Binuclear palladacycles with ionisable and non-ionisable tethers as anticancer agents

The search for novel anticancer agents to replace the current platinum-based treatments remains an ongoing process. Palladacycles have shown excellent promise as demonstrated by our previous work which yielded BTC2, a binuclear palladadycle with a non-ionisable polyethylene glycol (PEG) tether. Here, we explore the importance of the PEG-tether length on the anticancer activity of the binuclear palladacycles by comparing three analogous binuclear palladacycles, BTC2, BTC5 and BTC6, in the oestrogen receptor positive MCF7 and triple-negative MDA-MB-231 breast cancer cell lines. In addition, these are compared to another analogue with an ionisable morpholine tether, BTC7. Potent anticancer activity was revealed through cell viability studies (MTT assays) revealed that while BTC6 showed similar potent anticancer activity as BTC2, it was less toxic towards non-cancerous cell lines. Interestingly, BTC7 and BTCF were less potent than the PEGylated palladacycles but showed significantly improved selectivity towards the triple-negative breast cancer cells. Cell death analysis showed that BTC7 and BTCF significantly induced apoptosis in both the cancer cell lines while the PEGylated complexes induced both apoptosis and secondary necrosis. Furthermore, experimental and computational DNA binding studies indicated partial intercalation and groove binding as the modes of action for the PEGylated palladacycles. Similarly, experimental and computational BSA binding studies indicated and specific binding sites in BSA dependent on the nature of the tethers on the complexes.

Editorial for the Special Issue "Machine Learning in Computer Vision and Image Sensing: Theory and Applications"

Machine learning (ML) models have experienced remarkable growth in their application for multimodal data analysis over the past decade [...].

Quantum Machine-Based Decision Support System for the Detection of Schizophrenia from EEG Records

Schizophrenia is a serious chronic mental disorder that significantly affects daily life. Electroencephalography (EEG), a method used to measure mental activities in the brain, is among the techniques employed in the diagnosis of schizophrenia. The symptoms of the disease typically begin in childhood and become more pronounced as one grows older. However, it can be managed with specific treatments. Computer-aided methods can be used to achieve an early diagnosis of this illness. In this study, various machine learning algorithms and the emerging technology of quantum-based machine learning algorithm were used to detect schizophrenia using EEG signals. The principal component analysis (PCA) method was applied to process the obtained data in quantum systems. The data, which were reduced in dimensionality, were transformed into qubit form using various feature maps and provided as input to the Quantum Support Vector Machine (QSVM) algorithm. Thus, the QSVM algorithm was applied using different qubit numbers and different circuits in addition to classical machine learning algorithms. All analyses were conducted in the simulator environment of the IBM Quantum Platform. In the classification of this EEG dataset, it is evident that the QSVM algorithm demonstrated superior performance with a 100% success rate when using Pauli X and Pauli Z feature maps. This study serves as proof that quantum machine learning algorithms can be effectively utilized in the field of healthcare.

Assessing knowledge gaps and empowering Extension workers in Illinois with information on ticks and tickborne diseases through KAP surveys

Tickborne diseases (TBDs) are increasingly prevalent in Illinois and the Upper Midwest region. People who work in occupations that require time outdoors in agricultural or natural settings, such as some Extension workers, are at risk of tick bites and TBDs. Additionally, Extension workers are often a primary source of information about ticks and TBDs in rural communities. However, there is limited information on the level of awareness about ticks and TBDs in the Extension community. The goals of this study were to sequentially i) determine the baseline awareness of Extension workers in Illinois about ticks and TBDs using a knowledge, attitudes, and practices (KAP) survey tool, ii) provide comprehensive training on ticks and TBDs to this demographic, and iii) measure the uptake of knowledge after the training intervention through a post-training survey. The study period was from June 2022 until May 2023. We received 233 pre-training and 93 paired post-training survey responses. Most survey respondents were Extension volunteers, identified as women, and were over 50 years old. Knowledge about ticks and TBDs varied. We identified several gaps in their current tick awareness, most importantly, in tick prevention measures, tick identification, and TBDs in general. TBD knowledge, attitude, and practice scores all significantly improved after training (p < 0.001), with a mean difference of 10.47, 1.49, and 2.64 points, respectively. Additionally, both Extension professionals (79.2 %) and Extension volunteers (66.7 %) were more likely to feel confident in engaging with their stakeholders on ticks and TBDs after participating in training. Poisson models revealed that higher attitude and practice scores and greater self-reported knowledge were the factors most significantly associated with higher TBD knowledge. We found that greater concern for ticks and TBD (attitudes) and adherence to science-based prevention and management methods (practices) were also associated with higher knowledge scores. To our knowledge, this is the first study in Illinois to capture Extension workers' awareness of ticks and TBDs. The results highlight Extension workers' interest in filling knowledge gaps through learning, and the importance of training Extension workers to disseminate reliable and updated information on ticks and TBDs to their constituents, a critical step in preventing TBDs.

NNN manganese complex-catalyzed α-alkylation of methyl ketones using alcohols: an experimental and computational study

We present here a phosphine-free, quinoline-based pincer Mn catalyst for α-alkylation of methyl ketones using primary alcohols as alkyl surrogates. The C-C bond formation reaction proceeds via a hydrogen auto-transfer methodology. The sole by-product formed is water, rendering the protocol atom efficient. Electronic structure theory studies corroborated the proposed mechanism.

Deep learning techniques in PET/CT imaging: A comprehensive review from sinogram to image space

Positron emission tomography/computed tomography (PET/CT) is increasingly used in oncology, neurology, cardiology, and emerging medical fields. The success stems from the cohesive information that hybrid PET/CT imaging offers, surpassing the capabilities of individual modalities when used in isolation for different malignancies. However, manual image interpretation requires extensive disease-specific knowledge, and it is a time-consuming aspect of physicians' daily routines. Deep learning algorithms, akin to a practitioner during training, extract knowledge from images to facilitate the diagnosis process by detecting symptoms and enhancing images. This acquired knowledge aids in supporting the diagnosis process through symptom detection and image enhancement. The available review papers on PET/CT imaging have a drawback as they either included additional modalities or examined various types of AI applications. However, there has been a lack of comprehensive investigation specifically focused on the highly specific use of AI, and deep learning, on PET/CT images. This review aims to fill that gap by investigating the characteristics of approaches used in papers that employed deep learning for PET/CT imaging. Within the review, we identified 99 studies published between 2017 and 2022 that applied deep learning to PET/CT images. We also identified the best pre-processing algorithms and the most effective deep learning models reported for PET/CT while highlighting the current limitations. Our review underscores the potential of deep learning (DL) in PET/CT imaging, with successful applications in lesion detection, tumor segmentation, and disease classification in both sinogram and image spaces. Common and specific pre-processing techniques are also discussed. DL algorithms excel at extracting meaningful features, and enhancing accuracy and efficiency in diagnosis. However, limitations arise from the scarcity of annotated datasets and challenges in explainability and uncertainty. Recent DL models, such as attention-based models, generative models, multi-modal models, graph convolutional networks, and transformers, are promising for improving PET/CT studies. Additionally, radiomics has garnered attention for tumor classification and predicting patient outcomes. Ongoing research is crucial to explore new applications and improve the accuracy of DL models in this rapidly evolving field.

