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In this study, we examined the effects of rumen-protected L-tryptophan supplementation on the productivity and physiological metabolic indicators in lactating Holstein cows under heat stress conditions. The study involved eight early lactating Holstein cows (days in milk = 40 ± 9 days; milk yield 30 ± 1.5 kg/day; parity 1.09 ± 0.05, p < 0.05), four cows per experiment, with environmentally controlled chambers. In each experiment, two distinct heat stress conditions were created: a low-temperature and low-humidity (LTLH) condition at 25 °C with 35–50% humidity and a high-temperature and high-humidity (HTHH) condition at 31 °C with 80–95% humidity. During the adaptation phase, the cows were subjected to LTLH and HTHH conditions for 3 days. This was followed by a 4-day heat stress phase and then by a 7-day phase of heat stress, which were complemented by supplementation with rumen-protected L-tryptophan (ACT). The findings revealed that supplementation with ACT increased dry matter intake as well as milk yield and protein and decreased water intake, heart rate, and rectal temperature in the HTHH group (p < 0.05). For plateletcrit (PCT, p = 0.0600), the eosinophil percentage (EOS, p = 0.0880) showed a tendency to be lower, while the monocyte (MONO) and large unstained cells (LUC) amounts were increased in both groups (p < 0.05). Albumin and glucose levels were lower in the HTHH group (p < 0.05). The gene expressions of heat shock proteins 70 and 90 in the peripheral blood mononuclear cells were higher in the ACT group (HTHH, p < 0.05). These results suggest that ACT supplementation improved productivity, physiological indicators, blood characteristics, and gene expression in the peripheral blood mononuclear cells of early lactating Holstein cows under heat-stress conditions. In particular, ACT supplementation objectively relieved stress in these animals, suggesting that L-tryptophan has potential as a viable solution for combating heat-stress-induced effects on the cattle in dairy farming.

This letter presents a new aperture coupled microstrip antenna with a nonsymmetric U-shaped feed line and two rectangular slots on the ground plane. Simulation results show that the antenna has VSWR <2 from 10.5 to 22 GHz and the simulated gain bandwidth (above 7 dB) of the antenna is 6.6 GHz (47%). Good agreement is obtained between simulation and experimental results. © 2008 Wiley Periodicals, Inc. Microwave Opt Technol Lett 51: 259–260, 2009; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.24004 [ABSTRACT FROM AUTHOR]

Background: To evaluate comparative outcomes of pancreatic cancer resection with or without adjuvant chemotherapy in patients with stage I pancreatic cancer., Methods: A systematic search of MEDLINE, CENTRAL, and Web of Science and bibliographic reference lists were conducted. All comparative studies reporting outcomes of pancreatic cancer resection for stage I cancer with or without adjuvant chemotherapy were included, and their risk of bias was assessed using the Risk Of Bias In Non-randomized Studies-of Interventions tool. Survival outcomes were analyzed using the hazard ratio and odds ratio for the time-to-event and dichotomous outcomes, respectively., Results: We included 6 comparative studies reporting a total of 6,874 patients with resected stage 1 pancreatic cancer, of whom 3,951 patients had no adjuvant chemotherapy, and the remaining 2,923 patients received adjuvant chemotherapy. The use of adjuvant chemotherapy was associated with significantly higher overall survival (hazard ratio 0.71, 95% confidence interval 0.62-0.82, P < .00001) and 2-year survival (65.1% vs 57.4%, odds ratio 1.99; 95% confidence interval 1.01-1.41, P = .04) compared to no use of adjuvant chemotherapy. However, there was no statistically significant difference in 1-year (86.8% vs 78.4%, odds ratio 1.60; 95% confidence interval 0.72-3.57, P = .25), 3-year (46.0% vs 44.0%, odds ratio 1.07; 95% confidence interval 0.90-1.29, P = .43), or 5-year survival (24.8% vs 23.3%, odds ratio 1.03; 95% confidence interval 0.80-1.33, P = .81) between the 2 groups., Conclusion: Meta-analysis of best available evidence (level 2a with low to moderate certainty) demonstrates that adjuvant chemotherapy may confer survival benefits for stage I pancreatic cancer when compared to the use of surgery alone. Randomized control trials are required to escalate the level of evidence and confirm these findings with consideration of contemporary chemotherapy agents and regimens., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Aims: To evaluate comparative outcomes of laparoscopic spleen-preserving distal pancreatectomy (LSPDP) and laparoscopic distal pancreatectomy with splenectomy (LDPS)., Methods: A systematic search of multiple electronic data sources and bibliographic reference lists were conducted. Comparative studies reporting outcomes of LSPDP and LDPS were considered followed by evaluation of the associated risk of bias according to ROBINS-I tool. Perioperative complications, clinically important postoperative pancreatic fistula (POPF), infectious complications, blood loss, conversion to open, operative time and duration of hospital stay were the investigated outcome parameters., Results: Nineteen studies were identified enrolling 3739 patients of whom 1860 patients underwent LSPDP and the remaining 1879 patients had LDPS. The patients in the LSPDP and LDPS groups were of comparable age (p = 0.73), gender (p = 0.59), and BMI (p = 0.07). However, the patient in the LDPS group had larger tumour size (p = 0.0004) and more malignant lesions (p = 0.02). LSPDP was associated with significantly lower POPF (OR:0.65, p = 0.02), blood loss (MD:-28.30, p = 0.001), and conversion to open (OR:0.48, p < 0.0001) compared to LDPS. Moreover, it was associated with significantly shorter procedure time (MD: -22.06, p = 0.0009) and length of hospital stay (MD: -0.75, p = 0.005). However, no significant differences were identified in overall perioperative (OR:0.89, p = 0.25) or infectious (OR:0.67, p = 0.05) complications between two groups., Conclusions: LSPDP seems to be associated with lower POPF, bleeding and conversion to open compared to LDPS in patients with small-sized benign tumours. Moreover, it may be quicker and reduce hospital stay. Nevertheless, such advantages are of doubtful merit about large-sized or malignant tumours. The available evidence is subject to confounding by indication., Competing Interests: Declaration of competing interest All authors have no conflicts of interest or financial ties to disclose., (Copyright © 2023 Royal College of Surgeons of Edinburgh (Scottish charity number SC005317) and Royal College of Surgeons in Ireland. Published by Elsevier Ltd. All rights reserved.)

