Machine Learning in Electroconvulsive Therapy: A Systematic Review.

ABSTRACT: Despite years of research, we are still not able to reliably predict who might benefit from electroconvulsive therapy (ECT) treatment. As we exhaust what is possible using traditional statistical analysis, ECT remains a good candidate for machine learning approaches due to the large data sets with data captured through electroencephalography (EEG) and other objective measures. A systematic review of 6 databases led to the full-text examination of 26 articles using machine learning approaches in examining data predicting response to ECT treatment. The identified articles used a wide variety of data types covering structural and functional imaging data (n = 15), clinical data (n = 5), a combination of clinical and imaging data (n = 2), EEG (n = 3), and social media posts (n = 1). The clinical indications in which response prediction was assessed were depression (n = 21) and psychosis (n = 4). Changes in multiple anatomical regions in the brain were identified as holding a predictive value for response to ECT. These primarily centered on the limbic system and associated networks. Clinical features predicting good response to ECT in depression included shorter duration, lower severity, higher medication dose, psychotic features, low cortisol levels, and positive family history. It has also been possible to predict the likelihood of relapse of readmission with psychosis after ECT treatment, including a better response if higher transfer entropy was calculated from EEG signals. A transdisciplinary approach with an international consortium collecting a wide range of retrospective and prospective data may help to refine and extend these outcomes and translate them into clinical practice.

Swarm Intelligence in Internet of Medical Things: A Review.

Continuous advancements of technologies such as machine-to-machine interactions and big data analysis have led to the internet of things (IoT) making information sharing and smart decision-making possible using everyday devices. On the other hand, swarm intelligence (SI) algorithms seek to establish constructive interaction among agents regardless of their intelligence level. In SI algorithms, multiple individuals run simultaneously and possibly in a cooperative manner to address complex nonlinear problems. In this paper, the application of SI algorithms in IoT is investigated with a special focus on the internet of medical things (IoMT). The role of wearable devices in IoMT is briefly reviewed. Existing works on applications of SI in addressing IoMT problems are discussed. Possible problems include disease prediction, data encryption, missing values prediction, resource allocation, network routing, and hardware failure management. Finally, research perspectives and future trends are outlined.

UncertaintyFuseNet: Robust uncertainty-aware hierarchical feature fusion model with Ensemble Monte Carlo Dropout for COVID-19 detection.

The COVID-19 (Coronavirus disease 2019) pandemic has become a major global threat to human health and well-being. Thus, the development of computer-aided detection (CAD) systems that are capable of accurately distinguishing COVID-19 from other diseases using chest computed tomography (CT) and X-ray data is of immediate priority. Such automatic systems are usually based on traditional machine learning or deep learning methods. Differently from most of the existing studies, which used either CT scan or X-ray images in COVID-19-case classification, we present a new, simple but efficient deep learning feature fusion model, called U n c e r t a i n t y F u s e N e t , which is able to classify accurately large datasets of both of these types of images. We argue that the uncertainty of the model's predictions should be taken into account in the learning process, even though most of the existing studies have overlooked it. We quantify the prediction uncertainty in our feature fusion model using effective Ensemble Monte Carlo Dropout (EMCD) technique. A comprehensive simulation study has been conducted to compare the results of our new model to the existing approaches, evaluating the performance of competing models in terms of Precision, Recall, F-Measure, Accuracy and ROC curves. The obtained results prove the efficiency of our model which provided the prediction accuracy of 99.08% and 96.35% for the considered CT scan and X-ray datasets, respectively. Moreover, our U n c e r t a i n t y F u s e N e t model was generally robust to noise and performed well with previously unseen data. The source code of our implementation is freely available at: https://github.com/moloud1987/UncertaintyFuseNet-for-COVID-19-Classification.

X-ray image based COVID-19 detection using evolutionary deep learning approach.

