Diet, serving as a vital source of nutrients, exerts a profound influence on human health and disease progression. Recently, dietary interventions have emerged as promising adjunctive treatment strategies not only for cancer but also for neurodegenerative diseases, autoimmune diseases, cardiovascular diseases, and metabolic disorders. These interventions have demonstrated substantial potential in modulating metabolism, disease trajectory, and therapeutic responses. Metabolic reprogramming is a hallmark of malignant progression, and a deeper understanding of this phenomenon in tumors and its effects on immune regulation is a significant challenge that impedes cancer eradication. Dietary intake, as a key environmental factor, can influence tumor metabolism. Emerging evidence indicates that dietary interventions might affect the nutrient availability in tumors, thereby increasing the efficacy of cancer treatments. However, the intricate interplay between dietary interventions and the pathogenesis of cancer and other diseases is complex. Despite encouraging results, the mechanisms underlying diet-based therapeutic strategies remain largely unexplored, often resulting in underutilization in disease management. In this review, we aim to illuminate the potential effects of various dietary interventions, including calorie restriction, fasting-mimicking diet, ketogenic diet, protein restriction diet, high-salt diet, high-fat diet, and high-fiber diet, on cancer and the aforementioned diseases. We explore the multifaceted impacts of these dietary interventions, encompassing their immunomodulatory effects, other biological impacts, and underlying molecular mechanisms. This review offers valuable insights into the potential application of these dietary interventions as adjunctive therapies in disease management. 

Effects of dietary intervention on human diseases: molecular mechanisms and therapeutic potential.

Cancer has become a serious problem worldwide, as it represents the main cause of death, and its incidence has increased over the years. A potential strategy to counter the growing spread of various forms of cancer is the adoption of prevention strategies, in particular, the use of healthy lifestyles, such as maintaining a healthy weight, following a healthy diet; being physically active; avoiding smoking, alcohol consumption, and sun exposure; and vitamin D supplementation. These modifiable risk factors are associated with this disease, contributing to its development, progression, and severity. This review evaluates the relationship between potentially modifiable risk factors and overall cancer development, specifically breast, colorectal, and prostate cancer, and highlights updated recommendations on cancer prevention. The results of numerous clinical and epidemiological studies clearly show the influence of lifestyles on the development and prevention of cancer. An incorrect diet, composed mainly of saturated fats and processed products, resulting in increased body weight, combined with physical inactivity, alcohol consumption, and smoking, has induced an increase in the incidence of all three types of cancer under study. Given the importance of adopting correct and healthy lifestyles to prevent cancer, global institutions should develop strategies and environments that encourage individuals to adopt healthy and regular behaviors.

Healthy Lifestyle and Cancer Risk: Modifiable Risk Factors to Prevent Cancer

Ginger (Zingiber officinale) has been investigated for its potentially therapeutic effect on a range of chronic conditions and symptoms in humans. However, a simplified and easily understandable examination of the mechanisms behind these effects is lacking and, in turn, hinders interpretation and translation to practice, and contributes to overall clinical heterogeneity confounding the results. Therefore, drawing on data from nonhuman trials, the objective for this narrative review was to comprehensively describe the current knowledge on the proposed mechanisms of action of ginger on conferring therapeutic health effects in humans. Mechanistic studies support the findings from human clinical trials that ginger may assist in improving symptoms and biomarkers of pain, metabolic chronic disease, and gastrointestinal conditions. Bioactive ginger compounds reduce inflammation, which contributes to pain; promote vasodilation, which lowers blood pressure; obstruct cholesterol production, which regulates blood lipid profile; translocate glucose transporter type 4 molecules to plasma membranes to assist in glycemic control; stimulate fatty acid breakdown to aid weight management; and inhibit serotonin, muscarinic, and histaminergic receptor activation to reduce nausea and vomiting. Additional human trials are required to confirm the antimicrobial, neuroprotective, antineoplastic, and liver- and kidney-protecting effects of ginger. Interpretation of the mechanisms of action will help clinicians and researchers better understand how and for whom ginger may render therapeutic effects and highlight priority areas for future research.

Therapeutic health effects of ginger (Zingiber officinale): updated narrative review exploring the mechanisms of action.

Heartburn, Nausea, and Vomiting During Pregnancy.

