Short-Term Outcomes and Patient Perceptions after Paramedic Non-Transport during the COVID-19 Pandemic.

BACKGROUND: EMS frequently encounter patients who decline transport, yet there are little data to inform the safety of patient and/or paramedic-initiated assess, treat, and refer (ATR) protocols. We determined patient decision-making and short-term outcomes after non-transport by EMS during the COVID-19 pandemic. METHODS: This was a prospective, observational study of a random sample of patients evaluated and not transported by EMS from August 2020 to March 2021. From the EMS database, we randomly selected a daily sample of adult patients with disposition of ATR. We excluded patients dispositioned against medical advice (AMA) and those in police custody. Investigators contacted patients by phone to administer a standardized survey regarding decision-making, symptom progression, follow-up care, and satisfaction with non-transport decision. We also determined the proportion of patients who re-contacted 9-1-1 within 72 h, and unexpected deaths within 72 h using coroner data. Descriptive statistics were calculated. RESULTS: Of 4613 non-transported patients, 3330 (72%) patients for whom the disposition was ATR were included. Patients were 46% male with a median age of 49 (inter-quartile range (IQR) 31-67). Median vital signs measurements fell within the normal range. Investigators successfully contacted 584/3330 patients (18%). The most common reason for failure was lack of accurate phone number. The most common reasons patients reported for not going to the ED on initial encounter were: felt reassured after the paramedic assessment (151/584, 26%), medical complaint resolved (113/584, 19%), paramedic suggested transport was not required (73/584, 13%), concern for COVID-19 exposure (57/584, 10%), and initial concern was not medical (46/584, 8%). Ninety-five percent (552/584) were satisfied with the non-transport decision and 49% (284/584) had sought follow-up care. The majority (501/584, 86%) reported equal, improved, or resolved symptoms, while 80 patients (13%) reported worse symptoms, of whom (64/80, 80%) remained satisfied with the non-transport decision. Overall, there were 154 of 3330 (4.6%) 9-1-1 recontacts within 72 h. Based on coroner data, three unexpected deaths (0.09%) occurred within 72 h of the initial EMS calls. CONCLUSION: Paramedic disposition by ATR protocols resulted in a low rate of 9-1-1 recontact. Unexpected deaths were extremely rare. Patient satisfaction with the non-transport decision was high.

Network coupling and surgical treatment response in temporal lobe epilepsy: A proof-of-concept study.

OBJECTIVE: This proof-of-concept study aimed to examine the overlap between structural and functional activity (coupling) related to surgical response. METHODS: We studied intracranial rest and ictal stereoelectroencephalography (sEEG) recordings from 77 seizures in thirteen participants with temporal lobe epilepsy (TLE) who subsequently underwent resective/laser ablation surgery. We used the stereotactic coordinates of electrodes to construct functional (sEEG electrodes) and structural connectomes (diffusion tensor imaging). A Jaccard index was used to assess the similarity (coupling) between structural and functional connectivity at rest and at various intraictal timepoints. RESULTS: We observed that patients who did not become seizure free after surgery had higher connectome coupling recruitment than responders at rest and during early and mid seizure (and visa versa). SIGNIFICANCE: Structural networks provide a backbone for functional activity in TLE. The association between lack of seizure control after surgery and the strength of synchrony between these networks suggests that surgical intervention aimed to disrupt these networks may be ineffective in those that display strong synchrony. Our results, combined with findings of other groups, suggest a potential mechanism that explains why certain patients benefit from epilepsy surgery and why others do not. This insight has the potential to guide surgical planning (e.g., removal of high coupling nodes) following future research.

Understanding Consequences and Tradeoffs of Melt Processing as a Pretreatment for Enzymatic Depolymerization of Poly(ethylene terephthalate).

