Volatile anaesthesia and peri-operative outcomes related to cancer: a feasibility and pilot study for a large randomised control trial.

Published data suggest that the type of general anaesthesia used during surgical resection for cancer may impact on patient long-term outcome. However, robust prospective clinical evidence is essential to guide a change in clinical practice. We explored the feasibility of conducting a randomised controlled trial to investigate the impact of total intravenous anaesthesia with propofol vs. inhalational volatile anaesthesia on postoperative outcomes of patients undergoing major cancer surgery. We undertook a randomised, double-blind feasibility and pilot study of propofol total intravenous anaesthesia or volatile-based maintenance anaesthesia during cancer resection surgery at three tertiary hospitals in Australia and the USA. Patients were randomly allocated to receive propofol total intravenous anaesthesia or volatile-based maintenance anaesthesia. Primary outcomes for this study were successful recruitment to the study and successful delivery of the assigned anaesthetic treatment as per randomisation arm. Of the 217 eligible patients approached, 146 were recruited, a recruitment rate of 67.3% (95%CI 60.6-73.5%). One hundred and forty-five patients adhered to the randomised treatment arm, 99.3% (95%CI 96.2-100%). Intra-operative patient characteristics and postoperative complications were comparable between the two intervention groups. This feasibility and pilot study supports the viability of the protocol for a large, randomised controlled trial to investigate the effect of anaesthesia technique on postoperative cancer outcomes. The volatile anaesthesia and peri-operative outcomes related to cancer (VAPOR-C) study that is planned to follow this feasibility study is an international, multicentre trial with the aim of providing evidence-based guidelines for the anaesthetic management of patients undergoing major cancer surgery.

Impact of a risk-stratified thromboprophylaxis protocol on the incidence of postoperative venous thromboembolism and bleeding.

Efforts to reduce postoperative venous thromboembolism are challenging due to heterogeneity in thromboprophylaxis practice. As a result, a 'one-size-fits-all' approach that accounts for surgery-specific risk, but fails to account for patient-level variation, is often adopted by healthcare networks. Updated clinical practice guidelines have advocated an individualised risk-stratified approach that balances the risk:benefit ratio associated with thromboprophylaxis; however, there are limited data confirming effectiveness of these recommendations on the incidence of postoperative venous thromboembolism and bleeding. We developed the surgical-thrombo-embolism-prevention protocol, a novel risk-stratified algorithm that classified patients into low-, intermediate-, and high-risk profiles according to surgical procedure and patient baseline medical risk. Expert-endorsed risk-specific thromboprophylaxis strategies were then applied. A staged quality improvement program was developed to implement the protocol. We postulated that compliance with the protocol would reduce postoperative venous thromboembolism rates without increasing the incidence of postoperative bleeding. Between June 2013 and March 2018, we evaluated the efficacy, safety and sustainability of this risk-stratified approach in 24,953 surgical admissions at a dedicated cancer centre. By final implementation, program compliance was 91%. Postoperative venous thromboembolism rates reduced from 3.1 per 1000 surgical admissions to 0.6 per 1000 surgical admissions (relative risk reduction 79%; p < 0.005). Postoperative bleeding rates also declined from 10.0 per 1000 surgical admissions to 6.3 per 1000 surgical admissions (relative risk reduction 37%; p = 0.02). Sustained improvement was evident more than 3 years after implementation. Implementation of the surgical-thrombo-embolism-prevention protocol significantly reduced the incidence of postoperative venous thromboembolism supporting its validation at other institutions.

Strategies for success: a multi-institutional study on robot-assisted partial nephrectomy for complex renal lesions.

