Role and mechanism of PVN-sympathetic-adipose circuit in depression and insulin resistance induced by chronic stress.

Chronic stress induces depression and insulin resistance, between which there is a bidirectional relationship. However, the mechanisms underlying this comorbidity remain unclear. White adipose tissue (WAT), innervated by sympathetic nerves, serves as a central node in the interorgan crosstalk through adipokines. Abnormal secretion of adipokines is involved in mood disorders and metabolic morbidities. We describe here a brain-sympathetic nerve-adipose circuit originating in the hypothalamic paraventricular nucleus (PVN) with a role in depression and insulin resistance induced by chronic stress. PVN neurons are labelled after inoculation of pseudorabies virus (PRV) into WAT and are activated under restraint stress. Chemogenetic manipulations suggest a role for the PVN in depression and insulin resistance. Chronic stress increases the sympathetic innervation of WAT and downregulates several antidepressant and insulin-sensitizing adipokines, including leptin, adiponectin, Angptl4 and Sfrp5. Chronic activation of the PVN has similar effects. ß-adrenergic receptors translate sympathetic tone into an adipose response, inducing downregulation of those adipokines and depressive-like behaviours and insulin resistance. We finally show that AP-1 has a role in the regulation of adipokine expression under chronic stress.

Image-based artificial intelligence for the prediction of pathological complete response to neoadjuvant chemoradiotherapy in patients with rectal cancer: a systematic review and meta-analysis.

OBJECTIVE: Artificial intelligence (AI) holds enormous potential for noninvasively identifying patients with rectal cancer who could achieve pathological complete response (pCR) following neoadjuvant chemoradiotherapy (nCRT). We aimed to conduct a meta-analysis to summarize the diagnostic performance of image-based AI models for predicting pCR to nCRT in patients with rectal cancer. METHODS: This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A literature search of PubMed, Embase, Cochrane Library, and Web of Science was performed from inception to July 29, 2023. Studies that developed or utilized AI models for predicting pCR to nCRT in rectal cancer from medical images were included. The Quality Assessment of Diagnostic Accuracy Studies-AI was used to appraise the methodological quality of the studies. The bivariate random-effects model was used to summarize the individual sensitivities, specificities, and areas-under-the-curve (AUCs). Subgroup and meta-regression analyses were conducted to identify potential sources of heterogeneity. Protocol for this study was registered with PROSPERO (CRD42022382374). RESULTS: Thirty-four studies (9933 patients) were identified. Pooled estimates of sensitivity, specificity, and AUC of AI models for pCR prediction were 82% (95% CI: 76-87%), 84% (95% CI: 79-88%), and 90% (95% CI: 87-92%), respectively. Higher specificity was seen for the Asian population, low risk of bias, and deep-learning, compared with the non-Asian population, high risk of bias, and radiomics (all P < 0.05). Single-center had a higher sensitivity than multi-center (P = 0.001). The retrospective design had lower sensitivity (P = 0.012) but higher specificity (P < 0.001) than the prospective design. MRI showed higher sensitivity (P = 0.001) but lower specificity (P = 0.044) than non-MRI. The sensitivity and specificity of internal validation were higher than those of external validation (both P = 0.005). CONCLUSIONS: Image-based AI models exhibited favorable performance for predicting pCR to nCRT in rectal cancer. However, further clinical trials are warranted to verify the findings.

An interpretable machine learning model based on contrast-enhanced CT parameters for predicting treatment response to conventional transarterial chemoembolization in patients with hepatocellular carcinoma.

OBJECTIVE: To explore the potential of pre-therapy computed tomography (CT) parameters in predicting the treatment response to initial conventional TACE (cTACE) in intermediate-stage hepatocellular carcinoma (HCC) and develop an interpretable machine learning model. METHODS: This retrospective study included 367 patients with intermediate-stage HCC who received cTACE as first-line therapy from three centers. We measured the mean attenuation values of target lesions on multi-phase contrast-enhanced CT and further calculated three CT parameters, including arterial (AER), portal venous (PER), and arterial portal venous (APR) enhancement ratios. We used logistic regression analysis to select discriminative features and trained three machine learning models via 5-fold cross-validation. The performance in predicting treatment response was evaluated in terms of discrimination, calibration, and clinical utility. Afterward, a Shapley additive explanation (SHAP) algorithm was leveraged to interpret the outputs of the best-performing model. RESULTS: The mean diameter, ECOG performance status, and cirrhosis were the important clinical predictors of cTACE treatment response, by multiple logistic regression. Adding the CT parameters to clinical variables showed significant improvement in performance (net reclassification index, 0.318, P < 0.001). The Random Forest model (hereafter, RF-combined model) integrating CT parameters and clinical variables demonstrated the highest performance on external validation dataset (AUC of 0.800). The decision curve analysis illustrated the optimal clinical benefits of RF-combined model. This model could successfully stratify patients into responders and non-responders with distinct survival (P = 0.001). CONCLUSION: The RF-combined model can serve as a robust and interpretable tool to identify the appropriate crowd for cTACE sessions, sparing patients from receiving ineffective and unnecessary treatments.

