Designing a time-dependent therapeutic strategy using CDK4/6 inhibitors in an intracranial ATRT model

BACKGROUND

Inhibitors targeting cyclin-dependent kinases 4 and 6 (CDK4/6), crucial for cell cycle regulation, have shown promise in early-stage studies for treating central nervous system (CNS) tumors. However, challenges such as limited CNS penetration, optimal treatment duration, and systemic side effects have impeded their clinical translation for pediatric brain tumors (PBTs).

METHODS

We evaluated the potency of CDK4/6 inhibitors across various PBT cell lines, focusing particularly on palbociclib against atypical teratoid rhabdoid tumor (ATRT) with cell viability assays and gene expression analysis. Additionally, we assessed the efficacy and safety of intrathecal (IT) delivery of palbociclib through neurotoxicity and pharmacokinetic studies, along with survival assessments in murine leptomeningeal ATRT models.

RESULTS

Palbociclib showed the highest potency across various PBT cells, with extended treatments reducing growth inhibition 50 (GI50) values from the micromolar to nanomolar range. It suppressed critical cell cycle genes (CCNB1, CCNA2, CDK1) in BT16 ATRT cells. Neurotoxicity (GFAP, CD45, NeuN, Iba1) and pharmacokinetic assays confirmed IT route as a feasible and effective method for delivering palbociclib to the cerebrospinal fluid (CSF), avoiding systemic toxicity and enhancing drug concentration to the brain. Finally, metronomic IT delivery using an osmotic pump (OP, 48 mg/kg) increased survival in 2 murine leptomeningeal ATRT models, showcasing its potential as a novel therapy for leptomeningeal tumors.

CONCLUSIONS

Metronomic IT delivery of palbociclib enhances drug efficacy and safety, improves survival, and offers a promising treatment strategy for PBTs with CSF dissemination.

Machine learning to predict stroke risk from routine hospital data: A systematic review

PURPOSE

Stroke remains a leading cause of morbidity and mortality. Despite this, current risk stratification tools such as CHA2DS2-VASc and QRISK3 are of limited accuracy, particularly in those without a diagnosis of atrial-fibrillation. Hence, there is a need for more accurate stroke risk prediction models. Machine-learning (ML) may provide a solution to this by leveraging existing routine hospital databases to build accurate stroke risk prediction models and identify novel risk factors for stroke.

AIMS

In this systematic review we appraise current research using ML to predict stroke risk from routine hospital data. Based on these findings we then highlight common methodological limitations and recommendations for future research.

METHODS

In this review we identify 49 original research (38 in the general population and 11 in AF specific populations) articles from the PUBMED database from January-2013 to December-2024 using ML and routine hospital data to predict the risk of stroke.

RESULTS

ML models were able to accurately predict stroke risk in both AF specific and general populations, with AUCs ranging from 0.64 to 0.99. Where tested, ML also consistently outperformed traditional risk stratification tool, such as CHA2DS2-VASc. ML also appeared useful in identifying several novel risk factors from electrocardiogram, laboratory test and echocardiography data. However, the quality of datasets were often limited, there was a high suspicion of overfitting and models often lacked calibration, external validation and explainability analysis.

CONCLUSION

Whilst ML has shown great potential in stroke prediction and identifying novel risk factors for stroke, improvements in study methodology is required prior to integration of ML into routine healthcare. Future research should adhere to the EQUATOR guidance on prediction models and encourage interdisciplinary collaboration between computer scientists and clinicians. Further prospective RCTs are also required to validate models in the clinical setting and the identify barriers of integrating ML into routine healthcare.

Comparison between laparoscopic complete mesocolic excision and D2 radical operation in colon carcinoma resection: A propensity score matching analysis

BACKGROUND

Surgery remains the most effective treatment for colon cancer. However, there are still controversies regarding the tumor treatment effect, prognosis, and perioperative impact of complete mesocolic excision (CME) surgery in colon carcinoma resection.

OBJECTIVE

This study aims to compare laparoscopic complete mesocolic excision (LCME) and traditional open D2 radical surgery in colon carcinoma resection through a retrospective analysis using 1:1 propensity score matching (PSM).

METHODS

98 cases undergoing LCME or open D2 colon carcinoma resection at our hospital from January 2014 to November 2021 were retrospectively collected. After excluding cases and 1:1 matching using PSM based on baseline clinical data, 86 patients were assigned in research queue. 43 patients were in each group. Two groups were compared for general clinical baseline indicators. Surgical results and postoperative adverse events of patients were also compared. Disease-free survival (DFS) rate and overall survival (OS) rate was analyzed.

RESULTS

After 1:1 PSM matching, there was no statistically significant differences in baseline data between the LCME group and D2 group (P> 0.05). LCME was characterized by longer total duration of surgery (P< 0.001), less intraoperative bleeding volume (P< 0.001), more postoperative drainage fluid volume (P< 0.001), greater number of lymph nodes retrieved (P= 0.014). No statistically differences was observed regarding intraoperative blood transfusion, hospital stay, Clavien-Dindo complicating disease classification (all P> 0.05), 1 and 3-year DFS rate (P= 0.84) and OS rate (P⩾ 0.1).

CONCLUSION

LCME had a longer duration of surgery but less intraoperative bleeding volume and more postoperative drainage fluid volume and retrieved lymph nodes compared to D2 radical surgery. LCME surgery is comparable to D2 surgery in terms of postoperative prognosis, but LCME surgery shows a positive trend in the overall survival curve.

Obesogens and Energy Homeostasis: Definition, Mechanisms of Action, Exposure, and Adverse Effects on Human Health

BACKGROUND

Obesity is a major risk factor for noncommunicable diseases and is associated with a reduced life expectancy of up to 20 years, as well as with other consequences such as unemployment and increased economic burden for society. It is a multifactorial disease, and physiopathology of obesity involves dysregulated calorie utilization and energy balance, disrupted homeostasis of appetite and satiety, lifestyle factors including sedentary lifestyle, lower socioeconomic status, genetic predisposition, epigenetics, and environmental factors. Some endocrine-disrupting chemicals (EDCs) have been proposed as "obesogens" that stimulate adipogenesis leading to obesity. In this review, definition of obesogens, their adverse effects, underlying mechanisms, and metabolic implications will be updated and discussed.

SUMMARY

Disruption of lipid homeostasis by EDCs involves multiple mechanisms including increase in the number and size of adipocytes, disruption of endocrine-regulated adiposity and metabolism, alteration of hypothalamic regulation of appetite, satiety, food preference and energy balance, and modification of insulin sensitivity in the liver, skeletal muscle, pancreas, gastrointestinal system, and the brain. At a cellular level, obesogens can exert their endocrine disruptive effects by interfering with peroxisome proliferator-activated receptors and steroid receptors. Human exposure to chemical obesogens mainly occurs by ingestion and, to some extent, by inhalation and dermal uptake, usually in an unconscious manner. Persistent pollutants are lipophilic features; thus, they bioaccumulate in adipose tissue.

KEY MESSAGES

Although there are an increasing number of reports studying the effects of obesogens, their mechanisms of action remain to be elucidated. In addition, epidemiological studies are needed in order to evaluate human exposure to obesogens.

The Impact of the SMOTE Method on Machine Learning and Ensemble Learning Performance Results in Addressing Class Imbalance in Data Used for Predicting Total Testosterone Deficiency in Type 2 Diabetes Patients

BACKGROUND AND OBJECTIVE

Diabetes Mellitus is a long-term, multifaceted metabolic condition that necessitates ongoing medical management. Hypogonadism is a syndrome that is a clinical and/or biochemical indicator of testosterone deficiency. Cross-sectional studies have reported that 20-80.4% of all men with Type 2 diabetes have hypogonadism, and Type 2 diabetes is related to low testosterone. This study presents an analysis of the use of ML and EL classifiers in predicting testosterone deficiency. In our study, we compared optimized traditional ML classifiers and three EL classifiers using grid search and stratified k-fold cross-validation. We used the SMOTE method for the class imbalance problem.

METHODS

This database contains 3397 patients for the assessment of testosterone deficiency. Among these patients, 1886 patients with Type 2 diabetes were included in the study. In the data preprocessing stage, firstly, outlier/excessive observation analyses were performed with LOF and missing value analyses were performed with random forest. The SMOTE is a method for generating synthetic samples of the minority class. Four basic classifiers, namely MLP, RF, ELM and LR, were used as first-level classifiers. Tree ensemble classifiers, namely ADA, XGBoost and SGB, were used as second-level classifiers.

