Insulin Dependence Negatively Impacts Outcomes in Anterior Cervical Discectomy with Fusions: A Ten-Year Retrospective Analysis.

BACKGROUND CONTEXT: Although anterior cervical discectomy and fusion (ACDF) procedures for cervical spine disease have been increasing amid a growing diabetic patient population, there is a paucity of literature focusing on insulin-dependence as a risk-factor for post-operative ACDF complications. PURPOSE: To evaluate the differential impact of insulin dependence on perioperative outcomes including total length of stay, surgical, and medical complications within thirty days following ACDF. STUDY DESIGN/SETTING: A retrospective cohort, large multicenter database study. PATIENT SAMPLE: The American College of Surgeons National Surgical Quality Improvement Program database was queried to retrospectively identify patients who had undergone ACDF between 2011 and 2021 using the Current Procedural Terminology code 22551. OUTCOME MEASURES: Perioperative surgical and medical complications. METHODS: The study population was divided into three groups 1) insulin-dependent diabetes mellitus (IDDM), 2) insulin-independent diabetes mellitus (NIDDM), and 3) no diabetes mellitus (non-DM). One-way analysis of variance for continuous variables and chi-square tests for categorical variables were used to identify differences in perioperative variables between the three groups. Multivariable logistic regression analysis assessed the effect of diabetes mellitus status on post-operative medical and surgical outcomes. RESULTS: A total of 85,758 ACDF procedures were identified between 2011 and 2021, of which 5,178 were IDDM, 9,652 were NIDDM, and 70,982 were non-DM. The rates of surgical and medical complication varied between the three groups. IDDM patients had the highest rates of at least one medical complication (6.1%). Only IDDM increased the risk for medical complications (OR: 1.320, 95% CI [1.144-1.518]) and extended hospital length of stay (LOS) (OR: 1.244, 95% CI [1.071-1.441]) following a multivariate logistic regression analysis. CONCLUSION: Patients with IDDM were at an increased risk for postoperative medical complications and extended hospital LOS. Personalized postoperative management, guided by risk assessment is indicated for this population. These findings can be used to improve risk stratification and informed consent for DM patients who are insulin dependent.

Mechanosensitive ion channels MSL8, MSL9, and MSL10 have environmentally sensitive intrinsically disordered regions with distinct biophysical characteristics in vitro.

Intrinsically disordered protein regions (IDRs) are highly dynamic sequences that rapidly sample a collection of conformations over time. In the past several decades, IDRs have emerged as a major component of many proteomes, comprising ~30% of all eukaryotic protein sequences. Proteins with IDRs function in a wide range of biological pathways and are notably enriched in signaling cascades that respond to environmental stresses. Here, we identify and characterize intrinsic disorder in the soluble cytoplasmic N-terminal domains of MSL8, MSL9, and MSL10, three members of the MscS-like (MSL) family of mechanosensitive ion channels. In plants, MSL channels are proposed to mediate cell and organelle osmotic homeostasis. Bioinformatic tools unanimously predicted that the cytosolic N-termini of MSL channels are intrinsically disordered. We examined the N-terminus of MSL10 (MSL10N) as an exemplar of these IDRs and circular dichroism spectroscopy confirms its disorder. MSL10N adopted a predominately helical structure when exposed to the helix-inducing compound trifluoroethanol (TFE). Furthermore, in the presence of molecular crowding agents, MSL10N underwent structural changes and exhibited alterations to its homotypic interaction favorability. Lastly, interrogations of collective behavior via in vitro imaging of condensates indicated that MSL8N, MSL9N, and MSL10N have sharply differing propensities for self-assembly into condensates, both inherently and in response to salt, temperature, and molecular crowding. Taken together, these data establish the N-termini of MSL channels as intrinsically disordered regions with distinct biophysical properties and the potential to respond uniquely to changes in their physiochemical environment.

SHEPHARD: a modular and extensible software architecture for analyzing and annotating large protein datasets.

MOTIVATION: The emergence of high-throughput experiments and high-resolution computational predictions has led to an explosion in the quality and volume of protein sequence annotations at proteomic scales. Unfortunately, sanity checking, integrating, and analyzing complex sequence annotations remains logistically challenging and introduces a major barrier to entry for even superficial integrative bioinformatics. RESULTS: To address this technical burden, we have developed SHEPHARD, a Python framework that trivializes large-scale integrative protein bioinformatics. SHEPHARD combines an object-oriented hierarchical data structure with database-like features, enabling programmatic annotation, integration, and analysis of complex datatypes. Importantly SHEPHARD is easy to use and enables a Pythonic interrogation of largescale protein datasets with millions of unique annotations. We use SHEPHARD to examine three orthogonal proteome-wide questions relating protein sequence to molecular function, illustrating its ability to uncover novel biology. AVAILABILITY AND IMPLEMENTATION: We provided SHEPHARD as both a stand-alone software package (https://github.com/holehouse-lab/shephard), and as a Google Colab notebook with a collection of precomputed proteome-wide annotations (https://github.com/holehouse-lab/shephard-colab).

Using spreading activation to understand repetitive negative thinking.