Advances in Group VI Metal-Catalyzed Homogeneous Hydrogenation and Dehydrogenation Reactions

Transition metal-catalyzed homogeneous hydrogenation and dehydrogenation reactions for attaining plethora of organic scaffolds have evolved as a key domain of research in academia and industry. These protocols are atom-economic, greener, in line with the goal of sustainability, eventually pave the way for numerous novel environmentally benign methodologies. Appealing progress has been achieved in the realm of homogeneous catalysis utilizing noble metals. Owing to their high cost, less abundance along with toxicity issues led the scientific community to search for sustainable alternatives. In this context, earth- abundant base metals have gained substantial attention culminating enormous progress in recent years, predominantly with pincer-type complexes of nickel, cobalt, iron, and manganese. In this regard, group VI chromium, molybdenum and tungsten complexes have been overlooked and remain underdeveloped despite their earth-abundance and bio-compatibility. This review delineates a comprehensive overview in the arena of homogeneously catalysed (de)hydrogenation reactions using group VI base metals chromium, molybdenum, and tungsten till date. Various reactions have been described; hydrogenation, transfer hydrogenation, dehydrogenation, acceptorless dehydrogenative coupling, hydrogen auto transfer, along with their scope and brief mechanistic insights.

PFP-HOG: Pyramid and Fixed-Size Patch-Based HOG Technique for Automated Brain Abnormality Classification with MRI

Detecting neurological abnormalities such as brain tumors and Alzheimer's disease (AD) using magnetic resonance imaging (MRI) images is an important research topic in the literature. Numerous machine learning models have been used to detect brain abnormalities accurately. This study addresses the problem of detecting neurological abnormalities in MRI. The motivation behind this problem lies in the need for accurate and efficient methods to assist neurologists in the diagnosis of these disorders. In addition, many deep learning techniques have been applied to MRI to develop accurate brain abnormality detection models, but these networks have high time complexity. Hence, a novel hand-modeled feature-based learning network is presented to reduce the time complexity and obtain high classification performance. The model proposed in this work uses a new feature generation architecture named pyramid and fixed-size patch (PFP). The main aim of the proposed PFP structure is to attain high classification performance using essential feature extractors with both multilevel and local features. Furthermore, the PFP feature extractor generates low- and high-level features using a handcrafted extractor. To obtain the high discriminative feature extraction ability of the PFP, we have used histogram-oriented gradients (HOG); hence, it is named PFP-HOG. Furthermore, the iterative Chi2 (IChi2) is utilized to choose the clinically significant features. Finally, the k-nearest neighbors (kNN) with tenfold cross-validation is used for automated classification. Four MRI neurological databases (AD dataset, brain tumor dataset 1, brain tumor dataset 2, and merged dataset) have been utilized to develop our model. PFP-HOG and IChi2-based models attained 100%, 94.98%, 98.19%, and 97.80% using the AD dataset, brain tumor dataset1, brain tumor dataset 2, and merged brain MRI dataset, respectively. These findings not only provide an accurate and robust classification of various neurological disorders using MRI but also hold the potential to assist neurologists in validating manual MRI brain abnormality screening.

ARTIFICIAL INTELLIGENCE: CREATING NEW PARADIGMS IN THE MANAGEMENT OF NON-COMMUNICABLE DISEASES

The role of Artificial Intelligence (AI) in medical science is growing immensely. Since AI contains features that can address both preventive and therapeutic aspects of non-communicable diseases (NCDs), it can potentially lessen the massive burden of morbidity and mortality associated with NCDs. AI can help in various ways in NCDs including predicting disease occurrence, monitoring, ensuring treatment and follow-up of patients. Low- and middle-income countries can harness the benefit of AI for the management of chronic diseases and effectively address challenges like manpower shortage, accessibility to health care, etc. However, AI needs to be used responsibly and rationally in NCDs for its maximum benefit.

LSGP-USFNet: Automated Attention Deficit Hyperactivity Disorder Detection Using Locations of Sophie Germain's Primes on Ulam's Spiral-Based Features with Electroencephalogram Signals

Anxiety, learning disabilities, and depression are the symptoms of attention deficit hyperactivity disorder (ADHD), an isogenous pattern of hyperactivity, impulsivity, and inattention. For the early diagnosis of ADHD, electroencephalogram (EEG) signals are widely used. However, the direct analysis of an EEG is highly challenging as it is time-consuming, nonlinear, and nonstationary in nature. Thus, in this paper, a novel approach (LSGP-USFNet) is developed based on the patterns obtained from Ulam's spiral and Sophia Germain's prime numbers. The EEG signals are initially filtered to remove the noise and segmented with a non-overlapping sliding window of a length of 512 samples. Then, a time-frequency analysis approach, namely continuous wavelet transform, is applied to each channel of the segmented EEG signal to interpret it in the time and frequency domain. The obtained time-frequency representation is saved as a time-frequency image, and a non-overlapping n × n sliding window is applied to this image for patch extraction. An n × n Ulam's spiral is localized on each patch, and the gray levels are acquired from this patch as features where Sophie Germain's primes are located in Ulam's spiral. All gray tones from all patches are concatenated to construct the features for ADHD and normal classes. A gray tone selection algorithm, namely ReliefF, is employed on the representative features to acquire the final most important gray tones. The support vector machine classifier is used with a 10-fold cross-validation criteria. Our proposed approach, LSGP-USFNet, was developed using a publicly available dataset and obtained an accuracy of 97.46% in detecting ADHD automatically. Our generated model is ready to be validated using a bigger database and it can also be used to detect other children's neurological disorders.

Development of Debiasing Technique for Lung Nodule Chest X-ray Datasets to Generalize Deep Learning Models

Screening programs for early lung cancer diagnosis are uncommon, primarily due to the challenge of reaching at-risk patients located in rural areas far from medical facilities. To overcome this obstacle, a comprehensive approach is needed that combines mobility, low cost, speed, accuracy, and privacy. One potential solution lies in combining the chest X-ray imaging mode with federated deep learning, ensuring that no single data source can bias the model adversely. This study presents a pre-processing pipeline designed to debias chest X-ray images, thereby enhancing internal classification and external generalization. The pipeline employs a pruning mechanism to train a deep learning model for nodule detection, utilizing the most informative images from a publicly available lung nodule X-ray dataset. Histogram equalization is used to remove systematic differences in image brightness and contrast. Model training is then performed using combinations of lung field segmentation, close cropping, and rib/bone suppression. The resulting deep learning models, generated through this pre-processing pipeline, demonstrate successful generalization on an independent lung nodule dataset. By eliminating confounding variables in chest X-ray images and suppressing signal noise from the bone structures, the proposed deep learning lung nodule detection algorithm achieves an external generalization accuracy of 89%. This approach paves the way for the development of a low-cost and accessible deep learning-based clinical system for lung cancer screening.

Differential expression of genes during recovery of Nicotiana tabacum from tomato leaf curl Gujarat virus infection

MAIN CONCLUSION

Nicotiana tabacum exhibits recovery response towards tomato leaf curl Gujarat virus. Transcriptome analysis revealed the differential expression of defense-related genes. Genes encoding for cysteine protease inhibitor, hormonal- and stress-related to DNA repair mechanism are found to be involved in the recovery process. Elucidating the role of host factors in response to viral infection is crucial in understanding the plant host-virus interaction. Begomovirus, a genus in the family Geminiviridae, is reported throughout the globe and is known to cause serious crop diseases. Tomato leaf curl Gujarat virus (ToLCGV) infection in Nicotiana tabacum resulted in initial symptom expression followed by a quick recovery in the systemic leaves. Transcriptome analysis using next-generation sequencing (NGS) revealed a large number of differentially expressed genes both in symptomatic as well as recovered leaves when compared to mock-inoculated plants. The virus infected N. tabacum results in alteration of various metabolic pathways, phytohormone signaling pathway, defense related protein, protease inhibitor, and DNA repair pathway. RT-qPCR results indicated that Germin-like protein subfamily T member 2 (NtGLPST), Cysteine protease inhibitor 1-like (NtCPI), Thaumatin-like protein (NtTLP), Kirola-like (NtKL), and Ethylene-responsive transcription factor ERF109-like (NtERTFL) were down-regulated in symptomatic leaves when compared to recovered leaves of ToLCGV-infected plants. In contrast, the Auxin-responsive protein SAUR71-like (NtARPSL) was found to be differentially down-regulated in recovered leaves when compared to symptomatic leaves and the mock-inoculated plants. Lastly, Histone 2X protein like (NtHH2L) gene was found to be down-regulated, whereas Uncharacterized (NtUNCD) was up-regulated in both symptomatic as well as recovered leaves compared to the mock-inoculated plants. Taken together, the present study suggests potential roles of the differentially expressed genes that might govern tobacco's susceptibility and/or recovery response towards ToLCGV infection.