Radiological methodologies, such as chest x-rays and CT, are widely employed to help diagnose and monitor COVID-19 disease. COVID-19 displays certain radiological patterns easily detectable by X-rays of the chest. Therefore, radiologists can investigate these patterns for detecting coronavirus disease. However, this task is time-consuming and needs lots of trial and error. One of the main solutions to resolve this issue is to apply intelligent techniques such as deep learning (DL) models to automatically analyze the chest X-rays. Nevertheless, fine-tuning of architecture and hyperparameters of DL models is a complex and time-consuming procedure. In this paper, we propose an effective method to detect COVID-19 disease by applying convolutional neural network (CNN) to the chest X-ray images. To improve the accuracy of the proposed method, the last Softmax CNN layer is replaced with a K -nearest neighbors (KNN) classifier which takes into account the agreement of the neighborhood labeling. Moreover, we develop a novel evolutionary algorithm by improving the basic version of competitive swarm optimizer. To this end, three powerful evolutionary operators: Cauchy Mutation (CM), Evolutionary Boundary Constraint Handling (EBCH), and tent chaotic map are incorporated into the search process of the proposed evolutionary algorithm to speed up its convergence and make an excellent balance between exploration and exploitation phases. Then, the proposed evolutionary algorithm is used to automatically achieve the optimal values of CNN's hyperparameters leading to a significant improvement in the classification accuracy of the proposed method. Comprehensive comparative results reveal that compared with current models in the literature, the proposed method performs significantly more efficient.

Risk factors prediction, clinical outcomes, and mortality in COVID-19 patients.

Preventing communicable diseases requires understanding the spread, epidemiology, clinical features, progression, and prognosis of the disease. Early identification of risk factors and clinical outcomes might help in identifying critically ill patients, providing appropriate treatment, and preventing mortality. We conducted a prospective study in patients with flu-like symptoms referred to the imaging department of a tertiary hospital in Iran between March 3, 2020, and April 8, 2020. Patients with COVID-19 were followed up after two months to check their health condition. The categorical data between groups were analyzed by Fisher's exact test and continuous data by Wilcoxon rank-sum test. Three hundred and nineteen patients (mean age 45.48 ± 18.50 years, 177 women) were enrolled. Fever, dyspnea, weakness, shivering, C-reactive protein, fatigue, dry cough, anorexia, anosmia, ageusia, dizziness, sweating, and age were the most important symptoms of COVID-19 infection. Traveling in the past 3 months, asthma, taking corticosteroids, liver disease, rheumatological disease, cough with sputum, eczema, conjunctivitis, tobacco use, and chest pain did not show any relationship with COVID-19. To the best of our knowledge, a number of factors associated with mortality due to COVID-19 have been investigated for the first time in this study. Our results might be helpful in early prediction and risk reduction of mortality in patients infected with COVID-19.

A case-control study on factor V Leiden: an independent, gender-dependent risk factor for venous thromboembolism.

BACKGROUND: Activated protein C resistance (APCR) due to factor V Leiden (FVL) mutation (R506Q) is a major risk factor in patients with venous thromboembolism (VTE). The present study investigated the clinical manifestations and the risk of venous thromboembolism regarding multiple clinical, laboratory, and demographic properties in FVL patients. MATERIAL AND METHODS: A retrospective cross-sectional analysis was conducted on a total of 288 FVL patients with VTE according to APCR. In addition, 288 VET control samples, without FVL mutation, were also randomly selected. Demographic information, clinical manifestations, family and treatment history were recorded, and specific tests including t-test, chi-square and uni- and multi-variable regression tests applied. RESULTS: APCR was found to be 2.3 times significantly more likely in men (OR: 2.1, p < 0.05) than women. The risk of deep vein thrombosis (DVT) and pulmonary embolism (PE) in APCR patients was 4.5 and 3.2 times more than the control group, respectively (p < 0.05). However, APCR could not be an independent risk factor for arterial thrombosis (AT) and pregnancy complications. Moreover, patients were evaluated for thrombophilia panel tests and showed significantly lower protein C and S than the control group and patients without DVT (p < 0.0001). CONCLUSION: FVL mutation and APCR abnormality are noticeable risk factors for VTE. Screening strategies for FVL mutation in patients undergoing surgery, oral contraceptive medication, and pregnancy cannot be recommended, but a phenotypic test for activated protein C resistance should be endorsed in patients with VTE.