Aims: To evaluate short-term clinical and long-term survival outcomes of pancreatic resection for pancreatic metastasis from renal cell carcinoma (RCC)., Methods: A retrospective evaluation of patients undergoing pancreatic resection for metastasis from RCC over a 12-years period was conducted. Furthermore, a systematic search of electronic data sources and bibliographic reference lists were conducted to identify studies investigating the same clinical question. Short-term clinical and long-term survival outcomes were evaluated. Kaplan-Meier survival plots were constructed for survival outcomes. Cox-proportional regression analysis was performed to determine factors associated with survival. Finally, meta-analysis of survival outcomes was conducted using random-effects modelling., Results: Eighteen patients underwent pancreatic resections for RCC pancreatic metastasis within the study period. The mean age of the included patients was 63.8 ± 8.0 years. There were 10(55.6 %) male and 8(44.4 %) female patients. Pancreatectomy was associated with 4(25.0 %) Clavien-Dindo (C-D) I, 5(31.3 %) C-D II, and 7(43.7 %) C-D III complications, 7(38.8 %) pancreatic fistula, 3(16.7 %) post-pancreatectomy acute pancreatitis, 1(5.6 %) delayed gastric emptying, and 1(5.6 %) chyle leak. The mean length of hospital stay was 18 ± 16.3 days. The median survival was 64 months (95 % CI 60-78). The 3-and 5-year disease-free survival rates were 83.3 % and 55.5 %, respectively. The 3-and 5-year survival rates were 100 % and 55.6 %, respectively. The pooled analyses of 553 patients demonstrated 3-and 5-year survival rates of 77.6 % and 60.7 %, respectively., Conclusions: Pancreatectomy for RCC metastasis is associated with acceptable short-term clinical and promising long-term survival outcomes. Considering the rarity of the entity, escalation of level of evidence in this context is challenging., (Copyright © 2023 IAP and EPC. Published by Elsevier B.V. All rights reserved.)

Nowadays, many people worldwide suffer from brain disorders, and their health is in danger. So far, numerous methods have been proposed for the diagnosis of Schizophrenia (SZ) and attention deficit hyperactivity disorder (ADHD), among which functional magnetic resonance imaging (fMRI) modalities are known as a popular method among physicians. This paper presents an SZ and ADHD intelligent detection method of resting-state fMRI (rs-fMRI) modality using a new deep learning method. The University of California Los Angeles dataset, which contains the rs-fMRI modalities of SZ and ADHD patients, has been used for experiments. The FMRIB software library toolbox first performed preprocessing on rs-fMRI data. Then, a convolutional Autoencoder model with the proposed number of layers is used to extract features from rs-fMRI data. In the classification step, a new fuzzy method called interval type-2 fuzzy regression (IT2FR) is introduced and then optimized by genetic algorithm, particle swarm optimization, and gray wolf optimization (GWO) techniques. Also, the results of IT2FR methods are compared with multilayer perceptron, k-nearest neighbors, support vector machine, random forest, and decision tree, and adaptive neuro-fuzzy inference system methods. The experiment results show that the IT2FR method with the GWO optimization algorithm has achieved satisfactory results compared to other classifier methods. Finally, the proposed classification technique was able to provide 72.71% accuracy., (© The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

