Biomarkers related to hypertrophy of the ligamentum flavum: a systematic review of the literature.

OBJECTIVE: Spinal stenosis is one of the most common spinal disorders in the elderly. Hypertrophy of the ligamentum flavum (HLF) can contribute to spinal stenosis. The current literature suggests that various biomarkers may play important roles in the pathogenesis of HLF. However, the connection between these biomarkers and the development of HLF is still not well understood. This systematic review aims to explore the current literature on biomarkers related to the development of HLF. METHODS: A literature search was conducted using PubMed, Embase, Web of Science, and Cochrane Library. The search strategy looked for the titles, abstracts, and keywords of studies that contained a combination of the following phrases: "ligamentum flavum OR yellow ligament," "biomarkers," and "hypertrophy." Recorded data included study design, demographic characteristics (number of patients of each gender and mean age), study period, country where the study was conducted, biomarkers, and diagnostic modalities used. Risk of bias was assessed using the Newcastle-Ottawa Scale for case-control studies. RESULTS: The authors identified 39 studies. After screening, 26 full-text original articles assessing one or more biomarkers related to HLF were included. The included studies were conducted over a 22-year period. The most popular biomarkers studied, in order of frequency reported, were collagen types I and III (n = 10), transforming growth factor ß (TGF-ß) (n = 8), and interleukin (IL)-6 (n = 6). The authors found that mechanical stretching forces, tissue inhibitor of metalloproteinases 2 (TIMP-2) induction, and TGF-ß were associated with increased amounts of collagen I and III. IL-6 expression was increased by microRNA-21, as well as by leptin, through the nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) pathway. CONCLUSIONS: Biomarkers such as TGF-ß, IL-6, and collagen I and III have been consistently correlated with the development of HLF. However, the pathogenesis of HLF remains unclear due to the heterogeneity of the studies, patient populations, and research at the molecular level. Further studies are necessary to better characterize the pathogenesis of HLF and provide a more comprehensive understanding of how these biomarkers may aid in the diagnosis and treatment of HLF.

Simulated outcomes for durotomy repair in minimally invasive spine surgery.

Minimally invasive spine surgery (MISS) is increasingly performed using endoscopic and microscopic visualization, and the captured video can be used for surgical education and development of predictive artificial intelligence (AI) models. Video datasets depicting adverse event management are also valuable, as predictive models not exposed to adverse events may exhibit poor performance when these occur. Given that no dedicated spine surgery video datasets for AI model development are publicly available, we introduce Simulated Outcomes for Durotomy Repair in Minimally Invasive Spine Surgery (SOSpine). A validated MISS cadaveric dural repair simulator was used to educate neurosurgery residents, and surgical microscope video recordings were paired with outcome data. Objects including durotomy, needle, grasper, needle driver, and nerve hook were then annotated. Altogether, SOSpine contains 15,698 frames with 53,238 annotations and associated durotomy repair outcomes. For validation, an AI model was fine-tuned on SOSpine video and detected surgical instruments with a mean average precision of 0.77. In summary, SOSpine depicts spine surgeons managing a common complication, providing opportunities to develop surgical AI models.

Pilot Analysis of Surgeon Instrument Utilization Signatures Based on Shannon Entropy and Deep Learning for Surgeon Performance Assessment in a Cadaveric Carotid Artery Injury Control Simulation.

BACKGROUND AND OBJECTIVES: Assessment and feedback are critical to surgical education, but direct observational feedback by experts is rarely provided because of time constraints and is typically only qualitative. Automated, video-based, quantitative feedback on surgical performance could address this gap, improving surgical training. The authors aim to demonstrate the ability of Shannon entropy (ShEn), an information theory metric that quantifies series diversity, to predict surgical performance using instrument detections generated through deep learning. METHODS: Annotated images from a publicly available video data set of surgeons managing endoscopic endonasal carotid artery lacerations in a perfused cadaveric simulator were collected. A deep learning model was implemented to detect surgical instruments across video frames. ShEn score for the instrument sequence was calculated from each surgical trial. Logistic regression using ShEn was used to predict hemorrhage control success. RESULTS: ShEn scores and instrument usage patterns differed between successful and unsuccessful trials (ShEn: 0.452 vs 0.370, P < .001). Unsuccessful hemorrhage control trials displayed lower entropy and less varied instrument use patterns. By contrast, successful trials demonstrated higher entropy with more diverse instrument usage and consistent progression in instrument utilization. A logistic regression model using ShEn scores (78% accuracy and 97% average precision) was at least as accurate as surgeons' attending/resident status and years of experience for predicting trial success and had similar accuracy as expert human observers. CONCLUSION: ShEn score offers a summative signal about surgeon performance and predicted success at controlling carotid hemorrhage in a simulated cadaveric setting. Future efforts to generalize ShEn to additional surgical scenarios can further validate this metric.