Increased incidence of rhino-orbital mucormycosis in an educational therapeutic hospital during the COVID-19 pandemic in western Iran: An observational study.

BACKGROUND: COVID-19 patients, especially the patients requiring hospitalisation, have a high risk of several complications such as opportunistic bacterial and fungal infections. Mucormycosis is a rare and opportunistic fungal infection that mainly affects diabetic and immunocompromised patients. An increase has been observed in the number of rhino-orbital mucormycosis in patients with COVID-19 admitted to Imam Khomeini Hospital, Kermanshah, Iran, since October 2020. This is a report of the frequency, risk factors, clinical manifestations, treatment and prognosis of COVID-19 associated with mucormycosis infection. METHODS: The medical records of COVID-19 patients with rhino-orbital mucormycosis who were diagnosed in an educational therapeutic hospital in Kermanshah, west of Iran were surveyed. Several parameters were analysed including demographic, clinical, therapeutic and laboratory characteristics. RESULTS: Twelve patients with COVID-19-associated rhino-orbital mucormycosis were identified from 12 October to 18 November 2020. All cases reported as proven mucormycosis had a history of hospitalisation due to COVID-19. Comorbidities mainly included diabetes mellitus (83.33%) and hypertension (58.33%). Seventy-five per cent of patients received corticosteroids for COVID- 19 treatment. The sites of involvement were rhino-sino-orbital (83%) and rhino-sino (17%). Amphotericin B/liposomal amphotericin B alone or in combination with surgical debridement or orbital exenteration was used as the first-line therapy. The overall mortality rate was 66.7% (8/12). CONCLUSIONS: We found a high incidence of mucormycosis among COVID-19 patients. Diabetes mellitus and corticosteroid use were the dominant predisposing factor of mucormycosis. Mucormycosis is a life-threatening and opportunistic infection; therefore, physicians should know the signs and symptoms of the disease so that a timely diagnosis and therapy can be performed.

An oppositional-Cauchy based GSK evolutionary algorithm with a novel deep ensemble reinforcement learning strategy for COVID-19 diagnosis.

A novel coronavirus (COVID-19) has globally attracted attention as a severe respiratory condition. The epidemic has been first tracked in Wuhan, China, and has progressively been expanded in the entire world. The growing expansion of COVID-19 around the globe has made X-ray images crucial for accelerated diagnostics. Therefore, an effective computerized system must be established as a matter of urgency, to facilitate health care professionals in recognizing X-ray images from COVID-19 patients. In this work, we design a novel artificial intelligent-based automated X-ray image analysis framework based on an ensemble of deep optimized convolutional neural networks (CNNs) in order to distinguish coronavirus patients from non-patients. By developing a modified version of gaining-sharing knowledge (GSK) optimization algorithm using the Opposition-based learning (OBL) and Cauchy mutation operators, the architectures of the deployed deep CNNs are optimized automatically without performing the general trial and error procedures. After obtaining the optimized CNNs, it is also very critical to identify how to decrease the number of ensemble deep CNN classifiers to ensure the classification effectiveness. To this end, a selective ensemble approach is proposed for COVID-19 X-ray based image classification using a deep Q network that combines reinforcement learning (RL) with the optimized CNNs. This approach increases the model performance in particular and therefore decreases the ensemble size of classifiers. The experimental results show that the proposed deep RL optimized ensemble approach has an excellent performance over two popular X-ray image based COVID-19 datasets. Our proposed advanced algorithm can accurately identify the COVID-19 patients from the normal individuals with a significant accuracy of 0.991441, precision of 0.993568, recall (sensitivity) of 0.981445, F-measure of 0.989666 and AUC of 0.990337 for Kaggle dataset as well as an excellent accuracy of 0.987742, precision of 0.984334, recall (sensitivity) of 0.989123, F-measure of 0.984939 and AUC of 0.988466 for Mendely dataset.

Fertility and pregnancy in Iranian thalassemia patients: An update on transfusion complications.

