locations, with 220 samples taken from four specific sites within the city of Athens and 226 samples from five sites within Ohio University. The correlation between COVID-19 cases and wastewater viral concentrations, which was estimated using the Pearson correlation coefficient, was statistically significant and ranged from 0.6 to 0.9. In addition, time-lagged cross correlation was applied to identify the lag time between clinical and wastewater data, estimated 4 to 7 days. While we also explored the effect on the correlation coefficients of various normalizations of viral loads accounting for procedural loss or amount of fecal material and of estimated lag times, these alternative specifications did not change our substantive conclusions. Additionally, several linear and non-linear regression models were applied to predict the COVID-19 cases given wastewater data as input. The non-linear approach was found to yield the highest R-squared and Pearson correlation and lowest Mean Absolute Error values between the predicted and actual number of COVID-19 cases for both aggregated OHIO Campus and city data. Our results provide support for previous studies on correlation and time lag and new evidence that non-linear models, approximated with artificial neural networks, should be implemented for WBS of contagious diseases.

New prenyl flavanone and diarylbutanol from Uvaria siamensis stem bark and their α-glucosidase inhibitory activity.

One new prenyl flavanone (1), (2S)-8-prenyl-5,6-dihydroxy-7-methoxyflavanone, and one new diarylbutanol (2), (7'S)-3'-hydroxy-linderagatin-A, were isolated from the stem bark of Uvaria siamensis (Annonaceae), along with five known compounds, eriodictyol (3), quercetin (4), paprazine (5), N-trans-caffeoyltyramine (6), and N-trans-feruloyltyramine (7). Their structures were determined through extensive spectroscopic analyses and comparison with the literature. The α-glucosidase inhibitory potential of 1-7 was evaluated. Compound 6 showed the highest inhibitory activity against α-glucosidase and exhibited superior potency compared to the positive control, with an IC50 value of 0.12 µM.

Circularly Polarized MIMO Antenna with Wideband and High Isolation Characteristics for C-Band Communication Systems.

This paper presents a circularly polarized (CP) multiple-input multiple-output (MIMO) antenna using a microstrip patch and parasitic elements. The proposed design exhibits wideband characteristics for both impedance and axial ratio bandwidths. Especially, the mutual coupling between the MIMO elements is significantly depressed without using any decoupling network. To achieve these features, parasitic elements are positioned nearby and in different layers to the radiating elements. The measured results demonstrate that the proposed MIMO CP antenna has a wideband operation of 11.3% (5.0-5.6 GHz), which is defined by an overlap between -10-dB impedance and 3-dB axial ratio bandwidths. Across this band, the realized gain is better than 6.0 dBi, and the isolation is greater than 32 dB with the highest value of 45 dB. The MIMO parameters such as the envelope correlation coefficient, diversity gain, and channel capacity loss are also investigated thoroughly, which are found to be good on the scale of diversity standards.

Brunner's gland hyperplasia associated with lipomatous pseudohypertrophy of the pancreas presenting with gastrointestinal bleeding: A case report.

BACKGROUND: Brunner's gland hyperplasia (BGH) is a rare benign lesion of the duodenum. Lipomatous pseudohypertrophy (LiPH) of the pancreas is an extremely rare disease. Because each condition is rare, the probability of purely coincidental coexistence of both conditions is extremely low. CASE SUMMARY: We report a 26-year-old man presenting to our hospital with symptoms of recurrent upper gastrointestinal bleeding. Upper gastrointestinal endoscopy showed a huge pedunculated polypoid lesion in the duodenum with bleeding at the base of the lesion. Histopathological examination of the duodenal biopsy specimens showed BGH. Besides, abdominal computed tomography and magnetic resonance imaging revealed marked fat replacement over the entire pancreas, confirmed by histopathological evaluation on percutaneous pancreatic biopsies. Based on the radiological and histological findings, LiPH of the pancreas and BGH were diagnosed. The patient refused any surgical intervention. Therefore, he was managed with supportive treatment. The patient's symptoms improved and there was no further bleeding. CONCLUSION: This is the first well-documented case showing the coexistence of LiPH of the pancreas and BGH.

