Feasibility study of intubation in lateral position using Viva-sight double-lumen tube combined with video laryngoscope in patients undergoing pulmonary lobectomy.

BACKGROUND: Double-lumen tube (DLT) intubation in lateral decubitus position is rarely reported. We designed this study to evaluate the feasibility of VivaSight double-lumen tube (VDLT) intubation assisted by video laryngoscope in lateral decubitus patients. METHODS: Patients undergoing elective video-assisted thoracoscopic surgery (VATS) for lung lobectomy were assessed for eligibility between January 2022 and December, 2022. Eligible patients were randomly allocated into supine intubation group (group S) and lateral intubation group (group L) by a computer-generated table of random numbers. The prime objective was to observe whether the success rate of VDLT intubation in lateral position with the aid of video laryngoscope was not inferior to that in supine position. RESULTS: A total of 116 patients were assessed, and 88 eligible patients were randomly divided into group L (n = 44) and group S (n = 44). The success rate of the first attempt intubation in the L group was 90.5%, lower than that of S group (97.7%), but there was no statistical difference (p > 0.05). Patients in both groups were intubated with VDLT for no more than 2 attempts. The mean intubation time was 91.98 ± 26.70 s in L group, and 81.39 ± 34.35 s in S group (p > 0.05). The incidence of the capsular malposition in the group L was 4.8%, less than 36.4% of group S (p < 0.001). After 24 h of follow-up, it showed a higher incidence of sore throat in group S, compared to that in group L (p = 0.009). CONCLUSION: Our study shows the comprehensive success rate of intubation in lateral decubitus position with VDLT assisted by video laryngoscope is not inferior to that in supine position, with less risk of intraoperative tube malposition and postoperative sore throat. TRIAL REGISTRATION: Chinese Clinical Trail Register (ChiCTR2200062989).

Unsupervised Tissue Segmentation via Deep Constrained Gaussian Network.

Tissue segmentation is the mainstay of pathological examination, whereas the manual delineation is unduly burdensome. To assist this time-consuming and subjective manual step, researchers have devised methods to automatically segment structures in pathological images. Recently, automated machine and deep learning based methods dominate tissue segmentation research studies. However, most machine and deep learning based approaches are supervised and developed using a large number of training samples, in which the pixel-wise annotations are expensive and sometimes can be impossible to obtain. This paper introduces a novel unsupervised learning paradigm by integrating an end-to-end deep mixture model with a constrained indicator to acquire accurate semantic tissue segmentation. This constraint aims to centralise the components of deep mixture models during the calculation of the optimisation function. In so doing, the redundant or empty class issues, which are common in current unsupervised learning methods, can be greatly reduced. By validation on both public and in-house datasets, the proposed deep constrained Gaussian network achieves significantly (Wilcoxon signed-rank test) better performance (with the average Dice scores of 0.737 and 0.735, respectively) on tissue segmentation with improved stability and robustness, compared to other existing unsupervised segmentation approaches. Furthermore, the proposed method presents a similar performance (p-value >0.05) compared to the fully supervised U-Net.

Lesion synthesis to improve intracranial hemorrhage detection and classification for CT images.

Computer-aided diagnosis (CAD) for intracranial hemorrhage (ICH) is needed due to its high mortality rate and time sensitivity. Training a stable and robust deep learning-based model usually requires enough training examples, which may be impractical in many real-world scenarios. Lesion synthesis offers a possible solution to solve this problem, especially for the issue of the lack of micro bleedings. In this paper, we propose a novel strategy to generate artificial lesions on non-lesion CT images so as to produce additional labeled training examples. Artificial masks in any location, size, or shape can be generated through Artificial Mask Generator (AMG) and then be converted into hemorrhage lesions through Lesion Synthesis Network (LSN). Images with and without artificial lesions are combined for training an ICH detection with a novel Residual Score. We evaluate our method by the auxiliary diagnosis task of ICH. Our experiments demonstrate that the proposed approach can improve the AUC value from 84% to 91% in the ICH detection task and from 89% to 96% in the classification task. Moreover, by adding artificial lesions of small size, the sensitivity of micro bleeding is remarkably improved from 49% to 70%. Besides, the proposed method overcomes the other three synthetic approaches by a large margin.

