Awareness, Attitude, and Fertility Desire in Elective Oocyte Cryopreservation of Adults in Four Areas of China.

Purpose: Controversy exists on whether or not elective oocyte cryopreservation (eOC) can be conducted in public hospitals in China. Policymakers should take into account the benefits and risks in the Chinese population. This study explored basic data concerning the awareness, attitudes of eOC, and fertility desire of eOC in China to offer evidence for policy making. Methods: A total of 442 people in four areas of China responded to a survey. The questionnaire was divided into three parts: awareness, attitude, and fertility desire of eOC. Descriptive analysis and multivariable regression analysis were used in the study. Results: Generally, the respondents had a positive or neutral attitude towards eOC. However, about 90% of respondents did not know the cost of eOC. In general, a more positive attitude was found towards eOC among participants who had heard of the procedure compared with those who had not. Most women did not desire to reproduce by eOC. After adjusting for access to information, we found that female, older age groups, and singles were more likely to have increased awareness than their counterparts. The awareness of participants who accessed information from any source had a higher relative probability of having good awareness levels compared to participants who had not accessed the information. Undergraduates exhibited significantly higher levels of cognitive understanding, as indicated by their increased familiarity and comprehension, compared to high school students (relative risk ratio = 1.44, confidence interval = 0.48,4.29). Conclusion: Continued discussion is needed regarding the ethical, legal, and social aspects of performing eOC in public hospitals. Furthermore, policies are needed to regulate eOC to protect the reproductive freedom of healthy women.

A comparative study of analytical models of diffuse reflectance in homogeneous biological tissues: Gelatin-based phantoms and Monte Carlo experiments.

Information about tissue oxygen saturation (StO2) and other related important physiological parameters can be extracted from diffuse reflectance spectra measured through non-contact imaging. Three analytical optical reflectance models for homogeneous, semi-infinite, tissue have been proposed (Modified Beer-Lambert, Jacques 1999, Yudovsky 2009) but these have not been directly compared for tissue parameter extraction purposes. We compare these analytical models using Monte Carlo (MC) simulated diffuse reflectance spectra and controlled gelatin-based phantoms with measured diffuse reflectance spectra and known ground truth composition parameters. The Yudovsky model performed best against MC simulations and measured spectra of tissue phantoms in terms of goodness of fit and parameter extraction accuracy followed closely by Jacques' model. In this study, Yudovsky's model appeared most robust; however, our results demonstrated that both Yudovsky and Jacques models are suitable for modeling tissue that can be approximated as a single, homogeneous, semi-infinite slab.

Mechanism of oxalate decarboxylase Oxd_S12 from Bacillus velezensis BvZ45-1 in defence against cotton verticillium wilt.

Verticillium wilt, a soilborne vascular disease caused by Verticillium dahliae, strongly affects cotton yield and quality. In this study, an isolated rhizosphere bacterium, designated Bacillus velezensis BvZ45-1, exhibited >46% biocontrol efficacy against cotton verticillium wilt under greenhouse and field conditions. Moreover, through crude protein extraction and mass spectrometry analyses, we found many antifungal compounds present in the crude protein extract of BvZ45-1. The purified oxalate decarboxylase Odx_S12 from BvZ45-1 inhibited the growth of V. dahliae Vd080 by reducing the spore yield, causing mycelia to rupture, spore morphology changes, cell membrane rupture, and cell death. Subsequently, overexpression of Odx_S12 in Arabidopsis significantly improved plant resistance to V. dahliae. Through studies of the resistance mechanism of Odx_S12, V. dahliae was shown to produce oxalic acid (OA), which has a toxic effect on Arabidopsis leaves. Odx_S12 overexpression reduced Arabidopsis OA content, enhanced tolerance to OA, and improved resistance to verticillium wilt. Transcriptomics and quantitative real-time PCR analysis revealed that Odx_S12 promoted a reactive oxygen species burst and a salicylic acid- and abscisic acid-mediated defence response in Arabidopsis. In summary, this study not only identified B. velezensis BvZ45-1 as an efficient biological control agent, but also identified the resistance gene Odx_S12 as a candidate for cotton breeding against verticillium wilt.