Melt extrusion pretreatment of poly(ethylene terephthalate) (PET) prior to enzymatic depolymerization with an unpurified leaf branch compost cutinase enzyme cocktail is explored to ascertain the efficiency gained by different processing methods on the enzymatic depolymerization of PET. Specific surface area (SSA) is investigated as a key factor in reducing depolymerization time. Higher SSA substrates (>5.6 mm2  mg-1 ) show higher depolymerization rates (≈0.88 g L-1 terephthalic acid [TPA] per day) and no induction phase, while lower SSA substrates (≈4.3, 4.4, and 5.6 mm2  mg-1 ) show, after an initial induction phase, similar depolymerization rates (≈0.46, 0.45, and 0.44 g L-1 TPA per day) despite increases in SSA of up to 30%. The mechanism of enzymatic depolymerization manifests in the appearance of anisotropic pitting. Longer incubation time used to overcome the induction phase in low SSA substrates allows for nearly full recovery of monomeric products, but manual pregrinding of extruded PET sharply increases SSA, depolymerization rate, and substrate crystallinity which may decrease the maximum recycled yield of the product materials. An estimate of the energy cost of increasing SSA is made and its effects on material properties are discussed. This work highlights key material structure and pretreatment aspects influencing the enzymatic recycling of PET.

Dynamic Network Analysis Demonstrates the Formation of Stable Functional Networks During Rule Learning.

Variations in the functional connectivity of large-scale cortical brain networks may explain individual differences in learning ability. We used a dynamic network analysis of fMRI data to identify changes in functional brain networks that are associated with context-dependent rule learning. During fMRI scanning, naïve subjects performed a cognitive task designed to test their ability to learn context-dependent rules. Notably, subjects were given minimal instructions about the task prior to scanning. We identified several key network characteristics associated with fast and accurate rule learning. First, consistent with the formation of stable functional networks, a dynamic community detection analysis revealed regionally specific reductions in flexible switching between different functional communities in successful learners. Second, successful rule learners showed decreased centrality of ventral attention regions and increased assortative mixing of cognitive control regions as the rules were learned. Finally, successful subjects showed greater decoupling of default and attention communities throughout the entire task, whereas ventral attention and cognitive control regions became more connected during learning. Overall, the results support a framework by which a stable ventral attention community and more flexible cognitive control community support sustained attention and the formation of rule representations in successful learners.

A neural circuit model for a contextual association task inspired by recommender systems.

Behavioral data shows that humans and animals have the capacity to learn rules of associations applied to specific examples, and generalize these rules to a broad variety of contexts. This article focuses on neural circuit mechanisms to perform a context-dependent association task that requires linking sensory stimuli to behavioral responses and generalizing to multiple other symmetrical contexts. The model uses neural gating units that regulate the pattern of physiological connectivity within the circuit. These neural gating units can be used in a learning framework that performs low-rank matrix factorization analogous to recommender systems, allowing generalization with high accuracy to a wide range of additional symmetrical contexts. The neural gating units are trained with a biologically inspired framework involving traces of Hebbian modification that are updated based on the correct behavioral output of the network. This modeling demonstrates potential neural mechanisms for learning context-dependent association rules and for the change in selectivity of neurophysiological responses in the hippocampus. The proposed computational model is evaluated using simulations of the learning process and the application of the model to new stimuli. Further, human subject behavioral experiments were performed and the results validate the key observation of a low-rank synaptic matrix structure linking stimuli to responses.

Linking Theory to Practice: Predicting Ballistic Performance from Mechanical Properties of Aged Body Armor.

