Perils of Reduced Dose Apixaban In Atrial Fibrillation/Flutter - A Case Report with a Brief Literature Review.

Patients with atrial fibrillation/atrial flutter (AF) are notoriously predisposed to thromboembolic events secondary to the development of thrombi within the left atrium of the heart. AF accompanied by a fairly decent CHA2DS2-VASc score is a known indication for full anticoagulation, frequently with direct oral anticoagulants (DOACs), such as apixaban. Following extensive studies weighing the benefits of anticoagulation versus risks of major bleeding, patients meeting two or more of the dose-reduction criteria (age ≥80 years, weight ≤60 kg, and/or creatinine ≥ 1.5 mg/dL) require appropriate step-down to a reduced dose of apixaban. However, with rising concern for bleeding complications, many physicians are found to have been reducing the dose of apixaban despite the patient not completely meeting the defined criteria. Our study particularly emphasizes the risk of significantly increased thromboembolic complications in the setting of sub-therapeutic anticoagulation, in patients with both appropriately and inappropriately reduced doses of apixaban.

Genetic variants for head size share genes and pathways with cancer.

The size of the human head is highly heritable, but genetic drivers of its variation within the general population remain unmapped. We perform a genome-wide association study on head size (N = 80,890) and identify 67 genetic loci, of which 50 are novel. Neuroimaging studies show that 17 variants affect specific brain areas, but most have widespread effects. Gene set enrichment is observed for various cancers and the p53, Wnt, and ErbB signaling pathways. Genes harboring lead variants are enriched for macrocephaly syndrome genes (37-fold) and high-fidelity cancer genes (9-fold), which is not seen for human height variants. Head size variants are also near genes preferentially expressed in intermediate progenitor cells, neural cells linked to evolutionary brain expansion. Our results indicate that genes regulating early brain and cranial growth incline to neoplasia later in life, irrespective of height. This warrants investigation of clinical implications of the link between head size and cancer.

Predictors of glycemic worsening in the next year in adults with screen-detected type 2 diabetes.

Background and Aims: Identifying simple markers of risk for worsening glucose can allow care providers to target therapeutic interventions according to risk of worsening glycemic control. We aimed to determine which routine clinical measures herald near-term glycemic worsening in early type 2 diabetes(T2D). Methods: The Early Diabetes Intervention Program (EDIP) was a clinical trial in individuals with screendetected T2D [HbA1C 6.3+0.63%(45+5mmol/mol)]. During the trial some participants experienced worsening fasting blood glucose (FBG). We investigated the time course of FBG, HbA1c, weight, and other clinical factors to determine which might herald glycemic worsening over the next year. Results: Progressors (62/219, 28.5%) had higher FBG than non-progressors at baseline [118 vs 130mg/dL (6.6 vs 7.2 mmol/L), p=<0.001]. FBG was stable except in the year of progression, when progressors exhibited a large 1-year rise [mean change 14.2mg/dL(0.79 mmol/L)]. Current FBG and antecedent year change in FBG were associated with progression(p<0.01), although the magnitude of change was too small to be of clinical utility (0.19 mg/dL; 0.01 mmol/L). Current or antecedent year change in HbA1c, weight, TG or HDL were not associated with progression. In the year of glycemic worsening, rising glucose was strongly associated with a concurrent increase in weight (p<0.001). Conclusions: Elevated FBG but not HbA1c identified individuals at risk for imminent glycemic worsening; the subsequent large rise in glucose was associated with a short-term increase in weight. Glucose and weight surveillance provide actionable information for those caring for patients with early diabetes.

moPepGen: Rapid and Comprehensive Proteoform Identification.

Gene expression is a multi-step transformation of biological information from its storage form (DNA) into functional forms (protein and some RNAs). Regulatory activities at each step of this transformation multiply a single gene into a myriad of proteoforms. Proteogenomics is the study of how genomic and transcriptomic variation creates this proteoform diversity, and is limited by the challenges of modeling the complexities of gene-expression. We therefore created moPepGen, a graph-based algorithm that comprehensively enumerates proteoforms in linear time. moPepGen works with multiple technologies, in multiple species and on all types of genetic and transcriptomic data. In human cancer proteomes, it detects and quantifies previously unobserved noncanonical peptides arising from germline and somatic genomic variants, noncoding open reading frames, RNA fusions and RNA circularization. By enabling efficient identification and quantitation of previously hidden proteins in both existing and new proteomic data, moPepGen facilitates all proteogenomics applications. It is available at: https://github.com/uclahs-cds/package-moPepGen.

