The changing landscape of geriatric care in acute myeloid leukemia: a 5-year analysis of inpatient mortality predictors, trends in mortality, and chemotherapy use.

Background: This study examined inpatient mortality factors in geriatric patients with acute myeloid leukemia (AML) using data from the 2016 to 2020 National Inpatient Sample. Methods: Identifying patients through ICD-10 codes, a total of 127,985 individuals with AML were classified into age categories as follows: 50.58% were 65 to 74 years, 37.74% were 75 to 84 years, and 11.68% were 85 years or older. Statistical analysis, conducted with STATA, involved Fisher's exact and Student's t tests for variable comparisons. Mortality predictors were identified through multivariate logistic regression. Results: Various hospital and patient-level factors, including an increase in age, race, a higher Charlson Comorbidity Index score, insurance status, and specific comorbidities such as atrial fibrillation and protein-calorie malnutrition, independently elevated the risk of inpatient mortality. Asthma, hyperlipidemia, and inpatient chemotherapy were linked to lower mortality. Although there was no statistically significant mortality rate change from 2016 to 2020, a decline in chemotherapy use in the eldest age group was noted. Conclusion: This study highlights the complexity of factors influencing inpatient mortality among geriatric patients with AML, emphasizing the need for personalized clinical approaches in this vulnerable population.

An improved empirical mode decomposition method with ensemble classifiers for analysis of multichannel EEG in BCI emotion recognition.

Emotion recognition using EEG is a difficult study because the signals' unstable behavior, which is brought on by the brain's complex neuronal activity, makes it difficult to extract the underlying patterns inside it. Therefore, to analyse the signal more efficiently, in this article, a hybrid model based on IEMD-KW-Ens (Improved Empirical Mode Decomposition-Kruskal Wallis-Ensemble classifiers) technique is used. Here IEMD based technique is proposed to interpret EEG signals by adding an improved sifting stopping criterion with median filter to get the optimal decomposed EEG signals for further processing. A mixture of time, frequency and non-linear distinct features are extracted for constructing the feature vector. Afterward, we conducted feature selection using KW test to remove the insignificant ones from the feature set. Later the classification of emotions in three-dimensional model is performed in two categories i.e. machine learning based RUSBoosted trees and deep learning based convolutional neural network (CNN) for DEAP and DREAMER datasets and the outcomes are evaluated for valence, arousal, and dominance classes. The findings demonstrate that the hybrid model can successfully classify emotions in multichannel EEG signals. The decomposition approach is also instructive for improving the model's utility in emotional computing.

Out-of-Hospital Cardiac Arrest during the COVID-19 Pandemic: A Systematic Review.

Objective: Out-of-hospital cardiac arrest (OHCA) is a prominent cause of death worldwide. As indicated by the high proportion of COVID-19 suspicion or diagnosis among patients who had OHCA, this issue could have resulted in multiple fatalities from coronavirus disease 2019 (COVID-19) occurring at home and being counted as OHCA. Methods: We used the MeSH term "heart arrest" as well as non-MeSH terms "out-of-hospital cardiac arrest, sudden cardiac death, OHCA, cardiac arrest, coronavirus pandemic, COVID-19, and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)." We conducted a literature search using these search keywords in the Science Direct and PubMed databases and Google Scholar until 25 April 2022. Results: A systematic review of observational studies revealed OHCA and mortality rates increased considerably during the COVID-19 pandemic compared to the same period of the previous year. A temporary two-fold rise in OHCA incidence was detected along with a drop in survival. During the pandemic, the community's response to OHCA changed, with fewer bystander cardiopulmonary resuscitations (CPRs), longer emergency medical service (EMS) response times, and worse OHCA survival rates. Conclusions: This study's limitations include a lack of a centralised data-gathering method and OHCA registry system. If the chain of survival is maintained and effective emergency ambulance services with a qualified emergency medical team are given, the outcome for OHCA survivors can be improved even more.

LARNet: Real-Time Detection of Facial Micro Expression Using Lossless Attention Residual Network.