Fabrication of thin Molybdenum backed target using rolling method

A successful attempt was made to fabricate a thin foil of natural Mo target on a thick Au backing with Indium in between to improve adhesion between the foils. Rolling at elevated temperature was considered to fabricate Mo foil while gold foil was fabricated employing conventional rolling technique. The heating of Mo foil under natural environment lead to the oxidation or carbonization on foil surface which was confirmed through Energy Dispersive X-ray Spectroscopy (EDS) measurements. Indium of thickness ∼86μg/cm² was evaporated on Mo foil to improve adhesion between Mo and Au foils. The characterization of fabricated thin Mo foil was done using the Energy Dispersive X-ray Spectroscopy (EDS) and the Scanning Electron microscope (SEM) techniques. Thickness measurement of the target (Mo-Au) was done using Energy Dispersive X-ray Fluorescence (EDXRF) technique, in the measurements the thickness of the Mo foil and of gold backing are found out to be 1.3 mg/cm² and 9 mg/cm² respectively.

CCPNet136: automated detection of schizophrenia using carbon chain pattern and iterative TQWT technique with EEG signals

Objective.Schizophrenia (SZ) is a severe, chronic psychiatric-cognitive disorder. The primary objective of this work is to present a handcrafted model using state-of-the-art technique to detect SZ accurately with EEG signals.Approach.In our proposed work, the features are generated using a histogram-based generator and an iterative decomposition model. The graph-based molecular structure of the carbon chain is employed to generate low-level features. Hence, the developed feature generation model is called the carbon chain pattern (CCP). An iterative tunable q-factor wavelet transform (ITQWT) technique is implemented in the feature extraction phase to generate various sub-bands of the EEG signal. The CCP was applied to the generated sub-bands to obtain several feature vectors. The clinically significant features were selected using iterative neighborhood component analysis (INCA). The selected features were then classified using the k nearest neighbor (kNN) with a 10-fold cross-validation strategy. Finally, the iterative weighted majority method was used to obtain the results in multiple channels.Main results.The presented CCP-ITQWT and INCA-based automated model achieved an accuracy of 95.84% and 99.20% using a single channel and majority voting method, respectively with kNN classifier.Significance.Our results highlight the success of the proposed CCP-ITQWT and INCA-based model in the automated detection of SZ using EEG signals.

Wilson's disease - a tricky diagnosis on the acute take

Wilson's disease is a rare genetic disorder that affects copper metabolism in the body, leading to excess copper accumulation in various organs, including the liver and brain. It often presents to both primary and secondary care, with a combination of liver disease and neurological or psychiatric symptoms, but the presentation can be highly variable. Early recognition and treatment of Wilson's disease is important to prevent critical hepatic and neurological complications. In this case report, we describe the presentation of an 18-year-old male university student with a combination of dysphagia, tremors, and slurred speech, which progressed over several months. Through a series of investigations, the patient was diagnosed with Wilson's disease and received appropriate treatment. This report highlights the importance of considering Wilson's disease in patients with a wide range of symptoms and the need for a pragmatic approach to diagnosis, including routine and additional testing as necessary.

Intense chorus waves are the cause of flux-limiting in the heart of the outer radiation belt

Chorus waves play a key role in outer Van Allen electron belt dynamics through cyclotron resonance. Here, we use Van Allen Probes data to reveal a new and distinct population of intense chorus waves excited in the heart of the radiation belt during the main phase of geomagnetic storms. The power of the waves is typically ~ 2-3 orders of magnitude greater than pre-storm levels, and are generated when fluxes of ~ 10-100 keV electrons approach or exceed the Kennel-Petschek limit. These intense chorus waves rapidly scatter electrons into the loss cone, capping the electron flux to a value close to the limit predicted by Kennel and Petschek over 50 years ago. Our results are crucial for understanding the limits to radiation belt fluxes, with accurate models likely requiring the inclusion of this chorus wave-driven flux-limiting process, that is independent of the acceleration mechanism or source responsible for enhancing the flux.

Migrated and Impacted Foreign Body of Retropharyngeal Space: A Case Report

Foreign body upper aerodigestive tract is a common presentation but foreign body migrating into retropharyngeal space is not only uncommon, but its management also differs and is challenging too. Here we present a case of a foreign body migrating into the retropharyngeal space which was removed intraorally. A 28- year old male patient presented with complaints of pain while swallowing following consumption of sausage and pork two days earlier to the onset of symptoms. X-Ray Neck AP and lateral view were done which revealed a thin metallic foreign body at the level of the T4 vertebra. Upper gastrointestinal endoscopy and rigid esophagoscopy were done in which a foreign body was not visualized in the esophageal lumen. NCCT neck was done which gave precise location and was removed intraorally with the patient in Rose position. A repeat x-ray was done on the 5th day which revealed no foreign body, the patient was discharged on the 7th day. Although the upper aerodigestive tract foreign body is common, foreign body migrating to the posterior pharyngeal wall or into the retropharyngeal space is not common and it is difficult to remove a migrated foreign body many cases requiring open procedures, thoracoscopy, thoracotomy.

A review of automated sleep disorder detection

Automated sleep disorder detection is challenging because physiological symptoms can vary widely. These variations make it difficult to create effective sleep disorder detection models which support hu-man experts during diagnosis and treatment monitoring. From 2010 to 2021, authors of 95 scientific papers have taken up the challenge of automating sleep disorder detection. This paper provides an expert review of this work. We investigated whether digital technology and Artificial Intelligence (AI) can provide automated diagnosis support for sleep disorders. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines during the content discovery phase. We compared the performance of proposed sleep disorder detection methods, involving differ-ent datasets or signals. During the review, we found eight sleep disorders, of which sleep apnea and insomnia were the most studied. These disorders can be diagnosed using several kinds of biomedical signals, such as Electrocardiogram (ECG), Polysomnography (PSG), Electroencephalogram (EEG), Electromyogram (EMG), and snore sound. Subsequently, we established areas of commonality and distinctiveness. Common to all reviewed papers was that AI models were trained and tested with labelled physiological signals. Looking deeper, we discovered that 24 distinct algorithms were used for the detection task. The nature of these algorithms evolved, before 2017 only traditional Machine Learning (ML) was used. From 2018 onward, both ML and Deep Learning (DL) methods were used for sleep disorder detection. The strong emergence of DL algorithms has considerable implications for future detection systems because these algorithms demand significantly more data for training and testing when compared with ML. Based on our review results, we suggest that both type and amount of labelled data is crucial for the design of future sleep disorder detection systems because this will steer the choice of AI algorithm which establishes the desired decision support. As a guiding principle, more labelled data will help to represent the variations in symptoms. DL algorithms can extract information from these larger data quantities more effectively, therefore; we predict that the role of these algorithms will continue to expand.