It has long been a goal of the body armor testing community to establish an individualized, scientific-based protocol for predicting the ballistic performance end of life for fielded body armor. A major obstacle in achieving this goal is the test methods used to ascertain ballistic performance, which are destructive in nature and require large sample sizes. In this work, using both the Cunniff and Phoenix-Porwal models, we derived two separate but similar theoretical relationships between the observed degradation in mechanical properties of aged body armor and its decreased ballistic performance. We present two studies used to validate the derived functions. The first correlates the degradation in mechanical properties of fielded body armor to the degradation produced by a laboratory accelerated-aging protocol. The second examines the ballistic resistance and the extracted-yarn mechanical properties of new and laboratory-aged body armor made from poly(p-phenylene-2,6-benzobisoxazole), or PBO, and poly(p-phenylene terephthalamide), or PPTA. We present correlations found between the tensile strengths of yarns extracted from armor and the ballistic limit (V50) when significant degradation of the mechanical properties of the extracted yarns was observed. These studies provided the basis for a validation data set in which we compared the experimentally measured V50 ballistic limit results to the theoretically predicted V50 results. The theoretical estimates were generally shown to provide a conservative prediction of the ballistic performance of the armor. This approach is promising for the development of a tool for fielded armor performance surveillance relying upon mechanical testing of armor coupon samples.

Respiratory substrates regulate S-nitrosylation of mitochondrial proteins through a thiol-dependent pathway.

S-Nitrosylation is a reversible post-translational modification on cysteinyl thiols that can modulate the function of redox-sensitive proteins. The S-nitrosylation of mitochondrial proteins has been shown to regulate various mitochondrial activities involved in energy-transducing systems and mitochondrion-driven apoptosis. In isolated rat brain mitochondria, we demonstrate that mitochondrial protein S-nitrosylation is regulated by respiratory substrates (glutamate/malate) through a thiol-dependent pathway. Mitochondrial proteins become susceptible to S-nitrosoglutathione (GSNO)-induced S-nitrosylation in mitochondria with an oxidized environment (low glutathione (GSH), NADH, and NADPH, and high GSSG, NAD(+), and NADP(+)) caused by isolation of mitochondria using a discontinuous Percoll gradient. Activation of mitochondrial respiration by respiratory substrates leads to increased NAD(P)H and GSH levels, which in turn reduces mitochondrial S-nitrosylated proteins. 1-Chloro-2,4-dinitrobenzene (CDNB), which depletes mitochondrial GSH and inhibits the thioredoxin-thioredoxin reductase system, prevented the denitrosylation of mitochondrial proteins caused by respiratory substrate treatment. Using biotin-switch coupled with LC-MS/MS, several mitochondrial proteins were identified as targets of S-nitrosylation including adenine nucleotide translocase (ANT) and voltage-dependent anion channel (VDAC), important components of the mitochondria permeability transition pore (MPTP), as well as ATP synthase. The S-nitrosylation of ATP synthase by GSNO was found to inhibit its activity. These findings emphasize the importance of respiratory substrates in regulating S-nitrosylation through a thiol-dependent (GSH and/or thioredoxin) pathway, with implications for mitochondrial bioenergetics and mitochondrion-driven apoptosis.

Extracting data from electronic medical records: validation of a natural language processing program to assess prostate biopsy results.

OBJECTIVE: The extraction of specific data from electronic medical records (EMR) remains tedious and is often performed manually. Natural language processing (NLP) programs have been developed to identify and extract information within clinical narrative text. We performed a study to assess the validity of an NLP program to accurately identify patients with prostate cancer and to retrieve pertinent pathologic information from their EMR. MATERIALS AND METHODS: A retrospective review was performed of a prospectively collected database including patients from the Southern California Kaiser Permanente Medical Region that underwent prostate biopsies during a 2-week period. A NLP program was used to identify patients with prostate biopsies that were positive for prostatic adenocarcinoma from all pathology reports within this period. The application then processed 100 consecutive patients with prostate adenocarcinoma to extract 10 variables from their pathology reports. The extraction and retrieval of information by NLP was then compared to a blinded manual review. RESULTS: A consecutive series of 18,453 pathology reports were evaluated. NLP correctly detected 117 out of 118 patients (99.1%) with prostatic adenocarcinoma after TRUS-guided prostate biopsy. NLP had a positive predictive value of 99.1% with a 99.1% sensitivity and a 99.9% specificity to correctly identify patients with prostatic adenocarcinoma after biopsy. The overall ability of the NLP application to accurately extract variables from the pathology reports was 97.6%. CONCLUSIONS: Natural language processing is a reliable and accurate method to identify select patients and to extract relevant data from an existing EMR in order to establish a prospective clinical database.