Bougainvinones NP, three new flavonoids from Bougainvillea spectabilis.

In an effort to identify natural bioactive compounds, three new flavonoids (1-3) and six known compounds (4-9) were isolated from the stem bark of Bougainvillea spectabilis. The structures of these compounds were accomplished using comprehensive spectroscopic methods, including 1D and 2D NMR spectra with references to the literatures, as well as high-resolution mass spectrometric analysis. Their cytotoxicity against KB and HeLa S-3 cell lines was also evaluated.

Enhanced Image-Based Endoscopic Pathological Site Classification Using an Ensemble of Deep Learning Models.

In vivo diseases such as colorectal cancer and gastric cancer are increasingly occurring in humans. These are two of the most common types of cancer that cause death worldwide. Therefore, the early detection and treatment of these types of cancer are crucial for saving lives. With the advances in technology and image processing techniques, computer-aided diagnosis (CAD) systems have been developed and applied in several medical systems to assist doctors in diagnosing diseases using imaging technology. In this study, we propose a CAD method to preclassify the in vivo endoscopic images into negative (images without evidence of a disease) and positive (images that possibly include pathological sites such as a polyp or suspected regions including complex vascular information) cases. The goal of our study is to assist doctors to focus on the positive frames of endoscopic sequence rather than the negative frames. Consequently, we can help in enhancing the performance and mitigating the efforts of doctors in the diagnosis procedure. Although previous studies were conducted to solve this problem, they were mostly based on a single classification model, thus limiting the classification performance. Thus, we propose the use of multiple classification models based on ensemble learning techniques to enhance the performance of pathological site classification. Through experiments with an open database, we confirmed that the ensemble of multiple deep learning-based models with different network architectures is more efficient for enhancing the performance of pathological site classification using a CAD system as compared to the state-of-the-art methods.

Smoking habit and chemo-radiotherapy and/or surgery affect the sensitivity of EGFR plasma test in non-small cell lung cancer.

OBJECTIVE: This study aimed to identify the influential factors for the sensitivity of epidermal growth factor receptor (EGFR) plasma test in non-small cell lung cancer (NSCLC). The mutations were detected in tumor tissue and matched plasma samples from 125 newly diagnosed adenocarcinoma, clinical-stage IIIB-IV patients, and compared the diagnostic values of EGFR plasma test between groups of clinical characteristics. The influential factors for the sensitivity were identified and assessed by logistic regression. RESULTS: EGFR mutations were detected in 65 (52.0%) tumor tissue and 50 (40.0%) matched plasma samples (P = 0.028). Compared to the tissue method, the concordance rate, sensitivity, and specificity of the EGFR plasma test were 86.4%, 75.4%, and 98.3%, respectively. Notably, we found that sensitivity of the test is higher in non-smokers (84.1%) compared to smokers (57.1%, P = 0.018), and in treatment naïve subjects (85.7%) compared to whom undergone chemo-radiotherapy with/without surgery before testing (56.5%, P = 0.009). Furthermore, the highest sensitivity was attained in patients without these two factors (90.3%), whilst the lowest value was noted in those with both factors (40.0%, P = 0.004). The multivariable analysis confirmed that smoking habit and treatment history have independently negative impacts on sensitivity (OR = 0.24, P = 0.019, and OR = 0.36, P = 0.047, respectively).

SlimDeblurGAN-Based Motion Deblurring and Marker Detection for Autonomous Drone Landing.

Deep learning-based marker detection for autonomous drone landing is widely studied, due to its superior detection performance. However, no study was reported to address non-uniform motion-blurred input images, and most of the previous handcrafted and deep learning-based methods failed to operate with these challenging inputs. To solve this problem, we propose a deep learning-based marker detection method for autonomous drone landing, by (1) introducing a two-phase framework of deblurring and object detection, by adopting a slimmed version of deblur generative adversarial network (DeblurGAN) model and a You only look once version 2 (YOLOv2) detector, respectively, and (2) considering the balance between the processing time and accuracy of the system. To this end, we propose a channel-pruning framework for slimming the DeblurGAN model called SlimDeblurGAN, without significant accuracy degradation. The experimental results on the two datasets showed that our proposed method exhibited higher performance and greater robustness than the previous methods, in both deburring and marker detection.