Thy-Wise: An interpretable machine learning model for the evaluation of thyroid nodules.

Current risk stratification systems for thyroid nodules suffer from low specificity and high biopsy rates. Recently, machine learning (ML) is introduced to assist thyroid nodule diagnosis but lacks interpretability. Here, we developed and validated ML models on 3965 thyroid nodules, as compared to the American College of Radiology Thyroid Imaging, Reporting and Data System (ACR TI-RADS). Subsequently, a SHapley Additive exPlanation (SHAP) algorithm was leveraged to interpret the results of the best-performing ML model. Clinical characteristics including thyroid-function tests were collected from medical records. Five ACR TI-RADS ultrasonography (US) categories plus nodule size were assessed by experienced radiologists. Random forest (RF), support vector machine (SVM) and extreme gradient boosting (XGBoost) were used to build US-only and US-clinical ML models. The ML models and ACR TI-RADS were compared in terms of diagnostic performance and unnecessary biopsy rate. Among the ML models, the US-only RF model (hereafter, Thy-Wise) achieved the optimal performance. Compared to ACR TI-RADS, Thy-Wise showed higher accuracy (82.4% vs 74.8% for the internal validation; 82.1% vs 73.4% for external validation) and specificity (78.7% vs 68.3% for internal validation; 78.5% vs 66.9% for external validation) while maintaining sensitivity (91.7% vs 91.2% for internal validation; 91.9% vs 91.1% for external validation), as well as reduced unnecessary biopsies (15.3% vs 32.3% for internal validation; 15.7% vs 47.3% for external validation). The SHAP-based interpretation of Thy-Wise enables clinicians to better understand the reasoning behind the diagnosis, which may facilitate the clinical translation of this model.

Radiomics in precision medicine for gastric cancer: opportunities and challenges.

OBJECTIVES: Radiomic features derived from routine medical images show great potential for personalized medicine in gastric cancer (GC). We aimed to evaluate the current status and quality of radiomic research as well as its potential for identifying biomarkers to predict therapy response and prognosis in patients with GC. METHODS: We performed a systematic search of the PubMed and Embase databases for articles published from inception through July 10, 2021. The phase classification criteria for image mining studies and the radiomics quality scoring (RQS) tool were applied to evaluate scientific and reporting quality. RESULTS: Twenty-five studies consisting of 10,432 patients were included. 96% of studies extracted radiomic features from CT images. Association between radiomic signature and therapy response was evaluated in seven (28%) studies; association with survival was evaluated in 17 (68%) studies; one (4%) study analyzed both. All results of the included studies showed significant associations. Based on the phase classification criteria for image mining studies, 18 (72%) studies were classified as phase II, with two, four, and one studies as discovery science, phase 0 and phase I, respectively. The median RQS score for the radiomic studies was 44.4% (range, 0 to 55.6%). There was extensive heterogeneity in the study population, tumor stage, treatment protocol, and radiomic workflow amongst the studies. CONCLUSIONS: Although radiomic research in GC is highly heterogeneous and of relatively low quality, it holds promise for predicting therapy response and prognosis. Efforts towards standardization and collaboration are needed to utilize radiomics for clinical application. KEY POINTS: • Radiomics application of gastric cancer is increasingly being reported, particularly in predicting therapy response and survival. • Although radiomics research in gastric cancer is highly heterogeneous and relatively low quality, it holds promise for predicting clinical outcomes. • Standardized imaging protocols and radiomic workflow are needed to facilitate radiomics into clinical use.

Radiogenomic association between the T2-FLAIR mismatch sign and IDH mutation status in adult patients with lower-grade gliomas: an updated systematic review and meta-analysis.