BACKGROUND: Despite the significant advances in thalassemia pathobiology and efficacy of chelation regimens, complications of transfusion therapy have attenuated the reproductive health of thalassemia patients. Depending on clinical profiles, we aimed to assess the fertility status and stresses among thalassemia patients who desired to have children. MATERIAL AND METHODS: A total of 213 couples in reproductive ages were enrolled in this study in Tehran. Patients' demographic, clinical, fertility and spouse's health status were documented. We evaluated the pituitary-gonadal axis, serum ferritin, liver enzymes, and alloimmunization before planning a pregnancy and reported them as a function of spontaneous conception and transfusion dependency. RESULTS: Data showed that 131 patients (62%) had 228 spontaneous pregnancies leading to 198 (86.6%) successful pregnancies. A significant difference was observed in spontaneous pregnancy with respect to fertility complications and transfusion dependency. In addition, the clinical conditions of spouses in patients with any spontaneous pregnancy were more thalassemia carriers (P < .05). Moreover, serum ferritin levels had a significant negative correlation with the levels of Testosterone, Estradiol, luteinizing hormone, and follicle-stimulating hormone. Furthermore, a significant positive correlation was reported with the level of liver enzymes. Finally, alanine transaminase and aspartate transaminase had a significant negative correlation with pituitary hormones. CONCLUSION: We suggest that organised instruction in addition to good iron chelation, especially during the puberty period, would reduce the oxidative damage and related complications in thalassemia patients. Moreover, infertility seems to be attributed to iron deposition in various endocrine organs, pituitary, reproductive system and the liver, contributing to hormonal metabolism.

Automated Detection of Presymptomatic Conditions in Spinocerebellar Ataxia Type 2 Using Monte Carlo Dropout and Deep Neural Network Techniques with Electrooculogram Signals.

Application of deep learning (DL) to the field of healthcare is aiding clinicians to make an accurate diagnosis. DL provides reliable results for image processing and sensor interpretation problems most of the time. However, model uncertainty should also be thoroughly quantified. This paper therefore addresses the employment of Monte Carlo dropout within the DL structure to automatically discriminate presymptomatic signs of spinocerebellar ataxia type 2 in saccadic samples obtained from electrooculograms. The current work goes beyond the common incorporation of this special type of dropout into deep neural networks and uses the uncertainty derived from the validation samples to construct a decision tree at the register level of the patients. The decision tree built from the uncertainty estimates obtained a classification accuracy of 81.18% in automatically discriminating control, presymptomatic and sick classes. This paper proposes a novel method to address both uncertainty quantification and explainability to develop reliable healthcare support systems.

Autophagy regulation and its role in normal and malignant hematopoiesis.

Autophagy, the molecular machinery of self-eating, plays a dual role of a tumor promoter and tumor suppressor. This mechanism affects different clinical responses in cancer cells. Autophagy is targeted for treating patients resistant to chemotherapy or radiation. Limited reports investigate the significance of autophagy in cancer therapy, the regulation of hematopoietic and leukemic stem cells and leukemia formation. In the current review, the role of autophagy is discussed in various stages of hematopoiesis including quiescence, self-renewal, and differentiation.

Iranian patients with hemoglobin H disease: genotype-phenotype correlation.

Thalassemia is one of the most common monogenic hereditary disorders. Despite noticeable advances made in prevention strategies, it is still highly prevalent in the Iranian population. A key approach to management and early diagnosis of the disease is through revealing the regions with high prevalence and determining common genetic and phenotypic diversity. In the current study Hemoglobin H (HbH) disease patients were analyzed as the most common form of thalassemia intermedia in Iran. A total of 80 patients suspected of being thalassemic according to their mild to moderate anemia, microcytosis and normal iron levels were included in this study at the hemoglobinopathy and thalassemia center of Ahvaz University of Medical Science. Patients were analyzed for hematological parameters and HbH mutations using Multiplex Gap Polymerase Chain Reaction and Multiplex Amplification Refractory Mutation System. Twelve mutations were detected in the studied population. The most common genotype was -α3.7/--MED (45%) followed by Homozygote αPoly A2 (17.5%). A total of ten different alpha-globin (α-globin) mutations were observed in patients which --MED, being the most common mutation (26.27%), followed by -α3.7 (24.37%) and αpolyA2(A>G) (18.12%). Hematological parameters such as Hb, MCV, MCH and HbH were assessed and results showed that they varied significantly among genotypes, adjusted to age and gender. This study reveals a highly diverse range of HbH patients different from what was thought in terms of both genotype and phenotype in the Khuzestan region of Iran. These findings could contribute to improve the thalassemia managing policies in this province.