Presentation Attack Face Image Generation Based on a Deep Generative Adversarial Network.

Although face-based biometric recognition systems have been widely used in many applications, this type of recognition method is still vulnerable to presentation attacks, which use fake samples to deceive the recognition system. To overcome this problem, presentation attack detection (PAD) methods for face recognition systems (face-PAD), which aim to classify real and presentation attack face images before performing a recognition task, have been developed. However, the performance of PAD systems is limited and biased due to the lack of presentation attack images for training PAD systems. In this paper, we propose a method for artificially generating presentation attack face images by learning the characteristics of real and presentation attack images using a few captured images. As a result, our proposed method helps save time in collecting presentation attack samples for training PAD systems and possibly enhance the performance of PAD systems. Our study is the first attempt to generate PA face images for PAD system based on CycleGAN network, a deep-learning-based framework for image generation. In addition, we propose a new measurement method to evaluate the quality of generated PA images based on a face-PAD system. Through experiments with two public datasets (CASIA and Replay-mobile), we show that the generated face images can capture the characteristics of presentation attack images, making them usable as captured presentation attack samples for PAD system training.

Ultrasound Image-Based Diagnosis of Malignant Thyroid Nodule Using Artificial Intelligence.

Computer-aided diagnosis systems have been developed to assist doctors in diagnosing thyroid nodules to reduce errors made by traditional diagnosis methods, which are mainly based on the experiences of doctors. Therefore, the performance of such systems plays an important role in enhancing the quality of a diagnosing task. Although there have been the state-of-the art studies regarding this problem, which are based on handcrafted features, deep features, or the combination of the two, their performances are still limited. To overcome these problems, we propose an ultrasound image-based diagnosis of the malignant thyroid nodule method using artificial intelligence based on the analysis in both spatial and frequency domains. Additionally, we propose the use of weighted binary cross-entropy loss function for the training of deep convolutional neural networks to reduce the effects of unbalanced training samples of the target classes in the training data. Through our experiments with a popular open dataset, namely the thyroid digital image database (TDID), we confirm the superiority of our method compared to the state-of-the-art methods.

Effects of Diabetic Complications on Health-Related Quality of Life Impairment in Vietnamese Patients with Type 2 Diabetes.

Complications of type 2 diabetes mellitus (T2DM) adversely influence patients' health-related quality of life (HRQOL). This study is aimed at examining HRQOL of T2DM patients, as well as the effects of diabetic complications and comorbidities on HRQOL in this population. This was a hospital-based cross-sectional study on 214 T2DM patients in Hanoi, Vietnam. Short-form 12 version 2 (SF-12v2) and EuroQOL-5 Dimensions-5 Levels (EQ-5D-5L) were employed to measure the HRQOL. The median physical component summary score (PCS), mental component summary score (MCS), and EQ-5D index were 45.6, 56.3, and 0.94, respectively. Having at least one diabetic complication was associated with the reduction of SF-12 scores in social functioning (Diff. = -5.69, 95%CI = -9.24; -2.13), role emotional (Diff. = -1.81, 95%CI = -3.12; -0.51), and MCS (Diff. = -2.55, 95%CI = -5.01; -0.1). Significant decrement of physical functioning, role physical, social functioning, role emotional, and MCS was found in patients having diabetic heart diseases compared to those without diabetic complications. The study revealed that HRQOL of Vietnamese patients with diabetic complications was moderately low, especially in social and mental health perspectives. Strategies to prevent the onset of diabetic complications should be developed as a priority in diabetes management.

Cellular and synaptic adaptations of neural circuits processing skylight polarization in the fly.