EEG Ictal Power Dynamics, Function-Structure Associations, and Epilepsy Surgical Outcomes.

BACKGROUND AND OBJECTIVES: Postoperative seizure control in drug-resistant temporal lobe epilepsy (TLE) remains variable, and the causes for this variability are not well understood. One contributing factor could be the extensive spread of synchronized ictal activity across networks. Our study used novel quantifiable assessments from intracranial EEG (iEEG) to test this hypothesis and investigated how the spread of seizures is determined by underlying structural network topological properties. METHODS: We evaluated iEEG data from 157 seizures in 27 patients with TLE: 100 seizures from 17 patients with postoperative seizure control (Engel score I) vs 57 seizures from 10 patients with unfavorable surgical outcomes (Engel score II-IV). We introduced a quantifiable method to measure seizure power dynamics within anatomical regions, refining existing seizure imaging frameworks and minimizing reliance on subjective human decision-making. Time-frequency power representations were obtained in 6 frequency bands ranging from theta to gamma. Ictal power spectrums were normalized against a baseline clip taken at least 6 hours away from ictal events. Electrodes' time-frequency power spectrums were then mapped onto individual T1-weighted MRIs and grouped based on a standard brain atlas. We compared spatiotemporal dynamics for seizures between groups with favorable and unfavorable surgical outcomes. This comparison included examining the range of activated brain regions and the spreading rate of ictal activities. We then evaluated whether regional iEEG power values were a function of fractional anisotropy (FA) from diffusion tensor imaging across regions over time. RESULTS: Seizures from patients with unfavorable outcomes exhibited significantly higher maximum activation sizes in various frequency bands. Notably, we provided quantifiable evidence that in seizures associated with unfavorable surgical outcomes, the spread of beta-band power across brain regions is significantly faster, detectable as early as the first second after seizure onset. There was a significant correlation between beta power during seizures and FA in the corresponding areas, particularly in the unfavorable outcome group. Our findings further suggest that integrating structural and functional features could improve the prediction of epilepsy surgical outcomes. DISCUSSION: Our findings suggest that ictal iEEG power dynamics and the structural-functional relationship are mechanistic factors associated with surgical outcomes in TLE.

Retrospective Review of the Efficacy of Droperidol Compared to Prochlorperazine for Headache Management in the Emergency Department.

Introduction Headaches are a common presentation to the emergency department, representing approximately 3% of visits. The standard treatment of headaches has consisted of either monotherapy with an antidopaminergic agent or combination therapy with an antidopaminergic agent, a non-steroidal anti-inflammatory drug (NSAID), and diphenhydramine. Although droperidol is an antidopaminergic medication, it previously was not widely used in the treatment of headaches due to safety concerns. Given its pharmacokinetics, droperidol may provide faster relief in migrainous headaches compared to more commonly used antidopaminergic agents. Methods We conducted a single-center retrospective chart review to examine the impact of droperidol compared to other standard migraine therapies on pain scores. The study consisted of three treatment arms: droperidol monotherapy, a droperidol bundle (droperidol and ketorolac), and a prochlorperazine bundle (prochlorperazine and ketorolac). Patients who received medications in treatment arms and who had an encounter diagnosis including either "headache" or "migraine" were included. Patients were excluded if under 18 years of age, imprisoned, pregnant, or received potentially migraine-altering medications prior to the first documented pain score. The primary outcome was a mean reduction in pain scores. Secondary outcomes included length of emergency department stay, rates of inpatient admission, need for rescue therapies, and adverse events. Results A total of 361 droperidol orders were reviewed, of which 79 met the inclusion criteria. Of those included, 30 orders were within the droperidol monotherapy arm, 19 were within the droperidol bundle arm, and 30 were within the prochlorperazine bundle arm. There were no significant differences in reduction of pain scores, emergency department length of stay, rates of inpatient admission, rates of rescue therapy, or adverse events between the three treatment arms. Conclusion In this study, we found no statistical difference in migraine treatment efficacy between droperidol monotherapy and droperidol and prochlorperazine-based bundle therapies. Further studies are needed with larger sample sizes and predefined timing between pain score charting and medication administration.

