Global research trends in central nervous system tuberculosis - A bibliometric analysis.

Background: Central Nervous System Tuberculosis (CNS-TB) is a serious public health concern causing significant morbidity and mortality, especially in high TB burden countries. Despite the expanding research landscape of CNS-TB, there is no comprehensive map of this field. This work aims to (1) obtain a current and comprehensive overview of the CNS-TB research landscape, (2) investigate the intellectual and social structure of CNS-TB publications, and (3) detect geographical discrepancies in scientific production, highlighting regions requiring increased research focus. Methods: We conducted a bibliometric analysis on CNS-TB literature indexed in Web of Science from 2000 to 2022, evaluating 2130 articles. The dataset was analyzed in R for descriptive statistics. We used R-bibliometrix and VOSViewer for data visualization. Findings: Publication output grew annually at an average rate of 6·88%, driven primarily by India and China. International collaborations comprised 16·44% of total publications but contributed to 11 of the 15 top-cited papers. Additionally, we identified discrepancies of CNS-TB research in many low- and middleincome countries relative to their TB incidence. Interpretation: Our findings reveal a growing interest in CNS-TB research from China and India, countries with rapidly developing economies, high TB burdens, and a recent increase in research funding. Furthermore, we found that international collaborations are correlated with high impact and accessibility of CNS-TB research. Finally, we identified disparities in CNS-TB research in specific countries, particularly in many low- and middle-income countries, emphasizing the need for increased research focus in these regions.

Evaluating Computer Vision, Large Language, and Genome-Wide Association Models in a Limited Sized Patient Cohort for Pre-Operative Risk Stratification in Adult Spinal Deformity Surgery.

Background: Adult spinal deformities (ASD) are varied spinal abnormalities, often necessitating surgical intervention when associated with pain, worsening deformity, or worsening function. Predicting post-operative complications and revision surgery is critical for surgical planning and patient counseling. Due to the relatively small number of cases of ASD surgery, machine learning applications have been limited to traditional models (e.g., logistic regression or standard neural networks) and coarse clinical variables. We present the novel application of advanced models (CNN, LLM, GWAS) using complex data types (radiographs, clinical notes, genomics) for ASD outcome prediction. Methods: We developed a CNN trained on 209 ASD patients (1549 radiographs) from the Stanford Research Repository, a CNN pre-trained on VinDr-SpineXR (10,468 spine radiographs), and an LLM using free-text clinical notes from the same 209 patients, trained via Gatortron. Additionally, we conducted a GWAS using the UK Biobank, contrasting 540 surgical ASD patients with 7355 non-surgical ASD patients. Results: The LLM notably outperformed the CNN in predicting pulmonary complications (F1: 0.545 vs. 0.2881), neurological complications (F1: 0.250 vs. 0.224), and sepsis (F1: 0.382 vs. 0.132). The pre-trained CNN showed improved sepsis prediction (AUC: 0.638 vs. 0.534) but reduced performance for neurological complication prediction (AUC: 0.545 vs. 0.619). The LLM demonstrated high specificity (0.946) and positive predictive value (0.467) for neurological complications. The GWAS identified 21 significant (p < 10-5) SNPs associated with ASD surgery risk (OR: mean: 3.17, SD: 1.92, median: 2.78), with the highest odds ratio (8.06) for the LDB2 gene, which is implicated in ectoderm differentiation. Conclusions: This study exemplifies the innovative application of cutting-edge models to forecast outcomes in ASD, underscoring the utility of complex data in outcome prediction for neurosurgical conditions. It demonstrates the promise of genetic models when identifying surgical risks and supports the integration of complex machine learning tools for informed surgical decision-making in ASD.

Ten-Year Durability of Hypothalamic Deep Brain Stimulation in Treatment of Chronic Cluster Headaches: A Case Report and Literature Review.

Chronic cluster headache (CCH) is a debilitating primary headache that causes excruciating pain without remission. Various medical and surgical treatments have been implemented over the years, yet many provide only short-term relief. Deep brain stimulation (DBS) is an emerging treatment alternative that has been shown to dramatically reduce the intensity and frequency of headache attacks. However, reports of greater than 10-year outcomes after DBS for CCH are scant. Here, we report the durability of DBS in the posterior inferior hypothalamus after 10 years on a patient with CCH. Our patient experienced an 82% decrease in the frequency of headaches after DBS, which was maintained for over 10 years. The side effects observed included depression, irritability, anxiety, and dizziness, which were alleviated by changing programming settings. In the context of current literature, DBS shows promise for long-term relief of cluster headaches when other treatments fail.

The endophilin curvature-sensitive motif requires electrostatic guidance to recycle synaptic vesicles in vivo.

Curvature-sensing mechanisms assist proteins in executing particular actions on various membrane organelles. Here, we investigate the functional specificity of curvature-sensing amphipathic motifs in Caenorhabditis elegans through the study of endophilin, an endocytic protein for synaptic vesicle recycling. We generate chimeric endophilin proteins by replacing the endophilin amphipathic motif H0 with other curvature-sensing amphipathic motifs. We find that the role of amphipathic motifs cannot simply be extrapolated from the identity of their parental proteins. For example, the amphipathic motif of the nuclear pore complex protein NUP133 functionally replaces the synaptic role of endophilin H0. Interestingly, non-functional endophilin chimeras have similar defects-producing fewer synaptic vesicles but more endosomes-and this indicates that the curvature-sensing motifs in these chimeras have a common deficiency for reforming synaptic vesicles. Finally, we convert non-functional endophilin chimeras into functional proteins by changing the cationic property of amphipathic motifs, successfully reprogramming the functional specificity of curvature-sensing motifs in vivo.

Circumventing neural damage in a C. elegans chemosensory circuit using genetically engineered synapses.

Neuronal loss can considerably diminish neural circuit function, impairing normal behavior by disrupting information flow in the circuit. Here, we use genetically engineered electrical synapses to reroute the flow of information in a C. elegans damaged chemosensory circuit in order to restore organism behavior. We impaired chemotaxis by removing one pair of interneurons from the circuit then artificially coupled two other adjacent neuron pairs by ectopically expressing the gap junction protein, connexin, in them. This restored chemotaxis in the animals. We expected to observe linear and direct information flow between the connexin-coupled neurons in the recovered circuit but also revealed the formation of new potent left-right lateral electrical connections within the connexin-expressing neuron pairs. Our analysis suggests that these additional electrical synapses help restore circuit function by amplifying weakened neuronal signals in the damaged circuit in addition to emulating the wild-type circuit. A record of this paper's transparent peer review process is included in the Supplemental Information.