Virtual Reality Helps Describe the Progression of Thyroid Cartilage Calcification.

OBJECTIVES: Thyroid cartilage (TC) calcifications may impact surgical planning and clinical management. However, few studies to date have implemented virtual reality (VR) to evaluate these calcifications. This study assessed the feasibility of evaluating TC calcifications in various regions and measuring their volumes through VR models generated from computed tomography scans. We also investigated age and gender-related differences in calcification patterns. METHODS: Ninety-two participants were categorized into younger, middle-aged, and older age groups. Calcification patterns (degree in Hounsfield units and volume of calcification in cm3) in different TC regions were identified by VR analysis, which enabled comparisons between age groups and genders. RESULTS: Significant differences in calcification patterns were observed between males and females, particularly in the middle right, middle left, bottom left, and vertex regions. Age-related differences in the vertex region showed increased calcification in the older age group. CONCLUSION: This study points to the contribution of VR in the evaluation of complex anatomical structures. The findings revealed significant gender and age patterns in TC calcification. These insights can inform surgical planning and highlight the potential of using VR to gain a better understanding of TC calcification clinically.

Are LRRK2 p.G2019S or GBA1 variants associated with long-term outcomes of deep brain stimulation for Parkinson's disease?

BACKGROUND: Deep brain stimulation (DBS) is a well-established treatment option for individuals with advanced Parkinson's disease (PD). The potential influence of the LRRK2 p.G2019S or GBA1 variants on its lasting efficacy and adverse effects should be better characterized. METHODS: We conducted a retrospective single-center case-control study involving PD patients who were carriers of a GBA1 variant (GBA1-PD), the LRRK2 p.G2019S variant (LRRK2-PD), and non-carriers (Nc-PD). All participants underwent DBS and were followed up for at least a year. Assessments before surgery and at 1, 2, 3, 5, and 10 years post-DBS included the following: the Movement Disorder Society's Unified PD Rating Scale (MDS-UPDRS) Part III, Hoehn and Yahr scale, Levodopa Equivalent Daily Dose (LEDD) and non-motor symptoms (psychotic episodes, depressive symptoms, and cognitive decline). RESULTS: The sample was composed of 103 patients (72 males, mean age at DBS surgery 61.5 ± 8.7 years, mean postoperative follow-up 7.0 ± 4.1 years). Of these, 19 were LRRK2-PD, 20 GBA1-PD, and 64 were Nc-PD. No significant differences in motor outcomes were observed between the groups. Compared to the Nc-PD patients, the GBA1-PD patients were at increased risk of both psychotic episodes [hazard ratio (HR) 2.76 (95 % CI: 1.12-6.80), p = 0.027], and cognitive decline [HR 2.28 (95 % CI: 1.04-5.00), p = 0.04]. CONCLUSION: LRRK2 and GBA1 variant status did not affect the motor outcomes of DBS in PD patients. However, GBA1-PD patients were at increased risk for psychosis and cognitive decline. Further studies are required to determine the role of genetic stratification in referral to DBS.

Current state of the art and future directions for implantable sensors in medical technology: Clinical needs and engineering challenges.

Implantable sensors have revolutionized the way we monitor biophysical and biochemical parameters by enabling real-time closed-loop intervention or therapy. These technologies align with the new era of healthcare known as healthcare 5.0, which encompasses smart disease control and detection, virtual care, intelligent health management, smart monitoring, and decision-making. This review explores the diverse biomedical applications of implantable temperature, mechanical, electrophysiological, optical, and electrochemical sensors. We delve into the engineering principles that serve as the foundation for their development. We also address the challenges faced by researchers and designers in bridging the gap between implantable sensor research and their clinical adoption by emphasizing the importance of careful consideration of clinical requirements and engineering challenges. We highlight the need for future research to explore issues such as long-term performance, biocompatibility, and power sources, as well as the potential for implantable sensors to transform healthcare across multiple disciplines. It is evident that implantable sensors have immense potential in the field of medical technology. However, the gap between research and clinical adoption remains wide, and there are still major obstacles to overcome before they can become a widely adopted part of medical practice.

Virtual reality-based training and pre-operative planning for head and neck sentinel lymph node biopsy.

OBJECTIVE: Sentinel lymph node biopsy (SLNB) is crucial for managing head and neck skin cancer. However, variable lymphatic drainage can complicate SLN detection when using Single-Photon Emission Computed Tomography (SPECT) or lymphoscintigraphy. Virtual Reality (VR) can contribute to pre-operative planning by simulating a realistic 3D model, which improves orientation. VR can also facilitate real-patient training outside the operating room. This study explored using a VR platform for pre-operative planning in head and neck skin cancer patients undergoing SLNBs and assessed its value for residential training. MATERIALS AND METHODS: In this prospective technology pilot study, attending surgeons and residents who performed 21 SLNB operations on patients with head and neck skin cancers (81% males, mean age 69.2 ± 11.3) used a VR simulation model based on each patient's pre-operative SPECT scan to examine patient-specific anatomy. After surgery, they completed a questionnaire on the efficiency of the VR simulation as a pre-operative planning tool and training device for residents. RESULTS: The attending surgeons rated the VR model's accuracy at 8.3 ± 1.6 out of 10. Three-quarters (76%) of residents reported increased confidence after using VR. The physicians rated the platform's contribution to residents' training at 7.4 ± 2.1 to 8.9 ± 1.3 out of 10. CONCLUSION: A VR SLNB simulation can accurately portray marked sentinel lymph nodes. It was rated high as a surgical planning and teaching tool among attending surgeons and residents alike and may play a role in pre-operative planning and resident training. Further studies are needed to explore its applications in practice.