Interventions Risk Evaluation and Management in Aseptic Manufacturing

Interventions performed by personnel during an aseptic process can be a key source of microbiological contamination of sterile biopharmaceutical products, irrespective of the type of manufacturing system used. Understanding the relative risk of this source of contamination provides valuable information to help make decisions for the design, qualification, validation, operation, monitoring, and evaluation of the aseptic process. These decisions can be used to improve the aseptic process and provide assurance of the sterility of the products. To achieve these goals, an assessment of the contamination risk is needed. This risk assessment should be objective, accurate, and useful. This article presents an Intervention Risk Evaluation Model (IREM) philosophy and an objective, accurate, and useful method for intervention risk determination. The IREM uses a key word approach to identify, obtain, measure, and evaluate intervention risk factors. This article presents a general discussion of the method with the help of a case study to illustrate the development of the model, whereas subsequent parts would focus on application of this model with practical examples. This not only attempts to create objectivity of the entire process, but it develops awareness of the associated risks among shop floor operators, which can lead to a reduction of the overall risk level of the process and an improvement in the sterility assurance level.

Incidence of in-hospital all-cause mortality, resource utilization and complications in patients with adult congenital heart disease undergoing TAVR-a national inpatient sample study

Background

The prevalence of congenital heart disease (CHD) in adults in the United States is approximately 1.4 million. (1) With the advancement in diagnostic modalities and advanced treatments, including minimally invasive techniques, the life expectancy of individuals with CHD has greatly improved. (2) As these patients enter the 8th decade of their lives, the risk of calcification and aortic stenosis increases like the population without CHD. Current evidence supports transcatheter aortic valve replacement (TAVR) over surgical aortic valve replacement in individuals with moderate to high surgical risk. (3) Adults with acyanotic CHD (ACHD) with a higher risk for surgical complications are candidates for consideration of TAVR. There are sparse data about the cardiovascular outcome in these patients.

Purpose

With this National inpatient sample (NIS) study, the authors have shown the incidence of in-hospital all-cause mortality, resource utilization, and complications in adult patients with ACHD undergoing TAVR.

Methods

NIS 2016–2018 were utilized to conduct the study. Analyses were performed using STATA, version 16.0. Using appropriate ICD-10-PCS codes, authors identified adult patients with ACHD undergoing TAVR. The primary outcome of the study is to identify the impact of ACHD on all-cause in-hospital mortality and complications. Secondary outcomes of interest were resource utilization.

Results

134,170 patients were identified who had TAVR done between 2016–2018. Patients aged ≤18 years were excluded (N=25). Out of 134,170 patients that underwent TAVR, 1,170 (0.87%) were noted to have ACHD. Using the greedy algorithm, 1,115 matched pairs were generated. The ACHD group had a higher burden of co-morbidities including atrial fibrillation (46.2% vs. 38.8%, p=0.016), pulmonary hypertension (27.4% vs. 17.5%, p&lt;0.001), metabolic syndrome (1.3% vs. 0.3%, p=0.005), peripheral vascular disease (29.5% vs. 24.1%, p=0.049), alcohol use disorder (3.0% vs. 1.3%, p=0.018), coagulation disorder (22.7% vs. 12.8%, p&lt;0.001), drug abuse (1.3% vs. 0.4%, p=0.043), liver disease (7.3% vs. 3.1%, p&lt;0.001) and electrolyte disturbances (20.5% vs. 14.9%, p=0.017). We also noted a possible trend towards higher complication odds (cardiac complications such as the need for pericardial drain or cardiac implantable electronic device and cardiac arrest) in patients with ACHD undergoing TAVR without statistical significance based on multivariate analysis. On propensity matching, no difference was found in the incidence of overall cardiac complications between patients with ACHD and patients without ACHD, except STEMI (OR 4.16, 95% CI, 1.08–16.00, p=0.038).

Conclusion(s)

The study points towards the possible safety of pursuing TAVR in ACHD patients provided adequate technical support and operator competency.

Funding Acknowledgement

Type of funding sources: None.

Same-day discharge following transcatheter aortic valve replacement: a propensity-matched analysis from national readmission database

Background

The length of hospital stay following transcatheter aortic valve replacement (TAVR) has decreased in recent years, and next-day discharge strategy is being increasingly adopted in some centers. Whether it is safe to further expedite discharge post-TAVR in selected patients by allowing discharge on the same day as the procedure remains unknown. In addition to potentially decreasing hospitalization costs, it could also limit the inpatient footprint and strain on healthcare resources.

Purpose

The purpose of our study was to compare the 30-day readmission rate in patients receiving TAVR who were discharged the same day (same-day discharge or SDD group) with those who were discharged on a different day (different-day discharge or DDD group). Additionally, we aimed to identify risk factors for readmission after TAVR.

Methods

We used the United States Nationwide Readmission Database to identify all adults who underwent elective TAVR in the years 2015–2019. The primary outcome of this study was all-cause 30-day readmission rate. The secondary outcomes were total hospital costs for the index admission, and risk factors for 30-day readmission. Propensity score matching was conducted to compare the SDD and DDD groups. Independent risk factors of 30-day readmission were identified using multivariate Cox proportional hazards regression analysis of the unmatched cohort.

Results

Of the 196,618 patients who received TAVR (mean age 79.5±8.4 years, 45.0% females), 245 (0.12%) patients were discharged on the same day they received TAVR (SDD group), and the remaining 196,373 were discharged on a different day (DDD group). In the DDD group, the median length of hospital stay was 2 days (interquartile range 1–4 days). A 1:3 propensity score analysis generated a matched cohort including 245 and 889 patients in the SDD and DDD groups, respectively. The 30-day readmission rate was similar between the SDD and DDD groups (11.0% versus 10.8%, hazard ratio [HR] 1.01, 95% confidence interval [CI] 0.59–1.71, p=0.989). Hospitalization costs were significantly lower in the SDD group than the DDD group ($37,811±18,029 versus $49,130±27,007, p&lt;0.001) (see Picture 1). Age, female gender, history of diabetes, chronic kidney disease, chronic pulmonary disease, oxygen use, prior stroke, peripheral vascular disease, anemia, liver disease, and cancer were found to be independent risk factors for 30-day readmission after TAVR (see Picture 2).

Conclusion

In this large nationwide database analysis, patients receiving uncomplicated TAVR who were discharged on the same day as the procedure had a similar all-cause 30-day readmission rate and significantly lower hospital costs compared to those discharged on a different day. These results indicate that same-day discharge after TAVR may be a safe and feasible option in carefully selected patients.

Funding Acknowledgement

Type of funding sources: None.

Remote working in India during the COVID-19 crisis

India has been reeling from the effects of the COVID-19 pandemic and has enforced a nationwide lockdown to ensure the spread is contained, and the situation is under control. This has paved the way for corporate across the length and breadth of the country to embrace remote working as the only feasible option to continue their business. Earlier, it used to be the IT sector employees and a handful of employees from other industries who had the privilege of working remotely. Now, with remote working becoming the norm, we aim to capture how it has affected people’s working style and if interactions with family at home during work hours affect their work. We also aim to find out whether performance takes a hit due to the absence of co-workers.A questionnaire was filled up by employees working from home that sought out details about their working style, daily routine, interactions with other people (family member or colleague), and their thoughts on the remote working lifestyle. The major factors were measured on a five-point Likert scale.People work for a longer time when working from home, due to distractions caused by interactions with family members or other people and also the absence of colleagues causes problems to be solved at a slower pace. Women work longer hours when compared to men, additionally due to household chores. The absence of colleagues coupled with distractions at home cause people to prefer working from the office rather than from home. This study would help identify what sort of impact remote working has on an employee’s performance and how it can affect the working style.The paper analyses the effect of remote working and the presence of family at home on an employee’s performance.