Whole-lesion iodine map histogram analysis in the risk classification of gastrointestinal stromal tumors: comparison with single-slice iodine concentration measurements.

PURPOSE: To evaluate and compare the diagnostic performances of whole-lesion iodine map (IM) histogram analysis and single-slice IM measurement in the risk classification of gastrointestinal stromal tumors (GISTs). METHODS: Thirty-seven patients with GISTs, including 19 with low malignant underlying GISTs (LG-GISTs) and 18 with high malignant underlying GISTs (HG-GISTs), were evaluated with dual-energy computed tomography (DECT). Whole-lesion IM histogram parameters (mean; median; minimum; maximum; standard deviation; variance; 1st, 10th, 25th, 50th, 75th, 90th, and 99th percentile; kurtosis, skewness, and entropy) were computed for each lesion. In other sessions, iodine concentrations (ICs) were derived from the IM by placing regions of interest (ROIs) on the tumor slices and normalizing them to the iodine concentration in the aorta. Both quantitative analyses were performed on the venous phase images. The diagnostic accuracies of the two methods were assessed and compared. RESULTS: The minimum, maximum, 1st, 10th, and 25th percentile of the whole-lesion IM histogram and the IC and normalized IC (NIC) of the single-slice IC measurement significantly differed between LG- and HG-GISTs (p < 0.001 - p = 0.042). The minimum value in the histogram analysis (AUC = 0.844) and the NIC in the single-slice measurement analysis (AUC = 0.886) showed the best diagnostic performances. The NIC of single-slice measurements had a diagnostic performance similar to that of the whole-lesion IM histogram analysis (p = 0.618). CONCLUSIONS: Both whole-lesion IM histogram analysis and single-slice IC measurement can differentiate LG-GISTs and HG-GISTs with similar diagnostic performances.

Nutrient availability regulates the secretion and function of immune cell-derived extracellular vesicles through metabolic rewiring.

Extracellular vesicle (EV)-based immunotherapeutics have emerged as promising strategy for treating diseases, and thus, a better understanding of the factors that regulate EV secretion and function can provide insights into developing advanced therapies. Here, we report that nutrient availability, even changes in individual nutrient components, may affect EV biogenesis and composition of immune cells [e.g., macrophages (Mφs)]. As a proof of concept, EVs from M1-Mφ under glutamine-depleted conditions (EVGLN-) had higher yields, functional compositions, and immunostimulatory potential than EVs from conventional GLN-present medium (EVGLN+). Mechanistically, the systemic metabolic rewiring (e.g., altered energy and redox metabolism) induced by GLN depletion resulted in up-regulated pathways related to EV biogenesis/cargo sorting (e.g., ESCRT) and immunostimulatory molecule production (e.g., NF-κB and STAT) in Mφs. This study highlights the importance of nutrient status in EV secretion and function, and optimizing metabolic states and/or integrating them with other engineering methods may advance the development of EV therapeutics.

Communication-Efficient and Resilient Distributed Q-Learning.

This article investigates the problem of communication-efficient and resilient multiagent reinforcement learning (MARL). Specifically, we consider a setting where a set of agents are interconnected over a given network, and can only exchange information with their neighbors. Each agent observes a common Markov Decision Process and has a local cost which is a function of the current system state and the applied control action. The goal of MARL is for all agents to learn a policy that optimizes the infinite horizon discounted average of all their costs. Within this general setting, we consider two extensions to existing MARL algorithms. First, we provide an event-triggered learning rule where agents only exchange information with their neighbors if a certain triggering condition is satisfied. We show that this enables learning while reducing the amount of communication. Next, we consider the scenario where some of the agents can be adversarial (as captured by the Byzantine attack model), and arbitrarily deviate from the prescribed learning algorithm. We establish a fundamental trade-off between optimality and resilience when Byzantine agents are present. We then create a resilient algorithm and show almost sure convergence of all reliable agents' value functions to the neighborhood of the optimal value function of all reliable agents, under certain conditions on the network topology. When the optimal Q -values are sufficiently separated for different actions, we show that all reliable agents can learn the optimal policy under our algorithm.