OBJECTIVE: To describe our technique, illustrated with images and videos, of robot-assisted partial nephrectomy (RAPN) for challenging renal tumours. PATIENTS AND METHODS: A study of 249 patients who underwent RAPN in multiple institutions was performed. Patients were identified using prospective RAPN databases. Complex renal lesion were defined as those with a RENAL nephrometry score ≥10. Data were analysed and differences among groups examined. RESULTS: A total of 31 (12.4%) RAPNs were performed for complex renal tumours. The median (interquartile range [IQR]) patient age was 57 (50.5-70.5) years and 21 patients (67.7%) were men. The median (IQR) American Society of Anesthesiologists score was 2 (2-3). The median (IQR) operating time was 200 (50-265) min, warm ischaemia time was 23 (18.5-29) min, and estimated blood loss was 200 (50-265) mL. There were no intra-operative complications. Two patients (6.4%) had postoperative complications. One patient (3.2%) had a positive surgical margin. The median (IQR) length of stay was 3.5 (3-5) days and the median (IQR) follow-up was 12.5 (7-24) months. There were no recurrences. RAPN resulted in statistically significant changes in renal function 3 months after RAPN compared with preoperative renal function (P < 0.001). CONCLUSION: Our results showed that RAPN was a safe approach for selected patients with complex renal tumours and may facilitate tumour resection and renorrhaphy for challenging cases, offering a minimally invasive surgical option for patients who may otherwise require open surgery.

Determination of an optimally sensitive and specific chemical exchange saturation transfer MRI quantification metric in relevant biological phantoms.

The purpose of this study was to develop realistic phantom models of the intracellular environment of metastatic breast tumour and naïve brain, and using these models determine an analysis metric for quantification of CEST MRI data that is sensitive to only labile proton exchange rate and concentration. The ability of the optimal metric to quantify pH differences in the phantoms was also evaluated. Novel phantom models were produced, by adding perchloric acid extracts of either metastatic mouse breast carcinoma cells or healthy mouse brain to bovine serum albumin. The phantom model was validated using 1 H NMR spectroscopy, then utilized to determine the sensitivity of CEST MRI to changes in pH, labile proton concentration, T1 time and T2 time; six different CEST MRI analysis metrics (MTRasym , APT*, MTRRex , AREX and CESTR* with and without T1 /T2 compensation) were compared. The new phantom models were highly representative of the in vivo intracellular environment of both tumour and brain tissue. Of the analysis methods compared, CESTR* with T1 and T2 time compensation was optimally specific to changes in the CEST effect (i.e. minimal contamination from T1 or T2 variation). In phantoms with identical protein concentrations, pH differences between phantoms could be quantified with a mean accuracy of 0.6 pH units. We propose that CESTR* with T1 and T2 time compensation is the optimal analysis method for these phantoms. Analysis of CEST MRI data with T1 /T2 time compensated CESTR* is reproducible between phantoms, and its application in vivo may resolve the intracellular alkalosis associated with breast cancer brain metastases without the need for exogenous contrast agents.

Venous thromboembolism prevention during asparaginase-based therapy for acute lymphoblastic leukemia.

BACKGROUND: Venous thromboembolism (vte) is a recognized complication in patients treated with asparaginase-containing chemotherapy regimens; the optimal preventive strategy is unclear. We assessed the safety and efficacy of prophylaxis using low-dose low molecular weight heparin in adult patients with acute lymphoblastic leukemia in complete remission treated with an asparaginase-based post-remission chemotherapy regimen. METHODS: As part of the intensification phase of the Dana-Farber Cancer Institute 91-01 regimen, asparaginase was administered weekly to 41 consecutive patients for 21-30 weeks; these patients also received prophylaxis with enoxaparin 40 mg daily (60 mg for patients ≥80 kg). Outcomes were assessed against outcomes in a comparable cohort of 99 patients who received the same chemotherapy regimen without anticoagulation prophylaxis. RESULTS: The overall rate of symptomatic venous thrombosis was not significantly different in the prophylaxis and non-prophylaxis cohorts (18.92% and 21.74% respectively). Among patients receiving prophylaxis, vte occurred in higher proportion in those who weighed at least 80 kg (42.86% vs. 4.35%, p = 0.0070). No major bleeding complications occurred in the prophylaxis group (minor bleeding: 8.1%). CONCLUSIONS: Prophylaxis with low-dose enoxaparin during the intensification phase was safe, but was not associated with a lower overall proportion of vte.