Use of geographical information systems (GIS) in assessing ecological profile, fish community structure and production of a large reservoir of Himachal Pradesh

The present study demonstrates the spatial analysis and mapping of fish and different measures of environmental parameters and fish diversity of Pong reservoir, Himachal Pradesh, using Kriging spatial interpolation methods for geographical information system mapping. Seasonal data on environmental parameters, potential fish habitat and fish diversity was collected from lentic (dam), lentic (reservoir), transitional and lotic zone of the reservoir.. Important environmental parameters like water temperature, dissolved oxygen, electrical conductivity, water depth and transparency showed variations across the different zones of the reservoir. The sediment of the reservoir was sandy clay loam in nature as per texture analysis. Fish species richness, Shannon index and evenness index showed a similarity of the lotic and lentic (reservoir) zones of the reservoir. Six potential fish breeding grounds were identified in the reservoir indicating high conservation significance. The analysis of data showed a declining trend in fish production from 456.9 tonnes during the decade 1976-1987 to 347.91 tonnes during 2009-2020. The factors like anthropogenic climate change, predation of a stocked fish juvenile by water birds, undersized fish stocking and unscientific management are the probable reasons for the decreasing fish production. The spatial variation pattern of the water spread area, environmental parameters, fish catch and potential fish breeding grounds depicted in the GIS platform can be used as an important information base by the policy makers for fisheries management. The stocking of large size fish as a stocking material and adequate protection of the potential fish breeding grounds are the key advisories for the sustainable enhancement of fisheries as well as conservation.

Driving Influences of the Doppler Flash Observed by SuperDARN HF Radars in Response to Solar Flares

Sudden enhancement in high-frequency absorption is a well-known impact of solar flare-driven Short-Wave Fadeout (SWF). Less understood, is a perturbation of the radio wave frequency as it traverses the ionosphere in the early stages of SWF, also known as the Doppler flash. Investigations have suggested two possible sources that might contribute to it's manifestation: first, enhancements of plasma density in the D-and lower E-regions; second, the lowering of the F-region reflection point. Our recent work investigated a solar flare event using first principles modeling and Super Dual Auroral Radar Network (SuperDARN) HF radar observations and found that change in the F-region refractive index is the primary driver of the Doppler flash. This study analyzes multiple solar flare events observed across different SuperDARN HF radars to determine how flare characteristics, properties of the traveling radio wave, and geophysical conditions impact the Doppler flash. In addition, we use incoherent scatter radar data and first-principles modeling to investigate physical mechanisms that drive the lowering of the F-region reflection points. We found, (a) on average, the change in E- and F-region refractive index is the primary driver of the Doppler flash, (b) solar zenith angle, ray's elevation angle, operating frequency, and location of the solar flare on the solar disk can alter the ionospheric regions of maximum contribution to the Doppler flash, (c) increased ionospheric Hall and Pedersen conductance causes a reduction of the daytime eastward electric field, and consequently reduces the vertical ion-drift in the lower and middle latitude ionosphere, which results in lowering of the F-region ray reflection point.

Effect of telephone-monitored home-based cardiac rehabilitation exercise on functional capacity and quality of life in heart failure patients in a lower-middle-income country

Funding Acknowledgements

Type of funding sources: None.

Introduction

Chronic heart failure (CHF) prevails as one of the major cardiovascular diseases in lower-middle-income countries (LMICs) like Bangladesh. Home-Based Cardiac Rehabilitation (HBCR) is a cost-effective method of secondary prevention of chronic heart failure which, if provided, might not only improve the health status of the patients but might also reduce the financial and hospitalization burden on the health care system of these countries. The study aims to assess the scope and benefits of HBCR in such low resource settings.

Purpose

The study evaluates the effect of telephone-monitored HBCR exercise programme in improving the functional capacity and quality of life (QoL) in patients of CHF with reduced ejection fraction due to ischemic heart disease (IHD).

Method

This self-controlled interventional study was conducted from August 2019 to July 2020 at a heart failure clinic, a tertiary healthcare centre in Bangladesh. A total of 115 patients of CHF with ejection fraction &lt;40% and in NYHA class II and III were included in the study according to selection criteria. The functional capacity of the patients was evaluated by NYHA classification and 6-minute walk test. The quality of life of the patients was evaluated by Minnesota Living with Heart Failure Questionnaire (MLHFQ). All patients were advised to perform HBCR exercise as per recommended guideline and were telephone-monitored 2-weekly. After the 3-months study period, the participants were divided into compliant and partial compliant groups based on their adherence to the guideline. Repeat evaluation of patients' condition was carried out. Results were then compared within the groups and data was analyzed through appropriate statistical methods.

Results

Significant improvement of NYHA class (p&lt;0.05), and 6-minute walk test distance (6MWTD) (1102.01±215.90 feet vs 1243.30±217.86 feet; p&lt;0.001) were noticed after the rehabilitation programme. Improvement of total MLHFQ score was also observed (35.53±14 vs 28.22±12.84; p&lt;0.001) at 3-months follow up. The functional capacity and quality of life of the patients in both the compliant and partially compliant groups showed significant improvement after the rehabilitation programme (p&lt;0.001); though no difference was found in the indicators when compared between compliant vs partially compliant groups after rehabilitation except for 6MWTD (1302.86±219.61 feet vs 1230.71±212.284 feet, p&lt;0.001).

Conclusion

From the results, it can be concluded that any amount of routine exercise tends to improve quality of life and symptoms in patients of chronic heart failure with reduced ejection fraction. However, to achieve the best effect in functional capacity and overall health status, the addition of a structured exercise programme like HBCR can be beneficial for proper rehabilitation in low resource settings. Further validation of the results is recommended through randomized control trials in larger study groups.

Monitoring COVID-19 Cases and Vaccination in Indian States and Union Territories Using Unsupervised Machine Learning Algorithm

The worldwide spread of the novel coronavirus originating from Wuhan, China led to an ongoing pandemic as COVID-19. The disease being a contagion transmitted rapidly in India through the people having travel histories to the affected countries, and their contacts that tested positive. Millions of people across all states and union territories (UT) were affected leading to serious respiratory illness and deaths. In the present study, two unsupervised clustering algorithms namely k-means clustering and hierarchical agglomerative clustering are applied on the COVID-19 dataset in order to group the Indian states/UTs based on the pandemic effect and the vaccination program from the period of March, 2020 to early June, 2021. The aim of the study is to observe the plight of each state and UT of India combating the novel coronavirus infection and to monitor their vaccination status. The research study will be helpful to the government and to the frontline workers coping to restrict the transmission of the virus in India. Also, the results of the study will provide a source of information for future research regarding the COVID-19 pandemic in India.

Application of photoplethysmography signals for healthcare systems: An in-depth review

BACKGROUND AND OBJECTIVES

Photoplethysmography (PPG) is a device that measures the amount of light absorbed by the blood vessel, blood, and tissues, which can, in turn, translate into various measurements such as the variation in blood flow volume, heart rate variability, blood pressure, etc. Hence, PPG signals can produce a wide variety of biological information that can be useful for the detection and diagnosis of various health problems. In this review, we are interested in the possible health disorders that can be detected using PPG signals.

METHODS

We applied PRISMA guidelines to systematically search various journal databases and identified 43 PPG studies that fit the criteria of this review.