RESULTS

After the SMOTE, while the diagnostic accuracy decreased in all base classifiers except ELM, sensitivity values increased in all classifiers. Similarly, while the specificity values decreased in all classifiers, F1 score increased. The RF classifier gave more successful results on the base-training dataset. The most successful ensemble classifier in the training dataset was the ADA classifier in the original data and in the SMOTE data. In terms of the testing data, XGBoost is the most suitable model for your intended use in evaluating model performance. XGBoost, which exhibits a balanced performance especially when the SMOTE is used, can be preferred to correct class imbalance.

CONCLUSIONS

The SMOTE is used to correct the class imbalance in the original data. However, as seen in this study, when the SMOTE was applied, the diagnostic accuracy decreased in some models but the sensitivity increased significantly. This shows the positive effects of the SMOTE in terms of better predicting the minority class.

Understanding sexual homicide in Korea using machine learning algorithms

The current study was conducted to confirm the characteristics in sexual homicide and to explore variables that effectively differentiate sexual homicide and nonsexual homicide. Further, newer methods that have received attention in criminology, such as the machine learning method, were used to explore the ideal algorithm for classifying sexual homicide and patterns for sexual homicide in Korea. To do this, 542 homicide cases were analyzed utilizing eight algorithms, and the classification performance of each algorithm was analyzed along with the importance of variables. The results of the analysis revealed that the Naive Bayes, K-Nearest Neighbors, and RF algorithms demonstrate good classification accuracy, and generally, factors such as relationships, marriage, planning, personal weapons, and overkill were identified as crucial variables that distinguish sexual homicide in Korea. In addition, the crime scene information of the crime occurring in the dark (at night) and body disposal were found to have high importance. The current study proposes ways to enhance the efficacy of crime investigation and advance the research on sexual homicides in Korea through a more scientific understanding of sexual homicide that has not been thoroughly explored domestically.

Long Noncoding RNA VLDLR-AS1 Levels in Serum Correlate with Combat-Related Chronic Mild Traumatic Brain Injury and Depression Symptoms in US Veterans

More than 75% of traumatic brain injuries (TBIs) are mild (mTBI) and military service members often experience repeated combat-related mTBI. The chronic comorbidities concomitant with repetitive mTBI (rmTBI) include depression, post-traumatic stress disorder or neurological dysfunction. This study sought to determine a long noncoding RNA (lncRNA) expression signature in serum samples that correlated with rmTBI years after the incidences. Serum samples were obtained from Long-Term Impact of Military-Relevant Brain-Injury Consortium Chronic Effects of Neurotrauma Consortium (LIMBIC CENC) repository, from participants unexposed to TBI or who had rmTBI. Four lncRNAs were identified as consistently present in all samples, as detected via droplet digital PCR and packaged in exosomes enriched for CNS origin. The results, using qPCR, demonstrated that the lncRNA VLDLR-AS1 levels were significantly lower among individuals with rmTBI compared to those with no lifetime TBI. ROC analysis determined an AUC of 0.74 (95% CI: 0.6124 to 0.8741; p = 0.0012). The optimal cutoff for VLDLR-AS1 was ≤153.8 ng. A secondary analysis of clinical data from LIMBIC CENC was conducted to evaluate the psychological symptom burden, and the results show that lncRNAs VLDLR-AS1 and MALAT1 are correlated with symptoms of depression. In conclusion, lncRNA VLDLR-AS1 may serve as a blood biomarker for identifying chronic rmTBI and depression in patients.

Why is early detection of colon cancer still not possible in 2023?

Colorectal cancer (CRC) screening is a fundamental tool in the prevention and early detection of one of the most prevalent and lethal cancers. Over the years, screening, particularly in those settings where it is well organized, has succeeded in reducing the incidence of colon and rectal cancer and improving the prognosis related to them. Despite considerable advancements in screening technologies and strategies, the effectiveness of CRC screening programs remains less than optimal. This paper examined the multifaceted reasons behind the persistent lack of effectiveness in CRC screening initiatives. Through a critical analysis of current methodologies, technological limitations, patient-related factors, and systemic challenges, we elucidated the complex interplay that hampers the successful reduction of CRC morbidity and mortality rates. While acknowledging the advancements that have improved aspects of screening, we emphasized the necessity of addressing the identified barriers comprehensively. This study aimed to raise awareness of how important CRC screening is in reducing costs for this disease. Screening and early diagnosis are not only important in improving the prognosis of patients with CRC but can lead to an important reduction in the cost of treating a disease that is often diagnosed at an advanced stage. Spending more sooner can mean saving money later.

Visual transformer and deep CNN prediction of high-risk COVID-19 infected patients using fusion of CT images and clinical data

BACKGROUND

Despite the globally reducing hospitalization rates and the much lower risks of Covid-19 mortality, accurate diagnosis of the infection stage and prediction of outcomes are clinically of interest. Advanced current technology can facilitate automating the process and help identifying those who are at higher risks of developing severe illness. This work explores and represents deep-learning-based schemes for predicting clinical outcomes in Covid-19 infected patients, using Visual Transformer and Convolutional Neural Networks (CNNs), fed with 3D data fusion of CT scan images and patients' clinical data.

METHODS

We report on the efficiency of Video Swin Transformers and several CNN models fed with fusion datasets and CT scans only vs. a set of conventional classifiers fed with patients' clinical data only. A relatively large clinical dataset from 380 Covid-19 diagnosed patients was used to train/test the models.

RESULTS

Results show that the 3D Video Swin Transformers fed with the fusion datasets of 64 sectional CT scans + 67 clinical labels outperformed all other approaches for predicting outcomes in Covid-19-infected patients amongst all techniques (i.e., TPR = 0.95, FPR = 0.40, F0.5 score = 0.82, AUC = 0.77, Kappa = 0.6).

CONCLUSIONS

We demonstrate how the utility of our proposed novel 3D data fusion approach through concatenating CT scan images with patients' clinical data can remarkably improve the performance of the models in predicting Covid-19 infection outcomes.

SIGNIFICANCE

Findings indicate possibilities of predicting the severity of outcome using patients' CT images and clinical data collected at the time of admission to hospital.

Robust Machine Learning Framework for Modeling the Compressive Strength of SFRC: Database Compilation, Predictive Analysis, and Empirical Verification

In recent years, the field of construction engineering has experienced a significant paradigm shift, embracing the integration of machine learning (ML) methodologies, with a particular emphasis on forecasting the characteristics of steel-fiber-reinforced concrete (SFRC). Despite the theoretical sophistication of existing models, persistent challenges remain-their opacity, lack of transparency, and real-world relevance for practitioners. To address this gap and advance our current understanding, this study employs the extra gradient (XG) boosting algorithm, crafting a comprehensive approach. Grounded in a meticulously curated database drawn from 43 seminal publications, encompassing 420 distinct records, this research focuses predominantly on three primary fiber types: crimped, hooked, and mil-cut. Complemented by hands-on experimentation involving 20 diverse SFRC mixtures, this empirical campaign is further illuminated through the strategic use of partial dependence plots (PDPs), revealing intricate relationships between input parameters and consequent compressive strength. A pivotal revelation of this research lies in the identification of optimal SFRC formulations, offering tangible insights for real-world applications. The developed ML model stands out not only for its sophistication but also its tangible accuracy, evidenced by exemplary performance against independent datasets, boasting a commendable mean target-prediction ratio of 99%. To bridge the theory-practice gap, we introduce a user-friendly digital interface, thoroughly designed to guide professionals in optimizing and accurately predicting the compressive strength of SFRC. This research thus contributes to the construction and civil engineering sectors by enhancing predictive capabilities and refining mix designs, fostering innovation, and addressing the evolving needs of the industry.

Assessing Changes in Colon Cancer Care during the COVID-19 Pandemic: A Four-Year Analysis at a Romanian University Hospital

This retrospective study investigates the impact of the COVID-19 pandemic on the surgical management of patients with colon cancer in a tertiary University Hospital in Timisoara, Romania. Data from 867 patients who underwent surgical interventions for this condition between 26 February 2019 and 25 February 2023 were meticulously analyzed to evaluate substantial shifts in the management and outcomes of these patients in comparison to the pre-pandemic era. The results reveal a substantial decrease in elective surgical procedures (p < 0.001) and a significant increase in emergency interventions (p < 0.001). However, postoperative mortality did not show significant variations. Of concern is the diagnosis of patients at more advanced stages of colon cancer, with a significant increase in Stage IV cases in the second year of the pandemic (p = 0.045). Average hospitalization durations recorded a significant decrease (p < 0.001) during the pandemic, and an inverse correlation between patient age and surgery duration was reported (p = 0.01, r = -0.088). This analysis provides a comprehensive perspective on how the pandemic has influenced the management of colon cancer, highlighting significant implications for the management and outcomes of these patients.

