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Abstract Background Salivary gland-type lung carcinomas are uncommon neoplasms of the lung, representing less than 1% of all lung tumors. The two most common among them are adenoid cystic carcinoma and mucoepidermoid carcinoma. Although they usually have an indolent behavior, adenoid cystic carcinomas can be more aggressive, with 5-year survival as low as 55%. Very few cases are reported in literature. We report a similar rare case of salivary gland type lung carcinoma that presented for the first time with unilateral opacification of left hemithorax. Case presentation A 38-year-old man of North Indian origin, who was a a nonsmoker, presented with complaints of shortness of breath and cough for 1 year, which has increased in the last 2 months and was associated with significant weight loss. A frontal radiograph of the chest and computed tomography of the chest were performed, which showed a mass in the left upper lobe of the lung with its epicenter in the left main bronchus. A bronchoscopic guided biopsy was performed, and histopathology confirmed the diagnosis of lung carcinoma of salivary gland type (adenoid cystic carcinoma). There was invasion of major vessels, hence the patient was offered and started on palliative management instead of surgical treatment. In spite of palliative management of two cycles of chemotherapy and radiotherapy, the patient succumbed to the disease within 2 months from the time of diagnosis. Conclusion Lung carcinoma of the salivary gland type (especially adenoid cystic carcinoma) usually presents at a later stage. The resectability of the tumor depends on the involvement of the surrounding major vessels. Interestingly, these cancers have no association with smoking. The prognosis depends on the extent of the disease at the time of diagnosis. Hence, imaging plays a major role in deciding the further plan of management.

Summary: Haploinsufficiency of JAG1 is the primary cause of Alagille syndrome (ALGS), a rare, multisystem disorder. The identification of JAG1 intronic variants outside of the canonical splice region as well as missense variants, both of which lead to uncertain associations with disease, confuses diagnostics. Strategies to determine whether these variants affect splicing include the study of patient RNA or minigene constructs, which are not always available or can be laborious to design, as well as the utilization of computational splice prediction tools. These tools, including SpliceAI and Pangolin, use algorithms to calculate the probability that a variant results in a splice alteration, expressed as a Δ score, with higher Δ scores (>0.2 on a 0–1 scale) positively correlated with aberrant splicing. We studied the consequence of 10 putative splice variants in ALGS patient samples through RNA analysis and compared this to SpliceAI and Pangolin predictions. We identified eight variants with aberrant splicing, seven of which had not been previously validated. Combining these data with non-canonical and missense splice variants reported in the literature, we identified a predictive threshold for SpliceAI and Pangolin with high sensitivity (Δ score >0.6). Moreover, we showed reduced specificity for variants with low Δ scores (

Abstract The expanding application of deep brain stimulation (DBS) therapy both drives and is informed by our growing understanding of disease pathophysiology and innovations in neurosurgical care. Neurophysiological targeting, a mainstay for identifying optimal, motor responsive targets, has remained largely unchanged for decades. Utilizing deep learning-based computer vision and related computational methods, we developed an effective and simple intraoperative approach to objectively correlate neural signals with movements, automating and standardizing the otherwise manual and subjective process of identifying ideal DBS electrode placements. Kinematics are extracted from video recordings of intraoperative motor testing using a trained deep neural network and compared to multi-unit activity recorded from the subthalamic nucleus. Neuro-motor correlations were quantified using dynamic time warping with the strength of a given comparison measured by comparing against a null distribution composed of related neuro-motor correlations. This objective measure was then compared to clinical determinations as recorded in surgical case notes. In seven DBS cases for treatment of Parkinson’s disease, 100 distinct motor testing epochs were extracted for which clear clinical determinations were made. Neuro-motor correlations derived by our automated system compared favorably with expert clinical decision making in post-hoc comparisons, although follow-up studies are necessary to determine if improved correlation detection leads to improved outcomes. By improving the classification of neuro-motor relationships, the automated system we have developed will enable clinicians to maximize the therapeutic impact of DBS while also providing avenues for improving continued care of treated patients.