RESULTS

Twenty-five health issues were identified from these studies that were classified into six categories: cardiac, blood pressure, sleep health, mental health, diabetes, and miscellaneous. Various routes were employed in these PPG studies to perform the diagnosis: machine learning, deep learning, and statistical routes. The studies were reviewed and summarized.

CONCLUSIONS

We identified limitations such as poor standardization of sampling frequencies and lack of publicly available PPG databases. We urge that future work should consider creating more publicly available databases so that a wide spectrum of health problems can be covered. We also want to promote the use of PPG signals as a potential precision medicine tool in both ambulatory and hospital settings.

Impact of plasma glucose and duration of type 2 diabetes mellitus on SYNTAX Score II in patients suffering from non ST-elevation myocardial infarction

Aim    The objective was to assess the correlation of fasting plasma glucose (FPG), HbA1c, and the duration of type 2 diabetes mellitus (T2DM) with SYNTAX score (SS) II in patients with non-ST elevation myocardial infarction (NSTEMI).Material and methods    FPG and HbA1C were measured in 398 patients presenting with NSTEMI at admission. SS II was calculated using an online calculator. Patients were stratified according to SS II (≤21.5, 21.5-30.6, and ≥30.6), defined as SS II low, mid, and high, respectively.Results    37.7 % of subjects were diabetic. Correlations of FPG (R=0.402, R2=0.162, p&lt;0.001) and HbA1c (R=0.359, R2=0.129, p&lt;0.001) with SS II were weak in the overall population. Duration of T2DM showed very strong correlation with SS II (R=0.827, R2=0.347). For the prediction of high SS II in the study population, FPG≥98.5 mg / dl demonstrated a sensitivity of 58 % and a specificity of 60 %, and HbA1c ≥6.05 demonstrated a sensitivity of 63 % and a specificity of 69 %. Duration of T2DM (adjusted odds ratio (OR): 1.182; 95 % confidence interval (CI): 1.185-2.773) and FPG (OR: 0.987; 95 % CI: 0.976-0.9959) were significantly associated with high SS II after controlling for other risk factors. Duration of T2DM (Beta=0.439) contributed strongly to variance of SS II, whereas HbA1c (Beta=0.063) contributed weakly.Conclusion    Duration of T2DM is a very important risk factor for severity of coronary artery disease.

Molecular simulation of linear octacosane via a CG10 coarse grain scheme

Following our previous work on the united-atom simulation on octacosane (C₂₈H₅₈) (Dai et al., Phys. Chem. Chem. Phys., 2021, 23, 21262-21271), we developed a coarse grain scheme (CG10), which is able to reproduce the pivotal phase characteristics of octacosane with highly improved computational efficiency. The CG10 octacosane chain was composed of 10 consecutive beads, maintaining the fundamental zigzag chain morphology. When the potential functions were set up and the coefficients were parameterized, our CG10 models yielded solid phase diagrams and transitions during an annealing process. We also detected the melting point by various means: direct observation, bond order, density tracking, and an enthalpy plot. Furthermore, our CG10 successfully reproduced the liquid density with only 2% underestimation, indicating its applicability across the solid and liquid phases. Therefore, with the ability to reproduce critical structure and property characteristics, our CG10 scheme provides an effective means of numerically modelling octacosane with highly improved computational efficiency.

Hybrid Deep Feature Generation for Appropriate Face Mask Use Detection

Mask usage is one of the most important precautions to limit the spread of COVID-19. Therefore, hygiene rules enforce the correct use of face coverings. Automated mask usage classification might be used to improve compliance monitoring. This study deals with the problem of inappropriate mask use. To address that problem, 2075 face mask usage images were collected. The individual images were labeled as either mask, no masked, or improper mask. Based on these labels, the following three cases were created: Case 1: mask versus no mask versus improper mask, Case 2: mask versus no mask + improper mask, and Case 3: mask versus no mask. This data was used to train and test a hybrid deep feature-based masked face classification model. The presented method comprises of three primary stages: (i) pre-trained ResNet101 and DenseNet201 were used as feature generators; each of these generators extracted 1000 features from an image; (ii) the most discriminative features were selected using an improved RelieF selector; and (iii) the chosen features were used to train and test a support vector machine classifier. That resulting model attained 95.95%, 97.49%, and 100.0% classification accuracy rates on Case 1, Case 2, and Case 3, respectively. Having achieved these high accuracy values indicates that the proposed model is fit for a practical trial to detect appropriate face mask use in real time.

Estimation of the proportion of true null hypotheses under sparse dependence: Adaptive FDR controlling in microarray data

The proportion of non-differentially expressed genes is an important quantity in microarray data analysis and an appropriate estimate of the same is used to construct adaptive multiple testing procedures. Most of the estimators for the proportion of true null hypotheses based on the thresholding, maximum likelihood and density estimation approaches assume independence among the gene expressions. Usually, sparse dependence structure is natural in modelling associations in microarray gene expression data and hence it is necessary to develop methods for accommodating the sparse dependence well within the framework of existing estimators. We propose a clustering based method to put genes in the same group that are not coexpressed using the estimated high dimensional correlation structure under sparse assumption as dissimilarity matrix. This novel method is applied to three existing estimators for the proportion of true null hypotheses. Extensive simulation study shows that the proposed method improves an existing estimator by making it less conservative and the corresponding adaptive Benjamini-Hochberg algorithm more powerful. The proposed method is applied to a microarray gene expression dataset of colorectal cancer patients and the results show gain in terms of number of differentially expressed genes. The R code is available at https://github.com/aniketstat/Proportiontion-of-true-null-under-sparse-dependence-2021.

Interacting Genetic Lesions of Melanoma in the Tumor Microenvironment: Defining a Viable Therapy

Melanoma is the most aggressive form of skin cancer with an estimated 106,110 newly diagnosed cases in the United States of America in 2021 leading to an approximated 7180 melanoma-induced deaths. Cancer typically arises from an accumulation of somatic mutations and can be associated with mutagenic or carcinogenic exposure. A key characteristic of melanoma is the extensive somatic mutation rate of 16.8 mutations/Mb, which is largely attributed to UV exposure. Bearing the highest mutational load, many of them occur in key driver pathways, most commonly the BRAFV600E in the mitogen-activated protein kinase (MAPK) pathway. This driver mutation is targeted clinically with FDA-approved therapies using small molecule inhibitors of oncogenic BRAFV600E and MEK, which has greatly expanded therapeutic intervention following a melanoma diagnosis. Up until 2011, therapeutic options for metastatic melanoma were limited, and treatment typically fell under the spectrum of surgery, radiotherapy, and chemotherapy.Attributed to the extensive mutation rate, as well as having the highest number of neoepitopes, melanoma is deemed to be extremely immunogenic. However, despite this highly immunogenic nature, melanoma is notorious for inducing an immunosuppressive microenvironment which can be relieved by checkpoint inhibitor therapy. The two molecules currently approved clinically are ipilimumab and nivolumab, which target the molecules CTLA-4 and PD-1, respectively.A plethora of immunomodulatory molecules exist, many with redundant functions. Additionally, these molecules are expressed not only by immune cells but also by tumor cells within the tumor microenvironment. Tumor profiling of these cell surface checkpoint molecules is necessary to optimize a clinical response. The presence of immunomodulatory molecules in melanoma, using data from The Cancer Genome Atlas and validation of expression in two model systems, human melanoma tissues and patient-derived melanoma cells, revealed that the expression levels of B and T lymphocyte attenuator (BTLA), TIM1, and CD226, concurrently with the BRAFV600E mutation status, significantly dictated overall survival in melanoma patients. These molecules, along with herpesvirus entry mediator (HVEM) and CD160, two molecules that are a part of the HVEM/BTLA/CD160 axis, had a higher expression in human melanoma tissues when compared to normal skin melanocytes and have unique roles to play in T cell activation. New links are being uncovered between the expression of immunomodulatory molecules and the BRAFV600E genetic lesion in melanoma. Small molecule inhibitors of the MAPK pathway regulate the surface expression of this multifaceted molecule, making BTLA a promising target for immuno-oncology to be targeted in combination with small molecule inhibitors, potentially alleviating T regulatory cell activation and improving patient prognosis.