GhTLP1, a thaumatin-like protein 1, improves Verticillium wilt resistance in cotton via JA, ABA and MAPK signaling pathway-plant pathways.

Verticillium wilt of cotton is a very serious soil-borne disease and there is no effective control method. The mechanism of Gossypium hirsutum thaumatin-like protein 1(GhTLP1) in upland cotton regulating Verticillium wilt resistance has been an uncovered research approach. GhTLP1 is mainly localized in the cell wall. Overexpression of GhTLP1 significantly enhanced Arabidopsis plants resistance to Verticillium dahliae, while its homologous mutant tlp1 in Arabidopsis was more susceptible to the pathogen, and the heterologous complement line (EC) recovered resistance to V. dahliae. GhTLP1 responds to jasmonate acid (JA) and abscisic acid (ABA) hormones and regulates mitogen-activated protein kinase (MAPK) signaling pathway-plant pathway to enhance Arabidopsis plants resistance to V. dahliae. Silencing GhTLP1 resulted decrease in cotton plants resistance to V. dahliae. Moreover, the mutation of GhTLP1 at site Tyr97 and Tyr199 with the phosphorylation also decreased plant resistance to V. dahliae. Therefore, GhTLP1 phosphorylation was observed important in cotton plants against V. dahliae. Further analysis demonstrated that GhTLP1 interacted with gossypium hirsutum laccase 14 (GhLAC14) to enhance plants resistance to V. dahliae. Silencing GhLAC14 resulted decrease in cotton plants resistance to V. dahliae. Here, we propose that GhTLP1 is a potential molecular target for improving resistance to Verticillium wilt in cotton.

Identification and characterization of the Bicupin domain family and functional analysis of GhBCD11 in response to verticillium wilt in cotton.

Bicupin domain protein (BCD) family, an important component of Cupin domain superfamily, plays important roles in oxalic acid (OA) degradation and stress responses in high plants. However, no studies have been reported on the Cupin domain family in cotton up till now. In our study, a total 110 proteins including Cupin domain were identified from the upland cotton (Gossypium hirsutum). Among them, 17 proteins contained Bicupin domain. Subsequently, we found that V. dahliae produces OA leading to cotton leaf wilting. RT-qPCR analysis of GhBCDs revealed that OA and V. dahliae Vd080 significantly enhanced the expression of GhBCD11. The Virus-induced gene silencing and overexpression analysis showed that GhBCD11 positively regulates plant resistance to V. dahliae. Subcellular localization showed GhBCD11 located on the plasma membrane. The analysis of expression pattern showed that GhBCD11 can be induced via hormone-mediated signal pathway including salicylic acid (SA), ethephon (ET), methyl jasmonate (JA) and abscisic acid (ABA). In addition, we identified an interaction between 60 S ribosomal protein GhRPL12-3 and GhBCD11 by yeast double hybridization. Overall, this is the first study, where we identified Cupin domain family in cotton, clarified the role of GhBCD11 in cotton for resistance to V. dahliae and found an interaction between GhRPL12-3 and GhBCD11.

Synthetic white balancing for intra-operative hyperspectral imaging.