This study presents an integrated deep learning architecture with an object-detection algorithm and a convolutional neural network (CNN) for breast mass detection and visualization. Mammograms are analyzed to identify and localize breast mass lesions to aid clinician review. Two complementary forms of deep learning are used to identify the regions of interest (ROIs). An object-detection algorithm, YOLO v5, analyzes the entire mammogram to identify discrete image regions likely to represent masses. Object detections exhibit high precision, but the object-detection stage alone has insufficient overall accuracy for a clinical application. A CNN independently analyzes the mammogram after it has been decomposed into subregion tiles and is trained to emphasize sensitivity (recall). The ROIs identified by each analysis are highlighted in different colors to facilitate an efficient staged review. The CNN stage nearly always detects tumor masses when present but typically occupies a larger area of the image. By inspecting the high-precision regions followed by the high-sensitivity regions, clinicians can quickly identify likely lesions before completing the review of the full mammogram. On average, the ROIs occupy less than 20% of the tissue in the mammograms, even without removing pectoral muscle from the analysis. As a result, the proposed system helps clinicians review mammograms with greater accuracy and efficiency. 

A deep learning architecture with an object-detection algorithm and a convolutional neural network for breast mass detection and visualization

Background and Objective Anxiety influences job burnout and health. This study aimed to establish a nomogram to predict the anxiety status of medical staff during the coronavirus disease (COVID-19) pandemic. Methods A total of 600 medical members were randomized 7:3 and divided into training and validation sets. The data was collected using a questionnaire. Logistic regression analysis and Akaike information criterion (AIC) were applied to investigate the risk factors for anxiety. Odds ratio (OR) and 95% confidence interval (95% CI) were calculated to establish a nomogram. Results Participation time (OR=44.28, 95% CI=13.13~149.32), rest time (OR=38.50, 95% CI=10.43~142.19), epidemic prevention area (OR=10.16, 95% CI=3.51~29.40), epidemic prevention equipment (OR=15.24, 95% CI=5.73~40.55), family support (OR=9.63, 95% CI=3.55~26.11), colleague infection (OR=6.25, 95% CI=2.18~19.11), and gender (OR=3.30, 95% CI=1.15~9.47) were the independent risk factors (P<0.05) for anxiety in medical staff. The areas under the receiver operating characteristic (ROC) curves of the training and validation sets were 0.987 and 0.946, respectively. The decision curve’s net benefit shows the nomogram’s clinical utility. Conclusion The nomogram established in this study exhibited an excellent ability to predict anxiety status with sufficient discriminatory power and calibration. Our findings provide a protocol for predicting and identifying anxiety status in medical staff during the COVID-19 pandemic.

https://www.dovepress.com/getfile.php?fileID=85787

A Nomogram-Based Study: A Way Forward to Predict the Anxiety Status in Medical Staff During the COVID-19 Pandemic

Objective To evaluate the correlation of serum biological markers and related scales to the occurrence of delayed cerebral ischemia and clinical prognosis in patients with aneurysmal subarachnoid hemorrhage (aSAH) complicated with acute hydrocephalus before admission. Methods The clinical data of 227 patients with pre-admission aSAH complicated with acute hydrocephalus admitted to Henan Provincial People's Hospital from April 2017 to December 2020 were retrospectively analyzed. Patients were grouped according to the presence or absence of delayed cerebral ischemia (DCI) after surgery and the prognosis at 6 months after discharge. Univariate and multivariable logistic regression analysis were performed to analyze the relationship between serum biological indicators combined with aneurysm related clinical score scale and the occurrence and prognosis of delayed cerebral ischemia. ROC curves and nomogram were drawn. Results Multivariable Logistic regression analysis showed that high Hunt-Hess grade and surgical clipping were independent risk factors for postoperative DCI (P < 0.05). Older age, higher Hunt-Hess grade, higher CRP and neutrophil levels were independent risk factors for poor prognosis at 6 months after surgery (P < 0.05). ROC curve analysis showed that the area under the curve (AUC) of Hunt-Hess grade and surgical method for predicting DCI in patients with aSAH combined with hydrocephalus after surgery were 0.665 and 0.593. The combined AUC of Hunt-Hess grade and surgical method was 0.685, the sensitivity was 64.9%, and the specificity was 64.7%. The AUC of CRP, neutrophil, age and Hunt-Hess grade for predicting poor prognosis in patients with aSAH combined with hydrocephalus at 6 months after surgery were 0.804, 0.735, 0.596, 0.757, respectively. The combined AUC of CRP, neutrophil, age, Hunt-Hess grade was 0.879, the sensitivity was 79%, and the specificity was 84.5%. According to the correction curve, the predicted probability of the nomogram is basically consistent with the actual probability. Conclusion Hunt-Hess grade and surgical method are independent predictors of postoperative DCI in patients with aSAH complicated with hydrocephalus. “CRP,” “neutrophil,” “age” and “Hunt-Hess grade” at admission are independent predictors of clinical prognosis in patients with aSAH complicated with hydrocephalus. The combination of the above indicators has high predictive value.

