The role of gastric electrical stimulation in postsurgical gastroparesis: a retrospective analysis from 2 centers.

BACKGROUND: Postsurgical gastroparesis, resulting from surgical interventions on the stomach or vagal nerve injury, poses significant clinical challenges with patients presenting symptoms such as nausea, vomiting, and abdominal pain. Although gastric electrical stimulation (GES) offers potential relief, its efficacy in refractory postsurgical gastroparesis requires further examination. This study evaluated the clinical response to GES in patients with refractory postsurgical gastroparesis. METHODS: A retrospective study was conducted across 2 study sites, involving 185 patients with drug-refractory postsurgical gastroparesis who underwent both temporary and permanent GES placements. Patients were categorized based on their surgical history: bariatric surgery, Nissen fundoplication, and others. The impact of GES was evaluated using Food and Drug Administration-compliant patient-reported outcomes scores and other relevant clinical metrics at baseline, after temporary GES placement, and 6 months after permanent GES placement. All 3 groups were also analyzed by the symptom improved group vs the unimproved group at baseline and 6 months after GES placement. RESULTS: After GES implantation, all patient groups significantly improved upper gastrointestinal symptoms. The bariatric surgery group and Nissen fundoplication group specifically identified anorexia as the most severe symptom after GES after temporary GES placement among 3 groups (2.5 [0.4-3.5] and 1.5 [0.0-2.5], respectively). Nissen fundoplication patients had the highest score of anorexia among the 3 groups 6 months after GES (3.0 [2.0-3.5], P = .018). Despite these improvements, GES did not enhance gastric emptying test results. Symptomatic improvements were notably significant in patients who initially reported higher symptom severity than those who did not. CONCLUSION: GES shows promise in alleviating symptoms of refractory postsurgical gastroparesis, particularly in those with severe initial symptoms. However, its impact on gastric emptying remains inconclusive. Further research is needed to establish GES as a standard treatment for postsurgical gastroparesis.

Autonomic and Enteric Profiling May Help Predict Response to Diverse Obesity Therapies.

PURPOSE: Changes in autonomic (ANS) and enteric nervous systems (ENS) may be involved in pathogenesis of obesity. We hypothesized that baseline autonomic and enteric parameters may predict outcomes of diverse obesity therapies. MATERIAL AND METHODS: We studied ANS and ENS physiology in 37 patients (8 male, 29 female, age 45 years, weight 129.7 kg) at 4 centers in patients undergoing medical (9: low-calorie diet) versus invasive (22: 16 sleeve, 6 bypass) and semi-invasive (6: 2 band, 2 high energy stimulation, 2 aspiration) weight loss therapies. Weight loss was reported as percent weight loss from baseline to latest values at 1 year and in some up to 5 years; classified as < or > /= 20% for each group. ANS testing included sympathetic adrenergic function by measuring reflex vasoconstriction and postural adjustment ratio. ENS was measured non-invasively using cutaneous low-resolution electrogastrogram. RESULTS: Percent weight loss was greater with the invasive (28.5%) than semi-invasive (9.1%) or non-invasive low-calorie diet (4.4%) (p < .001). Percent weight loss at 1 year (and up to 5 years) corresponded to the adrenergic measure of postural adjustment ratio (r = .42, p = .012), total pulse amplitude at rest (r = .56, p < .001), and electrogastrogram standing-to-rest difference (r = .33, p = .056). CONCLUSION: Baseline autonomic and enteric function measures correspond to percentage with loss in this pilot study using diverse weight loss methods. Autonomic and enteric profiling has potential clinical use for evaluation and treatment of obesity but needed larger controlled trials.

Model-data-driven adversarial active learning for brain tumor segmentation.