Purpose: Hyperspectral imaging shows promise for surgical applications to non-invasively provide spatially resolved, spectral information. For calibration purposes, a white reference image of a highly reflective Lambertian surface should be obtained under the same imaging conditions. Standard white references are not sterilizable and so are unsuitable for surgical environments. We demonstrate the necessity for in situ white references and address this by proposing a novel, sterile, synthetic reference construction algorithm. Approach: The use of references obtained at different distances and lighting conditions to the subject were examined. Spectral and color reconstructions were compared with standard measurements qualitatively and quantitatively, using ΔE and normalized RMSE, respectively. The algorithm forms a composite image from a video of a standard sterile ruler, whose imperfect reflectivity is compensated for. The reference is modeled as the product of independent spatial and spectral components, and a scalar factor accounting for gain, exposure, and light intensity. Evaluation of synthetic references against ideal but non-sterile references is performed using the same metrics alongside pixel-by-pixel errors. Finally, intraoperative integration is assessed though cadaveric experiments. Results: Improper white balancing leads to increases in all quantitative and qualitative errors. Synthetic references achieve median pixel-by-pixel errors lower than 6.5% and produce similar reconstructions and errors to an ideal reference. The algorithm integrated well into surgical workflow, achieving median pixel-by-pixel errors of 4.77% while maintaining good spectral and color reconstruction. Conclusions: We demonstrate the importance of in situ white referencing and present a novel synthetic referencing algorithm. This algorithm is suitable for surgery while maintaining the quality of classical data reconstruction.

Differential diagnosis of lung cancer and tuberculosis based on 18 F-fluorodeoxyglucose PET/CT multi-time points imaging.

OBJECTIVE: To investigate the value of 18 F-fluorodeoxyglucose(FDG) PET/CT multi-time points imaging (MTPI) on the differential diagnosis between lung cancer (LC) and tuberculosis (TB). METHODS: Sixty-four patients underwent 18 F-FDG PET/CT MTPI. The stdSUVmax, stdSUVavg, retention index, metabolic tumor volume, total lesion glycolysis at four-time points and slope of metabolic curve were measured and calculated, and the sex, age, and uniformity of FDG uptake were recorded. The difference in each index between LC and TB was analyzed, and dynamic metabolic curves (DMCs) of LC and TB were fitted by significance indexes. Artificial neural network (ANN) prediction models were established between squamous cell carcinoma (SCC) and TB, as well as between adenocarcinomas and TB. RESULTS: Differences between SCC and TB, stdSUVmax/avg at four-time points, total lesion glycolysis, stdSUVmax/avg slope (1-2 h,1-3 h and 1-4 h), uniformity of FDG uptake and age were significant. stdSUVavg has the largest area under the 4 h curve; age was only significant between adenocarcinomas and TB. DMCs at 1-4 h fitted by stdSUVavg were more helpful in differentiating LC and TB than stdSUVmax. stdSUVavg(1 h and 4 h), stdSUVavg slope 1-4 h, age, and uniformity of FDG uptake were selected to establish an ANN prediction model between SCC and TB; the area under the curve (AUC) was 100.0%. The same indices were used to establish the prediction model between adenocarcinomas and TB; the AUC was up to 83.5, and after adding stdSUVavg (2 and 4 h) to adenocarcinomas and TB models, the AUC was 87.7%. CONCLUSION: 18 F-FDG PET/CT MTPI fitting DMCs and establishing an ANN prediction model would distinguish SCC from TB relatively accurately and provide certain help in the differentiation between adenocarcinomas and TB.

Host Genetic Factors, Comorbidities and the Risk of Severe COVID-19.