Asprosin, a novel glucogenic adipokine: a potential therapeutic implication in diabetes mellitus

OBJECTIVES

We aimed to investigate the effects of asprosin on diabetes with a focus on serum glucose, irisin, ghrelin, leptin levels and hepatic levels of triglycerides (TG), cholesterol, low-density lipoprotein (LDL).

METHODS

Asprosin (10 µg/kg) was administered intraperitoneally four times at 3-day intervals and then blood and hepatic parameters above mentioned were investigated in control and diabetic mice.

RESULTS

The administration of asprosin increased blood glucose level in healthy animals (p = .05) whereas it did not change blood glucose level in diabetic animals. In addition, while asprosin decreased irisin level and increased ghrelin level, it did not change leptin level in diabetic mice. Therewithal, asprosin decreased the increasing levels in hepatic TG, cholesterol, and LDL in diabetic mice.

CONCLUSIONS

Our novel findings implicate that asprosin may be a target molecule in preventing the development and complications of diabetes.

Prediction of hearing preservation after acoustic neuroma surgery based on SMOTE-XGBoost

Prior to the surgical removal of an acoustic neuroma, the majority of patients anticipate that their hearing will be preserved to the greatest possible extent following surgery. This paper proposes a postoperative hearing preservation prediction model for the characteristics of class-imbalanced hospital real data based on the extreme gradient boost tree (XGBoost). In order to eliminate sample imbalance, the synthetic minority oversampling technique (SMOTE) is applied to increase the number of underclass samples in the data. Multiple machine learning models are also used for the accurate prediction of surgical hearing preservation in acoustic neuroma patients. In comparison to research results from existing literature, the experimental results found the model proposed in this paper to be superior. In summary, the method this paper proposes can make a significant contribution to the development of personalized preoperative diagnosis and treatment plans for patients, leading to effective judgment for the hearing retention of patients with acoustic neuroma following surgery, a simplified long medical treatment process and saved medical resources.

Effects of intracerebroventricular MOTS-c infusion on thyroid hormones and uncoupling proteins

This study was conducted to determine the possible effects of intracerebroventricular MOTS-c infusion on thyroid hormones and uncoupling proteins (UCPs) in rats. Forty male Wistar Albino rats were divided into 4 groups with 10 animals in each group: control, sham, 10 and 100 µM MOTS-c. Hypothalamus, blood, muscle, adipose tissues samples were collected for thyrotropin-releasing hormone (TRH), UCP1 and UCP3 levels were determined by the RT-PCR and western blot analysis. Serum thyroid hormone levels were determined by the ELISA assays. MOTS-c infusion was found to increase food consumption but it did not cause any changes in the body weight. MOTS-c decreased serum TSH, T3, and T4 hormone levels. On the other hand, it was also found that MOTS-c administration increased UCP1 and UCP3 levels in peripheral tissues. The findings obtained in the study show that central MOTS-c infusion is a directly effective agent in energy metabolism.

Effects of chronic irisin treatment on brain monoamine levels in the hypothalamic and subcortical nuclei of adult male and female rats: An HPLC-ECD study

Monoaminergic systems are known to be involved in the pathophysiology of neuropsychiatric disorders and vegetative functions due to their established influence on hypothalamic and subcortical areas. These systems can be modulated by lifestyle factors, especially exercise, which is known to produce several beneficial effects on reproduction, brain health, and mental disorders. The fact that exercise is sensed by the brain shows that muscle-stimulated secretion of myokines allows direct crosstalk between the muscles and the brain. One of such exercise-induced beneficial effects on the brain is exhibited by irisin-a recently discovered PGC-1α-dependent adipo-myokine mainly secreted from skeletal muscle during exercise. Thus, we hypothesized that irisin may affect central monoamine levels and thus play an important role in the muscle-brain endocrine loop. To test this assertion, for 10 weeks, vehicle (deionized water) or 100 ng/kg irisin was injected intraperitoneally once a day to 12 male and 12 female rats after which the levels of monoamines and their metabolites were determined by HPLC-ECD. In the hypothalamic nuclei, irisin significantly decreased dopamine (DA) metabolite 3,4-dihydroxyphenylacetic acid (DOPAC) (p<0.05), DOPAC/DA ratio (p<0.01) and noradrenaline (NA, p<0.05) levels in the anteroventral periventricular nucleus (AVPV), and DOPAC and NA levels in the medial preoptic area (mPOA) (p<0.05), having a crucial role in reproduction and sexual motivation, respectively. On the other hand, irisin significantly increased DOPAC levels in the lateral hypothalamic area (LHA) (p<0.05), which acts as a hunger center, while it significantly decreased the levels of DA, NA, and its metabolite 3,4-dihydroxyphenylglycol (DHPG) in the ventromedial hypothalamic nucleus (VMH) as a known satiety center (p<0.05). In nucleus accumbens (NaC), irisin significantly reduced 5-hydroxyindoleacetic acid (5-HIAA) levels (p<0.05), which are implicated in autism spectrum disorder (ASD) physiopathology. It also significantly increased DA levels in this area, thus exhibiting positive effects on depression and sexual dysfunction in men. On the other hand, it significantly decreased serotonin (5-HT) (p<0.01) and its metabolite 5-HIAA levels in the medial amygdala (MeA) (p<0.05), indicating that it may play a role in social behaviors. Moreover, it significantly attenuated NA levels in the same hypothalamic area, which is directly involved in stress-induced activation of the central noradrenergic system. These findings demonstrate for the first time that irisin induces significant changes in monoamine levels in many hypothalamic nuclei involved in feeding behavior and vegetative functions, as well as in subcortical nuclei related to neuropsychiatric disorders.

Sarcopenia and Cognitive Decline in Older Adults: Targeting the Muscle-Brain Axis

Declines in physical performance and cognition are commonly observed in older adults. The geroscience paradigm posits that a set of processes and pathways shared among age-associated conditions may also serve as a molecular explanation for the complex pathophysiology of physical frailty, sarcopenia, and cognitive decline. Mitochondrial dysfunction, inflammation, metabolic alterations, declines in cellular stemness, and altered intracellular signaling have been observed in muscle aging. Neurological factors have also been included among the determinants of sarcopenia. Neuromuscular junctions (NMJs) are synapses bridging nervous and skeletal muscle systems with a relevant role in age-related musculoskeletal derangement. Patterns of circulating metabolic and neurotrophic factors have been associated with physical frailty and sarcopenia. These factors are mostly related to disarrangements in protein-to-energy conversion as well as reduced calorie and protein intake to sustain muscle mass. A link between sarcopenia and cognitive decline in older adults has also been described with a possible role for muscle-derived mediators (i.e., myokines) in mediating muscle-brain crosstalk. Herein, we discuss the main molecular mechanisms and factors involved in the muscle-brain axis and their possible implication in cognitive decline in older adults. An overview of current behavioral strategies that allegedly act on the muscle-brain axis is also provided.

Global elective breast- and colorectal cancer surgery performance backlogs, attributable mortality and implemented health system responses during the COVID-19 pandemic: A scoping review

Globally, 28.4 million non-emergent ('elective') surgical procedures have been deferred during the COVID-19 pandemic. This study evaluated the impact of the COVID-19 pandemic on elective breast- or colorectal cancer (CRC) procedure backlogs and attributable mortality, globally. Further, we evaluated the interaction between procedure deferrals and health systems, internationally. Relevant articles from any country, published between December 2019-24 November 2022, were identified through searches of online databases (MEDLINE, EMBASE) and by examining the reference lists of retrieved articles. We organised health system-related findings thematically per the Structures-Processes-Outcomes conceptual model by Donabedian (1966). Of 337 identified articles, we included 50. Eleven (22.0%) were reviews. The majority of included studies originated from high-income countries (n = 38, 76.0%). An ecological, modelling study elucidated that global 12-week procedure cancellation rates ranged from 68.3%-73%; Europe and Central Asia accounted for the majority of cancellations (n = 8,430,348) and sub-Saharan Africa contributed the least (n = 520,459). The percentage reduction in global, institutional elective breast cancer surgery activity ranged from 5.68%-16.5%. For CRC, this ranged from 0%-70.9%. Significant evidence is presented on how insufficient pandemic preparedness necessitated procedure deferrals, internationally. We also outlined ancillary determinants of delayed surgery (e.g., patient-specific factors). The following global health system response themes are presented: Structural changes (i.e., hospital re-organisation), Process-related changes (i.e., adapted healthcare provision) and the utilisation of Outcomes (i.e., SARS-CoV-2 infection incidence among patients or healthcare personnel, postoperative pulmonary complication incidence, hospital readmission, length of hospital stay and tumour staging) as indicators of health system response efficacy. Evidence on procedure backlogs and attributable mortality was limited, partly due to insufficient, real-time surveillance of cancer outcomes, internationally. Elective surgery activity has decreased and cancer services have adapted rapidly, worldwide. Further research is needed to understand the impact of COVID-19 on cancer mortality and the efficacy of health system mitigation measures, globally.