Active learning (AL) attempts to select informative samples in a dataset to minimize the number of required labels while maximizing the performance of the model. Current AL in segmentation tasks is limited to the expansion of popular classification-based methods including entropy, MC-dropout, etc. Meanwhile, most applications in the medical field are simply migrations that fail to consider the nature of medical images, such as high class imbalance, high domain difference, and data scarcity. In this study, we address these challenges and propose a novel AL framework for medical image segmentation task. Our approach introduces a pseudo-label-based filter addressing excessive blank patches in medical abnormalities segmentation tasks, e.g., lesions, and tumors, used before the AL selection. This filter helps reduce resource usage and allows the model to focus on selecting more informative samples. For the sample selection, we propose a novel query strategy that combines both model impact and data stability by employing adversarial attack. Furthermore, we harness the adversarial samples generated during the query process to enhance the robustness of the model. The experimental results verify our framework's effectiveness over various state-of-the-art methods. Our proposed method only needs less than 14% annotated patches in 3D brain MRI multiple sclerosis (MS) segmentation tasks and 20% for Low-Grade Glioma (LGG) tumor segmentation to achieve competitive results with full supervision. These promising outcomes not only improve performance but alleviate the time burden associated with expert annotation, thereby facilitating further advancements in the field of medical image segmentation. Our code is available at https://github.com/HelenMa9998/adversarial_active_learning.

Using deep learning-derived image features in radiologic time series to make personalised predictions: proof of concept in colonic transit data.

OBJECTIVES: Siamese neural networks (SNN) were used to classify the presence of radiopaque beads as part of a colonic transit time study (CTS). The SNN output was then used as a feature in a time series model to predict progression through a CTS. METHODS: This retrospective study included all patients undergoing a CTS in a single institution from 2010 to 2020. Data were partitioned in an 80/20 Train/Test split. Deep learning models based on a SNN architecture were trained and tested to classify images according to the presence, absence, and number of radiopaque beads and to output the Euclidean distance between the feature representations of the input images. Time series models were used to predict the total duration of the study. RESULTS: In total, 568 images of 229 patients (143, 62% female, mean age 57) patients were included. For the classification of the presence of beads, the best performing model (Siamese DenseNET trained with a contrastive loss with unfrozen weights) achieved an accuracy, precision, and recall of 0.988, 0.986, and 1. A Gaussian process regressor (GPR) trained on the outputs of the SNN outperformed both GPR using only the number of beads and basic statistical exponential curve fitting with MAE of 0.9 days compared to 2.3 and 6.3 days (p < 0.05) respectively. CONCLUSIONS: SNNs perform well at the identification of radiopaque beads in CTS. For time series prediction our methods were superior at identifying progression through the time series compared to statistical models, enabling more accurate personalised predictions. CLINICAL RELEVANCE STATEMENT: Our radiologic time series model has potential clinical application in use cases where change assessment is critical (e.g. nodule surveillance, cancer treatment response, and screening programmes) by quantifying change and using it to make more personalised predictions. KEY POINTS: • Time series methods have improved but application to radiology lags behind computer vision. Colonic transit studies are a simple radiologic time series measuring function through serial radiographs. • We successfully employed a Siamese neural network (SNN) to compare between radiographs at different points in time and then used the output of SNN as a feature in a Gaussian process regression model to predict progression through the time series. • This novel use of features derived from a neural network on medical imaging data to predict progression has potential clinical application in more complex use cases where change assessment is critical such as in oncologic imaging, monitoring for treatment response, and screening programmes.

Evaluation of a marker-less, intra-operative, augmented reality guidance system for robot-assisted laparoscopic radical prostatectomy.