Obesity and the cerebral cortex: Underlying neurobiology in mice and humans.

Obesity is a major modifiable risk factor for Alzheimer's disease (AD), characterized by progressive atrophy of the cerebral cortex. The neurobiology of obesity contributions to AD is poorly understood. Here we show with in vivo MRI that diet-induced obesity decreases cortical volume in mice, and that higher body adiposity associates with lower cortical volume in humans. Single-nuclei transcriptomics of the mouse cortex reveals that dietary obesity promotes an array of neuron-adverse transcriptional dysregulations, which are mediated by an interplay of excitatory neurons and glial cells, and which involve microglial activation and lowered neuronal capacity for neuritogenesis and maintenance of membrane potential. The transcriptional dysregulations of microglia, more than of other cell types, are like those in AD, as assessed with single-nuclei cortical transcriptomics in a mouse model of AD and two sets of human donors with the disease. Serial two-photon tomography of microglia demonstrates microgliosis throughout the mouse cortex. The spatial pattern of adiposity-cortical volume associations in human cohorts interrogated together with in silico bulk and single-nucleus transcriptomic data from the human cortex implicated microglia (along with other glial cells and subtypes of excitatory neurons), and it correlated positively with the spatial profile of cortical atrophy in patients with mild cognitive impairment and AD. Thus, multi-cell neuron-adverse dysregulations likely contribute to the loss of cortical tissue in obesity. The dysregulations of microglia may be pivotal to the obesity-related risk of AD.

Graham Patch Repair of a Prepyloric Ulcer Complicated by Recurrent Abdominal Abscesses and Leukocytosis: A Case Report.

This case report describes the clinical course of a 51-year-old Caucasian woman with a history of anemia who presented to the emergency department with worsening diffuse abdominal pain and weakness two days after dental surgery. The patient's condition rapidly deteriorated, manifesting as tachycardia, diaphoresis, and a peritonitic abdomen. A CT scan revealed a perforated gastric ulcer, prompting emergent laparoscopy, Graham patch repair, and abdominal washout. Postoperatively, the patient developed leukocytosis, and imaging indicated the formation of an abscess. Despite initial attempts at percutaneous drainage, a subsequent exploratory laparotomy was performed. The patient's leukocytosis eventually resolved, and she was discharged after 21 days with outpatient follow-up. The discussion delves into the declining incidence of peptic ulcer disease but a constant rate of complications, emphasizing the role of factors such as nonsteroidal anti-inflammatory drug use. The diagnostic approach using CT scans in suspected perforated peptic ulcers is highlighted. The study also explores risk stratification scoring systems, with a preference for operative management. The laparoscopic omental patch repair (Graham patch) is discussed, citing its safety and efficacy. The case presented an uncommon occurrence of failed primary percutaneous abscess drainage, leading to subsequent surgical drainage. The discussion concludes by noting variables that may contribute to drainage failure and emphasizes the need for further research to understand such complications.

Integrated image and location analysis for wound classification: a deep learning approach.

The global burden of acute and chronic wounds presents a compelling case for enhancing wound classification methods, a vital step in diagnosing and determining optimal treatments. Recognizing this need, we introduce an innovative multi-modal network based on a deep convolutional neural network for categorizing wounds into four categories: diabetic, pressure, surgical, and venous ulcers. Our multi-modal network uses wound images and their corresponding body locations for more precise classification. A unique aspect of our methodology is incorporating a body map system that facilitates accurate wound location tagging, improving upon traditional wound image classification techniques. A distinctive feature of our approach is the integration of models such as VGG16, ResNet152, and EfficientNet within a novel architecture. This architecture includes elements like spatial and channel-wise Squeeze-and-Excitation modules, Axial Attention, and an Adaptive Gated Multi-Layer Perceptron, providing a robust foundation for classification. Our multi-modal network was trained and evaluated on two distinct datasets comprising relevant images and corresponding location information. Notably, our proposed network outperformed traditional methods, reaching an accuracy range of 74.79-100% for Region of Interest (ROI) without location classifications, 73.98-100% for ROI with location classifications, and 78.10-100% for whole image classifications. This marks a significant enhancement over previously reported performance metrics in the literature. Our results indicate the potential of our multi-modal network as an effective decision-support tool for wound image classification, paving the way for its application in various clinical contexts.