Association between fat-free mass loss, changes in appetite and weight regain in individuals with obesity

BACKGROUND

The role of fat-free mass loss (FFML) in modulating weight regain, in individuals with obesity, as well as the potential mechanisms involved, remain inconsistent.

AIMS

To determine if % FFML following weight loss (WL) is a predictor of weight regain, and to investigate the association between %FFML and changes in appetite markers.

METHODS

Seventy individuals with obesity (BMI: 36±4kg/m²; age: 44±9 years; 29 males) underwent 8 weeks of a very low-energy diet (550-660 kcal/day), followed by 4 weeks of gradual refeeding and weight stabilization, and a 9-month maintenance program (eucaloric diet). Body weight and body composition (fat mass (FM) and FFM) (primary outcomes), as well as ß-hydroxybutyrate (ßHB) plasma concentration (a marker of ketosis) in fasting and appetite-related hormones (ghrelin, glucagon-like peptide 1, peptide YY, and cholecystokinin) and subjective appetite feelings, in fasting and every 30 minutes after a fixed breakfast for 2.5h (secondary outcomes), were measured at baseline, week 9 and 1 year (and week 13 in 35 subjects (25 males)). The association between FFML, weight regain and changes in appetite was assessed by linear regression.

RESULTS

WL at week 9 was 17.5±4.3kg and %FFML 20.4±10.6%. Weight regain at 1 year was 1.7±8.2kg (8.8±45.0%). After adjusting for WL and FM at baseline, %FFML at week 9 was not a significant predictor of weight regain. Similar results were seen at week 13. The greater the %FFML at week 9, but not 13, the smaller the reduction, or greater the increase in basal ghrelin concentration (ß:-3.2; 95% CI: -5.0, -1.1; P=0.003), even after adjusting for WL and ß-hydroxybutyrate.

CONCLUSION

%FFML was not a significant predictor of weight regain at 1-year in individuals with obesity. However, a greater %FFML was accompanied by a greater increase in ghrelin secretion under ketogenic conditions, suggesting a link between FFM and appetite regulation.

CLINICAL TRIAL REGISTRATION

ClinicalTrials.gov identifier NCT01834859.

Impact of COVID-19 on Cancer-Related Care in the United States: An Overview

COVID-19 impacted several health services, including cancer-related care. Its implications were significant due to the lapse in hospital resources, compounded by the delays stemming from the economic effects on patients' jobs and medical coverage. Furthermore, reports suggesting an increased risk for morbidity and mortality from COVID-19 in patients with cancer and those on active cancer treatment caused additional fear and potential delays in seeking medical services. This review provides an overview of the pandemic's impact on cancer care in the United States and suggests measures for tackling similar situations in the future.

Irisin and Energy Metabolism and the Role of Irisin on Metabolic Syndrome

Irisin is a thermogenic hormone that leads to causes energy expenditure by increasing brown adipose tissue (BAT). This protein hormone that enables the conversion of white adipose tissue (WAT) to BAT is the irisin protein. This causes energy expenditure during conversion. WAT stores triglycerides and fatty acids and contains very few mitochondria. They also involve in the development of insulin resistance (IR). WAT, which contains a very small amount of mitochondria, contributes to the formation of IR by storing triglycerides and fatty acids. WAT functions as endocrine tissue in the body, synthesizing various molecules such as leptin, ghrelin, NUCB2/nesfatin-1, and irisin along with fat storage. BAT is quite effective in energy expenditure, unlike WAT. The number of mitochondria and lipid droplets composed of multicellular cells in BAT is much higher when compared to WAT. BAT contains a protein called uncoupling protein-1 (UCP1) in the mitochondrial membranes. This protein pumps protons from the intermembrane space toward the mitochondrial matrix. When UCP1 is activated, heat dissipation occurs while ATP synthesis does not occur, because UCP1 is a division protein. At the same time, BAT regulates body temperature in infants. Its effectiveness in adults became clear after the discovery of irisin. The molecular mechanism of exercise, which increases calorie expenditure, became clear with the discovery of irisin. Thus, the isolation of irisin led to the clarification of metabolic events and fat metabolism. In this review, literature information will be given on the effect of irisin hormone on energy metabolism and metabolic syndrome (MetS).

LIME-based ensemble machine for predicting performance status of patients with liver cancer

OBJECTIVE

The Eastern Cooperative Oncology Group performance status (ECOG PS) is a widely recognized measure used to assess the functional abilities of cancer patients and predict their prognosis. It plays a crucial role in guiding treatment decisions made by physicians. This study aimed to build a stacking ensemble-based prognosis predictor model for predicting the ECOG PS of a liver cancer patient undergoing treatment.

METHODS

We used Light Gradient Boosting Machine (LightGBM) as the meta-model, and five base models, including Random Forest (RF), Extra Trees (ET), AdaBoost (Ada), Gradient Boosting Machine (GBM), and Extreme Gradient Boosting (XGBoost). After preprocessing the data and applying feature selection method, the stacking ensemble model was trained using 1622 liver cancer patients' data and 46 variables. We also integrated the stacking ensemble model with a LIME-based explainable model to obtain model prediction explainability.

RESULTS

According to the research, the best combination of the stacking ensemble model is ET + XGBoost + RF + GBM + Ada + LightGBM and achieved a ROC AUC of 0.9826 on the training set and 0.9675 on the test set.

CONCLUSIONS

This explainable stacking ensemble model can become a helpful tool for objectively predicting ECOG PS in liver cancer patients and aiding healthcare practitioners to adapt their treatment approach more effectively.

Association between Fat-Free Mass Loss after Diet and Exercise Interventions and Weight Regain in Women with Overweight

PURPOSE

This study aimed to determine if percent fat-free mass loss (% FFML) after diet alone, diet plus aerobic, or diet plus resistance exercise is a predictor of weight regain in women with overweight.

METHODS

One hundred and forty-one premenopausal women with overweight (body mass index, 28 ± 1 kg·m -2 ; age, 35 ± 6 yr) enrolled in a weight loss program to achieve a body mass index <25 kg·m -2 (diet alone, diet plus resistance, or diet plus aerobic exercise) and were followed for 1 yr. Body weight and composition (with dual-energy x-ray absorptiometry) were measured at baseline, after weight loss, and at 1 yr.

RESULTS

Participants lost 12.1 ± 2.6 kg of body weight, 11.3 ± 2.5 kg of fat mass, and 0.5 ± 1.6 kg of fat-free mass during the weight loss intervention, followed by weight regain at 1 yr (6.0 ± 4.4 kg, 51.3% ± 37.8%; P < 0.001 for all). % FFML was -3.6 ± 12.4, and a greater % FFML was associated with more weight regain ( r = -0.216, P = 0.01, n = 141), even after adjusting for the intervention group ( β = -0.07; 95% confidence interval, -0.13 to -0.01; P = 0.017).

CONCLUSIONS

% FFML is a significant predictor of weight regain in premenopausal women with overweight. These results support strategies for conserving fat-free mass during weight loss, such as resistance training. Future research should try to identify the mechanisms, at the level of both appetite and energy expenditure, responsible for this association.

Predictors of shorter- and longer-term mortality after COVID-19 presentation among dialysis patients: parallel use of machine learning models in Latin and North American countries

Background

We developed machine learning models to understand the predictors of shorter-, intermediate-, and longer-term mortality among hemodialysis (HD) patients affected by COVID-19 in four countries in the Americas.

Methods

We used data from adult HD patients treated at regional institutions of a global provider in Latin America (LatAm) and North America who contracted COVID-19 in 2020 before SARS-CoV-2 vaccines were available. Using 93 commonly captured variables, we developed machine learning models that predicted the likelihood of death overall, as well as during 0–14, 15–30, > 30 days after COVID-19 presentation and identified the importance of predictors. XGBoost models were built in parallel using the same programming with a 60%:20%:20% random split for training, validation, & testing data for the datasets from LatAm (Argentina, Columbia, Ecuador) and North America (United States) countries.

