Avoiding the use of long-term parenteral support in patients without intestinal failure: A position paper from the European Society of Clinical Nutrition & Metabolism, the European Society of Neurogastroenterology and Motility and the Rome Foundation for Disorders of Gut-Brain Interaction.

The role of long-term parenteral support in patients with underlying benign conditions who do not have intestinal failure (IF) is contentious, not least since there are clear benefits in utilising the oral or enteral route for nutritional support. Furthermore, the risks of long-term home parenteral nutrition (HPN) are significant, with significant impacts on morbidity and mortality. There has, however, been a recent upsurge of the use of HPN in patients with conditions such as gastro-intestinal neuromuscular disorders, opioid bowel dysfunction, disorders of gut-brain interaction and possibly eating disorders, who do not have IF. As a result, the European Society of Clinical Nutrition and Metabolism (ESPEN), the European Society of Neuro-gastroenterology and Motility (ESNM) and the Rome Foundation for Disorders of Gut Brain Interaction felt that a position statement is required to clarify - and hopefully reduce the potential for harm associated with - the use of long-term parenteral support in patients without IF. Consensus opinion is that HPN should not be prescribed for patients without IF, where the oral and/or enteral route can be utilised. On the rare occasions that PN commencement is required to treat life-threatening malnutrition in conditions such as those listed above, it should only be prescribed for a time-limited period to achieve nutritional safety, while the wider multi-disciplinary team focus on more appropriate biopsychosocial holistic and rehabilitative approaches to manage the patient's primary underlying condition.

Avoiding the use of long-term parenteral support in patients without intestinal failure: A position paper from the European Society of Clinical Nutrition & Metabolism, the European Society of Neurogastroenterology and Motility and the Rome Foundation for Disorders of Gut-Brain Interaction.

The publisher regrets that this article has been temporarily removed. A replacement will appear as soon as possible in which the reason for the removal of the article will be specified, or the article will be reinstated. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/policies/article-withdrawal.

Autofluorescence-spectral imaging for rapid and invasive characterization of soybean for pre-germination anaerobic stress tolerance.

The autofluorescence-spectral imaging (ASI) technique is based on the light-emitting ability of natural fluorophores. Soybean genotypes showing contrasting tolerance to pre-germination anaerobic stress can be characterized using the photon absorption and fluorescence emission of natural fluorophores occurring in seed coats. In this study, tolerant seeds were efficiently distinguished from susceptible genotypes at 405 nm and 638 nm excitation wavelengths. ASI approach can be employed as a new marker for the detection of photon-emitting compounds in the tolerant and susceptible soybean seed coats. Furthermore, the accuracy of rapid characterization of genotypes using this technique can provide novel insights into soybean breeding.

Zinc oxide and ferric oxide nanoparticles combination increase plant growth, yield, and quality of soybean under semiarid region.

Zinc (Zn) and iron (Fe) malnutrition are global health challenges that need immediate attention. Hence, to address these issues, a two-pronged approach involving the development and application of novel Zn and Fe products for crop fertilization may be a potential solution. Therefore, zinc oxide (ZnO) (∼13.2 nm) and ferric oxide (Fe2O3) (∼15 nm) nanoparticles (NPs) were synthesized and characterized. Seven nutrients treatments viz, control, ZnO- NPs (25 mg kg-1), Fe2O3-NPs (25 mg kg-1), ZnO + Fe2O3-NPs (25 mg kg-1each), ZnSO4 (55.8 mg kg-1), FeSO4 (60.4 mg kg-1) and ZnSO4+ FeSO4 (55.8 and 60.4 mg kg-1) were arranged in five-time replicated Completely Randomized Design model to test the effectiveness of ZnO and Fe2O3 NPs in two soybean cultivars over conventional zinc sulfate (ZnSO4) and ferrous sulfate (FeSO4) fertilizers. The results indicated that the photosynthetic rate (Pn) and chlorophyll content increased (33.9-86.2%) significantly at the flowering stage with ZnO and Fe2O3 NPs applications, compared to their conventional counterparts. Likewise, the combined application of ZnO and Fe2O3 NPs reduced H2O2 production by 17-19% and increased the superoxide dismutase (SOD) and catalase (CAT) activities by 15-17% and 9.6-11.4% over the combined use of ZnSO4 and FeSO4, respectively. The normalized difference vegetation index (NDVI) showed an increase of 6.9-44.2% under ZnO and Fe2O3 NPs, as well as ZnSO4 and FeSO4. Furthermore, the combined application of NPs enhanced soybean seed yield by 4.6-18.3% compared to conventional Zn and Fe fertilizers. Concerning seed Zn and Fe density, conjoint application of ZnO and Fe2O3 NPs increases Zn by 1.8-2.2-fold and Fe by 19.22-22.58% over the combined application of Zn SO4 and FeSO4, respectively. While the application of NPs significantly decreased seed phytic acid concentrations by 7.3-59.9% compared to the control. These findings suggest that the combined application of ZnO and Fe2O3 NPs effectively enhances soybean productivity, seed nutrient density, and overall produce quality. Therefore, the combined application of ZnO and Fe2O3 -NPs in soybean can be a potential approach for sustainable soybean production and to reduce/arrest Zn and Fe malnutrition in a growing population.