MRI-based deep learning can discriminate between temporal lobe epilepsy, Alzheimer's disease, and healthy controls.

BACKGROUND: Radiological identification of temporal lobe epilepsy (TLE) is crucial for diagnosis and treatment planning. TLE neuroimaging abnormalities are pervasive at the group level, but they can be subtle and difficult to identify by visual inspection of individual scans, prompting applications of artificial intelligence (AI) assisted technologies. METHOD: We assessed the ability of a convolutional neural network (CNN) algorithm to classify TLE vs. patients with AD vs. healthy controls using T1-weighted magnetic resonance imaging (MRI) scans. We used feature visualization techniques to identify regions the CNN employed to differentiate disease types. RESULTS: We show the following classification results: healthy control accuracy = 81.54% (SD = 1.77%), precision = 0.81 (SD = 0.02), recall = 0.85 (SD = 0.03), and F1-score = 0.83 (SD = 0.02); TLE accuracy = 90.45% (SD = 1.59%), precision = 0.86 (SD = 0.03), recall = 0.86 (SD = 0.04), and F1-score = 0.85 (SD = 0.04); and AD accuracy = 88.52% (SD = 1.27%), precision = 0.64 (SD = 0.05), recall = 0.53 (SD = 0.07), and F1 score = 0.58 (0.05). The high accuracy in identification of TLE was remarkable, considering that only 47% of the cohort had deemed to be lesional based on MRI alone. Model predictions were also considerably better than random permutation classifications (p < 0.01) and were independent of age effects. CONCLUSIONS: AI (CNN deep learning) can classify and distinguish TLE, underscoring its potential utility for future computer-aided radiological assessments of epilepsy, especially for patients who do not exhibit easily identifiable TLE associated MRI features (e.g., hippocampal sclerosis).

In people with temporal lobe epilepsy, seizures start in a particular part of the brain positioned behind the ears called the temporal lobe. It is difficult for a doctor to detect that a person has temporal lobe epilepsy using brain scans. In this study, we developed a computer model that was able to identify people with temporal lobe epilepsy from scans of their brain. This computer model could be used to help doctors identify temporal lobe epilepsy from brain scans in the future.

Enzyme selection, optimization, and production toward biodegradation of post-consumer poly(ethylene terephthalate) at scale.

Poly(ethylene terephthalate) (PET) is one of the world's most widely used polyester plastics. Due to its chemical stability, PET is extremely difficult to hydrolyze in a natural environment. Recent discoveries in new polyester hydrolases and breakthroughs in enzyme engineering strategies have inspired enormous research on biorecycling of PET. This study summarizes our research efforts toward large-scale, efficient, and economical biodegradation of post-consumer waste PET, including PET hydrolase selection and optimization, high-yield enzyme production, and high-capacity enzymatic degradation of post-consumer waste PET. First, genes encoding PETase and MHETase from Ideonella sakaiensis and the ICCG variant of leaf-branch compost cutinase (LCCICCG ) were codon-optimized and expressed in Escherichia coli BL21(DE3) for high-yield production. To further lower the enzyme production cost, a pelB leader sequence was fused to LCCICCG so that the enzyme can be secreted into the medium to facilitate recovery. To help bind the enzyme on the hydrophobic surface of PET, a substrate-binding module in a polyhydroxyalkanoate depolymerase from Alcaligenes faecalis (PBM) was fused to the C-terminus of LCCICCG . The resulting four different LCCICCG variants (LCC, PelB-LCC, LCC-PBM, and PelB-LCC-PBM), together with PETase and MHETase, were compared for PET degradation efficiency. A fed-batch fermentation process was developed to produce the target enzymes up to 1.2 g L-1 . Finally, the best enzyme, PelB-LCC, was selected and used for the efficient degradation of 200 g L-1 recycled PET in a well-controlled, stirred-tank reactor. The results will help develop an economical and scalable biorecycling process toward a circular PET economy.