Results

Among HD patients with COVID-19, 28.8% (1,001/3,473) died in LatAm and 20.5% (4,426/21,624) died in North America. Mortality occurred earlier in LatAm versus North America; 15.0% and 7.3% of patients died within 0–14 days, 7.9% and 4.6% of patients died within 15–30 days, and 5.9% and 8.6% of patients died > 30 days after COVID-19 presentation, respectively. Area under curve ranged from 0.73 to 0.83 across prediction models in both regions. Top predictors of death after COVID-19 consistently included older age, longer vintage, markers of poor nutrition and more inflammation in both regions at all timepoints. Unique patient attributes (higher BMI, male sex) were top predictors of mortality during 0–14 and 15–30 days after COVID-19, yet not mortality > 30 days after presentation.

Conclusions

Findings showed distinct profiles of mortality in COVID-19 in LatAm and North America throughout 2020. Mortality rate was higher within 0–14 and 15–30 days after COVID-19 in LatAm, while mortality rate was higher in North America > 30 days after presentation. Nonetheless, a remarkable proportion of HD patients died > 30 days after COVID-19 presentation in both regions. We were able to develop a series of suitable prognostic prediction models and establish the top predictors of death in COVID-19 during shorter-, intermediate-, and longer-term follow up periods.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12882-022-02961-x.

Role of brain-gut-muscle axis in human health and energy homeostasis

The interrelationship between brain, gut and skeletal muscle plays a key role in energy homeostasis of the body, and is becoming a hot topic of research. Intestinal microbial metabolites, such as short-chain fatty acids (SCFAs), bile acids (BAs) and tryptophan metabolites, communicate with the central nervous system (CNS) by binding to their receptors. In fact, there is a cross-talk between the CNS and the gut. The CNS, under the stimulation of pressure, will also affect the stability of the intestinal system, including the local intestinal transport, secretion and permeability of the intestinal system. After the gastrointestinal tract collects information about food absorption, it sends signals to the central system through vagus nerve and other channels to stimulate the secretion of brain-gut peptide and produce feeding behavior, which is also an important part of maintaining energy homeostasis. Skeletal muscle has receptors for SCFAs and BAs. Therefore, intestinal microbiota can participate in skeletal muscle energy metabolism and muscle fiber conversion through their metabolites. Skeletal muscles can also communicate with the gut system during exercise. Under the stimulation of exercise, myokines secreted by skeletal muscle causes the secretion of intestinal hormones, and these hormones can act on the central system and affect food intake. The idea of the brain-gut-muscle axis is gradually being confirmed, and at present it is important for regulating energy homeostasis, which also seems to be relevant to human health. This article focuses on the interaction of intestinal microbiota, central nervous, skeletal muscle energy metabolism, and feeding behavior regulation, which will provide new insight into the diagnostic and treatment strategies for obesity, diabetes, and other metabolic diseases.

Irisin, a fascinating field in our times

Irisin is a muscle-secreted hormone that is generated by cleavage of membrane protein FNDC-5 (fibronectin type III domain-containing protein 5). Irisin is considered to be a mediator of exercise-induced metabolic improvements, such as browning of white adipose tissue, and is known to alleviate several chronic non-metabolic diseases. Thus, irisin may be an ideal therapeutic target for metabolic and non-metabolic diseases. However, several controversies regarding irisin have hindered its clinical translation. We review the generation, regulation (especially in exercise), and metabolic as well as therapeutic effects of irisin on metabolic and non-metabolic diseases. Furthermore, we discuss controversies regarding irisin and highlight potential future research directions.

Can machines learn the mutation signatures of SARS-CoV-2 and enable viral-genotype guided predictive prognosis?

MOTIVATION

Continuous emergence of new variants through appearance/accumulation/disappearance of mutations is a hallmark of many viral diseases. SARS-CoV-2 variants have particularly exerted tremendous pressure on global healthcare system owing to their life threatening and debilitating implications. The sheer plurality of variants and huge scale of genomic data have added to the challenges of tracing the mutations/variants and their relationship to infection severity (if any).

RESULTS

We explored the suitability of virus-genotype guided machine-learning in infection prognosis and identification of features/mutations-of-interest. Total 199,519 outcome-traced genomes, representing 45,625 nucleotide-mutations, were employed. Among these, post data-cleaning, Low and High severity genomes were classified using an integrated model (employing virus genotype, epitopic-influence and patient-age) with consistently high ROC-AUC (Asia:0.97 ± 0.01, Europe:0.94 ± 0.01, N.America:0.92 ± 0.02, Africa:0.94 ± 0.07, S.America:0.93 ± 03). Although virus-genotype alone could enable high predictivity (0.97 ± 0.01, 0.89 ± 0.02, 0.86 ± 0.04, 0.95 ± 0.06, 0.9 ± 0.04), the performance was not found to be consistent and the models for a few geographies displayed significant improvement in predictivity when the influence of age and/or epitope was incorporated with virus-genotype (Wilcoxon p_BH < 0.05). Neither age or epitopic-influence or clade information could out-perform the integrated features. A sparse model (6 features), developed using patient-age and epitopic-influence of the mutations, performed reasonably well (>0.87 ± 0.03, 0.91 ± 0.01, 0.87 ± 0.03, 0.84 ± 0.08, 0.89 ± 0.05). High-performance models were employed for inferring the important mutations-of-interest using Shapley Additive exPlanations (SHAP). The changes in HLA interactions of the mutated epitopes of reference SARS-CoV-2 were then subsequently probed. Notably, we also describe the significance of a 'temporal-modeling approach' to benchmark the models linked with continuously evolving pathogens. We conclude that while machine learning can play a vital role in identifying relevant mutations and factors driving the severity, caution should be exercised in using the genotypic signatures for predictive prognosis.

Predicting compressive strength of high-performance concrete with high volume ground granulated blast-furnace slag replacement using boosting machine learning algorithms

Predicting the compressive strength of concrete is a complicated process due to the heterogeneous mixture of concrete and high variable materials. Researchers have predicted the compressive strength of concrete for various mixes using machine learning and deep learning models. In this research, compressive strength of high-performance concrete with high volume ground granulated blast-furnace slag replacement is predicted using boosting machine learning (BML) algorithms, namely, Light Gradient Boosting Machine, CatBoost Regressor, Gradient Boosting Regressor (GBR), Adaboost Regressor, and Extreme Gradient Boosting. In these studies, the BML model’s performance is evaluated based on prediction accuracy and prediction error rates, i.e., R², MSE, RMSE, MAE, RMSLE, and MAPE. Additionally, the BML models were further optimised with Random Search algorithms and compared to BML models with default hyperparameters. Comparing all 5 BML models, the GBR model shows the highest prediction accuracy with R² of 0.96 and lowest model error with MAE and RMSE of 2.73 and 3.40, respectively for test dataset. In conclusion, the GBR model are the best performing BML for predicting the compressive strength of concrete with the highest prediction accuracy, and lowest modelling error.

The modulatory effects of irisin on asprosin, leptin, glucose levels and lipid profile in healthy and obese male and female rats

OBJECTIVES

The main aim of this study was to investigate the effects of irisin on asprosin, leptin, glucose levels and lipid profile in healthy and obese male and female rats.

METHODS

Irisin was subcutaneously administered with osmotic minipumps at the dose of 100 ng/kg/day for 28 days and then, the serum levels of asprosin, leptin, glucose and lipid profile were investigated.

RESULTS

Irisin infusion increased asprosin levels in male rats (p = .02) but not in female rats. Irisin inhibited obesity-induced high glucose, low-density lipoprotein (LDL), triglyceride (TG) and leptin levels in all groups; however, it did not lead to any change in asprosin levels in both obese female and male rats.

CONCLUSIONS

It was determined that irisin increased serum asprosin levels and decreased LDL, TG, glucose and leptin levels, and this could indicate a protective role of irisin against obesity development.

The role of irisin in metabolic flexibility: beyond adipose tissue browning

Metabolic flexibility is the ability to adapt to physiological and environmental changes in metabolic demand. Irisin was originally discovered as an exercise-induced myokine involved in fat browning. In this review, we summarize emerging evidence for the role of irisin in regulating glucose metabolism and insulin sensitivity in skeletal muscle, neuroplasticity and satiety in central nervous system, β cell function and insulin secretion in the pancreas, bone remodeling, and adipose tissue function, which together orchestrate whole-body metabolic flexibility. Irisin is a key communicating mediator between skeletal muscle and other organs, and its manipulation could be a promising therapeutic strategy for treating obesity and related metabolic disorders. Teaser: This review summarizes recent progress in manipulating metabolic flexibility with irisin, and discusses its potential application as a drug target to treat obesity and related metabolic disorders.