BACKGROUND: Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was varied in disease symptoms. We aim to explore the effect of host genetic factors and comorbidities on severe COVID-19 risk. METHODS: A total of 20,320 COVID-19 patients in the UK Biobank cohort were included. Genome-wide association analysis (GWAS) was used to identify host genetic factors in the progression of COVID-19 and a polygenic risk score (PRS) consisted of 86 SNPs was constructed to summarize genetic susceptibility. Colocalization analysis and Logistic regression model were used to assess the association of host genetic factors and comorbidities with COVID-19 severity. All cases were randomly split into training and validation set (1:1). Four algorithms were used to develop predictive models and predict COVID-19 severity. Demographic characteristics, comorbidities and PRS were included in the model to predict the risk of severe COVID-19. The area under the receiver operating characteristic curve (AUROC) was applied to assess the models' performance. RESULTS: We detected an association with rs73064425 at locus 3p21.31 reached the genome-wide level in GWAS (odds ratio: 1.55, 95% confidence interval: 1.36-1.78). Colocalization analysis found that two genes (SLC6A20 and LZTFL1) may affect the progression of COVID-19. In the predictive model, logistic regression models were selected due to simplicity and high performance. Predictive model consisting of demographic characteristics, comorbidities and genetic factors could precisely predict the patient's progression (AUROC = 82.1%, 95% CI 80.6-83.7%). Nearly 20% of severe COVID-19 events could be attributed to genetic risk. CONCLUSION: In this study, we identified two 3p21.31 genes as genetic susceptibility loci in patients with severe COVID-19. The predictive model includes demographic characteristics, comorbidities and genetic factors is useful to identify individuals who are predisposed to develop subsequent critical conditions among COVID-19 patients.

Performance and emissions of a diesel engine fueled by coal-based diesel fuels and their blends with polyoxymethylene dimethyl ethers.

The objective of this study was to investigate the performance and emissions of a diesel engine fueled by coal-based diesel fuels and their blends with oxygenated fuel polyoxymethylene dimethyl ethers (PODEn). First, coal-based Fischer-Tropsch (FT) diesel fuel was blended with hydrogenated diesel fuel at three volume ratios of 40%/60%, 50%/50%, and 60%/40%, denoted as T6W4, T5W5, and T4W6, respectively. Then, PODEn were added into the T4W6 fuel with the volume ratios of 10%, 20%, and 30% to evaluate its effects on the performance and emissions of a coal-based diesel engine. The results showed that the output torques and powers of the three coal-based diesel blends were slightly lower than those of the petroleum diesel fuel. The brake specific fuel consumption (BSFC) of the coal-based diesel fuels was almost the same as that of the petroleum diesel fuel. The brake thermal efficiencies (BTE) of the coal-based diesel blends were slightly lower than that of the petroleum diesel fuel, and the maximum reduction was 1.59%. The pollutant emissions of T5W5 were the closest to those of petroleum diesel fuel. The nitrogen oxides (NOx) emissions of T4W6 were lower, with a maximum decrease of 11.18% compared with the petroleum diesel. The carbon monoxide (CO) and hydrocarbon (HC) emissions of T6W4 were the highest, with maximum increases of 36.79% and 29.05%, respectively. The smoke emissions of T4W6 and T6W4 were higher than those of petroleum diesel fuel. Adding PODEn into T4W6 lowered the engine power and torque but increased the BSFC and BTE. The output torque and power of the diesel engine were further reduced when PODEn were blended with T4W6, with the maximum reductions of 17.76% and 16.96%, respectively. With an increase in the PODEn blending ratio, BSFC and BTE increased gradually, and the maximum increase in the BTE was 1.57%. Blending PODEn with the fuel effectively improved the emission characteristics of the coal-based diesel fuels. The NOx emissions increased slightly, but the emissions of HC, CO, and smoke were reduced significantly, with maximum reductions of 24.42%, 31.67%, and 82.35%, respectively.

Radiomics-based prediction of response to immune checkpoint inhibitor treatment for solid cancers using computed tomography: a real-world study of two centers.