Genetics of Iranian Alpha-Thalassemia Patients: A Comprehensive Original Study.

Alpha thalassemia is the most prevalent monogenic gene disorder in the world, especially in Mediterranean countries. In the current hematological phenotype of patients with different genotypes, the effects of missense mutations on the protein function and also stability were evaluated in a large cohort study. A total of 1,560 subjects were enrolled in the study and divided into two groups: 259 normal subjects; and 1301 alpha-thalassemia carriers. Genomic DNA was extracted and analyzed using ARMS PCR, Multiplex Gap, and direct sequencing. The effects of single nucleotide change on the protein function and stability were predicted by freely available databases of human polymorphisms. Sixty-three different genotypes were seen in the patients. The more prevalent was heterozygote form of -α3.7 (41.4%) followed by -α3.7 homozygote (11.6%) and -MED (3.8%). The significant differences were seen in mean hemoglobin level [F = 20.5, p < 0.001] between the Alpha-globin genotypes, when adjusted for gender. Moreover, 28 different mutations were found in our study. A significant relationship was seen between ethnicity and the alpha-globin mutation frequency χ2 (df;8) = 38.36, p < 0.0001). Different genotypes could display as different phenotypes. The mutation frequency distributions in our region are different from those of other parts of Iran. Significant differences are seen in the spectrum of mutation frequency among various ethnicities. Finally, some missense mutations might not have considerable effect on the proteins, and they could be neutral mutations.

Evaluation of α-Globin Gene Mutations Among Different Ethnic Groups in Khuzestan Province, Southwest Iran.

α-Thalassemia (α-thal) is one of the most common inherited hemoglobin (Hb) disorders in the world. In addition to large deletions, over 50 different α-thal point mutations were detected around the world, thus, patients showed different phenotypes with regard to genotype. This study evaluated the genetic frequency of α-thal in Khuzestan Province, Southwest Iran, to help implement premarital and prenatal screening programs. The study was conducted on couples proposing to get married and parents who were referred to the genetic center of Shafa Hospital, Ahvaz, Iran, for prenatal diagnosis (PND) in 2012. Genomic DNA was purified by the salting-out method and tested using multiplex gap-polymerase chain reaction (gap-PCR), amplification refractory mutation system-PCR (ARMS-PCR), reverse hybridization test strips and DNA sequencing. Overall, 11 mutations were found on the α-globin genes. Based on gene frequency, the most common mutant allele was -α(3.7) (rightward) (71.3%) followed by the two gene deletion - -(MED) (9.7%). Other common mutations were α(codon 19)α (GCG>GC-, α2) (8.4%), the polyadenylation (polyA1) site α(polyA1)α (AATAAA>AATAAG) (2.8%), and α(-5 nt)α (-TGAGG) (2.0%). In addition, an extremely rare mutation at α(codon 21)α [Hb Fontainebleau, HBA2: c.64G > C (or HBA1)] was also found. The results of this study are critical for correct diagnosis of α-thal carriers, premarriage counseling and PND. This study suggests that the distribution of mutations on the α-globin genes differs among the ethnic groups in Khuzestan Province as well as in other provinces.

Early Acute Kidney Injury based on Serum Creatinine or Cystatin C in Intensive Care Unit after Major Trauma.