Effects of meteorin-like hormone on endocrine function of hypothalamo-hypophysial system and peripheral uncoupling proteins in rats

BACKGROUND

Meteorin-like hormone (Metrnl) is a peptide secreted from the adipose tissue and modulates the whole-body energy metabolism. Metrnl release into the circulation is influenced by obesity, cold exposure, and exercise. Thyroid hormones also exert many of their effects on metabolism through uncoupling proteins (UCPs). This study aimed to determine effect of Metrnl on hypothalamo-hypophysier-thyroid axis and energy metabolism and reveal the possible involvement of UCPs in this process.

METHODS AND RESULTS

Fourty male Sprague-Dawley rats were divided into 4 groups with 10 animals in each group: control, sham, 10 and 100 nM Metrnl. Hypothalamus, muscle, white adipose tissue (WAT) and brown adipose tissue (BAT) samples were collected to detect thyrotropin-releasing hormone (TRH), and UCP1 and UCP3 protein levels by western blot analysis. Serum thyroid-stimulating hormone (TSH), triiodothyronine (T3) and thyroxine (T4) hormone levels were determined by enzyme-linked immunosorbent assay. Central infusion of Metrnl caused significant increase in serum TSH, T3 and T4 levels compared to control (p < 0.05). After Metrnl treatment, there were significant increases in TRH in hypothalamus tissue, UCP1 in WAT and BAT; and UCP3 protein in the muscle tissue (p < 0.05).

CONCLUSIONS

The findings that Metrnl induced increases in the peripheral UCPs and hypothalamus-pituitary-thyroid axis hormones implicate a role for this hormone in body energy homeostasis through UCP-mediated mechanisms.

Towards an Approach for Filtration Efficiency Estimation of Consumer-Grade Face Masks Using Thermography

Due to the increasing need for continuous use of face masks caused by COVID-19, it is essential to evaluate the filtration quality that each face mask provides. In this research, an estimation method based on thermal image processing was developed; the main objective was to evaluate the effectiveness of different face masks while being used during breathing. For the acquisition of heat distribution images, a thermographic imaging system was built; moreover, a deep learning model detected the leakage percentage of each face mask with a mAP of 0.9345, recall of 0.842 and F1-score of 0.82. The results obtained from this research revealed that the filtration effectiveness depended on heat loss through the manufacturing material; the proposed estimation method is simple, fast, and can be replicated and operated by people who are not experts in the computer field.

Potential applications and performance of machine learning techniques and algorithms in clinical practice: A systematic review

PURPOSE

The advent of clinically adapted machine learning algorithms can solve numerous problems ranging from disease diagnosis and prognosis to therapy recommendations. This systematic review examines the performance of machine learning (ML) algorithms and evaluates the progress made to date towards their implementation in clinical practice.

METHODS

Systematic searching of databases (PubMed, MEDLINE, Scopus, Google Scholar, Cochrane Library and WHO Covid-19 database) to identify original articles published between January 2011 and October 2021. Studies reporting ML techniques in clinical practice involving humans and ML algorithms with a performance metric were considered.

RESULTS

Of 873 unique articles identified, 36 studies were eligible for inclusion. The XGBoost (extreme gradient boosting) algorithm showed the highest potential for clinical applications (n = 7 studies); this was followed jointly by random forest algorithm, logistic regression, and the support vector machine, respectively (n = 5 studies). Prediction of outcomes (n = 33), in particular Inflammatory diseases (n = 7) received the most attention followed by cancer and neuropsychiatric disorders (n = 5 for each) and Covid-19 (n = 4). Thirty-three out of the thirty-six included studies passed more than 50% of the selected quality assessment criteria in the TRIPOD checklist. In contrast, none of the studies could achieve an ideal overall bias rating of 'low' based on the PROBAST checklist. In contrast, only three studies showed evidence of the deployment of ML algorithm(s) in clinical practice.

CONCLUSIONS

ML is potentially a reliable tool for clinical decision support. Although advocated widely in clinical practice, work is still in progress to validate clinically adapted ML algorithms. Improving quality standards, transparency, and interpretability of ML models will further lower the barriers to acceptability.

Machine Learning in Action: Stroke Diagnosis and Outcome Prediction

The application of machine learning has rapidly evolved in medicine over the past decade. In stroke, commercially available machine learning algorithms have already been incorporated into clinical application for rapid diagnosis. The creation and advancement of deep learning techniques have greatly improved clinical utilization of machine learning tools and new algorithms continue to emerge with improved accuracy in stroke diagnosis and outcome prediction. Although imaging-based feature recognition and segmentation have significantly facilitated rapid stroke diagnosis and triaging, stroke prognostication is dependent on a multitude of patient specific as well as clinical factors and hence accurate outcome prediction remains challenging. Despite its vital role in stroke diagnosis and prognostication, it is important to recognize that machine learning output is only as good as the input data and the appropriateness of algorithm applied to any specific data set. Additionally, many studies on machine learning tend to be limited by small sample size and hence concerted efforts to collate data could improve evaluation of future machine learning tools in stroke. In the present state, machine learning technology serves as a helpful and efficient tool for rapid clinical decision making while oversight from clinical experts is still required to address specific aspects not accounted for in an automated algorithm. This article provides an overview of machine learning technology and a tabulated review of pertinent machine learning studies related to stroke diagnosis and outcome prediction.

The role of European health system characteristics in affecting Covid 19 lethality during the early days of the pandemic

This article examines the main factors affecting COVID-19 lethality across 16 European Countries with a focus on the role of health system characteristics during the first phase of the diffusion of the virus. Specifically, we investigate the leading causes of lethality at 10, 20, 30, 40 days in the first hit of the pandemic. Using a random forest regression (ML), with lethality as outcome variable, we show that the percentage of people older than 65 years (with two or more chronic diseases) is the main predictor variable of lethality by COVID-19, followed by the number of hospital intensive care unit beds, investments in healthcare spending compared to GDP, number of nurses and doctors. Moreover, the variable of general practitioners has little but significant predicting quality. These findings contribute to provide evidence for the prediction of lethality caused by COVID-19 in Europe and open the discussion on health policy and management of health care and ICU beds during a severe epidemic.

Intracerebroventricular asprosin administration strongly stimulates hypothalamic-pituitary-testicular axis in rats

Asprosin, a protein-based secretary product of white adipose tissue, stimulates appetite hepatic glucose production. It crosses blood-brain barrier and stimulates appetite center and causes sperm chemotaxis but exact role of this endogenous agent is not completely known. This study was conducted to investigate possible effects of central asprosin infusion on the hormones involved in the hypothalamic-pituitary-testicular (HPT) axis and sperm cells. Spraque Dawley male rats were divided into four groups; control, sham, low asprosin (34) and high asprosin (68 nM) groups, (n = 10 for each group). Control group remain intact while a brain infusion kit was placed in the lateral ventricles of the rats in the sham group (artificial cerebrospinal fluid) and asprosin (34 and 68 nM) was infused for 14 days. At the end of the experiment, the hypothalamus, blood, and epididymis tissues of the rats were collected. Gonadotropin-releasing hormone (GnRH) mRNA and tissue protein levels were determined in the hypothalamus tissue by RT-PCR and Western Blot methods. Serum luteinizing hormone (LH), follicle-stimulating hormone (FSH), and testosterone levels were examined using the ELISA method from blood samples and sperm cells were examined in the epididymis tissue. GnRH mRNA and protein expressions of asprosin administered groups were higher than control and sham groups (p < 0.05). Asprosin infusion was also found to increase serum FSH, LH, and testosterone levels (p < 0.05). In addition, sperm density, motility, and progressive movement were observed to increase in asprosin administered groups (p < 0.05). This study suggests that central asprosin stimulate the HPT axis and also epididymis tissue. Our results implicates potential role for asprosin in male infertility.

HANA: A Healthy Artificial Nutrition Analysis model during COVID-19 pandemic

BACKGROUND AND OBJECTIVE

The impact of diet on COVID-19 patients has been a global concern since the pandemic began. Choosing different types of food affects peoples' mental and physical health and, with persistent consumption of certain types of food and frequent eating, there may be an increased likelihood of death. In this paper, a regression system is employed to evaluate the prediction of death status based on food categories.

METHODS

A Healthy Artificial Nutrition Analysis (HANA) model is proposed. The proposed model is used to generate a food recommendation system and track individual habits during the COVID-19 pandemic to ensure healthy foods are recommended. To collect information about the different types of foods that most of the world's population eat, the COVID-19 Healthy Diet Dataset was used. This dataset includes different types of foods from 170 countries around the world as well as obesity, undernutrition, death, and COVID-19 data as percentages of the total population. The dataset was used to predict the status of death using different machine learning regression models, i.e., linear regression (ridge regression, simple linear regularization, and elastic net regression), and AdaBoost models.