OBJECTIVES: To reveal a radiogenomic correlation between the presence of the T2-fluid-attenuated inversion recovery resection (T2-FLAIR) mismatch sign on MR images and isocitrate dehydrogenase (IDH) mutation status in adult patients with lower-grade gliomas (LGGs). METHODS: A web-based systemic search for eligible literature up to April 13, 2021, was conducted on PubMed, Embase, and the Cochrane Library databases by two independent reviewers. This study was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. We included studies evaluating the accuracy of the T2-FLAIR mismatch sign in diagnosing the IDH mutation in adult patients with LGGs. The T2-FLAIR mismatch sign was defined as a T2-hyperintense lesion that is hypointense on FLAIR except for a hyperintense rim. RESULTS: Fourteen studies (n = 1986) were finally identified. The mean age of patients in the included studies ranged from 38.5 to 56 years. The pooled area under the curve (AUC), sensitivity, and specificity were obtained for each molecular profile: IDHmut-Codel: 0.46 (95% confidence interval [CI]: 0.42-0.50), 1% (95%CI: 0-7%), and 69% (95%CI: 62-75%), respectively; IDHmut-Noncodel: 0.75 (95%CI: 0.71-0.79), 42% (95%CI: 34-50%), and 99% (95%CI: 96-100%), respectively; IDH-Mutation regardless of 1p/19q codeletion status: 0.77 (95%CI: 0.73-0.80), 29% (95%CI: 21-40%), and 99% (95%CI: 92-100%), respectively. CONCLUSIONS: The T2-FLAIR mismatch sign was an insensitive but highly specific marker for IDHmut-Noncodel and IDH-Mutation LGGs, whereas it was not a useful marker for IDHmut-Codel LGGs. The findings might identify the T2-FLAIR mismatch sign as a non-invasive imaging biomarker for the selection of patients with IDH-mutant LGGs. KEY POINTS: • The T2-FLAIR mismatch sign was not a sensitive sign for IDH mutation in LGGs. • The T2-FLAIR mismatch sign was related to IDHmut-Noncodel with a specificity of 99%. • The pooled specificity (69%) of the T2-FLAIR mismatch sign for IDHmut-Codel was low.

Current status and quality of radiomic studies for predicting immunotherapy response and outcome in patients with non-small cell lung cancer: a systematic review and meta-analysis.

PURPOSE: Prediction of immunotherapy response and outcome in patients with non-small cell lung cancer (NSCLC) is challenging due to intratumoral heterogeneity and lack of robust biomarkers. The aim of this study was to systematically evaluate the methodological quality of radiomic studies for predicting immunotherapy response or outcome in patients with NSCLC. METHODS: We systematically searched for eligible studies in the PubMed and Web of Science datasets up to April 1, 2021. The methodological quality of included studies was evaluated using the phase classification criteria for image mining studies and the radiomics quality scoring (RQS) tool. A meta-analysis of studies regarding the prediction of immunotherapy response and outcome in patients with NSCLC was performed. RESULTS: Fifteen studies were identified with sample sizes ranging from 30 to 228. Seven studies were classified as phase II, and the remaining as discovery science (n = 2), phase 0 (n = 4), phase I (n = 1), and phase III (n = 1). The mean RQS score of all studies was 29.6%, varying from 0 to 68.1%. The pooled diagnostic odds ratio for predicting immunotherapy response in NSCLC using radiomics was 14.99 (95% confidence interval [CI] 8.66-25.95). In addition, radiomics could divide patients into high- and low-risk group with significantly different overall survival (pooled hazard ratio [HR]: 1.96, 95%CI 1.61-2.40, p < 0.001) and progression-free survival (pooled HR: 2.39, 95%CI 1.69-3.38, p < 0.001). CONCLUSIONS: Radiomics has potential to noninvasively predict immunotherapy response and outcome in patients with NSCLC. However, it has not yet been implemented as a clinical decision-making tool. Further external validation and evaluation within clinical pathway can facilitate personalized treatment for patients with NSCLC.

Effects of human platelet lysate on the growth of cultured human corneal endothelial cells.