Image-based phenotyping of seed architectural traits and prediction of seed weight using machine learning models in soybean.

Among seed attributes, weight is one of the main factors determining the soybean harvest index. Recently, the focus of soybean breeding has shifted to improving seed size and weight for crop optimization in terms of seed and oil yield. With recent technological advancements, there is an increasing application of imaging sensors that provide simple, real-time, non-destructive, and inexpensive image data for rapid image-based prediction of seed traits in plant breeding programs. The present work is related to digital image analysis of seed traits for the prediction of hundred-seed weight (HSW) in soybean. The image-based seed architectural traits (i-traits) measured were area size (AS), perimeter length (PL), length (L), width (W), length-to-width ratio (LWR), intersection of length and width (IS), seed circularity (CS), and distance between IS and CG (DS). The phenotypic investigation revealed significant genetic variability among 164 soybean genotypes for both i-traits and manually measured seed weight. Seven popular machine learning (ML) algorithms, namely Simple Linear Regression (SLR), Multiple Linear Regression (MLR), Random Forest (RF), Support Vector Regression (SVR), LASSO Regression (LR), Ridge Regression (RR), and Elastic Net Regression (EN), were used to create models that can predict the weight of soybean seeds based on the image-based novel features derived from the Red-Green-Blue (RGB)/visual image. Among the models, random forest and multiple linear regression models that use multiple explanatory variables related to seed size traits (AS, L, W, and DS) were identified as the best models for predicting seed weight with the highest prediction accuracy (coefficient of determination, R2=0.98 and 0.94, respectively) and the lowest prediction error, i.e., root mean square error (RMSE) and mean absolute error (MAE). Finally, principal components analysis (PCA) and a hierarchical clustering approach were used to identify IC538070 as a superior genotype with a larger seed size and weight. The identified donors/traits can potentially be used in soybean improvement programs.

Pteridine reductase (PTR1): initial structure-activity relationships studies of potential leishmanicidal arylindole derivatives compounds.

Leishmaniasis is a public health concern, especially in Brazil and India. The drugs available for therapy are old, cause toxicity and have reports of resistance. Therefore, this paper aimed to carry out initial structure-activity relationships (applying molecular docking and dynamic simulations) of arylindole scaffolds against the pteridine reductase (PTR1), which is essential target for the survival of the parasite. Thus, we used a series of 43 arylindole derivatives as a privileged skeleton, which have been evaluated previously for different biological actions. Compound 7 stood out among its analogues presenting the best results of average number of interactions with binding site (2.00) and catalytic triad (1.00). Additionally, the same compound presented the best binding free energy (-32.33 kcal/mol) in dynamic simulations. Furthermore, with computational studies, it was possible to comprehend and discuss the influences of the substituent sizes, positions of substitutions in the aromatic ring and electronic influences. Therefore, this study can be a starting point for the structural improvements needed to obtain a good leishmanicidal drug.

In silico analysis of angiotensin-converting enzyme inhibitory compounds obtained from soybean [Glycine max (L.) Merr.].

Cardiovascular diseases (CVDs) are one of the major reasons for deaths globally. The renin-angiotensin-aldosterone system (RAAS) regulates body hypertension and fluid balance which causes CVD. Angiotensin-converting enzyme I (ACE I) is the central Zn-metallopeptidase component of the RAAS playing a crucial role in maintaining homeostasis of the cardiovascular system. The available drugs to treat CVD have many side effects, and thus, there is a need to explore phytocompounds and peptides to be utilized as alternative therapies. Soybean is a unique legume cum oilseed crop with an enriched source of proteins. Soybean extracts serve as a primary ingredient in many drug formulations against diabetes, obesity, and spinal cord-related disorders. Soy proteins and their products act against ACE I which may provide a new scope for the identification of potential scaffolds that can help in the design of safer and natural cardiovascular therapies. In this study, the molecular basis for selective inhibition of 34 soy phytomolecules (especially of beta-sitosterol, soyasaponin I, soyasaponin II, soyasaponin II methyl ester, dehydrosoyasaponin I, and phytic acid) was evaluated using in silico molecular docking approaches and dynamic simulations. Our results indicate that amongst the compounds, beta-sitosterol exhibited a potential inhibitory action against ACE I.

Angiotensin-converting enzyme inhibitory peptides and isoflavonoids from soybean [Glycine max (L.) Merr.].