RESULTS

The death status was predicted with high accuracy, and the food categories related to death were identified with promising accuracy. The Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and R2 metrics and 20-fold cross-validation were used to evaluate the accuracy of the prediction models for the COVID-19 Healthy Diet Dataset. The evaluations demonstrated that elastic net regression was the most efficient prediction model. Based on an in-depth analysis of recent nutrition recommendations by WHO, we confirm the same advice already introduced in the WHO report1. Overall, the outcomes also indicate that the remedying effects of COVID-19 patients are most important to people which eat more vegetal products, oilcrops grains, beverages, and cereals - excluding beer. Moreover, people consuming more animal products, animal fats, meat, milk, sugar and sweetened foods, sugar crops, were associated with a higher number of deaths and fewer patient recoveries. The outcome of sugar consumption was important and the rates of death and recovery were influenced by obesity.

CONCLUSIONS

Based on evaluation metrics, the proposed HANA model may outperform other algorithms used to predict death status. The results of this study may direct patients to eat particular types of food to reduce the possibility of becoming infected with the COVID-19 virus.

Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application

Cardiovascular disease (CVD) is one of the leading causes of morbidity and mortality in the United States of America and globally. Carotid arterial plaque, a cause and also a marker of such CVD, can be detected by various non-invasive imaging modalities such as magnetic resonance imaging (MRI), computer tomography (CT), and ultrasound (US). Characterization and classification of carotid plaque-type in these imaging modalities, especially into symptomatic and asymptomatic plaque, helps in the planning of carotid endarterectomy or stenting. It can be challenging to characterize plaque components due to (I) partial volume effect in magnetic resonance imaging (MRI) or (II) varying Hausdorff values in plaque regions in CT, and (III) attenuation of echoes reflected by the plaque during US causing acoustic shadowing. Artificial intelligence (AI) methods have become an indispensable part of healthcare and their applications to the non-invasive imaging technologies such as MRI, CT, and the US. In this narrative review, three main types of AI models (machine learning, deep learning, and transfer learning) are analyzed when applied to MRI, CT, and the US. A link between carotid plaque characteristics and the risk of coronary artery disease is presented. With regard to characterization, we review tools and techniques that use AI models to distinguish carotid plaque types based on signal processing and feature strengths. We conclude that AI-based solutions offer an accurate and robust path for tissue characterization and classification for carotid artery plaque imaging in all three imaging modalities. Due to cost, user-friendliness, and clinical effectiveness, AI in the US has dominated the most.

Role of Irisin in Endocrine and Metabolic Disorders—Possible New Therapeutic Agent?

Irisin is a novel hormone that provides a possible solution for the treatment of metabolic disorders. Discovered in 2012 by Boström et al., irisin very quickly became an interesting subject in medical research. Irisin has been found in cerebrospinal fluid, the cerebellum, thyroid, pineal gland, liver, pancreas, testis, spleen, adult stomach, and human fetuses. Regarding the actions of irisin, both in animals and humans, the results are contradictory but interesting. Its capability to influence adipose tissue and glycemic homeostasis may be utilized in order to treat hypothyroidism, polycystic ovary syndrome, Prader–Willi syndrome, and other endocrine and metabolic disorders. Considering its osteogenic potential, irisin might be a therapeutic choice in diseases caused by a sedentary lifestyle. New data indicate that irisin treatment may serve in the treatment of severe acute respiratory syndrome coronavirus 2 (SARSCoV-2) infection. Furthermore, several therapeutic agents, such as insulin, metformin, fenofibrate, exenatide, and melatonin, influence the concentrations of irisin in animal models or in humans. Nutritional factors including polyunsaturated fatty acids may also have an effect on irisin concentrations. While it may be “too good to be true,” irisin offers many opportunities for future research that would aim to find its optimal therapeutical role in endocrine and metabolic diseases.

The Role of Peptide Hormones Discovered in the 21st Century in the Regulation of Adipose Tissue Functions

Peptide hormones play a prominent role in controlling energy homeostasis and metabolism. They have been implicated in controlling appetite, the function of the gastrointestinal and cardiovascular systems, energy expenditure, and reproduction. Furthermore, there is growing evidence indicating that peptide hormones and their receptors contribute to energy homeostasis regulation by interacting with white and brown adipose tissue. In this article, we review and discuss the literature addressing the role of selected peptide hormones discovered in the 21st century (adropin, apelin, elabela, irisin, kisspeptin, MOTS-c, phoenixin, spexin, and neuropeptides B and W) in controlling white and brown adipogenesis. Furthermore, we elaborate how these hormones control adipose tissue functions in vitro and in vivo.

The Role of Irisin in Exercise-Mediated Bone Health

Exercise training promotes physical and bone health, and is the first choice of non-drug strategies that help to improve the prognosis and complications of many chronic diseases. Irisin is a newly discovered peptide hormone that modulates energy metabolism and skeletal muscle mass. Here, we discuss the role of irisin in bone metabolism via exercise-induced mechanical forces regulation. In addition, the role of irisin in pathological bone loss and other chronic diseases is also reviewed. Notably, irisin appears to be a key determinant of bone mineral status and thus may serve as a novel biomarker for bone metabolism. Interestingly, the secretion of irisin appears to be mediated by different forms of exercise and pathological conditions such as diabetes, obesity, and inflammation. Understanding the mechanism by which irisin is regulated and how it regulates skeletal metabolism via osteoclast and osteoblast activities will be an important step toward applying new knowledge of irisin to the treatment and prevention of bone diseases such as osteolysis and other chronic disorders.

Natural Language Processing and Machine Learning for Identifying Incident Stroke From Electronic Health Records: Algorithm Development and Validation

Background

Stroke is an important clinical outcome in cardiovascular research. However, the ascertainment of incident stroke is typically accomplished via time-consuming manual chart abstraction. Current phenotyping efforts using electronic health records for stroke focus on case ascertainment rather than incident disease, which requires knowledge of the temporal sequence of events.

Objective

The aim of this study was to develop a machine learning–based phenotyping algorithm for incident stroke ascertainment based on diagnosis codes, procedure codes, and clinical concepts extracted from clinical notes using natural language processing.

Methods

The algorithm was trained and validated using an existing epidemiology cohort consisting of 4914 patients with atrial fibrillation (AF) with manually curated incident stroke events. Various combinations of feature sets and machine learning classifiers were compared. Using a heuristic rule based on the composition of concepts and codes, we further detected the stroke subtype (ischemic stroke/transient ischemic attack or hemorrhagic stroke) of each identified stroke. The algorithm was further validated using a cohort (n=150) stratified sampled from a population in Olmsted County, Minnesota (N=74,314).

Results

Among the 4914 patients with AF, 740 had validated incident stroke events. The best-performing stroke phenotyping algorithm used clinical concepts, diagnosis codes, and procedure codes as features in a random forest classifier. Among patients with stroke codes in the general population sample, the best-performing model achieved a positive predictive value of 86% (43/50; 95% CI 0.74-0.93) and a negative predictive value of 96% (96/100). For subtype identification, we achieved an accuracy of 83% in the AF cohort and 80% in the general population sample.

Conclusions

We developed and validated a machine learning–based algorithm that performed well for identifying incident stroke and for determining type of stroke. The algorithm also performed well on a sample from a general population, further demonstrating its generalizability and potential for adoption by other institutions.

Irisin and Incretin Hormones: Similarities, Differences, and Implications in Type 2 Diabetes and Obesity

Incretins are gut hormones that potentiate glucose-stimulated insulin secretion (GSIS) after meals. Glucagon-like peptide-1 (GLP-1) is the most investigated incretin hormone, synthesized mainly by L cells in the lower gut tract. GLP-1 promotes β-cell function and survival and exerts beneficial effects in different organs and tissues. Irisin, a myokine released in response to a high-fat diet and exercise, enhances GSIS. Similar to GLP-1, irisin augments insulin biosynthesis and promotes accrual of β-cell functional mass. In addition, irisin and GLP-1 share comparable pleiotropic effects and activate similar intracellular pathways. The insulinotropic and extra-pancreatic effects of GLP-1 are reduced in type 2 diabetes (T2D) patients but preserved at pharmacological doses. GLP-1 receptor agonists (GLP-1RAs) are therefore among the most widely used antidiabetes drugs, also considered for their cardiovascular benefits and ability to promote weight loss. Irisin levels are lower in T2D patients, and in diabetic and/or obese animal models irisin administration improves glycemic control and promotes weight loss. Interestingly, recent evidence suggests that both GLP-1 and irisin are also synthesized within the pancreatic islets, in α- and β-cells, respectively. This review aims to describe the similarities between GLP-1 and irisin and to propose a new potential axis-involving the gut, muscle, and endocrine pancreas that controls energy homeostasis.