Concurrent loss of co-stimulatory molecules and functional cytokine secretion attributes leads to proliferative senescence of CD8(+) T cells in HIV/TB co-infection.

The role of T-cell immunosenescence and functional CD8(+) T-cell responses in HIV/TB co-infection is unclear. We examined and correlated surrogate markers of HIV disease progression with immune activation, immunosenescence and differentiation using T-cell pools of HIV/TB co-infected, HIV-infected and healthy controls. Our investigations showed increased plasma viremia and reduced CD4/CD8 T-cell ratio in HIV/TB co-infected subjects relative to HIV-infected, and also a closer association with changes in the expression of CD38, a cyclic ADP ribose hydrolase and CD57, which were consistently expressed on late-senescent CD8(+) T cells. Up-regulation of CD57 and CD38 were directly proportional to lack of co-stimulatory markers on CD8(+) T cells, besides diminished expression of CD127 (IL-7Rα) on CD57(+)CD4(+) T cells. Notably, intracellular IFN-γ, perforin and granzyme B levels in HIV-specific CD8(+) T cells of HIV/TB co-infected subjects were diminished. Intracellular CD57 levels in HIV gag p24-specific CD8(+) T cells were significantly increased in HIV/TB co-infection. We suggest that HIV-TB co-infection contributes to senescence associated with chronic immune activation, which could be due to functional insufficiency of CD8(+) T cells.

Rhabdomyolysis in association with simvastatin and dosage increment in clarithromycin.

Clarithromycin is the most documented cytochrome P450 3A4 (CYP3A4) inhibitor to cause an adverse interaction with simvastatin. This particular case is of interest as rhabdomyolysis only occurred after an increase in the dose of clarithromycin. The patient developed raised cardiac biomarkers without any obvious cardiac issues, a phenomenon that has been linked to rhabdomyolysis previously. To date, there has been no reported effect of rhabdomyolysis on the structure and function of cardiac muscle. Clinicians need to be aware of prescribing concomitant medications that increase the risk of myopathy or inhibit the CYP3A4 enzyme. Our case suggests that troponin elevation could be associated with statin induced rhabdomyolysis, which may warrant further studies.

[Helicobacter pylori infection in children: a new focus].

Helicobacter pylori (Hp) is a high prevalence of chronic infectious pathogens, though not necessarily lead to symptoms, but it can affect the immune system. More than of the world's population harbors the bacterium, and most adult Hp infection was obtained in childhood. Hp infection is a major cause of peptic ulcer, although children rarely suffer from peptic ulcer disease. Hp infection is closely related to chronic gastritis, dyspepsia, chronic diarrhea and recurrent abdominal pain in children. In recent years, Hp infection may also participate in some of non-digestive diseases, such as children's nutritional iron deficiency anemia, growth retardation, malnutrition, autoimmune idiopathic thrombocytopenic purpura, chronic urticaria, as well as the development of adult atherosclerosis-related cardiovascular diseases and some nervous system diseases. Hp infection can be a lifetime issues of children. Hp infection of children will bring many socio-economic problems. In this paper, the correlation of Hp infection in stomach and oral cavity, and diagnostic technology, prevention as well as treatment strategies for Hp infection will be discussed.

Deep Variational Network Toward Blind Image Restoration.