Anti-Diabetic Therapy and Heart Failure: Recent Advances in Clinical Evidence and Molecular Mechanism.

Diabetic patients have a two- to four-fold increase in the risk of heart failure (HF), and the co-existence of diabetes and HF is associated with poor prognosis. In randomized clinical trials (RCTs), compelling evidence has demonstrated the beneficial effects of sodium-glucose co-transporter-2 inhibitors on HF. The mechanism includes increased glucosuria, restored tubular glomerular feedback with attenuated renin-angiotensin II-aldosterone activation, improved energy utilization, decreased sympathetic tone, improved mitochondria calcium homeostasis, enhanced autophagy, and reduced cardiac inflammation, oxidative stress, and fibrosis. The RCTs demonstrated a neutral effect of the glucagon-like peptide receptor agonist on HF despite its weight-reducing effect, probably due to it possibly increasing the heart rate via increasing cyclic adenosine monophosphate (cAMP). Observational studies supported the markedly beneficial effects of bariatric and metabolic surgery on HF despite no current supporting evidence from RCTs. Bromocriptine can be used to treat peripartum cardiomyopathy by reducing the harmful cleaved prolactin fragments during late pregnancy. Preclinical studies suggest the possible beneficial effect of imeglimin on HF through improving mitochondrial function, but further clinical evidence is needed. Although abundant preclinical and observational studies support the beneficial effects of metformin on HF, there is limited evidence from RCTs. Thiazolidinediones increase the risk of hospitalized HF through increasing renal tubular sodium reabsorption mediated via both the genomic and non-genomic action of PPARγ. RCTs suggest that dipeptidyl peptidase-4 inhibitors, including saxagliptin and possibly alogliptin, may increase the risk of hospitalized HF, probably owing to increased circulating vasoactive peptides, which impair endothelial function, activate sympathetic tones, and cause cardiac remodeling. Observational studies and RCTs have demonstrated the neutral effects of insulin, sulfonylureas, an alpha-glucosidase inhibitor, and lifestyle interventions on HF in diabetic patients.

Convolutional Neural Network Algorithm to Determine Lateralization of Seizure Onset in Patients With Epilepsy: A Proof-of-Principle Study.

BACKGROUND AND OBJECTIVES: A new frontier in diagnostic radiology is the inclusion of machine-assisted support tools that facilitate the identification of subtle lesions often not visible to the human eye. Structural neuroimaging plays an essential role in the identification of lesions in patients with epilepsy, which often coincide with the seizure focus. In this study, we explored the potential for a convolutional neural network (CNN) to determine lateralization of seizure onset in patients with epilepsy using T1-weighted structural MRI scans as input. METHODS: Using a dataset of 359 patients with temporal lobe epilepsy (TLE) from 7 surgical centers, we tested whether a CNN based on T1-weighted images could classify seizure laterality concordant with clinical team consensus. This CNN was compared with a randomized model (comparison with chance) and a hippocampal volume logistic regression (comparison with current clinically available measures). Furthermore, we leveraged a CNN feature visualization technique to identify regions used to classify patients. RESULTS: Across 100 runs, the CNN model was concordant with clinician lateralization on average 78% (SD = 5.1%) of runs with the best-performing model achieving 89% concordance. The CNN outperformed the randomized model (average concordance of 51.7%) on 100% of runs with an average improvement of 26.2% and outperformed the hippocampal volume model (average concordance of 71.7%) on 85% of runs with an average improvement of 6.25%. Feature visualization maps revealed that in addition to the medial temporal lobe, regions in the lateral temporal lobe, cingulate, and precentral gyrus aided in classification. DISCUSSION: These extratemporal lobe features underscore the importance of whole-brain models to highlight areas worthy of clinician scrutiny during temporal lobe epilepsy lateralization. This proof-of-concept study illustrates that a CNN applied to structural MRI data can visually aid clinician-led localization of epileptogenic zone and identify extrahippocampal regions that may require additional radiologic attention. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that in patients with drug-resistant unilateral temporal lobe epilepsy, a convolutional neural network algorithm derived from T1-weighted MRI can correctly classify seizure laterality.