Parameter evolution of the classifiers for disease diagnosis with offline data-driven hybrid systems

Automatic disease diagnosis is, in essence, a classification problem where the classifier has to be trained based on patients’ datasets and not entirely on doctors’ expert knowledge. In this paper, we present the design of such data-driven disease classifiers and fine-tuning classifier performance by a multi-objective evolutionary algorithm. We have used sequential minimal optimization (SMO) classifier as the base classifier and three evolutionary algorithms namely Cat Swarm Optimization (CSO), Invasive Weed Optimization (IWO) and Eagle Search based Invasive Weed Optimization (ESIWO) to diagnose disease from datasets available. In that sense, our approach is an offline data-driven approach with 18 benchmark medical datasets, and the obtained results demonstrate the superiority of the proposed diagnoses in terms of multiple objectives such as classification Prediction accuracy, Sensitivity, and Specificity. Relevant statistical tests have been carried out to substantiate the cogence of the obtained results.

Ischemic stroke: Process perspective, clinical and profile characteristics, and external factors

OBJECTIVE

To describe a method of analysis for understanding the health care process, enriched with information on the clinical and profile characteristics of the patients. To apply the proposed technique to analyze an ischemic stroke dataset.

MATERIALS AND METHODS

We analyzed 4,830 electronic health records (EHRs) from patients with ischemic stroke (2010-2017), containing information about events realized during treatment and clinical and profile information of the patients. The proposed method combined process mining techniques with data analysis, grouping the data by primary care units (PCU - units responsible for the primary care of patients residing in a geographical area).

RESULTS

A novel method, named process, data, and management (PDM) analysis method was used for ischemic stroke data and it provided the following outcomes: health care process for patients with ischemic stroke with time statistics; analysis of potential factors for slow hospital admission indicating an increase in the time to hospital admission of 3.4 h (mean value) for patients with an origin at the urgent care center (UCC) - 30% of patients; analysis of PCUs with distinct secondary stroke rates indicating that the social class of patients is the main difference between them; and the visualization of risk factors (before the stroke) by the PCU to inform the health manager about the potential of prevention.

DISCUSSION

PDM analysis describes a step-by-step method for combining process analysis with data analysis considering a management focus. The results obtained on the stroke context can support the definition of more refined action plans by the health manager, improving the stroke health care process and preventing new events.

CONCLUSION

When a patient is diagnosed with ischemic stroke, immediate treatment is needed. Moreover, it is possible to prevent new events to some degree by monitoring and treating risk factors. PDM analysis provides an overview of the health care process with time, combining elements that affect the treatment flow and factors, which can indicate a potential for preventing new events. We also can apply PDM analysis in different scenarios, when there is information about activities from treatment flow and other characteristics related to the treatment or the prevention of the analyzed disease. The management focus of the results aids in the formulation of service policies, action plans, and resource allocation.

The Use of Machine Learning Approaches for the Diagnosis of Acute Appendicitis

Acute appendicitis is one of the most common emergency diseases in general surgery clinics. It is more common, especially between the ages of 10 and 30 years. Additionally, approximately 7% of the entire population is diagnosed with acute appendicitis at some time in their lives and requires surgery. The study aims to develop an easy, fast, and accurate estimation method for early acute appendicitis diagnosis using machine learning algorithms. Retrospective clinical records were analyzed with predictive data mining models. The predictive success of the models obtained by various machine learning algorithms was compared. A total of 595 clinical records were used in the study, including 348 males (58.49%) and 247 females (41.51%). It was found that the gradient boosted trees algorithm achieves the best success with an accurate prediction success of 95.31%. In this study, an estimation method based on machine learning was developed to identify individuals with acute appendicitis. It is thought that this method will benefit patients with signs of appendicitis, especially in emergency departments in hospitals.

Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal

OBJECTIVE

To review and appraise the validity and usefulness of published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19 infection or being admitted to hospital with the disease.

DESIGN

Living systematic review and critical appraisal by the COVID-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group.

DATA SOURCES

PubMed and Embase through Ovid, up to 1 July 2020, supplemented with arXiv, medRxiv, and bioRxiv up to 5 May 2020.

STUDY SELECTION

Studies that developed or validated a multivariable covid-19 related prediction model.

DATA EXTRACTION

At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool).

RESULTS

37 421 titles were screened, and 169 studies describing 232 prediction models were included. The review identified seven models for identifying people at risk in the general population; 118 diagnostic models for detecting covid-19 (75 were based on medical imaging, 10 to diagnose disease severity); and 107 prognostic models for predicting mortality risk, progression to severe disease, intensive care unit admission, ventilation, intubation, or length of hospital stay. The most frequent types of predictors included in the covid-19 prediction models are vital signs, age, comorbidities, and image features. Flu-like symptoms are frequently predictive in diagnostic models, while sex, C reactive protein, and lymphocyte counts are frequent prognostic factors. Reported C index estimates from the strongest form of validation available per model ranged from 0.71 to 0.99 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.54 to 0.99 in prognostic models. All models were rated at high or unclear risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and unclear reporting. Many models did not include a description of the target population (n=27, 12%) or care setting (n=75, 32%), and only 11 (5%) were externally validated by a calibration plot. The Jehi diagnostic model and the 4C mortality score were identified as promising models.

CONCLUSION

Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that almost all pubished prediction models are poorly reported, and at high risk of bias such that their reported predictive performance is probably optimistic. However, we have identified two (one diagnostic and one prognostic) promising models that should soon be validated in multiple cohorts, preferably through collaborative efforts and data sharing to also allow an investigation of the stability and heterogeneity in their performance across populations and settings. Details on all reviewed models are publicly available at https://www.covprecise.org/. Methodological guidance as provided in this paper should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, prediction model authors should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline.

SYSTEMATIC REVIEW REGISTRATION

Protocol https://osf.io/ehc47/, registration https://osf.io/wy245.

READERS' NOTE

This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 3 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity.

Fuzzy cognitive map based approach for determining the risk of ischemic stroke

Stroke is the third major cause of mortality in the world. The diagnosis of stroke is a very complex issue considering controllable and uncontrollable factors. These factors include age, sex, blood pressure, diabetes, obesity, heart disease, smoking, and so on, having a considerable influence on the diagnosis of stroke. Hence, designing an intelligent system leading to immediate and effective treatment is essential. In this study, the soft computing method known as fuzzy cognitive mapping was proposed for diagnosis of the risk of ischemic stroke. Non-linear Hebbian learning method was used for fuzzy cognitive maps training. In the proposed method, the risk rate for each person was determined based on the opinions of the neurologists. The accuracy of the proposed model was tested using 10-fold cross-validation, for 110 real cases, and the results were compared with those of support vector machine and K-nearest neighbours. The proposed system showed a superior performance with a total accuracy of (93.6 ± 4.5)%. The data used in this study is available by emailing the first author for academic and non-commercial purposes.

Irisin increases the expression of anorexigenic and neurotrophic genes in mouse brain

BACKGROUND

Irisin, a newly discovered muscle-derived hormone, acts in different organs and tissues, improving energy homeostasis. In this study, we assessed, for the first time, the effects of intraperitoneal irisin injections on circulating levels of leptin and ghrelin, mRNA expression of the major hypothalamic appetite regulators and brain neurotrophic factors, as well as feeding behaviour in healthy mice.

METHODS

Twelve male 6-week-old C57BL/6 mice were randomized into two groups and intraperitoneally injected daily with irisin (0.5 μg/g body weight) or vehicle (phosphate-buffered saline [PBS]) for 14 days. On the last day of observation, leptin and ghrelin levels were measured with an enzyme-linked immunosorbent assay (ELISA). mRNA levels of genes of interest were analysed by quantitative reverse transcription polymerase chain reaction (qRT-PCR) in brain extracts.

RESULTS

Irisin administration did not change leptin or ghrelin serum concentrations. However, irisin injection increased CART, POMC, NPY, and BDNF mRNA levels, without affecting the mRNA expression of AgRP, orexin, PMCH, and UCP2. Finally, over the time frame of irisin treatment, body weight and feeding behaviour were unaltered.

CONCLUSIONS

These results suggest that intraperitoneal injection of irisin, although without effects on feeding behaviour and body weight, can increase the expression of anorexigenic and neurotrophic genes in mouse brain.