Purpose: Functional avoidance radiotherapy uses functional imaging to reduce pulmonary toxicity by designing radiotherapy plans that reduce doses to functional regions of the lung. A phase-II, multi-center, prospective study of 4DCT-ventilation functional avoidance was completed. Pre and post-treatment pulmonary function tests (PFTs) were acquired and assessed pulmonary function change. This study aims to evaluate which clinical, dose and dose-function factors predict PFT changes for patients treated with 4DCT-ventilation functional avoidance radiotherapy., Materials and Methods: 56 patients with locally advanced lung cancer receiving radiotherapy were accrued. PFTs were obtained at baseline and three months following radiotherapy and included forced expiratory volume in 1-second (FEV1), forced vital capacity (FVC), and FEV1/FVC. The ability of patient, clinical, dose (lung and heart), and dose-function metrics (metrics that combine dose and 4DCT-ventilation-based function) to predict PFT changes were evaluated using univariate and multivariate linear regression., Results: Univariate analysis showed that only dose-function metrics and the presence of chronic obstructive pulmonary disease (COPD) were significant (p<0.05) in predicting FEV1 decline. Multivariate analysis identified a combination of clinical (immunotherapy status, presence of thoracic comorbidities, smoking status, and age), along with lung dose, heart dose, and dose-function metrics in predicting FEV1 and FEV1/FVC changes., Conclusion: The current work evaluated factors predicting PFT changes for patients treated in a prospective functional avoidance radiotherapy study. The data revealed that lung dose- function metrics could predict PFT changes, validating the significance of reducing the dose to the functional lung to mitigate the decline in pulmonary function and providing guidance for future clinical trials., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Objectives: To evaluate the incidence of long-term de novo acid reflux-related complications following sleeve gastrectomy (SG) to determine whether routine postoperative surveillance endoscopy is necessary., Methods: A systematic search of Medline, Embase, CINAHL, CENTRAL, the Web of Science, and bibliographic reference lists was conducted. A proportion meta-analysis model was constructed to quantify the risk of the de novo gastro-oesophageal reflux disease (GORD), oesophagitis, and Barrett's oesophagus (BE) at least 4 years after SG. Random-effects modelling was applied to calculate pooled outcome data., Results: Thirty-two observational studies were included reporting a total of 7904 patients who underwent primary SG and were followed up for at least 4 years. The median follow-up period was 60 months (48-132). Preoperative acid-reflux symptoms existed in 19.1% ± 15.1% of the patients. The risk of development of de novo GORD, oesophagitis, and BE after SG was 24.8% (95% CI 18.6-31.0%), 27.9% (95% CI 17.7-38.1%), and 6.7% (95% CI 3.7-9.7%), respectively. The between-study heterogeneity was significant in all outcome syntheses. It was suspected that several of the included studies have not reported BE and oesophagitis because such events might not have happened in their cohorts., Conclusions: Long-term risk of de novo GORD after SG seems to be comparable with those of the general population which questions the merit of surveillance endoscopy after SG in asymptomatic patients. De novo BE and oesophagitis after SG have not been reported by most of the available studies which may lead to overestimation of the rates of both outcomes in any evidence synthesis. We recommend endoscopic surveillance for symptomatic patients only., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

The outbreak of the corona virus disease (COVID-19) has changed the lives of most people on Earth. Given the high prevalence of this disease, its correct diagnosis in order to quarantine patients is of the utmost importance in the steps of fighting this pandemic. Among the various modalities used for diagnosis, medical imaging, especially computed tomography (CT) imaging, has been the focus of many previous studies due to its accuracy and availability. In addition, automation of diagnostic methods can be of great help to physicians. In this paper, a method based on pre-trained deep neural networks is presented, which, by taking advantage of a cyclic generative adversarial net (CycleGAN) model for data augmentation, has reached state-of-the-art performance for the task at hand, i.e., 99.60% accuracy. Also, in order to evaluate the method, a dataset containing 3163 images from 189 patients has been collected and labeled by physicians. Unlike prior datasets, normal data have been collected from people suspected of having COVID-19 disease and not from data from other diseases, and this database is made available publicly. Moreover, the method's reliability is further evaluated by calibration metrics, and its decision is interpreted by Grad-CAM also to find suspicious regions as another output of the method and make its decisions trustworthy and explainable., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 Elsevier B.V. All rights reserved.)