Human platelet lysate (hPL) as a replacement for foetal bovine serum (FBS) in culturing human corneal endothelium is an emerging area of interest, although there are limited studies evaluating the quality of the hPL being used. Our study aimed to evaluate variations between sources of hPL and to explore the efficacy of hPL (with and without heparin) as a replacement for FBS in culturing human corneal endothelial cells in vitro. Immortalized human corneal endothelial cells (B4G12) and primary human corneal endothelial cells (PHCEnCs, n = 11 donors, age from 36 to 85 years old) were cultured with 5% hPL or FBS. A full characterisation of the effects of hPL and FBS on cell growth was conducted using IncuCyte Zoom (percentage cell confluence and population doubling time, PDT) to analyse cell proliferation. AlamarBlue assays were used to measure cell viability. The concentration of fibrinogen, PDGF, hEGF, VEGF and bFGF in two sources of hPL were analyzed by Enzyme-linked immunosorbent assay. Expression and localization of Na+/K+-ATPase, ZO-1 and CD166 on PHCEnCs and B4G12 cells were assessed with immunofluorescence and immunoblotting. Our results showed that a significant difference in fibrinogen, hEGF and VEGF concentrations was found between two sources of hPL. Heparin impaired the positive effect of hPL on cell growth. PDT and alamarBlue showed that hPL significantly increased proliferation and viability of PHCEnCs in two of three donors, and immunostaining indicated that hPL increased ZO-1 and CD166 expression but not Na+/K+-ATPase on PHCEnCs. In addition, heterogeneities on immunopositivity of Na+/K+-ATPase and ZO-1 and morphology were found on PHCEnCs derived from an individual donor cultured with hPL medium. In conclusion, hPL showed positive effect on primary corneal endothelial cell growth, and maintenance of their cellular characteristics compared to FBS. hPL can be considered as a supplement to replace FBS in PHCEnC culture. However, the variation observed between different hPL sources suggests that a standard quality control monitoring system such as storage time and a minimal concentration of growth factors may need to be established.

MRI-Based Deep-Learning Model for Distant Metastasis-Free Survival in Locoregionally Advanced Nasopharyngeal Carcinoma.

BACKGROUND: Distant metastasis is the primary cause of treatment failure in locoregionally advanced nasopharyngeal carcinoma (LANPC). PURPOSE: To develop a model to evaluate distant metastasis-free survival (DMFS) in LANPC and to explore the value of additional chemotherapy to concurrent chemoradiotherapy (CCRT) for different risk groups. STUDY TYPE: Retrospective. POPULATION: In all, 233 patients with biopsy-confirmed nasopharyngeal carcinoma (NPC) from two hospitals. FIELD STRENGTH: 1.5T and 3T. SEQUENCE: Axial T2 -weighted (T2 -w) and contrast-enhanced T1 -weighted (CET1 -w) images. ASSESSMENT: Deep learning was used to build a model based on MRI images (including axial T2 -w and CET1 -w images) and clinical variables. Hospital 1 patients were randomly divided into training (n = 169) and validation (n = 19) cohorts; Hospital 2 patients were assigned to a testing cohort (n = 45). LANPC patients were divided into low- and high-risk groups according to their DMFS (P < 0.05). Kaplan-Meier survival analysis was performed to compare the DMFS of different risk groups and subgroup analysis was performed to compare patients treated with CCRT alone and treated with additional chemotherapy to CCRT in different risk groups, respectively. STATISTICAL TESTS: Univariate analysis was performed to identify significant clinical variables. The area under the receiver operating characteristic (ROC) curve (AUC) was used to assess the model performance. RESULTS: Our deep-learning model integrating the deep-learning signature, node (N) stage (from TNM staging), plasma Epstein-Barr virus (EBV)-DNA, and treatment regimens yielded an AUC of 0.796 (95% confidence interval [CI]: 0.729-0.863), 0.795 (95% CI: 0.540-1.000), and 0.808 (95% CI: 0.654-0.962) in the training, internal validation, and external testing cohorts, respectively. Low-risk patients treated with CCRT alone had longer DMFS than patients treated with additional chemotherapy to CCRT (P < 0.05). DATA CONCLUSION: The proposed deep-learning model, based on MRI features and clinical variates, facilitated the prediction of DMFS in LANPC patients. LEVEL OF EVIDENCE: 3. TECHNICAL EFFICACY STAGE: 4.

SIRT1 in forebrain excitatory neurons produces sexually dimorphic effects on depression-related behaviors and modulates neuronal excitability and synaptic transmission in the medial prefrontal cortex.