Facial micro expressions are brief, spontaneous, and crucial emotions deep inside the mind, reflecting the actual thoughts for that moment. Humans can cover their emotions on a large scale, but their actual intentions and emotions can be extracted at a micro-level. Micro expressions are organic when compared with macro expressions, posing a challenge to both humans, as well as machines, to identify. In recent years, detection of facial expressions are widely used in commercial complexes, hotels, restaurants, psychology, security, offices, and education institutes. The aim and motivation of this paper are to provide an end-to-end architecture that accurately detects the actual expressions at the micro-scale features. However, the main research is to provide an analysis of the specific parts that are crucial for detecting the micro expressions from a face. Many states of the art approaches have been trained on the micro facial expressions and compared with our proposed Lossless Attention Residual Network (LARNet) approach. However, the main research on this is to provide analysis on the specific parts that are crucial for detecting the micro expressions from a face. Many CNN-based approaches extracts the features at local level which digs much deeper into the face pixels. However, the spatial and temporal information extracted from the face is encoded in LARNet for a feature fusion extraction on specific crucial locations, such as nose, cheeks, mouth, and eyes regions. LARNet outperforms the state-of-the-art methods with a slight margin by accurately detecting facial micro expressions in real-time. Lastly, the proposed LARNet becomes accurate and better by training with more annotated data.

Efficient Pneumonia Detection in Chest Xray Images Using Deep Transfer Learning.

Pneumonia causes the death of around 700,000 children every year and affects 7% of the global population. Chest X-rays are primarily used for the diagnosis of this disease. However, even for a trained radiologist, it is a challenging task to examine chest X-rays. There is a need to improve the diagnosis accuracy. In this work, an efficient model for the detection of pneumonia trained on digital chest X-ray images is proposed, which could aid the radiologists in their decision making process. A novel approach based on a weighted classifier is introduced, which combines the weighted predictions from the state-of-the-art deep learning models such as ResNet18, Xception, InceptionV3, DenseNet121, and MobileNetV3 in an optimal way. This approach is a supervised learning approach in which the network predicts the result based on the quality of the dataset used. Transfer learning is used to fine-tune the deep learning models to obtain higher training and validation accuracy. Partial data augmentation techniques are employed to increase the training dataset in a balanced way. The proposed weighted classifier is able to outperform all the individual models. Finally, the model is evaluated, not only in terms of test accuracy, but also in the AUC score. The final proposed weighted classifier model is able to achieve a test accuracy of 98.43% and an AUC score of 99.76 on the unseen data from the Guangzhou Women and Children's Medical Center pneumonia dataset. Hence, the proposed model can be used for a quick diagnosis of pneumonia and can aid the radiologists in the diagnosis process.

Internal medicine residents' point-of-care ultrasound skills and need assessment and the role of medical school training.

Introduction: Point-of-care-ultrasound (POCUS) as a useful bedside tool is growing. Few studies have examined residents' attitude towards POCUS or compared POCUS image interpretation skills between residents with and without POCUS training in medical school. Material and Methods: We distributed an anonymous survey and image interpretation test to assess residents' attitude towards POCUS, confidence, and skills in interpreting POCUS images and videos. Using independent samples t-tests, we compared mean confidence levels and test scores between residents with and without prior POCUS training. Results: Fifty-two residents responded to survey (response rate 68%) and 59 took the image interpretation test (77%). Most residents (90%) reported being interested in POCUS. Residents with prior POCUS training (n=13) were either PGY-1 (9) or PGY-2 (4). No PGY-3 resident had prior training. Most residents (83%) thought POCUS could be extremely useful in the inpatient setting compared to 29% for outpatient setting. PGY-1 residents with prior training had a higher mean confidence level than PGY-1 residents without prior training, but the difference was not statistically significant (3.26 vs 2.64; p=0.08). PGY-1 with prior training had a mean confidence level that was close to that of PGY-3 residents. PGY-1 residents with prior training scored significantly higher than PGY-1 residents without prior training in image interpretation test (10.25 vs 7; p=0.01). Residents felt most confident in interpreting inferior vena cava images (mean 3.7; max. 5), which also had the highest score in image interpretation test (correct response rate of 88%). Conclusion: Our residents seem very interested in POCUS. PGY-1 residents with prior POCUS training in medical school seem to have higher confidence in their POCUS skills than PGY-1 residents without prior training and outperformed them in image interpretation test. The study is very instructive in building our future POCUS curriculum for residents.