Blind image restoration (IR) is a common yet challenging problem in computer vision. Classical model-based methods and recent deep learning (DL)-based methods represent two different methodologies for this problem, each with its own merits and drawbacks. In this paper, we propose a novel blind image restoration method, aiming to integrate both the advantages of them. Specifically, we construct a general Bayesian generative model for the blind IR, which explicitly depicts the degradation process. In this proposed model, a pixel-wise non-i.i.d. Gaussian distribution is employed to fit the image noise. It is with more flexibility than the simple i.i.d. Gaussian or Laplacian distributions as adopted in most of conventional methods, so as to handle more complicated noise types contained in the image degradation. To solve the model, we design a variational inference algorithm where all the expected posteriori distributions are parameterized as deep neural networks to increase their model capability. Notably, such an inference algorithm induces a unified framework to jointly deal with the tasks of degradation estimation and image restoration. Further, the degradation information estimated in the former task is utilized to guide the latter IR process. Experiments on two typical blind IR tasks, namely image denoising and super-resolution, demonstrate that the proposed method achieves superior performance over current state-of-the-arts. The source code is available at https://github.com/zsyOAOA/VIRNet.

Coupling of air/metal and substrate/metal surface plasmon polaritons in Au slit arrays fabricated on quartz substrate.

We report on the coupling of the air/metal mode and the substrate/metal mode surface plasmon polaritons in one-dimensional metallic slit arrays fabricated on a dielectric substrate. Anti-crossing is exhibited at an incident angle where the two independent modes can be resonantly excited at a specific wavelength. The size of the anti-crossing gap was measured while changing the metal thickness.

Hope language in patients undergoing epilepsy surgery.

Candidates for epilepsy surgery often use the word "hope" to express their attitudes and beliefs about surgery. However, studies suggest that hope has a multiplicity of meanings that are not well understood. The goal of this analysis was to evaluate whether Candidates for epilepsy surgery use hope language to express a traditional, expected optimism during presurgery interviews. We examined patients' uses of the word "hope" and its derivatives (hoping, hopeful, hopefully) through a secondary analysis of 37 interviews of adult patients prior to epilepsy surgery. Approximately 1/3 of all hope statements were coded as expressions of optimism, while 1/3 were not optimistic, and 1/3 had unclear meanings. In addition to traditionally optimistic uses of the term, other themes surrounding use of this word included ideas of dread, worry, uncertainty, and temporizing language. This information may help clinicians communicate more effectively with patients, enhancing the informed consent process for epilepsy surgery.

Deep Face Video Inpainting via UV Mapping.

This paper addresses the problem of face video inpainting. Existing video inpainting methods target primarily at natural scenes with repetitive patterns. They do not make use of any prior knowledge of the face to help retrieve correspondences for the corrupted face. They therefore only achieve sub-optimal results, particularly for faces under large pose and expression variations where face components appear very differently across frames. In this paper, we propose a two-stage deep learning method for face video inpainting. We employ 3DMM as our 3D face prior to transform a face between the image space and the UV (texture) space. In Stage I, we perform face inpainting in the UV space. This helps to largely remove the influence of face poses and expressions and makes the learning task much easier with well aligned face features. We introduce a frame-wise attention module to fully exploit correspondences in neighboring frames to assist the inpainting task. In Stage II, we transform the inpainted face regions back to the image space and perform face video refinement that inpaints any background regions not covered in Stage I and also refines the inpainted face regions. Extensive experiments have been carried out which show our method can significantly outperform methods based merely on 2D information, especially for faces under large pose and expression variations. Project page: https://ywq.github.io/FVIP.

Radiograph-comparable image synthesis for spine alignment analysis using deep learning with prospective clinical validation.