Sirtuin 1 (SIRT1), an NAD+-dependent deacetylase, is a key regulator of cellular metabolism. Recent genome-wide association studies identified genetic variants of SIRT1 linked to major depressive disorders. SIRT1 is widely expressed in the brain; however, neuronal substrates that mediate SIRT1 action on depressive behaviors remain largely unknown. Here we show that selective deletion of SIRT1 in forebrain excitatory neurons causes depression-like phenotypes in male but not female mice. AAV-Cre-mediated SIRT1 knockdown in the medial prefrontal cortex (mPFC) of adult male mice induces depressive-like behaviors. Whole-cell patch-clamp recordings demonstrate that loss of SIRT1 decreases intrinsic excitability and spontaneous excitatory synaptic transmission in layer V pyramidal neurons in the prelimbic mPFC. Consistent with neuronal hypoexcitability, SIRT1 knockout reduces mitochondrial density and expression levels of genes involved in mitochondrial biogenesis and dynamics in the prelimbic mPFC. When a SIRT1 activator (SRT2104) is injected into the mPFC or lateral ventricle of wild-type mice, it reverses chronic unpredictable stress-induced anhedonia and behavioral despair, indicating an antidepressant-like effect. These results suggest that SIRT1 in mPFC excitatory neurons is required for normal neuronal excitability and synaptic transmission and regulates depression-related behaviors in a sex-specific manner.

Forward and Inverse Dynamics of a Six-Axis Accelerometer Based on a Parallel Mechanism.

The solution of the dynamic equations of the six-axis accelerometer is a prerequisite for sensor calibration, structural optimization, and practical application. However, the forward dynamic equations (FDEs) and inverse dynamic equations (IDEs) of this type of system have not been completely solved due to the strongly nonlinear coupling relationship between the inputs and outputs. This article presents a comprehensive study of the FDEs and IDEs of the six-axis accelerometer based on a parallel mechanism. Firstly, two sets of dynamic equations of the sensor are constructed based on the Newton-Euler method in the configuration space. Secondly, based on the analytical solution of the sensor branch chain length, the coordination equation between the output signals of the branch chain is constructed. The FDEs of the sensor are established by combining the coordination equations and two sets of dynamic equations. Furthermore, by introducing generalized momentum and Hamiltonian function and using Legendre transformation, the vibration differential equations (VDEs) of the sensor are derived. The VDEs and Newton-Euler equations constitute the IDEs of the system. Finally, the explicit recursive algorithm for solving the quaternion in the equation is given in the phase space. Then the IDEs are solved by substituting the quaternion into the dynamic equations in the configuration space. The predicted numerical results of the established FDEs and IDEs are verified by comparing with virtual and actual experimental data. The actual experiment shows that the relative errors of the FDEs and the IDEs constructed in this article are 2.21% and 7.65%, respectively. This research provides a new strategy for further improving the practicability of the six-axis accelerometer.

Novel coronavirus disease 2019 (COVID-19): relationship between chest CT scores and laboratory parameters.

PURPOSE: To quantify the severity of 2019 novel coronavirus disease (COVID-19) on chest CT and to determine its relationship with laboratory parameters. METHODS: Patients with real-time fluorescence polymerase chain reaction (RT-PCR)-confirmed COVID-19 between January 01 and February 18, 2020, were included in this study. Laboratory parameters were retrospectively collected from medical records. Severity of lung changes on chest CT of early, progressive, peak, and absorption stages was scored according to the percentage of lung involvement (5 lobes, scores 1-5 for each lobe, range 0-20). Relationship between CT scores and laboratory parameters was evaluated by the Spearman rank correlation. The Bonferroni correction adjusted significance level was at 0.05/4 = 0.0125. RESULTS: A total of 84 patients (mean age, 47.8 ± 12.0 years [standard deviation]; age range, 24-80 years) were evaluated. The patients underwent a total of 339 chest CT scans with a median interval of 4 days (interquartile range, 3-5 days). Median chest CT scores peaked at 4 days after the beginning of treatment and then declined. CT score of the early stage was correlated with neutrophil count (r = 0.531, P = 0.011). CT score of the progressive stage was correlated with neutrophil count (r = 0.502, P < 0.001), white blood cell count (r = 0.414, P = 0.001), C-reactive protein (r = 0.511, P < 0.001), procalcitonin (r = 0.423, P = 0.004), and lactose dehydrogenase (r = 0.369, P = 0.010). However, CT scores of the peak and absorption stages were not correlated with any parameter (P > 0.0125). No sex difference occurred regarding CT score (P > 0.05). CONCLUSION: Severity of lung abnormalities quantified on chest CT might correlate with laboratory parameters in the early and progressive stages. However, larger cohort studies are necessary.

Adiponectin modulates ventral tegmental area dopamine neuron activity and anxiety-related behavior through AdipoR1.