A methodology for the annotation of surgical videos for supervised machine learning applications.

PURPOSE: Surgical data science is an emerging field focused on quantitative analysis of pre-, intra-, and postoperative patient data (Maier-Hein et al. in Med Image Anal 76: 102306, 2022). Data science approaches can decompose complex procedures, train surgical novices, assess outcomes of actions, and create predictive models of surgical outcomes (Marcus et al. in Pituitary 24: 839-853, 2021; Røadsch et al. in Nat Mach Intell, 2022). Surgical videos contain powerful signals of events that may impact patient outcomes. A necessary step before the deployment of supervised machine learning methods is the development of labels for objects and anatomy. We describe a complete method for annotating videos of transsphenoidal surgery. METHODS: Endoscopic video recordings of transsphenoidal pituitary tumor removal surgeries were collected from a multicenter research collaborative. These videos were anonymized and stored in a cloud-based platform. Videos were uploaded to an online annotation platform. Annotation framework was developed based on a literature review and surgical observations to ensure proper understanding of the tools, anatomy, and steps present. A user guide was developed to trained annotators to ensure standardization. RESULTS: A fully annotated video of a transsphenoidal pituitary tumor removal surgery was produced. This annotated video included over 129,826 frames. To prevent any missing annotations, all frames were later reviewed by highly experienced annotators and a surgeon reviewer. Iterations to annotated videos allowed for the creation of an annotated video complete with labeled surgical tools, anatomy, and phases. In addition, a user guide was developed for the training of novice annotators, which provides information about the annotation software to ensure the production of standardized annotations. CONCLUSIONS: A standardized and reproducible workflow for managing surgical video data is a necessary prerequisite to surgical data science applications. We developed a standard methodology for annotating surgical videos that may facilitate the quantitative analysis of videos using machine learning applications. Future work will demonstrate the clinical relevance and impact of this workflow by developing process modeling and outcome predictors.

Entropy estimation within in vitro neural-astrocyte networks as a measure of development instability.

The brain demands a significant fraction of the energy budget in an organism; in humans, it accounts for 2% of the body mass, but utilizes 20% of the total energy metabolized. This is due to the large load required for information processing; spiking demands from neurons are high but are a key component to understanding brain functioning. Astrocytic brain cells contribute to the healthy functioning of brain circuits by mediating neuronal network energy and facilitating the formation and stabilization of synaptic connectivity. During development, spontaneous activity influences synaptic formation, shaping brain circuit construction, and adverse astrocyte mutations can lead to pathological processes impacting cognitive impairment due to inefficiencies in network spiking activity. We have developed a measure that quantifies information stability within in vitro networks consisting of mixed neural-astrocyte cells. Brain cells were harvested from mice with mutations to a gene associated with the strongest known genetic risk factor for Alzheimer's disease, APOE. We calculate energy states of the networks and using these states, we present an entropy-based measure to assess changes in information stability over time. We show that during development, stability profiles of spontaneous network activity are modified by exogenous astrocytes and that network stability, in terms of the rate of change of entropy, is allele dependent.

Medical Cannabis Certification Is Associated With Decreased Opiate Use in Patients With Chronic Pain: A Retrospective Cohort Study in Delaware.