Deep learning shows good reliability for automatic segmentation and volume measurement of brain hemorrhage, intraventricular extension, and peripheral edema.

OBJECTIVES: To evaluate for the first time the performance of a deep learning method based on no-new-Net for fully automated segmentation and volumetric measurements of intracerebral hemorrhage (ICH), intraventricular extension of intracerebral hemorrhage (IVH), and perihematomal edema (PHE) in primary ICH on CT. METHODS: Three hundred and eighty primary ICH patients who underwent CT at hospital arrival were divided into a training cohort (n = 300) and a validation cohort (n = 80). An independent cohort with 80 patients was used for testing. Ground truth (segmentation masks) was manually generated by radiologists. Model performance on lesion segmentation and volumetric measurement of ICH, IVH, and PHE were evaluated by comparing the model results with the segmentations performed by radiologists. RESULTS: In the test cohort, the Dice scores of lesion segmentation were 0.92, 0.79, and 0.71 for ICH, IVH, and PHE, respectively. The sensitivities were 0.93 for ICH, 0.88 for IVH, and 0.81 for PHE. The positive predictive values were 0.92, 0.76, and 0.69 for ICH, IVH, and PHE, respectively. Excellent concordance (concordance correlation coefficients [CCCs] ≥ 0.98) of ICH and IVH and good concordance of PHE (CCCs ≥ 0.92) were demonstrated between manually and automatically measured volumes. The model took approximately 15 s to provide automatic segmentation and volume analysis for each patient. CONCLUSION: Our model demonstrates good reliability for automatic segmentation and volume measurement of ICH, IVH, and PHE in primary ICH, which can be useful to reduce the effort and time of doctors to calculate volumes of ICH, IVH, and PHE. KEY POINTS: • Deep learning algorithms can provide automatic and reliable assessment of intracerebral hemorrhage, intraventricular hemorrhage, and perihematomal edema on CT. • Non-contrast CT-based deep learning method can be helpful to provide efficient and accurate measurements of ICH, IVH, and PHE in primary ICH patients, thereby reducing the effort and time of doctors to segment and calculate volumes of ICH, IVH, and PHE in primary ICH patients.

A mid-pregnancy risk prediction model for gestational diabetes mellitus based on the maternal status in combination with ultrasound and serological findings.

Although previous studies have proposed predictive models of gestational diabetes mellitus (GDM) based on maternal status, they do not always provide reliable results. The present study aimed to create a novel model that included ultrasound data of maternal fat distribution and serum inflammatory factors. The clinical data of 1,158 pregnant women treated at Tangshan Gongren Hospital and eight other flagship hospitals in Tangshan, including the First Hospital of Tangshan Gongren Hospital group, Ninth Hospital of Tangshan Gongren Hospital group, Tangshan Gongren Hospital group rehabilitation hospital, Tangshan railway central hospital, Tangshan Gongren Hospital group Fengnan hospital, Tangshan Gongren Hospital group Qianan Yanshan hospital, Tangshan Gongren Hospital group Qianxi Kangli hospital and Tangshan Gongren Hospital group Jidong Sub-hospital, were analyzed following the division of subjects into GDM and non-GDM groups according to their diagnostic results at 24-28 weeks of pregnancy. Univariate analysis was performed to investigate the significance of the maternal clinical parameters for GDM diagnosis and a GDM prediction model was established using stepwise regression analysis. The predictive value of the model was evaluated using a Homer-Lemeshow goodness-of-fit test and a receiver operating characteristic curve (ROC). The model demonstrated that age, pre-pregnancy body mass index, a family history of diabetes mellitus, polycystic ovary syndrome, a history of GDM, high systolic pressures, glycosylated hemoglobin levels, triglyceride levels, total cholesterol levels, low-density lipoprotein cholesterol levels, serum hypersensitive C-reactive protein, increased subcutaneous fat thickness and visceral fat thickness were all correlated with an increased GDM risk (all P<0.01). The area under the curve value was 0.911 (95% CI, 0.893-0.930). Overall, the results indicated that the current model, which included ultrasound and serological data, may be a more effective predictor of GDM compared with other single predictor models. In conclusion, the present study developed a tool to determine the risk of GDM in pregnant women during the second trimester. This prediction model, based on various risk factors, demonstrated a high predictive value for the GDM occurrence in pregnant women in China and may prove useful in guiding future clinical practice.