Six Thinking Hats model of learning-Creative teaching method in physiotherapy-A pilot study.

BACKGROUND: The fast growth of science and technology in the 21st century has made it necessary for advances in many sectors, including social, economic, educational, and health. Physiotherapists must have highly developed critical thinking abilities, such as creative, critical, problem-solving, and analytical thinking, to deal with these changes. Creativity is the capacity to view, shape, and organize the world in a unique way, whereas critical thinking is the ability to think outside the box, notice parts that others cannot see, be open to new ideas, and never be afraid to attempt new things. All of the continually developing and changing knowledge cannot be included in educational programs for physiotherapy students. The Six Thinking Hats were designed by Edward de Bono. The "Six Thinking Hats" concept is a teaching strategy for encouraging creative thinking by organizing and presenting ideas within a predetermined framework. The goal of the study was to apply the Six Thinking Hats model to physiotherapy themes in order to create a creative teaching technique and get feedback by fostering the critical thinking abilities of physiotherapy students. MATERIALS AND METHODS: During the 2021-2022 academic year, there were 42 students registered for III and IV BPT students enrolled for physiotherapy course; however, the research sample comprised 41 students. The course included 6 h of theory and 18 h of clinical sessions. Students were taught by two instructors via a PowerPoint presentation on SCI, obesity, and for further practice, they were given two case studies taught by Six Thinking Hats model. After the class, each group received a set of hats in one of six colors to use the Six Thinking Hats method effectively, and then students were asked to provide opinions on the Six Thinking Hats model of learning. Data analysis and response reporting were conducted, using literature and student comments. RESULTS: The Six Thinking Hats model was discovered to be more efficient in comprehending topics and provides all-encompassing care. The ability to understand patients' views, see both the positive and negative aspects in conditions, develop their ability to think critically, and create with unique concepts all improved for the students. About 90.2% of students stated that they learned to think from different aspects, 85.4% of the students stated that it ensured they considered the patient holistically, 87.8% of the students stated that the method allowed for sharing different ideas and thoughts, 75.6% that it helped them to produce creative ideas. Overall, 95% of students agreed that the Six Thinking Hats approach was better than conventional lectures and case studies, with 5% disagreeing. CONCLUSION: This study concluded that the Six Thinking Hats model of learning is a creative teaching method that is useful for the development of physiotherapy students' critical thinking. Skills and having this type of study in the physiotherapy literature add to the improvement and enrichment of physiotherapy education.

Addressing climate change with behavioral science: A global intervention tournament in 63 countries.

Effectively reducing climate change requires marked, global behavior change. However, it is unclear which strategies are most likely to motivate people to change their climate beliefs and behaviors. Here, we tested 11 expert-crowdsourced interventions on four climate mitigation outcomes: beliefs, policy support, information sharing intention, and an effortful tree-planting behavioral task. Across 59,440 participants from 63 countries, the interventions' effectiveness was small, largely limited to nonclimate skeptics, and differed across outcomes: Beliefs were strengthened mostly by decreasing psychological distance (by 2.3%), policy support by writing a letter to a future-generation member (2.6%), information sharing by negative emotion induction (12.1%), and no intervention increased the more effortful behavior-several interventions even reduced tree planting. Last, the effects of each intervention differed depending on people's initial climate beliefs. These findings suggest that the impact of behavioral climate interventions varies across audiences and target behaviors.

Intermittent fasting promotes rejuvenation of immunosenescent phenotypes in aged adipose tissue.