Adiponectin, a metabolic hormone secreted by adipocytes, can cross the blood-brain barrier to act on neurons in different brain regions, including those involved in stress-related disorders. Here we show that dopamine neurons in the ventral tegmental area (VTA) express adiponectin receptor 1 (AdipoR1). Intra-VTA infusion of adiponectin or the adiponectin mimetic AdipoRon in wild-type mice decreases basal dopamine neuron population activity and firing rate and reverses the restraint stress-induced increase in dopamine neuron activity and anxiety behavior. Adiponectin haploinsufficiency leads to increased dopamine neuron firing and anxiety behavior under basal conditions. Ablation of AdipoR1 specifically from dopamine neurons enhances neuronal and anxiogenic responses to restraint stress. The effects of intra-VTA infusion of adiponectin on neuronal activity and behavior were abolished in mice lacking AdipoR1 in dopamine neurons. These observations indicate that adiponectin can directly modulate VTA dopamine neuron activity and anxiety behavior, and that AdipoR1 is required for adiponectin-induced inhibition of dopamine neurons and anxiolytic effects. These results strengthen the idea of adiponectin as a key biological factor that links metabolic syndrome and emotional disorders.

Interlaboratory Reproducibility of Droplet Digital Polymerase Chain Reaction Using a New DNA Reference Material Format.

Use of droplet digital PCR technology (ddPCR) is expanding rapidly in the diversity of applications and number of users around the world. Access to relatively simple and affordable commercial ddPCR technology has attracted wide interest in use of this technology as a molecular diagnostic tool. For ddPCR to effectively transition to a molecular diagnostic setting requires processes for method validation and verification and demonstration of reproducible instrument performance. In this study, we describe the development and characterization of a DNA reference material (NMI NA008 High GC reference material) comprising a challenging methylated GC-rich DNA template under a novel 96-well microplate format. A scalable process using high precision acoustic dispensing technology was validated to produce the DNA reference material with a certified reference value expressed in amount of DNA molecules per well. An interlaboratory study, conducted using blinded NA008 High GC reference material to assess reproducibility among seven independent laboratories demonstrated less than 4.5% reproducibility relative standard deviation. With the exclusion of one laboratory, laboratories had appropriate technical competency, fully functional instrumentation, and suitable reagents to perform accurate ddPCR based DNA quantification measurements at the time of the study. The study results confirmed that NA008 High GC reference material is fit for the purpose of being used for quality control of ddPCR systems, consumables, instrumentation, and workflow.

Substance P in Flush Tears and Schirmer Strips of Healthy Participants.

PURPOSE: To determine the repeatability of the flush tear collection technique and the Schirmer strip for Substance P tear analysis. METHODS: The tears of 10 healthy non-contact-lens wearers were collected via Schirmer strip and microcapillary following instillation of either 20 µL (F-20) or 60 µL (F-60) of saline. Each technique was conducted on two occasions and in a randomized order. Total protein content (TPC) and Substance P concentrations were determined. The overall protein separation profile of each type of tears was examined using one-dimensional gel electrophoresis (1DGE). RESULTS: Collection rates were significantly faster for the F-60 compared to F-20 (17.3 ± 6.9 µL/min and 11.9 ± 5.3 µL/min, respectively, P < .001), with an average Schirmer strip length of 1.5 ± 2.1 mm/min. The coefficient of repeatability between days and eyes was greatest for the Schirmer strip, with eyes and days being significantly different (P = .03 and P = .03, respectively) for Schirmer strip Substance P. TPC was 3.8 ± 2.6 mg/mL, 3.3 ± 1.8 mg/mL, and 3.6 ± 3.0 mg/mL for F-20, F-60, and Schirmer strip techniques, respectively, with no significant difference between techniques (P = .85). Substance P concentration was 13.1 ± 14.8 ng/mL, 9.1 ± 6.1 ng/mL, and 14.9 ± 10.6 ng/mL for F-20, F-60, and Schirmer strip tears, respectively, with no significant difference between techniques (P = .57). 1DGE profile showed similar electrophoresis patterns among F-20, F-60, and basal tears. CONCLUSIONS: The F-60 method allows faster collection than F-20, but the latter results in better repeatability than both the F-60 and Schirmer sampling techniques. All three techniques return the same concentrations of TPC and Substance P. This indicates that tear collection using the F-20 may be more appropriate when conducting comparative analysis, whereas the F-60 may be more appropriate when more volume is required.

Corneal Nerve Morphology and Tear Film Substance P in Diabetes.