/pdf/risk-factors-and-predictive-models-of-poor-prognosis-and-lw09r5c4.pdf

Risk factors and predictive models of poor prognosis and delayed cerebral ischemia in aneurysmal subarachnoid hemorrhage complicated with hydrocephalus

Objectives The ratio of white blood cell to platelet count (WPR) is considered a promising biomarker in some diseases. However, its prediction of delayed cerebral ischemia (DCI) and prognosis after aneurysmal subarachnoid hemorrhage (aSAH) has not been studied. The primary objective of this study was to investigate the predictive value of WPR in DCI after aSAH and its impact on 90-day functional outcome. Materials and methods This study retrospectively analyzed the data of blood biochemical parameters in 447 patients with aSAH at early admission. Univariate and multivariate analyses were used to determine the risk factors for DCI. According to multivariate analysis results, a nomogram for predicting DCI is developed and verified by R software. The influence of WPR on 90-day modified Rankin score (mRS) was also analyzed. Results Among 447 patients with aSAH, 117 (26.17%) developed DCI during hospitalization. Multivariate logistic regression analysis showed that WPR [OR = 1.236; 95%CI: 1.058–1.444; p = 0.007] was an independent risk factor for DCI. The receiver operating characteristic (ROC) curve analysis was used to evaluate the predictive ability of WPR for DCI, and the cut-off value of 5.26 (AUC 0.804, 95% CI: 0.757–0.851, p < 0.001). The ROC curve (AUC 0.875, 95% CI: 0.836–0.913, p < 0.001) and calibration curve (mean absolute error = 0.017) showed that the nomogram had a good predictive ability for the occurrence of DCI. Finally, we also found that high WPR levels at admission were closely associated with poor prognosis. Conclusion WPR level at admission is a novel serum marker for DCI and the poor prognosis after aSAH. A nomogram model containing early WPR will be of great value in predicting DCI after aSAH.

/pdf/prognostic-significance-of-white-blood-cell-to-platelet-2nbrhtvu.pdf

Prognostic significance of white blood cell to platelet ratio in delayed cerebral ischemia and long-term clinical outcome after aneurysmal subarachnoid hemorrhage

Nausea and vomiting of pregnancy (NVP) is a common condition that affects up to 70% of pregnant women. Hyperemesis gravidarum (HG) is considered the serious form of NVP, which is reported in 0.3–10.8% of pregnant women. NVP has a relatively benign course, but HG can be linked with some poor maternal, fetal, and offspring outcomes. The exact causes of NVP and HG are unknown, but various factors have been hypothesized to be associated with pathogenesis. With the advance of precision medicine and molecular biology, some genetic factors such as growth/differentiation factor 15 (GDF15) have become therapeutic targets. In our review, we summarize the historical hypotheses of the pathogenesis of NVP and HG including hormonal factors, Helicobacter pylori, gastrointestinal dysmotility, placenta-related factors, psychosocial factors, and new factors identified by genetics. We also highlight some approaches to the management of NVP and HG, including pharmacological treatment, complementary treatment, and some supporting treatments. Looking to the future, progress in understanding NVP and HG may reduce the adverse outcomes and improve the maternal quality of life during pregnancy.

https://www.frontiersin.org/articles/10.3389/fmed.2021.809270/pdf

Emerging Progress in Nausea and Vomiting of Pregnancy and Hyperemesis Gravidarum: Challenges and Opportunities

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A Nomogram-Based Study: A Way Forward to Predict the Anxiety Status in Medical Staff During the COVID-19 Pandemic

Risk factors and predictive models of poor prognosis and delayed cerebral ischemia in aneurysmal subarachnoid hemorrhage complicated with hydrocephalus

Prognostic significance of white blood cell to platelet ratio in delayed cerebral ischemia and long-term clinical outcome after aneurysmal subarachnoid hemorrhage

Emerging Progress in Nausea and Vomiting of Pregnancy and Hyperemesis Gravidarum: Challenges and Opportunities