Repetitive negative thinking (RNT) describes a recursive, unproductive pattern of thought that is commonly observed in individuals who experience anxiety and depression. Past research on RNT has primarily relied on self-report, which fails to capture the potential mechanisms that underlie the persistence of maladaptive thought. We investigated whether RNT may be maintained by a negatively biased semantic network. The present study used a modified free association task to assess state RNT. Following the presentation of a valenced (positive, neutral, negative) cue word, participants generated a series of free associates, which allowed for the dynamic progression of responses. State RNT was conceptualised as the length of consecutive, negatively valenced free associates (i.e. chains). Participants also completed two self-report measures that assessed trait RNT and trait negative affect. Within a structural equation model, negative (but not positive or neutral) response chain length positively predicted trait RNT and negative affect, and this was only the case for positive (but not negative or neutral) cue words. These results suggest that RNT tendencies may be reflected in semantic retrieval and can be assessed without self-report.

PTSD Avoidance Symptoms Associated With Alcohol Craving in Treatment-Seeking Veteran Population.

Objective: Posttraumatic stress disorder (PTSD) is one of the most frequently treated behavioral health conditions within the Department of Veterans Affairs and often co-occurs with alcohol or substance use. Past research suggests that alcohol and/or substance use may be used to cope with PTSD symptoms but there are inconsistent findings in how specific PTSD symptom clusters are associated with alcohol use disorder (AUD) or substance use disorder (SUD). Evaluating the relationship between PTSD symptom clusters and craving for individual drug of dependence may help explain these ambiguous results. Methods: Veterans (N = 167) recently engaged in mental health residential treatment were recruited to participate in a semi-structured diagnostic interview (Structured Clinical Interview for the DSM-5, Research Version [SCID-5-RV]) to assess for past 12-month history of AUD/SUD. Participants also completed the Posttraumatic Stress Disorder Checklist for DSM-5 (PCL-5) to assess PTSD symptoms. Results: Covarying for severity of alcohol use, avoidance symptoms were significantly associated with alcohol craving for veterans with alcohol as their drug of dependence. Covarying for severity of stimulant use, no PTSD symptom clusters were associated with stimulant craving for veterans with stimulants as their drug of dependence. Conclusions: Veterans with high levels of PTSD avoidance symptoms may experience alcohol craving symptoms because they believe that alcohol use will eliminate or alleviate thoughts, feelings, or external reminders of the trauma. These results have important clinical implications in the treatment of co-occurring PTSD and AUD.

Developing Acute Event Risk Profiles for Older Adults with Dementia in Long-Term Care Using Motor Behavior Clusters Derived from Deep Learning.

OBJECTIVES: This paper uses deep (machine) learning techniques to develop and test how motor behaviors, derived from location and movement sensor tracking data, may be associated with falls, delirium, and urinary tract infections (UTIs) in long-term care (LTC) residents. DESIGN: Longitudinal observational study. SETTING AND PARTICIPANTS: A total of 23 LTC residents (81,323 observations) with cognitive impairment or dementia in 2 northeast Department of Veterans Affairs LTC facilities. METHODS: More than 18 months of continuous (24/7) monitoring of motor behavior and activity levels used objective radiofrequency identification sensor data to track and record movement data. Occurrence of acute events was recorded each week. Unsupervised deep learning models were used to classify motor behaviors into 5 clusters; supervised decision tree algorithms used these clusters to predict acute health events (falls, delirium, and UTIs) the week before the week of the event. RESULTS: Motor behaviors were classified into 5 categories (Silhouette score = 0.67), and these were significantly different from each other. Motor behavior classifications were sensitive and specific to falls, delirium, and UTI predictions 1 week before the week of the event (sensitivity range = 0.88-0.91; specificity range = 0.71-0.88). CONCLUSION AND IMPLICATIONS: Intraindividual changes in motor behaviors predict some of the most common and detrimental acute events in LTC populations. Study findings suggest real-time locating system sensor data and machine learning techniques may be used in clinical applications to effectively prevent falls and lead to the earlier recognition of risk for delirium and UTIs in this vulnerable population.

Molecular dissection of RbpA-mediated regulation of fidaxomicin sensitivity in mycobacteria.

RNA polymerase (RNAP) binding protein A (RbpA) is essential for mycobacterial viability and regulates transcription initiation by increasing the stability of the RNAP-promoter open complex (RPo). RbpA consists of four domains: an N-terminal tail (NTT), a core domain (CD), a basic linker, and a sigma interaction domain. We have previously shown that truncation of the RbpA NTT and CD increases RPo stabilization by RbpA, implying that these domains inhibit this activity of RbpA. Previously published structural studies showed that the NTT and CD are positioned near multiple RNAP-σA holoenzyme functional domains and predict that the RbpA NTT contributes specific amino acids to the binding site of the antibiotic fidaxomicin (Fdx), which inhibits the formation of the RPo complex. Furthermore, deletion of the NTT results in decreased Mycobacterium smegmatis sensitivity to Fdx, but whether this is caused by a loss in Fdx binding is unknown. We generated a panel of rbpA mutants and found that the RbpA NTT residues predicted to directly interact with Fdx are partially responsible for RbpA-dependent Fdx activity in vitro, while multiple additional RbpA domains contribute to Fdx activity in vivo. Specifically, our results suggest that the RPo-stabilizing activity of RbpA decreases Fdx activity in vivo. In support of the association between RPo stability and Fdx activity, we find that another factor that promotes RPo stability in bacteria, CarD, also impacts to Fdx sensitivity. Our findings highlight how RbpA and other factors may influence RNAP dynamics to affect Fdx sensitivity.