PURPOSE: Robot-assisted laparoscopic radical prostatectomy (RALRP) using the da Vinci surgical robot is a common treatment for organ-confined prostate cancer. Augmented reality (AR) can help during RALRP by showing the surgeon the location of anatomical structures and tumors from preoperative imaging. Previously, we proposed hand-eye and camera intrinsic matrix estimation procedures that can be carried out with conventional instruments within the patient during surgery, take < 3 min to perform, and fit seamlessly in the existing surgical workflow. In this paper, we describe and evaluate a complete AR guidance system for RALRP and quantify its accuracy. METHODS: Our AR system requires three transformations: the transrectal ultrasound (TRUS) to da Vinci transformation, the camera intrinsic matrix, and the hand-eye transformation. For evaluation, a 3D-printed cross-wire was visualized in TRUS and stereo endoscope in a water bath. Manually triangulated cross-wire points from stereo images were used as ground truth to evaluate overall TRE between these points and points transformed from TRUS to camera. RESULTS: After transforming the ground-truth points from the TRUS to the camera coordinate frame, the mean target registration error (TRE) (SD) was [Formula: see text] mm. The mean TREs (SD) in the x-, y-, and z-directions are [Formula: see text] mm, [Formula: see text] mm, and [Formula: see text] mm, respectively. CONCLUSIONS: We describe and evaluate a complete AR guidance system for RALRP which can augment preoperative data to endoscope camera image, after a deformable magnetic resonance image to TRUS registration step. The streamlined procedures with current surgical workflow and low TRE demonstrate the compatibility and readiness of the system for clinical translation. A detailed sensitivity study remains part of future work.

Fabrication, optimization, and in vitro evaluation of docetaxel-loaded nanostructured lipid carriers for improved anticancer activity.

Lung cancer is the leading cause of cancer-related deaths in both men and women worldwide. It is the leading cancer killer in both men and women in every Ethnic Group. A major problem associated with chemotherapies against their lung cancer is the lack of selective toxicity, which results in a narrow therapeutic index thereby compromising clinical prognosis. To circumvent these challenges, the present investigation sought to develop a docetaxel-loaded nanostructured lipid carrier system (DTX-NLCS) for the treatment of lung cancer. A 3-factor/3-level Box-Behnken Design was applied to systematically optimize the DTX-NLCS parameters. The amount of drug, emulsifier concentration, and homogenization speed was selected as independent variables, while the particle size and % entrapment efficiency (%EE) were selected as dependent variables. The optimized batch parameters were 29.81 mg drug, 19.97% w/w emulsifier, and 13 200 (rpm) speed of homogenization with a mean particle size of 154.1 ± 3.13 nm and a mean %EE of 86.12 ± 3.48%. The in vitro lipolysis experiments revealed that the optimized DTX-NLCs were stabilized by 10% w/w PEG 4000 mono-stearate and exhibited an anti-lipolytic effect. Furthermore, the in vitro gastrointestinal stability studies (at pH-1.2, pH-4.5, pH-6.8, and pH-7.4) revealed that the optimized developed system could withstand various GI tract media. The in vitro dissolution studies depicted a pH-independent controlled-release consistent with the Weibull model. In vitro cytotoxicity studies using NCI-H460 cell lines further revealed that there was a reduction in IC50 values in the DTX-NLCS treated cells as compared to those treated with the pure drug, indicating an improved efficiency for the developed system.

Oral Delivery of Anticancer Agents Using Nanoparticulate Drug Delivery System.

BACKGROUND: Conventionally, anti-cancer agents were administered through the intravenous route. The major drawbacks associated with the intravenous route of administration are: severe side effects, need of hospitalization, nursing care, and palliative treatment. In order to overcome the drawbacks associated with the intravenous route of administration, oral delivery of anti-cancer agents has gained tremendous interest among the scientific fraternity. Oral delivery of anti-cancer agents principally leads to a reduction in the overall cost of treatment, and aids in improving the quality of life of patients. Bioavailability of drugs and inter-subject variability are the major concerns with oral administration of anti-cancer agents. Factors viz. physicochemical and biological barriers (pre-systemic metabolism and transmembrane efflux of the drug) are accountable for hampering oral bioavailability of anti-cancer agents can be efficiently overcome by employing nanocarrier based drug delivery systems. Oral delivery of anticancer agents by employing these drug delivery systems will not only improve the quality of life of patients but will also provide pharmacoeconomic advantage and lead to a reduction in the overall cost of treatment of life-threatening disease like cancer. OBJECTIVE: This article aims to familiarize the readers with some of the recent advancements in the field of nanobased drug delivery systems for oral delivery of anticancer agents. CONCLUSION: Advancement in the field of nanotechnology-based drug delivery systems has opened up gateways for the delivery of drugs that are difficult to administer orally. Oral delivery of anti-cancer agents by these drug delivery systems will not only improve the quality of life of patients but will also provide pharmacoeconomic advantage and lead to a reduction in the overall cost of treatment of life-threatening disease like cancer.