Emerging Therapy for Diabetic Cardiomyopathy: From Molecular Mechanism to Clinical Practice.

Diabetic cardiomyopathy is characterized by abnormal myocardial structure or performance in the absence of coronary artery disease or significant valvular heart disease in patients with diabetes mellitus. The spectrum of diabetic cardiomyopathy ranges from subtle myocardial changes to myocardial fibrosis and diastolic function and finally to symptomatic heart failure. Except for sodium-glucose transport protein 2 inhibitors and possibly bariatric and metabolic surgery, there is currently no specific treatment for this distinct disease entity in patients with diabetes. The molecular mechanism of diabetic cardiomyopathy includes impaired nutrient-sensing signaling, dysregulated autophagy, impaired mitochondrial energetics, altered fuel utilization, oxidative stress and lipid peroxidation, advanced glycation end-products, inflammation, impaired calcium homeostasis, abnormal endothelial function and nitric oxide production, aberrant epidermal growth factor receptor signaling, the activation of the renin-angiotensin-aldosterone system and sympathetic hyperactivity, and extracellular matrix accumulation and fibrosis. Here, we summarize several important emerging treatments for diabetic cardiomyopathy targeting specific molecular mechanisms, with evidence from preclinical studies and clinical trials.

Semantic Categorization of Naming Responses Based on Prearticulatory Electrical Brain Activity.

PURPOSE: Object naming requires visual decoding, conceptualization, semantic categorization, and phonological encoding, all within 400 to 600 ms of stimulus presentation and before a word is spoken. In this study, we sought to predict semantic categories of naming responses based on prearticulatory brain activity recorded with scalp EEG in healthy individuals. METHODS: We assessed 19 healthy individuals who completed a naming task while undergoing EEG. The naming task consisted of 120 drawings of animate/inanimate objects or abstract drawings. We applied a one-dimensional, two-layer, neural network to predict the semantic categories of naming responses based on prearticulatory brain activity. RESULTS: Classifications of animate, inanimate, and abstract responses had an average accuracy of 80%, sensitivity of 72%, and specificity of 87% across participants. Across participants, time points with the highest average weights were between 470 and 490 milliseconds after stimulus presentation, and electrodes with the highest weights were located over the left and right frontal brain areas. CONCLUSIONS: Scalp EEG can be successfully used in predicting naming responses through prearticulatory brain activity. Interparticipant variability in feature weights suggests that individualized models are necessary for highest accuracy. Our findings may inform future applications of EEG in reconstructing speech for individuals with and without speech impairments.

Iron regulatory protein 2 turnover through a nonproteasomal pathway.

Iron regulatory protein 2 (IRP2) controls the synthesis of many proteins involved in iron metabolism, and the level of IRP2 itself is regulated by varying the rate of its degradation. The proteasome is known to mediate degradation, with specificity conferred by an iron-sensing E3 ligase. Most studies on the degradation of IRP2 have employed cells overexpressing IRP2 and also rendered iron deficient to further increase IRP2 levels. We utilized a sensitive, quantitative assay for IRP2, which allowed study of endogenous IRP2 degradation in HEK293A cells under more physiologic conditions. We found that under these conditions, the proteasome plays only a minor role in the degradation of IRP2, with almost all the IRP2 being degraded by a nonproteasomal pathway. This new pathway is calcium-dependent but is not mediated by calpain. Elevating the cellular level of IRP2 by inducing iron deficiency or by transfection causes the proteasomal pathway to account for the major fraction of IRP2 degradation. We conclude that under physiological, iron-sufficient conditions, the steady-state level of IRP2 in HEK293A cells is regulated by the nonproteasomal pathway.