Specialized ommatidia harboring polarization-sensitive photoreceptors exist in the 'dorsal rim area' (DRA) of virtually all insects. Although downstream elements have been described both anatomically and physiologically throughout the optic lobes and the central brain of different species, little is known about their cellular and synaptic adaptations and how these shape their functional role in polarization vision. We have previously shown that in the DRA of Drosophila melanogaster, two distinct types of modality-specific 'distal medulla' cell types (Dm-DRA1 and Dm-DRA2) are post-synaptic to long visual fiber photoreceptors R7 and R8, respectively. Here we describe additional neuronal elements in the medulla neuropil that manifest modality-specific differences in the DRA region, including DRA-specific neuronal morphology, as well as differences in the structure of pre- or post-synaptic membranes. Furthermore, we show that certain cell types (medulla tangential cells and octopaminergic neuromodulatory cells) specifically avoid contacts with polarization-sensitive photoreceptors. Finally, while certain transmedullary cells are specifically absent from DRA medulla columns, other subtypes show specific wiring differences while still connecting the DRA to the lobula complex, as has previously been described in larger insects. This hints towards a complex circuit architecture with more than one pathway connecting polarization-sensitive DRA photoreceptors with the central brain.

Artificial Intelligence-Based Thyroid Nodule Classification Using Information from Spatial and Frequency Domains.

Image-based computer-aided diagnosis (CAD) systems have been developed to assist doctors in the diagnosis of thyroid cancer using ultrasound thyroid images. However, the performance of these systems is strongly dependent on the selection of detection and classification methods. Although there are previous researches on this topic, there is still room for enhancement of the classification accuracy of the existing methods. To address this issue, we propose an artificial intelligence-based method for enhancing the performance of the thyroid nodule classification system. Thus, we extract image features from ultrasound thyroid images in two domains: spatial domain based on deep learning, and frequency domain based on Fast Fourier transform (FFT). Using the extracted features, we perform a cascade classifier scheme for classifying the input thyroid images into either benign (negative) or malign (positive) cases. Through expensive experiments using a public dataset, the thyroid digital image database (TDID) dataset, we show that our proposed method outperforms the state-of-the-art methods and produces up-to-date classification results for the thyroid nodule classification problem.

Emergence role of nucleated red blood cells in molecular response evaluation for chronic myeloid leukemia.

PURPOSE: To investigate and evaluate the role of nucleated red blood cells (NRBCs) and other markers in predicting remission failure in chronic myeloid leukemia (CML) patients treated with imatinib. METHODS: Seventy-one CML patients with BCR-ABL(+) in bone marrow cells were selected for this study. Molecular response evaluations were done every three months according to the recommendations of European LeukemiaNet (ELN). Patients were defined as remission failure if BCR-ABL transcripts >10% after 6 months (T6), >1% after 12 months (T12), and >0.1% after 18 (T18) months of treatment. The logistic regression was used to determine the optimal cut-off point of each marker and test the association of marker level with remission failure. RESULTS: The median NRBC, white blood cells, blast cells, basophils, and platelets were declined parallel with the decreases of BCR-ABL transcripts in bone marrow cells after 6 months of treatment (P<0.001). In addition, NRBC was almost not found in the blood of patients who archived good response at T6, T12, and T18 time-points. Interestingly, patients with a high level of NRBC (cut-off: 0.003×109/L) have higher BCR-ABL transcripts compared to others. The elevated NRBC at T6 (OR=6.49, P=0.042), T12 (OR=6.73, P=0.007), and T18 (OR=5.96, P=0.009) time-points was identified as an independent factor for the remission failure. CONCLUSION: The results of this study showed that a high number of NRBC in peripheral blood of CML patients is associated with higher BCR-ABL transcripts in bone marrow cells. The elevated NRBC might serve as an independent marker for molecular remission failure in CML.

Modality-Specific Circuits for Skylight Orientation in the Fly Visual System.