The aging of white adipose tissue (WAT) involves senescence of adipose stem and progenitor cells (ASPCs) and dysregulation of immune cell populations, serving as a major driver of age-associated adipose dysfunction and metabolic diseases. Conversely, the elimination of senescent ASPCs is associated with improvements in overall health. Intermittent fasting (IF), a dietary intervention that incorporates periodic cycles of fasting and refeeding, has been reported to promote weight loss and fat mass reduction and improve glucose and insulin homeostasis in both murine and human studies. While previous studies have assessed the effects of IF on obesity-associated metabolic dysfunction, few studies have examined the aging-specific changes to ASPCs and immune cell populations in WAT. Here, we show that IF in 18-20-month-old mice reduced senescent phenotypes of ASPCs and restored their adipogenic potential. Intriguingly, IF-treated mice exhibited an increase in adipose eosinophils, which has been reported to be associated with improved WAT homeostasis and immunological fitness in aged mice. The observed cellular and metabolic changes suggest that IF may be a feasible lifestyle regimen to reduce cellular senescence which could result in attenuation of downstream aging-induced WAT dysfunction and metabolic diseases.

NFTest: automated testing of Nextflow pipelines.

MOTIVATION: The ongoing expansion in the volume of biomedical data has contributed to a growing complexity in the tools and technologies used in research with an increased reliance on complex workflows written in orchestration languages such as Nextflow to integrate algorithms into processing pipelines. The growing use of workflows involving various tools and algorithms has led to increased scrutiny of software development practices to avoid errors in individual tools and in the connections between them. RESULTS: To facilitate test-driven development of Nextflow pipelines, we created NFTest, a framework for automated pipeline testing and validation with customizability options for Nextflow features. It is open-source, easy to initialize and use, and customizable to allow for testing of complex workflows with test success configurable through a broad range of assertions. NFTest simplifies the testing burden on developers by automating tests once defined and providing a flexible interface for running tests to validate workflows. This reduces the barrier to rigorous biomedical workflow testing and paves the way toward reducing computational errors in biomedicine. AVAILABILITY AND IMPLEMENTATION: NFTest is an open-source Python framework under the GPLv2 license and is freely available at https://github.com/uclahs-cds/tool-NFTest. The call-sSNV Nextflow pipeline is available at: https://github.com/uclahs-cds/pipeline-call-sSNV.

PipeVal: light-weight extensible tool for file validation.

MOTIVATION: The volume of biomedical data generated each year is growing exponentially as high-throughput molecular, imaging and mHealth technologies expand. This rise in data volume has contributed to an increasing reliance on and demand for computational methods, and consequently to increased attention to software quality and data integrity. RESULTS: To simplify data verification in diverse data-processing pipelines, we created PipeVal, a light-weight, easy-to-use, extensible tool for file validation. It is open-source, easy to integrate with complex workflows, and modularized for extensibility for new file formats. PipeVal can be rapidly inserted into existing methods and pipelines to automatically validate and verify inputs and outputs. This can reduce wasted compute time attributed to file corruption or invalid file paths, and significantly improve the quality of data-intensive software. AVAILABILITY AND IMPLEMENTATION: PipeVal is an open-source Python package under the GPLv2 license and it is freely available at https://github.com/uclahs-cds/package-PipeVal. The docker image is available at: https://github.com/uclahs-cds/package-PipeVal/pkgs/container/pipeval.

Quantifying the seed sensitivity of cancer subclonal reconstruction algorithms.

