28 research outputs found
Editorial: Integrative multi-omics and artificial intelligence (AI)-driven approaches for superior nutritional quality and stress resilience in crops
No full textGallstone Ileus Mimicking a Colonic Tumor: A Case Report
No full textIntroduction: Gall stone ileus is a rare complication of cholelithiasis which typically presents with obstruction in the small intestine. However, it can rarely mimic a mass when it presents in unusual sites like the sigmoid colon as in our case. Case Presentation: We present a 42-year-old woman with a history of bariatric surgery, diverticulitis status post sigmoid colectomy and decompensated cirrhosis complicated by hepatic encephalopathy who presented to the hospital with concern for altered mental status and was diagnosed with grade III hepatic encephalopathy due to lactulose non adherence. During the hospitalization, patient developed rectal bleeding with suspected colonic mass on imaging that was ultimately identified as a large sigmoid gallstone ileus on endoscopic evaluation. Conclusion: Our report aimed to highlight the importance of considering gallstone ileus in the differential diagnosis of colonic masses, especially in patients with relevant clinical history
Comparative analysis of deep learning and machine learning-based models for simultaneous prediction of minerals in perilla (Perilla frutescens L.) seeds using near-infrared reflectance spectroscopy
No full textPerilla seeds contain a rich array of essential minerals, thus having the potential to address multiple micronutrient deficiencies at a time. However, traditional methods of mineral estimation are complex, time-consuming, expensive, and require technical expertise. This study includes the development of Near-Infrared Reflectance Spectroscopy (NIRS)-based prediction models for predicting five important minerals (Calcium, Copper, Magnesium, Manganese, and Phosphorus) using machine learning and deep learning techniques. Four models, including 1D Convolutional Neural Networks (1D CNNs), Artificial Neural Networks (ANNs), Random Forests (RFs), and Support Vector Regression (SVR), were developed and evaluated. The developed 1D CNN model outperformed other considered models in predicting calcium, magnesium, and phosphorus content with RPD (Residual Prediction Deviation) values of 1.75, 1.83, and 2.96, respectively. Whereas, SVR performed best in predicting copper and manganese with an RPD of 1.82 and 2.2, respectively. The 1D CNN model demonstrated R2 (Coefficient of determination) values above 0.65 for all minerals, with a maximum of 0.88 for phosphorus. In addition, the developed models performed superior as compared to the Partial Least Square Regression method (R2= 0.32). The developed models provide efficient tools for rapidly screening perilla germplasm available in global repositories, thus aiding in the selection of mineral-rich genotypes to mitigate micronutrient deficiencies
Uterine preservation in low-grade endometrial stromal sarcoma
No full textData on uterine preservation in the management of low grade endometrial stromal sarcoma (LGESS) is scarce due to rarity of this tumor type. Standard management of LGESS involves extrafascial hysterectomy with bilateral salpingo-oophorectomy with debulking of any extrauterine metastatic disease. High estrogen and progesterone receptor expression facilitates adjuvant hormone therapy post-surgery. LGESS frequently affects young women, thus fertility preservation is an important issue in management. Here we describe uterine preservation in two young women diagnosed with LGESS followed by GnRH analogue therapy with favorable outcome. The first case was diagnosed with recurrent endometrial polyp invading myometrium requiring wedge resection of uterus with free margins. Second case presented with a vaginal mass arising from cervix and excision was done through vaginal route. Both patients were prescribed GnRH analogue therapy for six months post-surgery and are currently on follow-up. These case reports add to literature on feasibility of uterine preservation in the management of LGESS
Knowledge, attitudes, and perceptions towards waterpipe tobacco smoking amongst college or university students: a systematic review
No full textBackground
Despite evidence for the harms of waterpipe tobacco smoking (WTS), its use is increasing amongst college and university students worldwide. This systematic review aims to assess the knowledge of, attitudes towards and perceptions of WTS among college or university students.
Methods
We electronically searched MEDLINE, EMBASE, CINAHL, PSYCHINFO and ISI the Web of Science in October 2018, restricting our search to studies published since January 1990. We included studies among university or college students that used qualitative or quantitative methods, and addressed either knowledge, attitudes, or perceptions towards WTS. We excluded studies where WTS could not be distinguished from other forms of tobacco use and studies reported as abstracts where the full text could not be identified. Data were synthesised qualitatively and analysed data by region (global north/ south), and by reasons for use, knowledge of health hazards, how knowledge influences use, perceptions towards dependence, and policy knowledge.
Results
Eighty-six studies were included; 45 from the global north and 41 from the global south. Socio-cultural and peer influences were major contributing factors that encouraged students to initiate WTS. Furthermore, WTS dependence had two components: psychological and social. This was compounded by the general perception that WTS is a less harmful, less addictive and more sociable alternative to cigarette smoking. Knowledge of WTS harms failed to correlate with a reduced risk of WTS use, and some students reported symptoms of WTS addiction. A large proportion of students believed that quitting WTS was easy, yet few were able to do so successfully. Finally, students believed current public health campaigns to educate on WTS harms were inadequate and, particularly in the global north, were not required.