Role of Artificial Intelligence in COVID-19 Detection

The global pandemic of coronavirus disease (COVID-19) has caused millions of deaths and affected the livelihood of many more people. Early and rapid detection of COVID-19 is a challenging task for the medical community, but it is also crucial in stopping the spread of the SARS-CoV-2 virus. Prior substantiation of artificial intelligence (AI) in various fields of science has encouraged researchers to further address this problem. Various medical imaging modalities including X-ray, computed tomography (CT) and ultrasound (US) using AI techniques have greatly helped to curb the COVID-19 outbreak by assisting with early diagnosis. We carried out a systematic review on state-of-the-art AI techniques applied with X-ray, CT, and US images to detect COVID-19. In this paper, we discuss approaches used by various authors and the significance of these research efforts, the potential challenges, and future trends related to the implementation of an AI system for disease detection during the COVID-19 pandemic.

Factors determining generalization in deep learning models for scoring COVID-CT images

The COVID-19 pandemic has inspired unprecedented data collection and computer vision modelling efforts worldwide, focused on the diagnosis of COVID-19 from medical images. However, these models have found limited, if any, clinical application due in part to unproven generalization to data sets beyond their source training corpus. This study investigates the generalizability of deep learning models using publicly available COVID-19 Computed Tomography data through cross dataset validation. The predictive ability of these models for COVID-19 severity is assessed using an independent dataset that is stratified for COVID-19 lung involvement. Each inter-dataset study is performed using histogram equalization, and contrast limited adaptive histogram equalization with and without a learning Gabor filter. We show that under certain conditions, deep learning models can generalize well to an external dataset with F1 scores up to 86%. The best performing model shows predictive accuracy of between 75% and 96% for lung involvement scoring against an external expertly stratified dataset. From these results we identify key factors promoting deep learning generalization, being primarily the uniform acquisition of training images, and secondly diversity in CT slice position.

Application of Deep Learning Models for Automated Identification of Parkinson's Disease: A Review (2011-2021)

Parkinson’s disease (PD) is the second most common neurodegenerative disorder affecting over 6 million people globally. Although there are symptomatic treatments that can increase the survivability of the disease, there are no curative treatments. The prevalence of PD and disability-adjusted life years continue to increase steadily, leading to a growing burden on patients, their families, society and the economy. Dopaminergic medications can significantly slow down the progression of PD when applied during the early stages. However, these treatments often become less effective with the disease progression. Early diagnosis of PD is crucial for immediate interventions so that the patients can remain self-sufficient for the longest period of time possible. Unfortunately, diagnoses are often late, due to factors such as a global shortage of neurologists skilled in early PD diagnosis. Computer-aided diagnostic (CAD) tools, based on artificial intelligence methods, that can perform automated diagnosis of PD, are gaining attention from healthcare services. In this review, we have identified 63 studies published between January 2011 and July 2021, that proposed deep learning models for an automated diagnosis of PD, using various types of modalities like brain analysis (SPECT, PET, MRI and EEG), and motion symptoms (gait, handwriting, speech and EMG). From these studies, we identify the best performing deep learning model reported for each modality and highlight the current limitations that are hindering the adoption of such CAD tools in healthcare. Finally, we propose new directions to further the studies on deep learning in the automated detection of PD, in the hopes of improving the utility, applicability and impact of such tools to improve early detection of PD globally.

Effect of thrombocytopenia in patients with infective endocarditis: an insight from the National Inpatient Sample database

Introduction

Infective endocarditis (IE) is one of the feared diseases in septic patients, and incidences are rising due to the intravenous drug abuse epidemic. Sepsis causes an escalation of the platelet destructions leading to thrombocytopenia (1). Few independent hospital-based studies have proposed increase mortality with thrombocytopenia in patients with IE (2–5). We aim to evaluate the significance of thrombocytopenia in IE subjects from the national inpatient sample (NIS) database.

Method

We analyzed the NIS database from Jan-2016 to Dec-2018 using Stata 16.0. NIS is the largest publicly available all-payer inpatient care database in the United States, containing data on more than seven million hospital stays per year. We identified patients with IE with or without thrombocytopenia using ICD-10 codes. The primary outcome of interest was in-hospital mortality comparison. We adjusted potential confounders (age, sex, diabetes, hypertension, etc.) with multivariate logistic regression analysis. Further analysis was done after balancing the population co-morbidity using a Greedy propensity match for accuracy.

Results

A total of 174,495 subjects were included in this study with a diagnosis of IE. Among these individuals, 33,285 patients had a concurrent diagnosis of thrombocytopenia. The mean ages were 53±19.5 years for the thrombocytopenia group and 55±19.8 years for others. Females were equally represented in both cohorts. There were 4,945 (14.86%) vs 2,835 (2.01%) mortalities reported in with and without thrombocytopenia group respectively. After propensity matching, there was a pronounced increase in mortality [Odds ratio (OR): 1.93 (1.72 – 2.15), p-value: &lt;0.001] in the group with thrombocytopenia comparing to others. Complications such as Major bleeding requiring blood transfusion [OR: 1.45 (1.35–1.57)], acute myocardial infarction [OR: 1.56 (1.35–1.70)], complete heart block [OR: 1.44 (1.16–1.53)], cardiac arrest [OR: 1.44 (1.25–1.72)], acute respiratory failure [OR: 1.51 (1.39–1.73)] and pressor support requirement [(OR: 1.73 (1.57–2.01)] were notably higher in the cohort of thrombocytopenia with statistically significant p-value (&lt;0.001). The difference in length of stay between both cohorts after propensity match wasn't statistically significant.

Conclusion

In conclusion, IE patients with thrombocytopenia have higher incidences of in-patient mortality and poor outcomes than cohort without thrombocytopenia. Some of the adverse consequences could be temporally explained by complications related to underlying thrombocytopenia. Further investigations are needed to delineate the outcome in this group of subjects.

Funding Acknowledgement

Type of funding sources: None.

Impact of atrial fibrillation in patients with colorectal cancer: a national inpatient sample database analysis

Background

Atrial fibrillation (AF) is the most common cardiac arrhythmia affecting approximately 1–2% overall population (1). Its causal relationship with colorectal cancer (CRC) is much for debate. According to one hypothesis, the presence of autoantibodies directed against ionic channels or acetylcholine receptors can predispose to the development of atrial fibrillation (2–3). Thus, AF may be regarded as an inflammatory complication in patients with colon cancer. Our study objective was to determine if AF impacts the outcome of patients with CRC.

Method

We analyzed the National Inpatient Sample (NIS) database from Oct-2015 to Dec 2018 using Stata 16.0. The NIS databases are released under the Healthcare Cost and Utilization Project, which includes inpatient admissions from the United States' participating hospitals. Total population with CRC were identified using their respective ICD-10 diagnostic codes then divided based on AF. To determine atrial fibrillation association with mortality and complications, we used multivariate logistic regression analysis using weights to generate nationally representative results. Later, a propensity-matched population analysis was done for the accuracy of the results.