BACKGROUND: Acute kidney injury (AKI) is a common problem in critically ill patients and is independently associated with increased morbidity and mortality. Recently, serum cystatin C has been shown to be superior to creatinine in early detection of renal function impairment. We compared estimated GFR based on serum cystatin C with estimated GFR based on serum creatinine for early detection of renal dysfunction according to the RIFLE criteria. METHODS: During 9 months, three hundred post trauma patients that were referred to the intensive care unit of a referral trauma hospital were recruited. Serum creatinine and serum cystatin C were measured and the estimated GFR within 24 hours of ICU admission was calculated. The primary outcome was the incidence of AKI according to the RIFLE criteria within 2(nd) to 7(th) day of admission. RESULTS: During the first week of ICU admission, 21% of patients experienced AKI. After adjusting for major confounders, only the patients with first day's serum cystatin level higher than 0.78 mg/l were at higher risk of first week AKI (OR=6.14, 95% CI: 2.5-14.7, P<0.001). First day's serum cystatin C and injury severity score were the major risk factors for ICU mortality (OR=3.54, 95% CI: 1.7-7.4, P=0.001) and (OR=4.6, 95% CI: 1.5-14, P=0.007), respectively. CONCLUSION: Within 24 hours after admission in ICU due to multiple trauma, high serum cystatin C level may have prognostic value in predicting early AKI and mortality during ICU admission. However, such correlation was not seen neither with creatinine nor cystatin C based GFR.

The Application of Umbilical Cord Blood-derived Platelet Gel for Skin Ulcers Associated With Chronic Graft-Versus-Host Disease in Pediatrics: A Randomized Trial.

Allogeneic hematopoietic stem cell transplantation (allo-HSCT) is a curative strategy against a variety of malignant and nonmalignant disorders. However, acute and chronic graft-versus-host disease (aGVHD and cGVHD, respectively) commonly complicate this approach, culminating in substantial morbidities and mortalities. The integumentary system is the preponderant organ involved in cGVHD, and its response to existing treatments, including well-versed immunosuppressants and novel targeted therapies, is not desirable. Despite the rarity, ulcers of sclerotic skin cGVHD are treatment-refractory and associated with significant morbidities and an exaggerated risk of infectious complications. Platelet-rich plasma (PRP) and its derivatives are endowed with growth factors and proangiogenic molecules and hold regenerative potential. This study aimed to assess the safety and efficacy of the application of platelet gel-containing dressing against ulcerative skin cGVHD in pediatric patients. This randomized trial is conducted at the hematopoietic stem cell transplantation unit of the Children's Medical Center Hospital in Tehran, Iran. Twenty-one pediatric patients (aged between 5 and 15 years) were initially enrolled, and 16 met the inclusion criteria. All cases (4 females) were recipients of allo-HSCT who had been complicated with symmetrically or near-symmetrically ulcerative sclerotic skin cGVHD. Fresh umbilical cord blood (UCB) was obtained from healthy donors and underwent centrifugation using a novel PRP preparation kit in a single-step process. Platelet gel was produced by adding thrombin to the isolated buffy coat layer. Two similar ulcers of each patient were randomized to receive either conventional dressing or platelet gels up to 6 times. At each time point evaluation, ulcer size and its relative reduction compared to the basal size were recorded. Included patients received a total of 80 platelet gel-containing dressings. While the mean sizes of randomized ulcers at the beginning of the study were similar, their differences became significant 15 days after the initiation of intervention (P = .019). In addition, the mean reduction in the ulcers' surface area (in comparison to their baseline values) was significantly higher for the intervention arm at all evaluation points (P = .001 for day 5 and P < .001 for subsequent time points). At the end of the trial, the number of ulcers with a more than 50% reduction in size was 14 (87.5%) in the intervention arm (including 6 completely healed ulcers) versus 1 (6.25%, which was not completely healed) in the control arm (P < .001). None of the patients exhibited any localized or systemic treatment-related adverse events. In this study, using a relatively large number of cases, we showed that UCB-derived platelet gel is a safe, feasible, and effective curative approach for skin ulcers of sclerotic skin cGVHD in pediatric patients. Designing upcoming trials on the efficacy of this therapeutic approach for ocular, mucosal, and acute skin GVHD is prudent. Retrospectively registered at the Iranian Registry of Clinical Trials (registration number IRCT20190101042197N1) on August 24, 2020.

Verification of a simplified aneurysm dimensionless flow parameter to predict intracranial aneurysm rupture status.