Conclusion
Reasons for WTS amongst university students are multi-faceted. Overall, interventions at both the individual and community level, but also policy measures to portray a message of increased harm amongst students, are required. Additional studies are necessitated to understand temporal changes in students’ beliefs, thus allowing for better targeted interventions
Effectiveness of Anaerobic Soil Disinfestation for Weed and Nematode Management in Organic Sweetpotato Production
No full textWeeds and nematodes are particularly problematic in organic sweetpotato production due to a lack of effective pesticides. Anaerobic soil disinfestation (ASD) has the potential to fit into current pest management practices as an alternative to pesticide application. Greenhouse studies were conducted at the Clemson Coastal Research and Education Center (CREC) in Charleston, SC, to investigate the impact of carbon source amendment and a no carbon source treatment, and soil type on cumulative anaerobicity, weed control, nematode population, and sweetpotato vigor. Microcosms were filled with one of three different soil types (Charleston—loamy/native; Blackville—high coarse sand content; and Clemson—high clay content) and were mixed with cottonseed meal (CSM) or no carbon amendment. The pots were then sealed with plastic totally impenetrable film (Tif) for 6 weeks, followed by the transplanting of sweetpotato (cv Bayou Belle) slips. The results suggested that the CSM-treated microcosms spent more time under anaerobic conditions than those treated with the no carbon amendment. The microcosms that experienced a longer duration of anaerobicity had a lower percent weed cover (49%), fewer nematode egg masses, and a lower gall index when compared to microcosms which experienced a shorter duration of anaerobicity. Significantly higher instances of leaf necrosis were observed in the sweetpotato slips sown in the CSM-treated microcosms. The addition of CSM as a carbon source to facilitate ASD resulted in similar above-ground biomasses of the sweetpotato plants compared to the treatments containing no carbon amendment. However, a significantly lower below-ground biomass of the sweetpotato plants was observed in the CSM-treated microcosms
Comparative analysis of modified partial least squares regression and hybrid deep learning models for predicting protein content in Perilla (Perilla frutescens L.) seed meal using NIR spectroscopy
No full textPerilla seed meal (PSM), a byproduct of oil extraction from Perilla frutescens L. seeds, is rich in protein (24.26–42.85%) and holds potential as an economical and sustainable animal feed. Traditional methods for assessing protein content are labor-intensive and costly. This study explores Near-Infrared Reflectance Spectroscopy (NIRS) for the rapid, precise, and non-destructive determination of PSM protein content in 126 samples. We developed and evaluated Modified Partial Least Squares (MPLS) regression and deep learning (DL) models, including 1D-CNN (Convolutional Neural Network), LSTM Long Short-Term Memory), and hybrid architectures incorporating skip connections, inception modules, and spectral derivatives. Model performance was validated externally using parameters such as RSQexternal (R-squared), bias, SEP(C) (Standard Error of Prediction), RPD (Residual Prediction Deviation), slope, SD (Standard Deviation), p-value (≥0.05), and the correlation between reference and predicted values. The 1D CNN-LSTM-Inception derivative 1 model achieved the best performance (RPD: 8.0, RSQexternal: 0.98), followed by the MPLS-based model (RPD: 4.88, RSQexternal: 0.96) and the 1D CNN derivative 1 model (RPD: 3.07, RSQexternal: 0.96). These models provide a reliable and advanced technology for the non-destructive screening of PSM protein content, thus aiding in the rapid identification and selection of superior perilla chemotypes from varied backgrounds
Anaerobic Soil Disinfestation as a Tool for Nematode and Weed Management in Organic Sweetpotato
No full textAnaerobic soil disinfestation (ASD) is a promising alternative to synthetic chemical-driven pest management methods facilitated by incorporating carbon sources into the soil, tarping the soil with plastic mulch, and irrigating to soil saturation. To evaluate the impact of ASD on southern root-knot nematode [Meloidogyne incognita (Kofoid & White), SRKN] and yellow nutsedge (Cyperus esculentus L.) management in organically grown sweetpotato, greenhouse studies were conducted. The treatments were structured as a factorial of two carbon amendments [chicken manure + molasses (CM + M), and no additional carbon (control)] by 20 sweetpotato genotypes with 4 replications using a randomized complete block design. The results suggest that the microcosms receiving the carbon amendment spent the most time under anaerobic conditions (<200 mvh). Planting of sweetpotato genotypes in CM + M-treated microcosms resulted in 60–90% and 56–92% suppression of soil population and egg reproduction of SRKN as compared to no additional carbon. The application of CM + M reduced overall weed cover by 79% relative to the control. Sweetpotatoes in CM + M-treated microcosms had significantly higher dry above-ground biomass (6.8 g) as compared to the control (3.6 g). The results of this study demonstrated that ASD has the potential to manage nematodes and weeds in organic sweetpotato production systems
Comparative analysis of modified partial least squares regression and hybrid deep learning models for predicting protein content in Perilla (Perilla frutescens L.) seed meal using NIR spectroscopy
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