The arrowhead ministernotomy with rigid sternal plate fixation: a minimally invasive approach for surgery of the ascending aorta and aortic root.

Background. Ministernotomy incisions have been increasingly used in a variety of settings. We describe a novel approach to ministernotomy using arrowhead incision and rigid sternal fixation with a standard sternal plating system. Methods. A small, midline, vertical incision is made from the midportion of the manubrium to a point just above the 4th intercostal mark. The sternum is opened in the shape of an inverted T using two oblique horizontal incisions from the midline to the sternal edges. At the time of chest closure, the three bony segments are aligned and approximated, and titanium plates (Sternalock, Jacksonville, Florida) are used to fix the body of the sternum back together. Results. This case series includes 11 patients who underwent arrowhead ministernotomy with rigid sternal plate fixation for aortic surgery. The procedures performed were axillary cannulation (n = 2), aortic root replacement (n = 3), valve sparing root replacement (n = 3), and replacement of the ascending aorta (n = 11) and/or hemiarch (n = 2). Thirty-day mortality was 0%; there were no conversions, strokes, or sternal wound infections. Conclusions. Arrowhead ministernotomy with rigid sternal plate fixation is an adequate minimally invasive approach for surgery of the ascending aorta and aortic root.

Percutaneous transcatheter closure of the aortic valve to treat cardiogenic shock in a left ventricular assist device patient with severe aortic insufficiency.

In this case report, we present a patient status post left ventricular assist device implantation complicated by de novo aortic insufficiency. At 8 months postimplant, the patient underwent a reoperative aortic valve repair, without complete closure of the valve. Three months after reoperation, the patient developed cardiogenic shock secondary to recurrent, severe aortic insufficiency. Ultimately, the patient underwent percutaneous, transcatheter closure of the aortic valve with an Amplatzer Cribiform device (AGA Medical Corp, Plymouth, MN). Two months post procedure, the patient remains stable with improved symptoms and functional status, and without evidence of further aortic insufficiency or device migration.

Langerhans cell histiocytosis of long bones: MR imaging and complete follow up study.

Langerhans cell histiocytosis (LCH) is a relatively rare disease affecting the reticuloendothelial system in the pediatric age group. It can affect bones, lung, liver, spleen, lymph nodes and skin. MR imaging is particularly informative in diagnosis and management of bone LCH. In this report, we present the initial and 23 months post-treatment MR images of a femoral LCH lesion in a 12-year-old child to describe the role of MRI in bone LCH.

Rare magnetic resonance imaging findings in medium-chain acyl-coenzyme A dehydrogenase deficiency.

A 3-year-old boy receiving valproate for 1.5 months presented with sudden-onset unprovoked seizures and unconsciousness. Hypoketotic hypoglycemia, hyperammonemia, and deranged liver function were detected. Elevated medium-chain urinary acylglycines and plasma acylcarnitine were detected. His serum valproate level was elevated. Valproate toxicity had been precipitated in presence of medium-chain acyl-CoA dehydrogenase deficiency. Cranial magnetic resonance imaging brain indicated unilateral basal ganglia ischemia instead of the bilateral changes expected in metabolic disease.

Trigeminal Neuralgia in an HIV Patient.

Trigeminal neuralgia is a painful condition affecting face. Its commonest cause is the tortuous vessels in prepontine cistern. There are other causes also, like brainstem lesions and mass lesions, as well as inflammatory causes. We present a case of an HIV patient with marked involvement of trigeminal nerves, which is a unique finding in immunocompromised patients.

Biliary ascariasis on magnetic resonance cholangiopancreatography.

A 17-year-old girl presented with features of biliary obstruction. Magnetic resonance cholangi-pancreatography revealed typical linear signals in common bile duct, which appears like Ascaris lumbricoides. The diagnosis was confirmed by endoscopic removal of the worm.