OBJECTIVES: Aneurysm number (An) is a novel prediction tool utilizing parameters of pulsatility index (PI) and aneurysm geometry. An has been shown to have the potential to differentiate intracranial aneurysm (IA) rupture status. The objective of this study is to investigate the feasibility and accuracy of An for IA rupture status prediction using Australian based clinical data. METHODS: A retrospective study was conducted across three tertiary referral hospitals between November 2017 and November 2020 and all saccular IAs with known rupture status were included. Two sets of An values were calculated based on two sets of PI values previously reported in the literature. RESULTS: Five hundred and four IA cases were included in this study. The results demonstrated no significant difference between ruptured and unruptured status when using An ≥1 as the discriminator. Further analysis showed no strong correlation between An and IA subtypes. The area under the curve (AUC) indicated poor performance in predicting rupture status (AUC1 = 0.55 and AUC2 = 0.56). CONCLUSIONS: This study does not support An ≥1 as a reliable parameter to predict the rupture status of IAs based on a retrospective cohort. Although the concept of An is supported by hemodynamic aneurysm theory, further research is needed before it can be applied in the clinical setting. ADVANCES IN KNOWLEDGE: This study demonstrates that the novel prediction tool, An, proposed in 2020 is not reliable and that further research of this hemodynamic model is needed before it can be incorporated into the prediction of IA rupture status.

Resveratrol; a Double-Edged Sword Antioxidant Agent for Preserving Platelet Cell Functions During Storage; Molecular Insights.

Background: In the current study we have aimed to find the effects of Resveratrol treatment on platelet concentrates (PCs) at the dose dependent manner. We have also attempted to find the molecular mechanism of the effects. Methods: The PCs, have received from Iranian blood transfusion organization (IBTO). Totally 10 PCs were studied. The PCs divided into 4 groups including untreated (control) and treated by different dose of Resveratrol; 10, 30 and 50 µM. Platelet aggregation and total reactive oxygen species (ROS) levels were evaluated at day 3 of PCs storage. In silico analysis was carried out to find out the potential involved mechanisms. Results: The aggregation against collagen has fallen dramatically in all studied groups but at the same time, aggregation was significantly higher in the control versus treated groups (p<0.05). The inhibitory effect was dose dependent. The aggregation against Ristocetin did not significantly affect by Resveratrol treatment. The mean of total ROS significantly increased in all studied groups except those PCs treated with 10 µM of Resveratrol (P=0.9). The ROS level significantly increased with increasing Resveratrol concentration even more than control group (slope=11.6, P=0.0034). Resveratrol could potently interact with more than 15 different genes which, 10 of them enrolled in cellular regulation of the oxidative stress. Conclusions: Our findings indicated that the Resveratrol affect the platelet aggregation at the dose dependent manner. Moreover, we have also found that the Resveratrol play as double-edged sword in the controlling oxidative state of the cells. Therefore, Using the optimal dose of Resveratrol is the great of importance.

Low muscle mass, malnutrition, sarcopenia, and associations with survival in adults with cancer in the UK Biobank cohort.