Background: Adolescent idiopathic scoliosis (AIS) is the most common type of spinal disorder affecting children. Clinical screening and diagnosis require physical and radiographic examinations, which are either subjective or increase radiation exposure. We therefore developed and validated a radiation-free portable system and device utilising light-based depth sensing and deep learning technologies to analyse AIS by landmark detection and image synthesis. Methods: Consecutive patients with AIS attending two local scoliosis clinics in Hong Kong between October 9, 2019, and May 21, 2022, were recruited. Patients were excluded if they had psychological and/or systematic neural disorders that could influence the compliance of the study and/or the mobility of the patients. For each participant, a Red Green Blue-Depth (RGBD) image of the nude back was collected using our in-house radiation-free device. Manually labelled landmarks and alignment parameters by our spine surgeons were considered as the ground truth (GT). Images from training and internal validation cohorts (n = 1936) were used to develop the deep learning models. The model was then prospectively validated on another cohort (n = 302) which was collected in Hong Kong and had the same demographic properties as the training cohort. We evaluated the prediction accuracy of the model on nude back landmark detection as well as the performance on radiograph-comparable image (RCI) synthesis. The obtained RCIs contain sufficient anatomical information that can quantify disease severities and curve types. Findings: Our model had a consistently high accuracy in predicting the nude back anatomical landmarks with a less than 4-pixel error regarding the mean Euclidian and Manhattan distance. The synthesized RCI for AIS severity classification achieved a sensitivity and negative predictive value of over 0.909 and 0.933, and the performance for curve type classification was 0.974 and 0.908, with spine specialists' manual assessment results on real radiographs as GT. The estimated Cobb angle from synthesized RCIs had a strong correlation with the GT angles (R2 = 0.984, p < 0.001). Interpretation: The radiation-free medical device powered by depth sensing and deep learning techniques can provide instantaneous and harmless spine alignment analysis which has the potential for integration into routine screening for adolescents. Funding: Innovation and Technology Fund (MRP/038/20X), Health Services Research Fund (HMRF) 08192266.

Gain recovery in a quantum dot semiconductor optical amplifier and corresponding pattern effects in amplified optical signals at 1.5 µm.

Fast gain recovery observed in quantum-dot semiconductor-optical-amplifiers (QDSOAs) is useful for amplifying high-speed optical signals. The small but finite slow recovery component can deteriorate the signal amplification due to the accumulation of gain saturation during 10 Gb/s operation. A study of the gain recoveries and pattern effects in signals amplified using a 1.5 µm InAs/InGaAsP QDSOA reveals that the gain recovery is always fast, and pattern-effect-free amplification is observed at the ground state. However, at the excited state, the slow component increases with the current, and significant pattern effects are observed. Simulations of the pattern effects agreed with the observed experimental trends.

Deep Photometric Stereo for Non-Lambertian Surfaces.

This paper addresses the problem of photometric stereo, in both calibrated and uncalibrated scenarios, for non-Lambertian surfaces based on deep learning. We first introduce a fully convolutional deep network for calibrated photometric stereo, which we call PS-FCN. Unlike traditional approaches that adopt simplified reflectance models to make the problem tractable, our method directly learns the mapping from reflectance observations to surface normal, and is able to handle surfaces with general and unknown isotropic reflectance. At test time, PS-FCN takes an arbitrary number of images and their associated light directions as input and predicts a surface normal map of the scene in a fast feed-forward pass. To deal with the uncalibrated scenario where light directions are unknown, we introduce a new convolutional network, named LCNet, to estimate light directions from input images. The estimated light directions and the input images are then fed to PS-FCN to determine the surface normals. Our method does not require a pre-defined set of light directions and can handle multiple images in an order-agnostic manner. Thorough evaluation of our approach on both synthetic and real datasets shows that it outperforms state-of-the-art methods in both calibrated and uncalibrated scenarios.

An artificial intelligence powered platform for auto-analyses of spine alignment irrespective of image quality with prospective validation.