Neuroprotective Effect of Astragalus Polysacharin on Streptozotocin (STZ)-Induced Diabetic Rats.

BACKGROUND In the recent years, there has been increasing interest in traditional Chinese medicine as a neuroprotective nutrient in the management of chronic neurodegenerative disease, such as diabetic cognitive decline. Astragalus polysacharin (APS), a Chinese herb extract, is a biologically active treatment for neurodegenerative diseases. Therefore, in the present study, we investigated the neuroprotective effects of APS (20 mg/kg) on diabetes-induced memory impairments in Sprague-Dawley (SD) rats and explored its underlying mechanisms of action. MATERIAL AND METHODS Thirty SD rats were randomly divided into a control group (CON group, n=10), a diabetic model (DM) group (n=10), and an APS group (n=10). We administered 55 mg/kg streptozotocin (STZ, Sigma) by intraperitoneal injection to induce a diabetic model. Food and water intake, body weight, and blood fasting plasma glucose (FPG) were measured. The Morris water maze test (MWM) was used to assess learning and memory ability, and we measured levels of N-methyl-D-aspartate receptor (NMDA), calcium/calmodulin-dependent protein kinase II (CaMKII), and cAMP response element-binding protein (CREB) in the hippocampus. RESULTS APS (20 mg/kg) administration decreased the rats' fasting plasma glucose (FPG) levels and body weight. APS (20 mg/kg) administration improved the cognitive performance of diabetes-induced rats in the Morris water maze test. APS (20 mg/kg) administration reduced the number of dead cells in the CA1 region of the hippocampus. Furthermore, APS (20 mg/kg) administration obviously upregulated the phosphorylation levels CREB, NMDA, and CaMK II. CONCLUSIONS These results suggest that APS has the neuroprotective effects, and it may be a candidate for treatment of neurodegenerative diseases such as diabetic cognitive impairment.

Challenges in the surgical treatment of undiagnosed functional paragangliomas: A case report.

INTRODUCTION: Paragangliomas (PGs) or extra-adrenal pheochromocytomas are rare neuroendocrine neoplasms of ubiquitous distribution. Those that produce excess catecholamine are categorized as functional, and those that do not are categorized as nonfunctional. Although modern medical technology is becoming more widespread, there are still substantial risks of misdiagnosis or missed diagnosis of PGs. CASE PRESENTATION: A 38-year-old woman who lived in an autonomous region of inner Mongolia presented complaining of having experienced coughing for approximately the past month. Chest computed tomography (CT) and magnetic resonance imaging (MRI) revealed a lesion on the right side of thoracic vertebra 5-8 of approximately 66 mm × 54 mm, and it was deemed to be a mediastinal tumor that required surgical treatment. The patient exhibited severe hemodynamic instability during the operation, resulting in substantial challenges and risks with regard to anesthesia management. CONCLUSION: When a patient is suspected having PG, whether the surgery should be continued or not depends on their overall condition and whether hemodynamic fluctuation can be controlled to within the normal range. Both are factors that should be considered during intraoperative management. Communication between the surgeon and anesthesiologist is necessary, in order to accurately assess the risks associated with the operation. The combination of central venous pressure and the Flotrac/Vigileo system may provide precise guidance for complementary liquid therapy and reduce cardiopulmonary complications. After the operation, hemodynamic changes should be monitored continuously in the intensive care unit, and vasoactive drugs are required to avoid postoperative hypotension. Dramatic hemodynamic changes are certainly a challenge for patients and anesthesiologists, regardless of their origin, and sufficient attention should be paid to avoid serious consequences.