BACKGROUND: Low muscle mass (MM) is a common component of cancer-related malnutrition and sarcopenia, conditions that are all independently associated with an increased risk of mortality. This study aimed to (1) compare the prevalence of low MM, malnutrition, and sarcopenia and their association with survival in adults with cancer from the UK Biobank and (2) explore the influence of different allometric scaling (height [m2 ] or body mass index [BMI]) on low MM estimates. METHODS: Participants in the UK Biobank with a cancer diagnosis within 2 years of the baseline assessment were identified. Low MM was estimated by appendicular lean soft tissue (ALST) from bioelectrical impedance analysis derived fat-free mass. Malnutrition was determined using the Global Leadership in Malnutrition criteria. Sarcopenia was defined using the European Working Group on Sarcopenia in Older People criteria (version 2). All-cause mortality was determined from linked national mortality records. Cox-proportional hazards models were fitted to estimate the effect of low MM, malnutrition, and sarcopenia on all-cause mortality. RESULTS: In total, 4122 adults with cancer (59.8 ± 7.1 years; 49.2% male) were included. Prevalence of low MM (8.0% vs. 1.7%), malnutrition (11.2% vs. 6.2%), and sarcopenia (1.4% vs. 0.2%) was higher when MM was adjusted using ALST/BMI compared with ALST/height2 , respectively. Low MM using ALST/BMI identified more cases in participants with obesity (low MM 56.3% vs. 0%; malnutrition 50% vs. 18.5%; sarcopenia 50% vs. 0%). During a median 11.2 (interquartile range: 10.2, 12.0) years of follow up, 901 (21.7%) of the 4122 participants died, and of these, 744 (82.6%) deaths were cancer-specific All conditions were associated with a higher hazard of mortality using either method of MM adjustment: low MM (ALST/height2 : HR 1.9 [95% CI 1.3, 2.8], P = 0.001; ALST/BMI: HR 1.3 [95% CI 1.1, 1.7], P = 0.005; malnutrition (ALST/height2 : HR 2.5 [95% CI 1.1, 1.7], P = 0.005; ALST/BMI: HR 1.3 [95% CI 1.1, 1.7], P = 0.005; sarcopenia (ALST/height2 : HR 2.9 [95% CI 1.3, 6.5], P = 0.013; ALST/BMI: HR 1.6 [95% CI 1.0, 2.4], P = 0.037). CONCLUSIONS: In adults with cancer, malnutrition was more common than low MM or sarcopenia, although all conditions were associated with a higher mortality risk, regardless of the method of adjusting for MM. In contrast, adjustment of low MM for BMI identified more cases of low MM, malnutrition, and sarcopenia overall and in participants with obesity compared with height adjustment, suggesting it is the preferred adjustment.

Machine learning models to predict outcomes at 30-days using Global Leadership Initiative on Malnutrition combinations with and without muscle mass in people with cancer.

BACKGROUND: Equipment to assess muscle mass is not available in all health services. Yet we have limited understanding of whether applying the Global Leadership Initiative on Malnutrition (GLIM) criteria without an assessment of muscle mass affects the ability to predict adverse outcomes. This study used machine learning to determine which combinations of GLIM phenotypic and etiologic criteria are most important for the prediction of 30-day mortality and unplanned admission using combinations including and excluding low muscle mass. METHODS: In a cohort of 2801 participants from two cancer malnutrition point prevalence studies, we applied the GLIM criteria with and without muscle mass. Phenotypic criteria were assessed using ≥5% unintentional weight loss, body mass index, subjective assessment of muscle stores from the PG-SGA. Aetiologic criteria included self-reported reduced food intake and inflammation (metastatic disease). Machine learning approaches were applied to predict 30-day mortality and unplanned admission using models with and without muscle mass. RESULTS: Participants with missing data were excluded, leaving 2494 for analysis [49.6% male, mean (SD) age: 62.3 (14.2) years]. Malnutrition prevalence was 19.5% and 17.5% when muscle mass was included and excluded, respectively. However, 48 (10%) of malnourished participants were missed if muscle mass was excluded. For the nine GLIM combinations that excluded low muscle mass the most important combinations to predict mortality were (1) weight loss and inflammation and (2) weight loss and reduced food intake. Machine learning metrics were similar in models excluding or including muscle mass to predict mortality (average accuracy: 84% vs. 88%; average sensitivity: 41% vs. 38%; average specificity: 85% vs. 89%). Weight loss and reduced food intake was the most important combination to predict unplanned hospital admission. Machine learning metrics were almost identical in models excluding or including muscle mass to predict unplanned hospital admission, with small differences observed only if reported to one decimal place (average accuracy: 77% vs. 77%; average sensitivity: 29% vs. 29%; average specificity: 84% vs. 84%). CONCLUSIONS: Our results indicate predictive ability is maintained, although the ability to identify all malnourished patients is compromised, when muscle mass is excluded from the GLIM diagnosis. This has important implications for assessment in health services where equipment to assess muscle mass is not available. Our findings support the robustness of the GLIM approach and an ability to apply some flexibility in excluding certain phenotypic or aetiologic components if necessary, although some cases will be missed.