BACKGROUND: Immune checkpoint inhibitors (ICIs) represent an approved treatment for various cancers; however, only a small proportion of the population is responsive to such treatment. We aimed to develop and validate a plain CT-based tool for predicting the response to ICI treatment among cancer patients. METHODS: Data for patients with solid cancers treated with ICIs at two centers from October 2019 to October 2021 were randomly divided into training and validation sets. Radiomic features were extracted from pretreatment CT images of the tumor of interest. After feature selection, a radiomics signature was constructed based on the least absolute shrinkage and selection operator regression model, and the signature and clinical factors were incorporated into a radiomics nomogram. Model performance was evaluated using the training and validation sets. The Kaplan-Meier method was used to visualize associations with survival. RESULTS: Data for 122 and 30 patients were included in the training and validation sets, respectively. Both the radiomics signature (radscore) and nomogram exhibited good discrimination of response status, with areas under the curve (AUC) of 0.790 and 0.814 for the training set and 0.831 and 0.847 for the validation set, respectively. The calibration evaluation indicated goodness-of-fit for both models, while the decision curves indicated that clinical application was favorable. Both models were associated with the overall survival of patients in the validation set. CONCLUSIONS: We developed a radiomics model for early prediction of the response to ICI treatment. This model may aid in identifying the patients most likely to benefit from immunotherapy.

Flagellin C decreases the expression of the Gossypium hirsutum cation/proton exchanger 3 gene to promote calcium ion, hydrogen peroxide, and nitric oxide and synergistically regulate the resistance of cotton to Verticillium wilt.

To date, no ideal effective method for controlling Verticillium wilt in upland cotton (Gossypium hirsutum) has been defined. The purpose of this study was to determine the effects and mechanism through which flagellin C (FLiC) regulates the Gossypium hirsutum cation/proton exchanger 3 gene (GhCAX3), induces plant immunity, and increases resistance to Verticillium wilt. The FLiC gene was cloned from an endophytic bacterium (Pseudomonas) isolated from roots of the upland cotton cultivar Zhongmiansuo 41. The biocontrol effects of FLiC purified in vitro on resistant and susceptible upland cotton cultivars were 47.50 and 32.42%, respectively. FLiC induced a hypersensitive response (HR) in leaves of tobacco and immune responses in upland cotton. Transcriptome data showed that treatment with FLiC significantly enriched the calcium antiporter activity-associated disease-resistant metabolic pathway in seedlings. Moreover, FLiC downregulated GhCAX3 expression to increase intracellular calcium ion (Ca2+) content and stimulate increases in the intracellular hydrogen peroxide (H2O2) and nitric oxide (NO) contents. The coordinated regulation of Ca2+, H2O2, and NO enhanced cotton resistance to Verticillium wilt. Furthermore, transgenic Arabidopsis plants overexpressing FLiC showed significantly improved resistance to Verticillium wilt. FLiC may be used as a resistance gene and a regulator to improve resistance to Verticillium dahliae (VD) in upland cotton.

Hepatic perivascular epithelioid cell tumor: A case report.

BACKGROUND: Perivascular epithelioid cell tumor (PEComa) is a mesenchymal tumor with histologic and immunophenotypic characteristics of perivascular epithelioid cells, has a low incidence, and can involve multiple organs. PEComa originating in the liver is extremely rare, with most cases being benign, and only a few cases are malignant. Good outcomes are achieved with radical surgical resection, but there is no effective treatment for some large tumors and specific locations that are contraindicated for surgery. CASE SUMMARY: A 32-year-old woman was admitted to our hospital with a palpable abdominal mass and progressive deterioration since the previous month. An ultrasound-guided percutaneous liver aspiration biopsy was performed. Postoperative pathological immunohistochemical staining was HMB45, Melan-A, and smooth muscle actin positive. Perivascular epithelioid tumor was diagnosed. The tumor was large and could not be completely resected by surgery. Further digital subtraction angiography revealed a rich tumor blood supply, and interventional embolization followed by surgery was recommended. Finally, the patient underwent transarterial embolization (TAE) combined with sorafenib for four cycles. Angiography reexamination indicated no clear vascular staining of the tumor, and the tumor had shrunk. The patient was followed up for a short period of time, achieved a stable condition, and surgery was recommended. CONCLUSION: Adjuvant combination treatment with TAE and sorafenib is safe and feasible as it shrinks the tumor preoperatively and facilitates surgery.