In the fly optic lobe, ∼800 highly stereotypical columnar microcircuits are arranged retinotopically to process visual information. Differences in cellular composition and synaptic connectivity within functionally specialized columns remain largely unknown. Here, we describe the cellular and synaptic architecture in medulla columns located downstream of photoreceptors in the dorsal rim area (DRA), where linearly polarized skylight is detected for guiding orientation responses. We show that only in DRA medulla columns both R7 and R8 photoreceptors target to the bona fide R7 target layer where they form connections with previously uncharacterized, modality-specific Dm neurons: two morphologically distinct DRA-specific cell types (termed Dm-DRA1 and Dm-DRA2) stratify in separate sublayers and exclusively contact polarization-sensitive DRA inputs, while avoiding overlaps with color-sensitive Dm8 cells. Using the activity-dependent GRASP and trans-Tango techniques, we confirm that DRA R7 cells are synaptically connected to Dm-DRA1, whereas DRA R8 form synapses with Dm-DRA2. Finally, using live imaging of ingrowing pupal photoreceptor axons, we show that DRA R7 and R8 termini reach layer M6 sequentially, thus separating the establishment of different synaptic connectivity in time. We propose that a duplication of R7→Dm circuitry in DRA ommatidia serves as an ideal adaptation for detecting linearly polarized skylight using orthogonal e-vector analyzers.

Deep Learning-Based Multinational Banknote Type and Fitness Classification with the Combined Images by Visible-Light Reflection and Infrared-Light Transmission Image Sensors.

Automatic sorting of banknotes in payment facilities, such as automated payment machines or vending machines, consists of many tasks such as recognition of banknote type, classification of fitness for recirculation, and counterfeit detection. Previous studies addressing these problems have mostly reported separately on each of these classification tasks and for a specific type of currency only. In other words, there has been little research conducted considering a combination of these multiple tasks, such as classification of banknote denomination and fitness of banknotes, as well as considering a multinational currency condition of the method. To overcome this issue, we propose a multinational banknote type and fitness classification method that both recognizes the denomination and input direction of banknotes and determines whether the banknote is suitable for reuse or should be replaced by a new one. We also propose a method for estimating the fitness value of banknotes and the consistency of the estimation results among input trials of a banknote. Our method is based on a combination of infrared-light transmission and visible-light reflection images of the input banknote and uses deep-learning techniques with a convolutional neural network. The experimental results on a dataset composed of Indian rupee (INR), Korean won (KRW), and United States dollar (USD) banknote images with mixture of two and three fitness levels showed that the proposed method gives good performance in the combination condition of currency types and classification tasks.

Visible-Light Camera Sensor-Based Presentation Attack Detection for Face Recognition by Combining Spatial and Temporal Information.

Face-based biometric recognition systems that can recognize human faces are widely employed in places such as airports, immigration offices, and companies, and applications such as mobile phones. However, the security of this recognition method can be compromised by attackers (unauthorized persons), who might bypass the recognition system using artificial facial images. In addition, most previous studies on face presentation attack detection have only utilized spatial information. To address this problem, we propose a visible-light camera sensor-based presentation attack detection that is based on both spatial and temporal information, using the deep features extracted by a stacked convolutional neural network (CNN)-recurrent neural network (RNN) along with handcrafted features. Through experiments using two public datasets, we demonstrate that the temporal information is sufficient for detecting attacks using face images. In addition, it is established that the handcrafted image features efficiently enhance the detection performance of deep features, and the proposed method outperforms previous methods.

Deep Learning-Based Enhanced Presentation Attack Detection for Iris Recognition by Combining Features from Local and Global Regions Based on NIR Camera Sensor.

Iris recognition systems have been used in high-security-level applications because of their high recognition rate and the distinctiveness of iris patterns. However, as reported by recent studies, an iris recognition system can be fooled by the use of artificial iris patterns and lead to a reduction in its security level. The accuracies of previous presentation attack detection research are limited because they used only features extracted from global iris region image. To overcome this problem, we propose a new presentation attack detection method for iris recognition by combining features extracted from both local and global iris regions, using convolutional neural networks and support vector machines based on a near-infrared (NIR) light camera sensor. The detection results using each kind of image features are fused, based on two fusion methods of feature level and score level to enhance the detection ability of each kind of image features. Through extensive experiments using two popular public datasets (LivDet-Iris-2017 Warsaw and Notre Dame Contact Lens Detection 2015) and their fusion, we validate the efficiency of our proposed method by providing smaller detection errors than those produced by previous studies.