Background Opioid medications are commonly used to treat chronic pain around the world. While these medications are quite effective at reducing pain, they can create opioid dependence and lead to further drug addiction. Long-term opioid use has significantly contributed to the "opioid epidemic" that is currently ravaging the United States, leading to opioid overdoses and unintentional deaths, particularly in Delaware. Objective To determine if medical marijuana certification helps patients in Delaware with chronic pain reduce their opiate use. Methods In this study, we examined individuals who were provided with legal; medical cannabis certifications in the state of Delaware between June 2018 and October 2019 and were concurrently being treated with opioid medications for chronic pain at a private pain management practice. Using a posthoc analysis, we conducted a retrospective cohort study on the individuals (n = 81) to determine if there was a decrease in their opioid use following medical cannabis certification. Opioid use was measured in morphine milligram equivalent (MME) through the Delaware prescription monitoring program (PMP) database. Results Overall, the average change in prescribed opioid use was found to be -12.3 morphine milligram equivalent (MME) units when including all individuals (p < 0.00001). Among the included individuals with baseline opioid use, medical cannabis certification was associated with a 31.3% average decrease in opioid use (n = 63). When examining subgroups based upon pain location, individuals with neck pain displayed a 41.5% average decrease in MME (n = 27), while individuals with low back pain were observed to have a 29.4% decrease in opioid use (n = 58). Similarly, individuals with knee pain (n = 14) reduced their opioid use by 32.6%. Conclusion The results display an association between medical cannabis certification and a decrease in opiate use among the study group individuals. This study suggests that medical cannabis use may help individuals to reduce their opiate requirements along with physician intervention. More research is needed to validate these findings with appropriate controls and verification of cannabis use.

Possible Bias in the Publication Trends of High Impact Factor Anesthesiology and Gastroenterology Journals -An Analysis of 5 Years' Data.

BACKGROUND: We hypothesize that being an editorial board member (EBM) in a high impact factor specialty medical journal increases the chances of publishing in the same journal. MATERIALS AND METHODS: The publication trends of the first five EBMs in the five highest impact factor Anesthesiology and Gastroenterology journals were analyzed. Preceding 5 years' publications appearing on PubMed were grouped into as follows: number of publications in the journal in which the EBM serves (N1), number of publications by the same author in the other four highest impact factor (IF) journals (N2) and number of publications in all the other journals (N3). We evaluated the probability of the observed distribution of publications in the five highest IF journals happening by chance alone, assuming that all the EBMs had the same opportunity of publishing in any of these journals. The probability of publishing in their own journal was assumed to be one fifth. RESULTS: The EBMs published their manuscripts in their own journal at a very high frequency. Encompassing all ten journals, the calculated P value for such a distribution was <0.001. In two journals, Anesthesia and Analgesia and Anaesthesia, the EBMs' publications in their journal were more than twice the cumulative total in the remaining four journals. In three of the five gastroenterology journals analyzed, combined publications of the five EBMs were greater in their own journal than the remaining four journals combined. CONCLUSIONS: Despite proclaimed fair peer review process, EBMs seem to get preference in their own journals.

Repetitive Transcranial Magnetic Stimulation in Chronic Pain: A Meta-analysis.

BACKGROUND: In this meta-analysis, we explore the role of repetitive transcranial magnetic stimulation (rTMS), a noninvasive neuromodulation technique in the treatment of chronic pain. METHODS: Studies comparing rTMS and conventional treatment for chronic pain were searched. The comparison was made for decrease in the pain scores with and without (sham) the use of rTMS after a follow-up interval of 4-8 weeks. All reported pain scores were converted into a common scale ranging from "0" (no pain) to "10" (worst pain). RESULTS: Nine trials with 183 patients in each of the groups were included in the analysis. The decrease in pain scores with rTMS was 1.12 (95% confidence interval [CI] being 1.46-0.78) (fixed effects, I2 = 0%, P < 0.001) and in sham-rTMS was 0.28 (95% CI being 0.49-0.07) (Fixed effects, I2 = 0, P = 0.01). The pooled mean drop in pain scores with rTMS therapy was higher by 0.79 (95% CI being 0.26-1.33) (fixed effects, I2 = 0, P < 0.01). The duration and frequency of rTMS were highly variable across trials. Publication bias was unlikely (Egger's test, X-intercept = 0.13, P = 0.75). CONCLUSIONS: Use of rTMS improves the efficacy of conventional medical treatment in chronic pain patients. This treatment is not associated with any direct adverse effects. However, the duration and frequency of rTMS therapy is presently highly variable and needs standardization.