Angiotensin-converting enzyme I (ACE I) is a zinc-containing metallopeptidase involved in the renin-angiotensin system (RAAS) that helps in the regulation of hypertension and maintains fluid balance otherwise, which results in cardiovascular diseases (CVDs). One of the leading reasons of global deaths is due to CVDs. RAAS also plays a central role in maintaining homeostasis of the CV system. The commercial drugs available to treat CVDs possess several fatal side effects. Hence, phytochemicals like peptides having plant-based origin should be explored and utilized as alternative therapies. Soybean is an important leguminous crop that simultaneously possesses medicinal properties. Soybean extracts are used in many drug formulations for treating diabetes and other disorders and ailments. Soy proteins and its edible products such as tofu have shown potential inhibitory activity against ACE. Thus, this review briefly describes various soy proteins and products that can be used to inhibit ACE thereby providing new scope for the identification of potential candidates that can help in the design of safer and natural treatments for CVDs.

Harnessing heterosis and male sterility in soybean [Glycine max (L.) Merrill]: A critical revisit.

Soybean is a predominantly self-pollinated crop. It is also one of the important oilseed legumes. Soybean is an excellent crop having industrial, traditional, culinary, feeding, and cultural roles. Genetic diversity in breeding programs is of prime importance as it ensures the success of any breeding by enhancing the outcomes and results of the plants. The phenomenon wherein the progeny exhibits greater biomass (yield) and a faster rate of development and fertility than its parents is referred to as heterosis. As of now, heterosis is mainly limited to the trait of seed yield and is considered the basis for the development of better (superior) varieties. Male sterility (MS) is extensively used for the production of seeds and the improvement of crops coupled with the traditional breeding programs and molecular technology. Therefore, deployment of MS and heterosis in breeding soybean could yield better outcomes. This review aims to focus on two aspects, namely, MS and heterosis in soybean with its scope for crop improvement.

Identifications of good and bad structural fragments of hydrazone/2,5-disubstituted-1,3,4-oxadiazole hybrids with correlation intensity index and consensus modelling using Monte Carlo based QSAR studies, their molecular docking and ADME analysis.

The application of QSAR along with other in silico tools like molecular docking, and molecular dynamics provide a lot of promise for finding new treatments for life-threatening diseases like Type 2 diabetes mellitus (T2DM). The present study is an attempt to develop Monte Carlo algorithm-based QSAR models using freely available CORAL software. The experimental data on the α-amylase inhibition by a series of benzothiazole-linked hydrazone/2,5-disubstituted-1,3,4-oxadiazole hybrids were selected as endpoint for the model generation. Initially, a total of eight QSAR models were built using correlation intensity index (CII) as a criterion of predictive potential. The model developed from split 6 using CII was the most reliable because of the highest numerical value of the determination coefficient of the validation set (r2VAL = 0.8739). The important structural fragments responsible for altering the endpoint were also extracted from the best-built model. With the goal of improved prediction quality and lower prediction errors, the validated models were used to build consensus models. Molecular docking was used to know the binding mode and pose of the selected derivatives. Further, to get insight into their metabolism by living beings, ADME studies were investigated using internet freeware, SwissADME.

Clinico-epidemiological profile and outcomes of adults with COVID-19: A hospital-based retrospective study in Kerala, India.

Introduction: The clinical and epidemiological presentations of patients with coronavirus disease 2019 (COVID-19) in India is still not well explored. We studied the epidemiological and clinical profile and outcomes of COVID-19 patients admitted to a tertiary care private hospital in Kerala, India. Methods: In this retrospective study, we analyzed data of 476 adult (≥18 years) COVID-19 patients admitted to a tertiary care hospital in Kerala from September 1, 2020 to March 31, 2021. The patients were categorized into mild, moderate, and severe cases and followed till discharge or death. Data were analyzed using Statistical Package for the Social Sciences (SPSS) version 23.0 with a significance set at P < 0.05. Results: The median age was 57 years (56% men). Mild, moderate, and severe cases accounted for 17%, 65%, and 18%, respectively. Around 75% had at least one comorbidity, and 51% had multiple comorbidities. The most common comorbidities were diabetes (45%), hypertension (44%), dyslipidemia (15%), and cardiac problems (12%). The elevated D-dimer values among patients in different categories were significantly different, with 74% in severe, 46% in moderate, and 19% in mild category patients. Serum ferritin, C-reactive protein, lactic acid dehydrogenase, and neutrophil to lymphocyte ratio values were significantly higher for severely ill patients. Thirty deaths (67% men) occurred during the study period, with a case fatality rate of 6.3%. Mortality mainly happened in the older age group (80%) and those with multimorbidity (90%). Conclusion: Age and multimorbidity are the major contributing factors for death in hospitalized COVID-19 patients. Generalization of the findings necessitates well-designed large-scale studies.