Accurate and robust deep learning-based segmentation of the prostate clinical target volume in ultrasound images.

The goal of this work was to develop a method for accurate and robust automatic segmentation of the prostate clinical target volume in transrectal ultrasound (TRUS) images for brachytherapy. These images can be difficult to segment because of weak or insufficient landmarks or strong artifacts. We devise a method, based on convolutional neural networks (CNNs), that produces accurate segmentations on easy and difficult images alike. We propose two strategies to achieve improved segmentation accuracy on difficult images. First, for CNN training we adopt an adaptive sampling strategy, whereby the training process is encouraged to pay more attention to images that are difficult to segment. Secondly, we train a CNN ensemble and use the disagreement among this ensemble to identify uncertain segmentations and to estimate a segmentation uncertainty map. We improve uncertain segmentations by utilizing the prior shape information in the form of a statistical shape model. Our method achieves Hausdorff distance of 2.7 ±â€¯2.3 mm and Dice score of 93.9 ±â€¯3.5%. Comparisons with several competing methods show that our method achieves significantly better results and reduces the likelihood of committing large segmentation errors. Furthermore, our experiments show that our approach to estimating segmentation uncertainty is better than or on par with recent methods for estimation of prediction uncertainty in deep learning models. Our study demonstrates that estimation of model uncertainty and use of prior shape information can significantly improve the performance of CNN-based medical image segmentation methods, especially on difficult images.

A "pickup" stereoscopic camera with visual-motor aligned control for the da Vinci surgical system: a preliminary study.

PURPOSE: The current state-of-the-art surgical robotic systems use only a single endoscope to view the surgical field. Research has been conducted to introduce additional cameras to the surgical system, giving rise to new camera angles that cannot be achieved using the endoscope alone. While this additional visualization certainly aids in surgical performance, current systems lack visual-motor compatibility with respect to the additional camera views. We propose a new system that overcomes this limitation. METHODS: In this paper, we introduce a novel design of an additional "pickup" camera that can be integrated into the da Vinci Surgical System. We also introduce a solution to work comfortably in the various arbitrary views this camera provides by eliminating visual-motor misalignment. This is done by changing the working frame of the surgical instruments to work with respect to the coordinate system at the "pickup" camera instead of the endoscope. RESULTS: Human user trials ([Formula: see text]) were conducted to evaluate the effect of visual-motor alignment with respect to the "pickup" camera on surgical performance. An inanimate surgical peg transfer task from the validated Fundamentals of Laparoscopic Surgery (FLS) Training Curriculum was used, and an improvement of 73% in task completion time and 80% in accuracy was observed with the visual-motor alignment over the case without it. CONCLUSION: Our study shows that there is a requirement to achieve visual-motor alignment when utilizing views from external cameras in current clinical surgical robotics setups. We introduce a complete system that provides additional camera views with visual-motor aligned control. Such a system would be useful in existing surgical procedures and could also impact surgical planning and navigation.

On the feasibility of transperineal 3D ultrasound image guidance for robotic radical prostatectomy.