Clinical assessments of movement disorders currently rely on the administration of rating scales, which, while clinimetrically validated and reliable, depend on clinicians’ subjective analyses, resulting in interrater differences. Intraoperative microelectrode recording for deep brain stimulation targeting similarly relies on clinicians’ subjective evaluations of movement-related neural activity. Digital motion tracking can improve the diagnosis, assessment, and treatment of movement disorders by generating objective, standardized measures of patients’ kinematics. Motion tracking with concurrent neural recording also enables motor neuroscience studies to elucidate the neurophysiology underlying movements. Despite these promises, motion tracking has seen limited adoption in clinical settings due to the drawbacks of conventional motion tracking systems and practical limitations associated with clinical settings. However, recent advances in deep learning based computer vision algorithms have made accurate, robust markerless motion tracking viable in any setting where digital video can be captured. Here, we review and discuss the potential clinical applications and technical limitations of deep learning based markerless motion tracking methods with a focus on DeepLabCut (DLC), an open-source software package that has been extensively applied in animal neuroscience research. We first provide a general overview of DLC, discuss its present usage, and describe the advantages that DLC confers over other motion tracking methods for clinical use. We then present our preliminary results from three ongoing studies that demonstrate the use of DLC for 1) movement disorder patient assessment and diagnosis, 2) intraoperative motor mapping for deep brain stimulation targeting and 3) intraoperative neural and kinematic recording for basic human motor neuroscience.

Aim: The purpose of this study was to determine the prevalence of developmental dental anomalies in the East Indian subpopulation. Materials and Methods: The study was based on clinical examination, evaluation of dental casts, and radiographs of 2385 Indian patients (1169 males and 1216 females), who visited Dental Institute, Rajendra Institute of Medical Sciences, Ranchi. These patients were examined for shape anomalies, number anomalies, structural anomalies, and positional anomalies. Results: It was observed that 5.83% of patients reported with a dental anomaly. Males reported with higher incidence with a male-to-female ratio of 1: 0.96. Microdontia was the most common anomaly. Unilateral microdontia was more common than bilateral and was more prominent in males (9.05%). It was observed that peg laterals were frequently encountered developmental anomaly at 1.34%, while the incidence of amelogenesis imperfecta and dentinogenesis imperfecta was 0.29% and 0.33%, respectively. The present demography reported a very low incidence of dens, fusion, hyperdontia, hypodontia, and macrodontia at

Optimal placement of deep brain stimulation (DBS) therapy for treating movement disorders routinely relies on intraoperative motor testing for target determination. However, in current practice, motor testing relies on subjective interpretation and correlation of motor and neural information. Recent advances in computer vision could improve assessment accuracy. We describe our application of deep learning-based computer vision to conduct markerless tracking for measuring motor behaviors of patients undergoing DBS surgery for the treatment of Parkinson’s disease. Video recordings were acquired during intraoperative kinematic testing (N = 5 patients), as part of standard of care for accurate implantation of the DBS electrode. Kinematic data were extracted from videos post-hoc using the Python-based computer vision suite DeepLabCut. Both manual and automated (80.00% accuracy) approaches were used to extract kinematic episodes from threshold derived kinematic fluctuations. Active motor epochs were compressed by modeling upper limb deflections with a parabolic fit. A semi-supervised classification model, support vector machine (SVM), trained on the parameters defined by the parabolic fit reliably predicted movement type. Across all cases, tracking was well calibrated (i.e., reprojection pixel errors 0.016–0.041; accuracies >95%). SVM predicted classification demonstrated high accuracy (85.70%) including for two common upper limb movements, arm chain pulls (92.30%) and hand clenches (76.20%), with accuracy validated using a leave-one-out process for each patient. These results demonstrate successful capture and categorization of motor behaviors critical for assessing the optimal brain target for DBS surgery. Conventional motor testing procedures have proven informative and contributory to targeting but have largely remained subjective and inaccessible to non-Western and rural DBS centers with limited resources. This approach could automate the process and improve accuracy for neuro-motor mapping, to improve surgical targeting, optimize DBS therapy, provide accessible avenues for neuro-motor mapping and DBS implantation, and advance our understanding of the function of different brain areas.

Using novel virtual cities, we investigated the influence of verbal and visual strategies on the encoding of navigation-relevant information in a large-scale virtual environment. In 2 experiments, participants watched videos of routes through 4 virtual cities and were subsequently tested on their memory for observed landmarks and their ability to make judgments regarding the relative directions of the different landmarks along the route. In the first experiment, self-report questionnaires measuring visual and verbal cognitive styles were administered to examine correlations between cognitive styles, landmark recognition, and judgments of relative direction. Results demonstrate a tradeoff in which the verbal cognitive style is more beneficial for recognizing individual landmarks than for judging relative directions between them, whereas the visual cognitive style is more beneficial for judging relative directions than for landmark recognition. In a second experiment, we manipulated the use of verbal and visual strategies by varying task instructions given to separate groups of participants. Results confirm that a verbal strategy benefits landmark memory, whereas a visual strategy benefits judgments of relative direction. The manipulation of strategy by altering task instructions appears to trump individual differences in cognitive style. Taken together, we find that processing different details during route encoding, whether due to individual proclivities (Experiment 1) or task instructions (Experiment 2), results in benefits for different components of navigation-relevant information. These findings also highlight the value of considering multiple sources of individual differences as part of spatial cognition investigations.