BACKGROUND: Assessment of spine alignment is crucial in the management of scoliosis, but current auto-analysis of spine alignment suffers from low accuracy. We aim to develop and validate a hybrid model named SpineHRNet+, which integrates artificial intelligence (AI) and rule-based methods to improve auto-alignment reliability and interpretability. METHODS: From December 2019 to November 2020, 1,542 consecutive patients with scoliosis attending two local scoliosis clinics (The Duchess of Kent Children's Hospital at Sandy Bay in Hong Kong; Queen Mary Hospital in Pok Fu Lam on Hong Kong Island) were recruited. The biplanar radiographs of each patient were collected with our medical machine EOS™. The collected radiographs were recaptured using smartphones or screenshots, with deidentified images securely stored. Manually labelled landmarks and alignment parameters by a spine surgeon were considered as ground truth (GT). The data were split 8:2 to train and internally test SpineHRNet+, respectively. This was followed by a prospective validation on another 337 patients. Quantitative analyses of landmark predictions were conducted, and reliabilities of auto-alignment were assessed using linear regression and Bland-Altman plots. Deformity severity and sagittal abnormality classifications were evaluated by confusion matrices. FINDINGS: SpineHRNet+ achieved accurate landmark detection with mean Euclidean distance errors of 2·78 and 5·52 pixels on posteroanterior and lateral radiographs, respectively. The mean angle errors between predictions and GT were 3·18° and 6·32° coronally and sagittally. All predicted alignments were strongly correlated with GT (p < 0·001, R2 > 0·97), with minimal overall difference visualised via Bland-Altman plots. For curve detections, 95·7% sensitivity and 88·1% specificity was achieved, and for severity classification, 88·6-90·8% sensitivity was obtained. For sagittal abnormalities, greater than 85·2-88·9% specificity and sensitivity were achieved. INTERPRETATION: The auto-analysis provided by SpineHRNet+ was reliable and continuous and it might offer the potential to assist clinical work and facilitate large-scale clinical studies. FUNDING: RGC Research Impact Fund (R5017-18F), Innovation and Technology Fund (ITS/404/18), and the AOSpine East Asia Fund (AOSEA(R)2019-06).

Spine-GFlow: A hybrid learning framework for robust multi-tissue segmentation in lumbar MRI without manual annotation.

Most learning-based magnetic resonance image (MRI) segmentation methods rely on the manual annotation to provide supervision, which is extremely tedious, especially when multiple anatomical structures are required. In this work, we aim to develop a hybrid framework named Spine-GFlow that combines the image features learned by a CNN model and anatomical priors for multi-tissue segmentation in a sagittal lumbar MRI. Our framework does not require any manual annotation and is robust against image feature variation caused by different image settings and/or underlying pathology. Our contributions include: 1) a rule-based method that automatically generates the weak annotation (initial seed area), 2) a novel proposal generation method that integrates the multi-scale image features and anatomical prior, 3) a comprehensive loss for CNN training that optimizes the pixel classification and feature distribution simultaneously. Our Spine-GFlow has been validated on 2 independent datasets: HKDDC (containing images obtained from 3 different machines) and IVDM3Seg. The segmentation results of vertebral bodies (VB), intervertebral discs (IVD), and spinal canal (SC) are evaluated quantitatively using intersection over union (IoU) and the Dice coefficient. Results show that our method, without requiring manual annotation, has achieved a segmentation performance comparable to a model trained with full supervision (mean Dice 0.914 vs 0.916).

Specific mesenchymal/epithelial induction of olfactory receptor, vomeronasal, and gonadotropin-releasing hormone (GnRH) neurons.

We asked whether specific mesenchymal/epithelial (M/E) induction generates olfactory receptor neurons (ORNs), vomeronasal neurons (VRNs), and gonadotropin-releasing hormone (GnRH) neurons, the major neuron classes associated with the olfactory epithelium (OE). To assess specificity of M/E-mediated neurogenesis, we compared the influence of frontonasal mesenchyme on frontonasal epithelium, which becomes the OE, with that of the forelimb bud. Despite differences in position, morphogenetic and cytogenic capacity, both mesenchymal tissues support neurogenesis, expression of several signaling molecules and neurogenic transcription factors in the frontonasal epithelium. Only frontonasal mesenchyme, however, supports OE-specific patterning and activity of a subset of signals and factors associated with OE differentiation. Moreover, only appropriate pairing of frontonasal epithelial and mesenchymal partners yields ORNs, VRNs, and GnRH neurons. Accordingly, the position and molecular identity of specialized frontonasal epithelia and mesenchyme early in gestation and subsequent inductive interactions specify the genesis and differentiation of peripheral chemosensory and neuroendocrine neurons.