Result

We found 245,305 patients admitted with CRC between Oct 2015 to Dec-2018 in the USA, out of which 28,170 (11.5%) were having AF. The mean age for the patients with AF was 77±10 compare to 65±14 years in those without AF. Patients with AF were associated with higher comorbidities and had a high population percentage with Carlson category three or above. There were 1,456 (5.2%) mortalities in the AF group compared to 5,689 (2.6%) in the other. The higher odds of mortality in patients with AF was present in multivariate logistic regression analysis in both non-propensity matched [1.71 (1.45–2.02), P-value: &lt;0.000] and propensity-matched [1.44 (1.18–1.75), P-value: &lt;0.001] cohorts. Patients with AF were hospitalized longer (9.20±7.8 vs. 6.85±7.0 days), leading to a high admission costs (US$ 25,875±22,875 vs. 20,087±19,314). Odds of complications such as need for blood transfusions [1.61 (1.05–1.29), P-value: 0.005], hemorrhage requiring blood transfusion [1.17 (1.05–1.29), P-value: 0.003], lower-GI bleed [1.31 (1.21–1.43), P-value: &lt;0.001], sepsis [1.45 (1.30–1.62), P-value: &lt;0.001], respiratory failure [1.39 (1.15–1.67), P-value: 0.001] etc. were also higher in group of patients with CRC and AF.

Conclusion

In our retrospective, propensity-matched national inpatient sample analyses of patients admitted with colorectal cancer, atrial fibrillation is associated with higher morbidity and mortality. AF was associated with a high burden of complications with prolonged hospital stay leading to increased health care expenditures.

Funding Acknowledgement

Type of funding sources: None.

Gender based outcome of IABP implantation in patients with acute coronary syndrome and cardiogenic shock: a national inpatient sample database analysis

Background

Intra-Aortic Balloon counter-pulsation is frequently used as a circulatory support device in patients requiring hemodynamic support - in cardiogenic shock and in patients at risk of hemodynamic decompensation during a high-risk coronary intervention. Impact of IABP in this patient population has been variable. Certain studies have shown a beneficial effect of IABP on selected populations having acute coronary syndrome with cardiogenic shock (1–3). Our objective was to compare the outcomes based on gender in the ACS population with cardiogenic shock and IABP placement.

Methods

We analyzed the National Inpatient Sample database from Oct-2015 to Dec-2017 released under Healthcare Cost utilization Project in the USA using Stata 16.0. The population was identified using respective ICD-10 codes. We excluded the population with sudden cardiac arrest, pulmonary embolism, and patients with anatomical post-MI complications. Multivariate logistic regression analysis was done to determine the difference in outcomes based on gender using clinically relevant variables. Later, propensity-matched cohort analysis was performed based on the regression variables.

Results

Of 36, 990 patients who met our inclusion criteria 25,670 (69%) were male and 11,320 (31%) were female. The average age for male and female populations was 66±11 and 69±12 years. Femnales were more likely to have higher Charlson co-morbidity index three or above. We found higher mortality in the female population [3,146 (27.79%)] compared to male [5,884 (22.92%)] in univariate analyses. Propensity-matched multivariate regression analysis showed no difference [OR: 1.06 (0.91–1.22) with P-value: 0.482] in mortality after adjusting for clinically relevant variables. Subgroup analysis in STEMI and NSTEMI populations did not show a difference. The average hospital stay was similar in both cohorts, with the male having a higher cost per stay. We found no difference in most of the complications included in our study except for higher coronary artery dissection [OR: 2.98 (1.73–5.13), P-value: &lt;0.001] and lower rates of AKI [OR: 0.72 (0.63–0.83), P-value: &lt;0.001], AKI requiring hemodialysis [OR: 0.74 (0.56–0.97), P-value:0.031] and ventricular tachycardia [OR: 0.73 (0.64–0.84), P-value: &lt;0.001] in the female population.

Conclusion

The inpatient population of ACS with Cardiogenic shock and IABP insertion showed no significant difference in mortality between males and females which was valid for subgroup analysis of NSTEMI and STEMI groups. Complications such as coronary artery dissection were higher, whereas AKI, AKI requiring hemodialysis, and ventricular tachycardias, were lower in females than males.

Funding Acknowledgement

Type of funding sources: None.

Interactions between poloxamer, PEOx-PPOy-PEOx, and non-ionic surfactant, sucrose monolaurate: A study on potential allergenic effect using model phospholipid membrane

Sugar-based surfactants are involved in skin related allergy cases in the past decade. Skin irritation starts with the interaction of the surfactant with the skin lipids leading to lipid emulsification and eventual barrier damage. Polymers or co-surfactants can be used to mitigate the allergenic effect but the mechanism of formulation mildness on skin remains unclear. We have used the quartz crystal microbalance (QCM) together with dissipative particle dynamics (DPD) simulation, small angle x-ray scattering (SAXS) as well as cell viability tests to decipher the interactions between poloxamers and sucrose monolaurate (SML), and how these interactions could prevent the disruption of a model supported phospholipid bilayer (SLB). Poloxamer addition to the SML solution can delay or totally prevent the disruption of the SLB depending on poloxamer type and concentration. Poloxamer P407 (Pluronic® F127) delays the onset of disruption while poloxamer P188 (Pluronic® F68) does not preserve the bilayer integrity even at high concentration of up to 15% w/w. Preservation of the SLB is likely due to the differences in the aggregates formation between SML-F127 and SML-F68 mixtures with corresponding retarded motion of SML micelles through the SML-F127 polymer matrix that improved cell viability.

Deep Mining Generation of Lung Cancer Malignancy Models from Chest X-ray Images

Lung cancer is the leading cause of cancer death and morbidity worldwide. Many studies have shown machine learning models to be effective in detecting lung nodules from chest X-ray images. However, these techniques have yet to be embraced by the medical community due to several practical, ethical, and regulatory constraints stemming from the “black-box” nature of deep learning models. Additionally, most lung nodules visible on chest X-rays are benign; therefore, the narrow task of computer vision-based lung nodule detection cannot be equated to automated lung cancer detection. Addressing both concerns, this study introduces a novel hybrid deep learning and decision tree-based computer vision model, which presents lung cancer malignancy predictions as interpretable decision trees. The deep learning component of this process is trained using a large publicly available dataset on pathological biomarkers associated with lung cancer. These models are then used to inference biomarker scores for chest X-ray images from two independent data sets, for which malignancy metadata is available. Next, multi-variate predictive models were mined by fitting shallow decision trees to the malignancy stratified datasets and interrogating a range of metrics to determine the best model. The best decision tree model achieved sensitivity and specificity of 86.7% and 80.0%, respectively, with a positive predictive value of 92.9%. Decision trees mined using this method may be considered as a starting point for refinement into clinically useful multi-variate lung cancer malignancy models for implementation as a workflow augmentation tool to improve the efficiency of human radiologists.

Molecular dynamics simulation of octacosane for phase diagrams and properties via the united-atom scheme

We used the united-atom scheme to build three types of crystalline structures for octacosane (C₂₈H₅₈) and carried out molecular dynamics simulations to investigate their phase properties. By gradually heating the three polymorphs, we managed to reproduce the sequence of experimentally reported crystalline phases and rotator phases. By studying the system density, molecule morphology, chain tilt angle and cell anisotropy, we hypothesized three mechanisms behind the observed system deformations and phase transformations during the annealing process. Furthermore, our model successfully predicted the melting temperature and heat of fusion. We also reproduced the characteristics of the rotator phases and the liquid phase, validating the transferability of the united-atom scheme among the different condensed phases of octacosane. Our methodology represents an effective and efficient means of numerical study for octacosane and may be used for other members of the n-alkane family.