PURPOSE: This work aims to characterize the relationship between tear film neuropeptide substance P and the structural integrity of the sub-basal nerve plexus in diabetes. METHODS: Seventeen healthy control participants and nine participants with diabetes were recruited in this cross-sectional study. Total protein content and substance P concentrations were determined in the flush tears of participants. Corneal nerve morphology was assessed by capturing the corneal sub-basal nerve plexus using the Heidelberg Retinal Tomograph II with the Rostock Corneal Module (Heidelberg Engineering GmbH, Heidelberg, Germany) in the central cornea. Corneal nerve fiber density (CNFD) was measured using ACCMetrics (M.A. Dabbah, Imaging Science and Biomedical Engineering, Manchester, UK) on eight captured images. Comparisons between groups were made using independent samples t-tests. Correlations between parameters were analyzed using Pearson's correlations. RESULTS: Substance P concentrations were significantly higher in the tears of the control group compared to participants with diabetes (4150 ± 4752 and 1473 ± 1671 pg/mL, respectively, P = .047). There was no significant difference in total protein content between the groups (3.4 ± 1.8 and 2.6 ± 1.7 mg/mL in the control and diabetes groups, respectively, P = .262). CNFD was significantly lower in the participants with diabetes compared to the control group (16.1 ± 5.7 and 21.5 ± 7.0 mm/mm, respectively, P = .041). There was a moderate correlation between substance P and CNFD (r = 0.48, P = .01). CONCLUSIONS: Substance P is expressed at a significantly lower level in the tears of people with diabetes compared with healthy controls. The positive correlation between substance P and corneal nerve density indicates that substance P may be a potential biomarker for corneal nerve health.

Absolute quantification of human tear lactoferrin using multiple reaction monitoring technique with stable-isotopic labeling.

The mass spectrometry technique of multiple reaction monitoring (MRM) was used to quantify and compare the expression level of lactoferrin in tear films among control, prostate cancer (CaP), and benign prostate hyperplasia (BPH) groups. Tear samples from 14 men with CaP, 15 men with BPH, and 14 controls were analyzed in the study. Collected tears (2 µl) of each sample were digested with trypsin overnight at 37 °C without any pretreatment, and tear lactoferrin was quantified using a lactoferrin-specific peptide, VPSHAVVAR, both using natural/light and isotopic-labeled/heavy peptides with MRM. The average tear lactoferrin concentration was 1.01 ± 0.07 µg/µl in control samples, 0.96 ± 0.07 µg/µl in the BPH group, and 0.98 ± 0.07 µg/µl in the CaP group. Our study is the first to quantify tear proteins using a total of 43 individual (non-pooled) tear samples and showed that direct digestion of tear samples is suitable for MRM studies. The calculated average lactoferrin concentration in the control group matched that in the published range of human tear lactoferrin concentration measured by enzyme-linked immunosorbent assay (ELISA). Moreover, the lactoferrin was stably expressed across all of the samples, with no significant differences being observed among the control, BPH, and CaP groups.

Development and Validation of a Feature-Based Broad-Learning System for Opportunistic Osteoporosis Screening Using Lumbar Spine Radiographs.

RATIONALE AND OBJECTIVES: Osteoporosis is primarily diagnosed using dual-energy X-ray absorptiometry (DXA); yet, DXA is significantly underutilized, causing osteoporosis, an underdiagnosed condition. We aimed to provide an opportunistic approach to screen for osteoporosis using artificial intelligence based on lumbar spine X-ray radiographs. MATERIALS AND METHODS: In this institutional review board-approved retrospective study, female patients aged ≥50 years who received both X-ray scans and DXA of the lumbar vertebrae, in three centers, were included. A total of 1180 cases were used for training and 145 cases were used for testing. We proposed a novel broad-learning system (BLS) and then compared the performance of BLS models using radiomic features and deep features as a source of input. The deep features were extracted using ResNet18 and VGG11, respectively. The diagnostic performances of these BLS models were evaluated with the area under the curve (AUC), sensitivity, and specificity. RESULTS: The incidence rate of osteoporosis in the training and test sets was 35.9% and 37.9%, respectively. The radiomic feature-based BLS model achieved higher testing AUC (0.802 vs. 0.654 vs. 0.632, both P = .002), sensitivity (78.2% vs. 56.4% vs. 50.9%), and specificity (82.2% vs. 74,4% vs. 75.6%) than the two deep feature-based BLS models. CONCLUSION: Our proposed radiomic feature-based BLS model has the potential to expand osteoporosis screening to a broader population by identifying osteoporosis on lumbar spine X-ray radiographs.