Identification of Candidate Therapeutic Genes for More Precise Treatment of Esophageal Squamous Cell Carcinoma and Adenocarcinoma.

The standard therapy administered to patients with advanced esophageal cancer remains uniform, despite its two main histological subtypes, namely esophageal squamous cell carcinoma (SCC) and esophageal adenocarcinoma (AC), are being increasingly considered to be different. The identification of potential drug target genes between SCC and AC is crucial for more effective treatment of these diseases, given the high toxicity of chemotherapy and resistance to administered medications. Herein we attempted to identify and rank differentially expressed genes (DEGs) in SCC vs. AC using ensemble feature selection methods. RNA-seq data from The Cancer Genome Atlas and the Fudan-Taizhou Institute of Health Sciences (China). Six feature filters algorithms were used to identify DEGs. We built robust predictive models for histological subtypes with the random forest (RF) classification algorithm. Pathway analysis also be performed to investigate the functional role of genes. 294 informative DEGs (87 of them are newly discovered) have been identified. The areas under receiver operator curve (AUC) were higher than 99.5% for all feature selection (FS) methods. Nine genes (i.e., ERBB3, ATP7B, ABCC3, GALNT14, CLDN18, GUCY2C, FGFR4, KCNQ5, and CACNA1B) may play a key role in the development of more directed anticancer therapy for SCC and AC patients. The first four of them are drug targets for chemotherapy and immunotherapy of esophageal cancer and involved in pharmacokinetics and pharmacodynamics pathways. Research identified novel DEGs in SCC and AC, and detected four potential drug targeted genes (ERBB3, ATP7B, ABCC3, and GALNT14) and five drug-related genes.

Development and Validation of a Clinical-Based Signature to Predict the 90-Day Functional Outcome for Spontaneous Intracerebral Hemorrhage.

We aimed to develop and validate an objective and easy-to-use model for identifying patients with spontaneous intracerebral hemorrhage (ICH) who have a poor 90-day prognosis. This three-center retrospective study included a large cohort of 1,122 patients with ICH who presented within 6 h of symptom onset [training cohort, n = 835; internal validation cohort, n = 201; external validation cohort (center 2 and 3), n = 86]. We collected the patients' baseline clinical, radiological, and laboratory data as well as the 90-day functional outcomes. Independent risk factors for prognosis were identified through univariate analysis and multivariate logistic regression analysis. A nomogram was developed to visualize the model results while a calibration curve was used to verify whether the predictive performance was satisfactorily consistent with the ideal curve. Finally, we used decision curves to assess the clinical utility of the model. At 90 days, 714 (63.6%) patients had a poor prognosis. Factors associated with prognosis included age, midline shift, intraventricular hemorrhage (IVH), subarachnoid hemorrhage (SAH), hypodensities, ICH volume, perihematomal edema (PHE) volume, temperature, systolic blood pressure, Glasgow Coma Scale (GCS) score, white blood cell (WBC), neutrophil, and neutrophil-lymphocyte ratio (NLR) (p < 0.05). Moreover, age, ICH volume, and GCS were identified as independent risk factors for prognosis. For identifying patients with poor prognosis, the model showed an area under the receiver operating characteristic curve of 0.874, 0.822, and 0.868 in the training cohort, internal validation, and external validation cohorts, respectively. The calibration curve revealed that the nomogram showed satisfactory calibration in the training and validation cohorts. Decision curve analysis showed the clinical utility of the nomogram. Taken together, the nomogram developed in this study could facilitate the individualized outcome prediction in patients with ICH.

Minimal apparent diffusion coefficient in predicting the Ki-67 proliferation index of pancreatic neuroendocrine tumors.