Long-term safety of medical cannabis in Parkinson's disease: A retrospective case-control study.

BACKGROUND: Whole-plant medical cannabis (MC) products are widely used for controlling symptoms associated with Parkinson's disease (PD). Despite its widespread use, few studies have investigated the long-term impact of MC on the progression of PD or its safety profile. This study examined the effects of MC on PD in a real-life setting. METHODS: A retrospective case-control study of 152 idiopathic PD patients (mean age 69.1 ± 9.0 years), followed at the Sheba Medical Center Movement Disorders Institute (SMDI) from 2008 to 2022 was conducted. Seventy-six patients who used licensed whole-plant medical cannabis (MC) for at least a year were compared to a matched group who did not receive MC in terms of their Levodopa Equivalent Daily Dose (LEDD), Hoehn and Yahr (H&Y) stage, and cognitive, depressive, and psychotic symptoms. RESULTS: The median monthly dose of MC was 20 g (IQR: 20-30), with a median Tetrahydrocannabinol (THC) percentage of 10 (IQR: 9.5-14.15) and a median Cannabidiol (CBD) percentage of 4 (IQR: 2-10). There were no significant differences between the MC and the control groups for LEDD or H&Y stage progression (p = 0.90, 0.77, respectively). A Kaplan-Meier analysis showed no evidence of relative worsening of psychotic, depressive, or cognitive symptoms reported by patients to their treating physicians over time in the MC group (p = 0.16-0.50). CONCLUSION: Over the 1-3 years of follow-ups, the MC treatment regimens appeared to be safe. MC did not exacerbate neuropsychiatric symptoms and had no detrimental effects on disease progression.

Proof of concept: Comparative accuracy of semiautomated VR modeling for volumetric analysis of the heart ventricles.

Introduction: Simpson's rule is generally used to estimate cardiac volumes. By contrast, modern methods such as Virtual Reality (VR) utilize mesh modeling to present the object's surface spatial structure, thus enabling intricate volumetric calculations. In this study, two types of semiautomated VR models for cardiac volumetric analysis were compared to the standard Philips dedicated cardiac imaging platform (PDP) which is based on Simpson's rule calculations. Methods: This retrospective report examined the cardiac computed tomography angiography (CCTA) of twenty patients with atrial fibrillation obtained prior to a left atrial appendage occlusion procedure. We employed two VR models to evaluate each CCTA and compared them to the PDP: a VR model with Philips-similar segmentations (VR-PS) that included the trabeculae and the papillary muscles within the luminal volume, and a VR model that only included the inner blood pool (VR-IBP). Results: Comparison of the VR-PS and the PDP left ventricle (LV) volumes demonstrated excellent correlation with a ρ c of 0.983 (95% CI 0.96, 0.99), and a small mean difference and range. The calculated volumes of the right ventricle (RV) had a somewhat lower correlation of 0.89 (95% CI 0.781, 0.95), a small mean difference, and a broader range. The VR-IBP chamber size estimations were significantly smaller than the estimates based on the PDP. Discussion: Simpson's rule and polygon summation algorithms produce similar results in normal morphological LVs. However, this correlation failed to emerge when applied to RVs and irregular chambers. Conclusions: The findings suggest that the polygon summation method is preferable for RV and irregular LV volume and function calculations.

Association between age at type 1 diabetes diagnosis and metabolic outcome at young adulthood: a real-life observational study.

BACKGROUND: Younger age at diagnosis of type 1 diabetes (T1D) may affect the clinical course and outcome. We examined whether age at diagnosis was associated with glycemic control and metabolic outcome in young adulthood. METHODS: This observational study included 105 young adults with T1D (current mean age: 21.2 ± 3.0 years, mean age at diagnosis 12.0 ± 4.0 years) followed during 2012 to 2019. Data on HbA1c, glucose variability, continuous glucose monitoring (CGM) metrics, body mass index (BMI), blood pressure (BP), and body composition were collected from medical records from age 18 years until last visit, and the association between age at diagnosis and outcomes was assessed. RESULTS: Age at T1D diagnosis was negatively associated with HbA1c levels (r = -0.368, P = .001), BMI (r = -0.218, P = .026), and diastolic BP (r = -0.215, P = .028). Younger age at diagnosis predicted poorer glycemic control after controlling for T1D duration, sex, socioeconomic status, BMI, and CGM use (r2 = 0.19, P = .002). There was a 0.1% greater HbA1c reduction for every yearly increase in age at diagnosis (ß = -0.090, P = .042). The mean metabolic age of females diagnosed at <10 years of age was older than their chronological age (P = .049). CONCLUSIONS: Younger age at T1D diagnosis predicts worse glycemic control at young adulthood, independent of recognized confounding risk factors (disease duration, sex, socioeconomic status, weight, and use of diabetes technology). Female patients diagnosed at a young age have an older metabolic age, indicating the need for lifestyle alteration to improve their basal metabolic rate.