Background: Intra-tumoural heterogeneity complicates cancer prognosis and impairs treatment success. One of the ways subclonal reconstruction (SRC) quantifies intra-tumoural heterogeneity is by estimating the number of subclones present in bulk DNA sequencing data. SRC algorithms are probabilistic and need to be initialized by a random seed. However, the seeds used in bioinformatics algorithms are rarely reported in the literature. Thus, the impact of the initializing seed on SRC solutions has not been studied. To address this gap, we generated a set of ten random seeds to systematically benchmark the seed sensitivity of three probabilistic SRC algorithms: PyClone-VI, DPClust, and PhyloWGS. Results: We characterized the seed sensitivity of three algorithms across fourteen whole-genome sequences of head and neck squamous cell carcinoma and nine SRC pipelines, each composed of a single nucleotide variant caller, a copy number aberration caller and an SRC algorithm. This led to a total of 1470 subclonal reconstructions, including 1260 single-region and 210 multi-region reconstructions. The number of subclones estimated per patient vary across SRC pipelines, but all three SRC algorithms show substantial seed sensitivity: subclone estimates vary across different seeds for the same set of input using the same SRC algorithm. No seed consistently estimated the mode number of subclones across all patients for any SRC algorithm. Conclusions: These findings highlight the variability in quantifying intra-tumoural heterogeneity introduced by the seed sensitivity of probabilistic SRC algorithms. We recommend that authors, reviewers and editors adopt guidelines to both report and randomize seed choices. It may also be valuable to consider seed-sensitivity in the benchmarking of newly developed SRC algorithms. These findings may be of interest in other areas of bioinformatics where seeded probabilistic algorithms are used and suggest consideration of formal seed reporting standards to enhance reproducibility.

Tissue-specific RNA-seq defines genes governing male tail tip morphogenesis in C. elegans.

Caenorhabditis elegans males undergo sex-specific tail tip morphogenesis (TTM) under the control of the transcription factor DMD-3. To find genes regulated by DMD-3, We performed RNA-seq of laser-dissected tail tips. We identified 564 genes differentially expressed (DE) in wild-type males vs. dmd-3(-) males and hermaphrodites. The transcription profile of dmd-3(-) tail tips is similar to that in hermaphrodites. For validation, we analyzed transcriptional reporters for 49 genes and found male-specific or male-biased expression for 26 genes. Only 11 DE genes overlapped with genes found in a previous RNAi screen for defective TTM. GO enrichment analysis of DE genes finds upregulation of genes within the UPR (unfolded protein response) pathway and downregulation of genes involved in cuticle maintenance. Of the DE genes, 40 are transcription factors, indicating that the gene network downstream of DMD-3 is complex and potentially modular. We propose modules of genes that act together in TTM and are coregulated by DMD-3, among them the chondroitin synthesis pathway and the hypertonic stress response.

Glycolate oxidase-1 gene variants influence the risk of hyperoxaluria and renal stone development.

PURPOSE: Oxalate is an excellent calcium ion attractor with great abundance in the human body, and the liver is the major source of oxalate. The Glycolate oxidase-1 (GOX1) gene is solely responsible for the glycolate and glyoxylate metabolism and produces oxalate. This study has been designed to comprehend the association of genetic variants of the GOX1 gene with the risk of hyperoxaluria and renal stone disease in the Indian population. METHOD: The present study is a candidate gene approach prospective case-control study carried out on 300 participants (150 cases and 150 controls) at Muljibhai Patel Urological Hospital, Gujarat, India. Biochemical parameters, including serum levels of calcium, creatinine, parathyroid hormone, and 24-h urine metabolites, were performed. The genotyping of GOX1 gene variants rs6086287, rs2235250, rs2255183, and rs2294303 was performed using a customized TaqMan assay probe by RT-PCR. RESULT: Parathyroid hormone, serum creatinine, and urine metabolites were significantly elevated in nephrolithiasis compared to healthy individuals. All mutated homozygous genotypes GG (rs6086287), TT (rs2235250), GG (rs2255183), and CC (rs2294303) were significantly associated with a high risk of renal stone disease. Individuals diagnosed with hyperoxaluria and carrying TG (rs6086287), AG (rs2255183), and TT (rs2294303) genotypes have a significantly high risk of renal stone disease. Moreover, haplotype analysis and correlation analysis also confirmed the strong association between genetic variants and nephrolithiasis. CONCLUSION: Genetic variants of the GOX1 genes were associated with renal stone disease. In the presence of risk genotype and hyperoxaluria, the susceptibility to develop renal stone disease risk gets modulated.

Reinforcement learning assisted recursive QAOA.