One-Year Report of COVID-19 Impact on Geriatric Patients: a Bio-Psycho-Social Approach.

Background: Since December of 2019, coronavirus 19 (COVID-19) has spread rapidly around the world. Our understanding of the infection has grown over the past year, and its impact on older adults is particularly significant. Apart from the direct impacts of COVID-19 infections, it has also led to lockdowns which, in turn, result in isolation and loneliness. Method: We conducted a literature review of publicly available articles of the COVID-19 pandemic impact on the geriatric population between December 2019 and April 2021, a total of 748 articles. Results: The review will be presented with the Bio-Psycho-Social model, covering how the biological, psychological, and sociological aspects of health are intertwined and impact older adults. Early studies have also highlighted the prevalence of post-COVID infection symptoms that typically fall under geriatric medicine care. We highlight the bidirectional impact of isolation and COVID-19 infections on geriatric health, as well as discuss pertinent topics such as vaccine efficacy, long-term sequelae of COVID-19 infections, and ageism. Conclusion: This review seeks to present a one-year report of what is known about COVID-19 and geriatric medicine, as well as provide guidance to practitioners who care for older adults based on the most up-to-date literature.

Physical health status of music students in a post-secondary institution: A cross-sectional study.

BACKGROUND: Musicians' health is an essential field of healthcare that is specifically tailored to the needs of musicians, which encompasses multiple facets of health. OBJECTIVE: The research seeks to determine the prevalence of physical injuries in music students and musicians, and to identify possible causes. METHODS: A previously unvalidated 42-item survey was distributed to music students, non-music students, and professional musicians. The questions addressed demographics, physical health, mental health, medication use, and interest in musicians' health. The study was conducted from Fall semester 2017 to Winter semester 2019 at McGill University, with analysis completed in August 2019. RESULTS: A total of 585 complete responses were obtained. Music students (35%) had higher prevalence of physical injuries than non-music students (18%), and professional musicians had the highest prevalence (56%). Multiple factors dictate the prevalence of physical injuries among musicians, including gender, age, program of enrollment, and instrument of choice. Of note, daily duration of practice was not one of these factors. CONCLUSIONS: Several factors were identified through this cross-sectional analysis to be associated with musicians' physical injuries. These findings can serve as a foundation through which physicians and post-secondary institutions may implement changes to better enhance the physical health of musicians. It also cast doubts on previous assumptions associated with physical injury of musicians.

A computational reward learning account of social media engagement.

Social media has become a modern arena for human life, with billions of daily users worldwide. The intense popularity of social media is often attributed to a psychological need for social rewards (likes), portraying the online world as a Skinner Box for the modern human. Yet despite such portrayals, empirical evidence for social media engagement as reward-based behavior remains scant. Here, we apply a computational approach to directly test whether reward learning mechanisms contribute to social media behavior. We analyze over one million posts from over 4000 individuals on multiple social media platforms, using computational models based on reinforcement learning theory. Our results consistently show that human behavior on social media conforms qualitatively and quantitatively to the principles of reward learning. Specifically, social media users spaced their posts to maximize the average rate of accrued social rewards, in a manner subject to both the effort cost of posting and the opportunity cost of inaction. Results further reveal meaningful individual difference profiles in social reward learning on social media. Finally, an online experiment (n = 176), mimicking key aspects of social media, verifies that social rewards causally influence behavior as posited by our computational account. Together, these findings support a reward learning account of social media engagement and offer new insights into this emergent mode of modern human behavior.