A Systematic Review of Symptoms of Pernicious Anemia.

BACKGROUND: Pernicious anemia (PA) is a type of macrocytic anemia caused by autoimmune gastritis. To facilitate timely diagnosis and treatment of PA there is a pressing need for improved understanding among Healthcare providers of the condition's symptoms and diagnostic criteria. OBJECTIVE: This systematic review aims to extend existing clinical knowledge on the presentation of PA by determining which symptoms and clinical complications are reported in published adult case studies. METHODS: Relevant studies were identified through electronic searches of PsycINFO, Embase, and MEDLINE, via OvidSP. During data extraction symptoms were categorized according to the International Classification of Diseases and were grouped based on frequency. RESULTS: Symptoms were documented for 103 adults with a diagnosis of PA; the most frequent symptoms were fatigue (55%), loss of sensation in limbs (32%), excessive weight loss (27%), and a sore tongue (23%). CONCLUSIONS: This review highlights the diverse symptomology of adults who are diagnosed with PA. Most symptoms documented in case studies are consistent with the core signs of B12 and folate deficiencies. Research is needed to identify if there are common clusters of PA symptoms that can be used as prompts for diagnostic testing in patients with suspected B12 deficiency.

Plain language titleA Review of Symptoms of Pernicious AnemiaPlain language summaryThis study reviewed case studies that have been written about adults with pernicious anemia, it has documented the frequency of the core symptoms and the impact these have on health.

CRISPR-Cas9 for selective targeting of somatic mutations in pancreatic cancers.

Somatic mutations are desirable targets for selective elimination of cancer, yet most are found within noncoding regions. We have adapted the CRISPR-Cas9 gene editing tool as a novel, cancer-specific killing strategy by targeting the subset of somatic mutations that create protospacer adjacent motifs (PAMs), which have evolutionally allowed bacterial cells to distinguish between self and non-self DNA for Cas9-induced double strand breaks. Whole genome sequencing (WGS) of paired tumor minus normal (T-N) samples from three pancreatic cancer patients (Panc480, Panc504, and Panc1002) showed an average of 417 somatic PAMs per tumor produced from single base substitutions. Further analyses of 591 paired T-N samples from The International Cancer Genome Consortium found medians of ∼455 somatic PAMs per tumor in pancreatic, ∼2800 in lung, and ∼3200 in esophageal cancer cohorts. Finally, we demonstrated 69-99% selective cell death of three targeted pancreatic cancer cell lines using 4-9 sgRNAs designed using the somatic PAM discovery approach. We also showed no off-target activity from these tumor-specific sgRNAs in either the patient's normal cells or an irrelevant cancer using WGS. This study demonstrates the potential of CRISPR-Cas9 as a novel and selective anti-cancer strategy, and supports the genetic targeting of adult cancers.

Advancing post-traumatic seizure classification and biomarker identification: Information decomposition based multimodal fusion and explainable machine learning with missing neuroimaging data.

A late post-traumatic seizure (LPTS), a consequence of traumatic brain injury (TBI), can potentially evolve into a lifelong condition known as post-traumatic epilepsy (PTE). Presently, the mechanism that triggers epileptogenesis in TBI patients remains elusive, inspiring the epilepsy community to devise ways to predict which TBI patients will develop PTE and to identify potential biomarkers. In response to this need, our study collected comprehensive, longitudinal multimodal data from 48 TBI patients across multiple participating institutions. A supervised binary classification task was created, contrasting data from LPTS patients with those without LPTS. To accommodate missing modalities in some subjects, we took a two-pronged approach. Firstly, we extended a graphical model-based Bayesian estimator to directly classify subjects with incomplete modality. Secondly, we explored conventional imputation techniques. The imputed multimodal information was then combined, following several fusion and dimensionality reduction techniques found in the literature, and subsequently fitted to a kernel- or a tree-based classifier. For this fusion, we proposed two new algorithms: recursive elimination of correlated components (RECC) that filters information based on the correlation between the already selected features, and information decomposition and selective fusion (IDSF), which effectively recombines information from decomposed multimodal features. Our cross-validation findings showed that the proposed IDSF algorithm delivers superior performance based on the area under the curve (AUC) score. Ultimately, after rigorous statistical comparisons and interpretable machine learning examination using Shapley values of the most frequently selected features, we recommend the two following magnetic resonance imaging (MRI) abnormalities as potential biomarkers: the left anterior limb of internal capsule in diffusion MRI (dMRI), and the right middle temporal gyrus in functional MRI (fMRI).

Fruit and Vegetable Intake, Food Security, Barriers to Healthy Eating, and Empowerment among Dietetic Interns and Physician Assistant Interns: A Cross-Sectional Pilot Study.

Students are required to complete supervised practice hours prior to becoming Registered Dietitians and Physician Assistants. Research suggests that environmental and social factors affect dietetic interns' diets during their internship, although these factors have not been studied among physician assistant interns. This cross-sectional study utilized an online survey to compare dietetic interns' (n = 81) and physician assistant interns' (n = 79) fruit and vegetable intake, food security, barriers to healthy eating, and empowerment for making healthy dietary choices during an internship. Differences were assessed via independent t-tests and chi-square distributions. The significance was set at p < 0.05. Dietetic interns had a higher vegetable intake (p = 0.002) while physician assistant interns had higher rates of food insecurity (p = 0.040). Dietetic interns reported a greater impact on their dietary choices due to mental fatigue (p = 0.006), while physician assistant interns' dietary choices were more heavily impacted by peer influence, interactions with patients, and interactions with preceptors (p < 0.05). There was not a group difference in overall empowerment (p = 0.157), although both groups rated empowerment for asking for help with food and nutrition challenges the lowest of the empowerment sub-items. Addressing interns' unique needs may support students' educational success and wellbeing once they are professionals, promote a diverse workforce, and ensure optimal care for patients.