In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of mortality globally. At early stages, CVDs appear with minor symptoms and progressively get worse. The majority of people experience symptoms such as exhaustion, shortness of breath, ankle swelling, fluid retention, and other symptoms when starting CVD. Coronary artery disease (CAD), arrhythmia, cardiomyopathy, congenital heart defect (CHD), mitral regurgitation, and angina are the most common CVDs. Clinical methods such as blood tests, electrocardiography (ECG) signals, and medical imaging are the most effective methods used for the detection of CVDs. Among the diagnostic methods, cardiac magnetic resonance imaging (CMRI) is increasingly used to diagnose, monitor the disease, plan treatment and predict CVDs. Coupled with all the advantages of CMR data, CVDs diagnosis is challenging for physicians as each scan has many slices of data, and the contrast of it might be low. To address these issues, deep learning (DL) techniques have been employed in the diagnosis of CVDs using CMR data, and much research is currently being conducted in this field. This review provides an overview of the studies performed in CVDs detection using CMR images and DL techniques. The introduction section examined CVDs types, diagnostic methods, and the most important medical imaging techniques. The following presents research to detect CVDs using CMR images and the most significant DL methods. Another section discussed the challenges in diagnosing CVDs from CMRI data. Next, the discussion section discusses the results of this review, and future work in CVDs diagnosis from CMR images and DL techniques are outlined. Finally, the most important findings of this study are presented in the conclusion section., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Purpose: Four-dimensional computed tomography (4DCT)-ventilation-based functional avoidance uses 4DCT images to generate plans that avoid functional regions of the lung with the goal of reducing pulmonary toxic effects. A phase 2, multicenter, prospective study was completed to evaluate 4DCT-ventilation functional avoidance radiation therapy. The purpose of this study was to report the results for pretreatment to posttreatment pulmonary function test (PFT) changes for patients treated with functional avoidance radiation therapy., Methods and Materials: Patients with locally advanced lung cancer receiving chemoradiation were accrued. Functional avoidance plans based on 4DCT-ventilation images were generated. PFTs were obtained at baseline and 3 months after chemoradiation. Differences for PFT metrics are reported, including diffusing capacity for carbon monoxide (DLCO), forced expiratory volume in 1 second (FEV 1 ), and forced vital capacity (FVC). PFT metrics were compared for patients who did and did not experience grade 2 or higher pneumonitis., Results: Fifty-six patients enrolled on the study had baseline and posttreatment PFTs evaluable for analysis. The mean change in DLCO, FEV 1 , and FVC was -11.6% ± 14.2%, -5.6% ± 16.9%, and -9.0% ± 20.1%, respectively. The mean change in DLCO was -15.4% ± 14.4% for patients with grade 2 or higher radiation pneumonitis and -10.8% ± 14.1% for patients with grade <2 radiation pneumonitis ( P  = .37). The mean change in FEV 1 was -14.3% ± 22.1% for patients with grade 2 or higher radiation pneumonitis and -3.9% ± 15.4% for patients with grade <2 radiation pneumonitis ( P  = .09)., Conclusions: The current work is the first to quantitatively characterize PFT changes for patients with lung cancer treated on a prospective functional avoidance radiation therapy study. In comparison with patients treated with standard thoracic radiation planning, the data qualitatively show that functional avoidance resulted in less of a decline in DLCO and FEV 1 . The presented data can help elucidate the potential pulmonary function improvement with functional avoidance radiation therapy., (© 2022 The Author(s).)

Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD is also associated with communication deficits and repetitive behavior in affected individuals. Various ASD detection methods have been developed, including neuroimaging modalities and psychological tests. Among these methods, magnetic resonance imaging (MRI) imaging modalities are of paramount importance to physicians. Clinicians rely on MRI modalities to diagnose ASD accurately. The MRI modalities are non-invasive methods that include functional (fMRI) and structural (sMRI) neuroimaging methods. However, diagnosing ASD with fMRI and sMRI for specialists is often laborious and time-consuming; therefore, several computer-aided design systems (CADS) based on artificial intelligence (AI) have been developed to assist specialist physicians. Conventional machine learning (ML) and deep learning (DL) are the most popular schemes of AI used for diagnosing ASD. This study aims to review the automated detection of ASD using AI. We review several CADS that have been developed using ML techniques for the automated diagnosis of ASD using MRI modalities. There has been very limited work on the use of DL techniques to develop automated diagnostic models for ASD. A summary of the studies developed using DL is provided in the Supplementary Appendix. Then, the challenges encountered during the automated diagnosis of ASD using MRI and AI techniques are described in detail. Additionally, a graphical comparison of studies using ML and DL to diagnose ASD automatically is discussed. We suggest future approaches to detecting ASDs using AI techniques and MRI neuroimaging., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Moridian, Ghassemi, Jafari, Salloum-Asfar, Sadeghi, Khodatars, Shoeibi, Khosravi, Ling, Subasi, Alizadehsani, Gorriz, Abdulla and Acharya.)

Epilepsy is a disorder of the brain denoted by frequent seizures. The symptoms of seizure include confusion, abnormal staring, and rapid, sudden, and uncontrollable hand movements. Epileptic seizure detection methods involve neurological exams, blood tests, neuropsychological tests, and neuroimaging modalities. Among these, neuroimaging modalities have received considerable attention from specialist physicians. One method to facilitate the accurate and fast diagnosis of epileptic seizures is to employ computer-aided diagnosis systems (CADS) based on deep learning (DL) and neuroimaging modalities. This paper has studied a comprehensive overview of DL methods employed for epileptic seizures detection and prediction using neuroimaging modalities. First, DL-based CADS for epileptic seizures detection and prediction using neuroimaging modalities are discussed. Also, descriptions of various datasets, preprocessing algorithms, and DL models which have been used for epileptic seizures detection and prediction have been included. Then, research on rehabilitation tools has been presented, which contains brain-computer interface (BCI), cloud computing, internet of things (IoT), hardware implementation of DL techniques on field-programmable gate array (FPGA), etc. In the discussion section, a comparison has been carried out between research on epileptic seizure detection and prediction. The challenges in epileptic seizures detection and prediction using neuroimaging modalities and DL models have been described. In addition, possible directions for future works in this field, specifically for solving challenges in datasets, DL, rehabilitation, and hardware models, have been proposed. The final section is dedicated to the conclusion which summarizes the significant findings of the paper., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Schizophrenia (SZ) is a mental disorder that typically emerges in late adolescence or early adulthood. It reduces the life expectancy of patients by 15 years. Abnormal behavior, perception of emotions, social relationships, and reality perception are among its most significant symptoms. Past studies have revealed that SZ affects the temporal and anterior lobes of hippocampus regions of the brain. Also, increased volume of cerebrospinal fluid (CSF) and decreased volume of white and gray matter can be observed due to this disease. Magnetic resonance imaging (MRI) is the popular neuroimaging technique used to explore structural/functional brain abnormalities in SZ disorder, owing to its high spatial resolution. Various artificial intelligence (AI) techniques have been employed with advanced image/signal processing methods to accurately diagnose SZ. This paper presents a comprehensive overview of studies conducted on the automated diagnosis of SZ using MRI modalities. First, an AI-based computer aided-diagnosis system (CADS) for SZ diagnosis and its relevant sections are presented. Then, this section introduces the most important conventional machine learning (ML) and deep learning (DL) techniques in the diagnosis of diagnosing SZ. A comprehensive comparison is also made between ML and DL studies in the discussion section. In the following, the most important challenges in diagnosing SZ are addressed. Future works in diagnosing SZ using AI techniques and MRI modalities are recommended in another section. Results, conclusion, and research findings are also presented at the end., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Extracellular matrixes (ECMs), such as the cell walls and biofilms, are important for supporting cell integrity and function and regulating intercellular communication. These biomaterials are also of significant interest to the production of biofuels and the development of antimicrobial treatment. Solid-state nuclear magnetic resonance (ssNMR) and magic-angle spinning-dynamic nuclear polarization (MAS-DNP) are uniquely powerful for understanding the conformational structure, dynamical characteristics, and supramolecular assemblies of carbohydrates and other biomolecules in ECMs. This review highlights the recent high-resolution investigations of intact ECMs and native cells in many organisms spanning across plants, bacteria, fungi, and algae. We spotlight the structural principles identified in ECMs, discuss the current technical limitation and underexplored biochemical topics, and point out the promising opportunities enabled by the recent advances of the rapidly evolving ssNMR technology.