Liraglutide improves cognitive impairment via the AMPK and PI3K/Akt signaling pathways in type 2 diabetic rats.

Liraglutide is a type of glucagon­like­peptide 1 receptor agonist, which has been reported as a novel type of antidiabetic agent with numerous benefits, including cardiovascular and neuroprotective effects. To the best of our knowledge, few studies to date have reported the potential mechanism underlying the neuroprotective effects of liraglutide on rats with type 2 diabetes mellitus (T2DM). The present study aimed to investigate the neuroprotective actions of liraglutide in diabetic rats and to determine the mechanisms underlying these effects. A total of 30 male T2DM Goto­Kakizaki (GK) rats (age, 32 weeks; weight, 300­350 g) and 10 male Wistar rats (age, 32 weeks; weight, 300­350 g) were used in the present study. Wistar rats received vehicle treatment, and GK rats randomly received treatment with vehicle, low dose of liraglutide (75 µg/kg) or high dose of liraglutide (200 µg/kg) for 28 days. Cognitive deficits were evaluated using the Morris water maze test. The expression levels of phosphoinositide 3­kinase (PI3K), protein kinase B (Akt), phosphorylated (p)­Akt, AMP­activated protein kinase (AMPK), mammalian target of rapamycin (mTOR), Beclin­1, microtubule­associated protein light chain 3 (LC)­3 II, caspase­3, B­cell lymphoma 2 (Bcl­2)­associated X protein and Bcl­2 were assessed by western blot analysis. The results demonstrated that diabetic GK rats exhibited cognitive dysfunction, whereas treatment with liraglutide alleviated the learning and memory deficits, particularly in the high­dose liraglutide group. The expression levels of Beclin­1 and LC­3 II were decreased in GK rats; however, this decrease was alleviated in the presence of liraglutide. Liraglutide also reversed T2DM model­induced increases in mTOR, and decreases in p­AMPK, PI3K and p­Akt expression, and modulated the expression of apoptosis­associated proteins. Furthermore, the administration of liraglutide inhibited apoptosis and exerted a protective effect against cognitive deficits via the activation of autophagy. In conclusion, the protective effects of liraglutide may be associated with increased mTOR expression via activation of the AMPK and PI3K/Akt signaling pathways.

Sulforaphane Prevents Neuronal Apoptosis and Memory Impairment in Diabetic Rats.

BACKGROUND/AIMS: To explore the effects of sulforaphane (SFN) on neuronal apoptosis in hippocampus and memory impairment in diabetic rats. METHODS: Thirty male rats were randomly divided into normal control, diabetic model and SFN treatment groups (N = 10 in each group). Streptozotocin (STZ) was applied to establish diabetic model. Water Morris maze task was applied to test learning and memory. Tunel assaying was used to detect apoptosis in hippocampus. The expressions of Caspase-3 and myeloid cell leukemia 1(MCL-1) were detected by western blotting. Neurotrophic factor levels and AKT/GSK3ß pathway were also detected. RESULTS: Compared with normal control, learning and memory were apparently impaired, with up-regulation of Caspase-3 and down-regulation of MCL-1 in diabetic rats. Apoptotic neurons were also found in CA1 region after diabetic modeling. By contrast, SFN treatment prevented the memory impairment, decreased the apoptosis of hippocampal neurons. SFN also attenuated the abnormal expression of Caspase-3 and MCL-1 in diabetic model. Mechanically, SFN treatment reversed diabetic modeling-induced decrease of p-Akt, p-GSK3ß, NGF and BDNF expressions. CONCLUSION: SFN could prevent the memory impairment and apoptosis of hippocampal neurons in diabetic rat. The possible mechanism was related to the regulation of neurotropic factors and Akt/GSK3ß pathway.