PURPOSE: To investigate the diagnostic performance of the minimal apparent diffusion coefficient (ADCmin) to distinguish between pancreatic neuroendocrine tumors (Pan-NETs) with low and high Ki-67 proliferation index values and to evaluate the relationship between ADCmin and the Ki-67 proliferation index. METHODS: Pre-operative magnetic resonance imaging data and postoperative Ki-67 proliferation index data of 42 patients with primary neuroendocrine tumor of the pancreas from November 2014 to March 2021 were included in this retrospective study. According to the Ki-67 proliferation index value, Pan-NETs were divided into a high-expression group (Ki-67 ≥ 10%, n = 17) and low-expression group (Ki-67 < 10%, n = 25), and mean ADC (ADCmean) and ADCmin values were compared between groups using receiver operating characteristic (ROC) curves to evaluate the performance of ADCmean and ADCmin in judging the expression level of Ki-67 proliferation index. The relationship between ADCmin and the Ki-67 proliferation index was also evaluated. RESULTS: The ADCmin was significantly higher in the low-expression group (Z = - 3.537, p < 0.01). The area under the ROC curve (AUC) for ADCmin was 0.825, which was higher than that for ADCmean (0.781). Using 1.32 × 10-3 mm2/s as the optimal discriminating threshold, the sensitivity, specificity, accuracy, and positive and negative predictive values of the two groups were 80%, 88.2%, 83.3%, 90%, and 75%, respectively. The ADCmin of Pan-NETs showed a significant negative correlation with the Ki-67 proliferation index (rs = - 0.634, p < 0.001). CONCLUSION: The ADCmin is a potential imaging biomarker, which may be helpful for non-invasive preoperative prediction of the Ki-67 proliferation index of Pan-NETs and the subsequent planning of appropriate treatment.

Optical properties of human brain and tumour tissue: An ex vivo study spanning the visible range to beyond the second near-infrared window.

Neuro-oncology surgery would benefit from detailed intraoperative tissue characterization provided by noncontact, contrast-agent-free, noninvasive optical imaging methods. In-depth knowledge of target tissue optical properties across a wide-wavelength spectrum could inform the design of optical imaging and computational methods to enable robust tissue analysis during surgery. We adapted a dual-beam integrating sphere to analyse small tissue samples and investigated ex vivo optical properties of five types of human brain tumour (meningioma, pituitary adenoma, schwannoma, low- and high-grade glioma) and nine different types of healthy brain tissue across a wavelength spectrum of 400 to 1800 nm. Fresh and frozen tissue samples were analysed. All tissue types demonstrated similar absorption spectra, but the reduced scattering coefficients of tumours show visible differences in the obtained optical spectrum compared to those of surrounding normal tissue. These results underline the potential of optical imaging technologies for intraoperative tissue characterization.

Intraoperative hyperspectral label-free imaging: from system design to first-in-patient translation.

Despite advances in intraoperative surgical imaging, reliable discrimination of critical tissue during surgery remains challenging. As a result, decisions with potentially life-changing consequences for patients are still based on the surgeon's subjective visual assessment. Hyperspectral imaging (HSI) provides a promising solution for objective intraoperative tissue characterisation, with the advantages of being non-contact, non-ionising and non-invasive. However, while its potential to aid surgical decision-making has been investigated for a range of applications, to date no real-time intraoperative HSI (iHSI) system has been presented that follows critical design considerations to ensure a satisfactory integration into the surgical workflow. By establishing functional and technical requirements of an intraoperative system for surgery, we present an iHSI system design that allows for real-time wide-field HSI and responsive surgical guidance in a highly constrained operating theatre. Two systems exploiting state-of-the-art industrial HSI cameras, respectively using linescan and snapshot imaging technology, were designed and investigated by performing assessments against established design criteria and ex vivo tissue experiments. Finally, we report the use of our real-time iHSI system in a clinical feasibility case study as part of a spinal fusion surgery. Our results demonstrate seamless integration into existing surgical workflows.