Automated and reusable deep learning (AutoRDL) framework for predicting response to neoadjuvant chemotherapy and axillary lymph node metastasis in breast cancer using ultrasound images: a retrospective, multicentre study.

Background: Previous deep learning models have been proposed to predict the pathological complete response (pCR) and axillary lymph node metastasis (ALNM) in breast cancer. Yet, the models often leveraged multiple frameworks, required manual annotation, and discarded low-quality images. We aimed to develop an automated and reusable deep learning (AutoRDL) framework for tumor detection and prediction of pCR and ALNM using ultrasound images with diverse qualities. Methods: The AutoRDL framework includes a You Only Look Once version 5 (YOLOv5) network for tumor detection and a progressive multi-granularity (PMG) network for pCR and ALNM prediction. The training cohort and the internal validation cohort were recruited from Guangdong Provincial People's Hospital (GPPH) between November 2012 and May 2021. The two external validation cohorts were recruited from the First Affiliated Hospital of Kunming Medical University (KMUH), between January 2016 and December 2019, and Shunde Hospital of Southern Medical University (SHSMU) between January 2014 and July 2015. Prior to model training, super-resolution via iterative refinement (SR3) was employed to improve the spatial resolution of low-quality images from the KMUH. We developed three models for predicting pCR and ALNM: a clinical model using multivariable logistic regression analysis, an image model utilizing the PMG network, and a combined model that integrates both clinical and image data using the PMG network. Findings: The YOLOv5 network demonstrated excellent accuracy in tumor detection, achieving average precisions of 0.880-0.921 during validation. In terms of pCR prediction, the combined modelpost-SR3 outperformed the combined modelpre-SR3, image modelpost-SR3, image modelpre-SR3, and clinical model (AUC: 0.833 vs 0.822 vs 0.806 vs 0.790 vs 0.712, all p < 0.05) in the external validation cohort (KMUH). Consistently, the combined modelpost-SR3 exhibited the highest accuracy in ALNM prediction, surpassing the combined modelpre-SR3, image modelpost-SR3, image modelpre-SR3, and clinical model (AUC: 0.825 vs 0.806 vs 0.802 vs 0.787 vs 0.703, all p < 0.05) in the external validation cohort 1 (KMUH). In the external validation cohort 2 (SHSMU), the combined model also showed superiority over the clinical and image models (0.819 vs 0.712 vs 0.806, both p < 0.05). Interpretation: Our proposed AutoRDL framework is feasible in automatically predicting pCR and ALNM in real-world settings, which has the potential to assist clinicians in optimizing individualized treatment options for patients. Funding: National Key Research and Development Program of China (2023YFF1204600); National Natural Science Foundation of China (82227802, 82302306); Clinical Frontier Technology Program of the First Affiliated Hospital of Jinan University, China (JNU1AF-CFTP-2022-a01201); Science and Technology Projects in Guangzhou (202201020022, 2023A03J1036, 2023A03J1038); Science and Technology Youth Talent Nurturing Program of Jinan University (21623209); and Postdoctoral Science Foundation of China (2022M721349).

Tear levels of SFRP1 are significantly reduced in keratoconus patients.

PURPOSE: To measure secreted frizzled-related protein 1 (SFRP1) levels in human tears and to investigate tear SFRP1 as a potential biomarker for keratoconus (KC). METHODS: Tears were collected from control (n = 33) and KC patients (n = 33) using micropipette tubes. Total tear protein was measured using a FluoroProfile Protein Quantification kit. An in-house enzyme-linked immunosorbent assay (ELISA) was developed to measure SFRP1 in control and KC tears. Statistical analyses of age, gender, the association of SFRP1, and total tear protein with KC were conducted. RESULTS: Tear SFRP1 was significantly decreased in KC, compared to age-matched controls (3.41 ng/µl ± 3.12 versus 5.55 ng/µl ± 5.62, respectively; p = 0.039). Conversely, total tear protein was significantly increased in KC, compared to age-matched controls (12.38 µg/µl ± 4.76 versus 9.40 µg/µl ± 3.88, respectively; p = 0.038). The ratio of SFRP1/total tear protein was also found to be significantly decreased in the KC group (p = 0.007). No significant association between tear SFRP1 and total tear protein was detected. CONCLUSIONS: Tear SFRP1 was significantly decreased in age-matched KC versus control patients, and may be further reduced in moderate KC. Tear-SFRP1 levels alone do not provide an obvious biomarker for KC; however, our results provide further evidence that tear-protein profiles are altered in KC, and suggest the involvement of SFRPs in the pathogenesis of KC.