PURPOSE: Prostate cancer is the most prevalent form of male-specific cancers. Robot-assisted laparoscopic radical prostatectomy (RALRP) using the da Vinci surgical robot has become the gold-standard treatment for organ-confined prostate cancer. To improve intraoperative visualization of anatomical structures, many groups have developed techniques integrating transrectal ultrasound (TRUS) into the surgical workflow. TRUS, however, is intrusive and does not provide real-time volumetric imaging. METHODS: We propose a proof-of-concept system offering an alternative noninvasive transperineal view of the prostate and surrounding structures using 3D ultrasound (US), allowing for full-volume imaging in any anatomical plane desired. The system aims to automatically track da Vinci surgical instruments and display a real-time US image registered to preoperative MRI. We evaluate the approach using a custom prostate phantom, an iU22 (Philips Healthcare, Bothell, WA) US machine with an xMATRIX X6-1 transducer, and a custom probe fixture. A novel registration method between the da Vinci kinematic frame and 3D US is presented. To evaluate the entire registration pipeline, we use a previously developed MRI to US deformable registration algorithm. RESULTS: Our US calibration technique yielded a registration error of 0.84 mm, compared to 1.76 mm with existing methods. We evaluated overall system error with a prostate phantom, achieving a target registration error of 2.55 mm. CONCLUSION: Transperineal imaging using 3D US is a promising approach for image guidance during RALRP. Preliminary results suggest this system is comparable to existing guidance systems using TRUS. With further development and testing, we believe our system has the potential to improve patient outcomes by imaging anatomical structures and prostate cancer in real time.

Marker-less real-time intra-operative camera and hand-eye calibration procedure for surgical augmented reality.

Accurate medical Augmented Reality (AR) rendering requires two calibrations, a camera intrinsic matrix estimation and a hand-eye transformation. We present a unified, practical, marker-less, real-time system to estimate both these transformations during surgery. For camera calibration we perform calibrations at multiple distances from the endoscope, pre-operatively, to parametrize the camera intrinsic matrix as a function of distance from the endoscope. Then, we retrieve the camera parameters intra-operatively by estimating the distance of the surgical site from the endoscope in less than 1 s. Unlike in prior work, our method does not require the endoscope to be taken out of the patient; for the hand-eye calibration, as opposed to conventional methods that require the identification of a marker, we make use of a rendered tool-tip in 3D. As the surgeon moves the instrument and observes the offset between the actual and the rendered tool-tip, they can select points of high visual error and manually bring the instrument tip to match the virtual rendered tool tip. To evaluate the hand-eye calibration, 5 subjects carried out the hand-eye calibration procedure on a da Vinci robot. Average Target Registration Error of approximately 7mm was achieved with just three data points.

Pharmaceutical aspects of silver nanoparticles.

Silver nanoparticles are particles in the size ranging between 1 and 100 nm. The two major methods used for synthesis of silver nanoparticle are the physical and chemical methods with the disadvantage that they are expensive and can also have toxicity. Biological method is being used as an expedient alternative, as this approach is environment-friendly and less toxic and it includes plant extracts, microorganism, fungi, etc. The major applications of silver nanoparticles in the medical field include diagnostic applications and therapeutic applications, apart from its antimicrobial activity. Due to their nanotoxicity, AgNPs have a several drawbacks too. This review presents a complete view of the mechanism of action, synthesis, the pharmacokinetics of silver nanoparticles, different formulations of AgNPs used in biomedical applications, infertility management, antibacterial effects, skin damage, burns, cancer treatment, etc. and various applications of silver nanoparticles together with the possible toxicological challenge.

Pharmaceutical potential of quantum dots.

Quantum dots (QDs) or fluorescent nanocrystals are designed nanoparticles that are promising for several biological and bio-medical applications as well as drug delivery and simultaneous cellular imaging. QD's have exhibited promising potential primarily in receptor based targeting as a result of their distinctive physicochemical properties. Functionalized QDs (f-QDs) have been developed as effective, safe, nano-sized smart systems to deliver a wide range of bio-actives. Surface modified fluorescent carbon QDs with surface modification have attracted attention as targeting ligand to accomplish cellular targeting with enhanced specificity. Several surface engineered and conjugated fluorescent carbon QDs are presently being explored for the treatment of cancer and the outcome is eagerly awaited.