In recent years, variational quantum algorithms such as the Quantum Approximation Optimization Algorithm (QAOA) have gained popularity as they provide the hope of using NISQ devices to tackle hard combinatorial optimization problems. It is, however, known that at low depth, certain locality constraints of QAOA limit its performance. To go beyond these limitations, a non-local variant of QAOA, namely recursive QAOA (RQAOA), was proposed to improve the quality of approximate solutions. The RQAOA has been studied comparatively less than QAOA, and it is less understood, for instance, for what family of instances it may fail to provide high-quality solutions. However, as we are tackling NP-hard problems (specifically, the Ising spin model), it is expected that RQAOA does fail, raising the question of designing even better quantum algorithms for combinatorial optimization. In this spirit, we identify and analyze cases where (depth-1) RQAOA fails and, based on this, propose a reinforcement learning enhanced RQAOA variant (RL-RQAOA) that improves upon RQAOA. We show that the performance of RL-RQAOA improves over RQAOA: RL-RQAOA is strictly better on these identified instances where RQAOA underperforms and is similarly performing on instances where RQAOA is near-optimal. Our work exemplifies the potentially beneficial synergy between reinforcement learning and quantum (inspired) optimization in the design of new, even better heuristics for complex problems.

A Mixed-Methods Assessment of the Feasibility of Conducting Neurosurgical Clinical Research in Uganda.

BACKGROUND: Clinical research is necessary to evaluate neurosurgical interventions, yet clinical trials are conducted less frequently in low- and middle-income countries. Because specific barriers, facilitating factors, and strategies for neurosurgical clinical research in Uganda have not been previously identified, this study evaluated neurosurgical providers' perspectives on clinical research and documentation patterns of neurosurgical variables at Mulago National Referral Hospital. METHODS: Retrospective review of 166 neurosurgical patient charts assessed the frequency of documentation of key variables. Twenty-two providers working in neurosurgery participated in 6 focus group discussions with qualitative analysis utilizing the framework method. RESULTS: Chart review showed that primary diagnosis (99.4%), pupil light response (97.6%), and computed tomography scan results (93.3%) were documented for most patients. Cranial nerve exam (61.5%), pupil size (69.9%), and time to neurosurgical intervention (45%) were documented less frequently. On average, Glasgow Coma Scale was documented for 86.6% of days hospitalized, while vital signs were documented for 12.3%. In most focus group discussions, participants identified follow-up, financing, recruitment, time, approval, and sociocultural factors as research barriers. Participants described how the current health workforce facilitates successful research. To improve research capacity, suggested strategies focused on research networks, data collection, leadership, participant recruitment, infrastructure, and implementation. CONCLUSIONS: At Mulago National Referral Hospital, there was variability in the frequency of documentation of neurosurgical variables, which may impact data collection for future studies. While multiple barriers were identified, sociocultural, financing, and time barriers greatly impacted neurosurgical clinical research. Despite that, identified facilitating factors and strategies could be utilized to support neurosurgical research capacity growth.

Development of a human glioblastoma model using humanized DRAG mice for immunotherapy.

Glioblastoma (GBM) is the most common and lethal primary brain tumor. The development of alternative humanized mouse models with fully functional human immune cells will potentially accelerate the progress of GBM immunotherapy. We successfully generated humanized DRAG (NOD.Rag1KO.IL2RγcKO) mouse model by transplantation of human DR4+ hematopoietic stem cells (hHSCs), and effectively grafted GBM patient-derived tumorsphere cells to form xenografted tumors intracranially. The engrafted tumors recapitulated the pathological features and the immune cell composition of human GBM. Administration of anti-human PD-1 antibodies in these tumor-bearing humanized DRAG mice decreased the major tumor-infiltrating immunosuppressive cell populations, including CD4+PD-1+ and CD8+PD-1+ T cells, CD11b+CD14+HLA-DR+ macrophages, CD11b+CD14+HLA-DR-CD15- and CD11b+CD14-CD15+ myeloid-derived suppressor cells, indicating the humanized DRAG mice as a useful model to test the efficacy of GBM immunotherapy. Taken together, these results suggest that the humanized DRAG mouse model is a reliable preclinical platform for studying brain cancer immunotherapy and beyond.