Inflammatory illnesses, such as periodontitis and atherosclerotic coronary heart disease (ASCHD), trigger the production of pro-inflammatory mediators. The aim of this study was to assess the accuracy of using salivary interleukin-1β (IL-1β), interleukin-18 (IL-18), and gasdermin D (GSDMD) in discerning patients with periodontitis with and without ASCHD from healthy individuals, and to assess their correlation with clinical periodontal parameters and low-density lipoprotein (LDL) levels. The study involved 120 participants: 30 were healthy subjects (control group, C), 30 had generalized periodontitis (group P), 30 had ASCHD and clinically healthy periodontium (group AS-C), and 30 had ASCHD and generalized periodontitis (group AS-P). Saliva and blood samples were collected, and periodontal characteristics such as plaque index, bleeding on probing, probing pocket depth, and clinical attachment loss were examined. IL-1β, IL-18, and GSDMD levels from saliva were determined using ELISA. LDL levels were determined from the blood samples. Groups P, AS-C, and AS-P had higher levels of salivary IL-1β, IL-18, and GSDMD than group C. The receiver operating characteristic (ROC) curves of all biomarkers showed high diagnostic accuracy, with a significant positive correlation with the clinical parameters and LDL levels. The observed correlations between the studied pro-inflammatory mediators and disease severity suggest that these biomarkers could serve as indicators of disease progression in conditions such as periodontitis and ASCHD.

UNASSIGNED: Patients with pulmonary tuberculosis (PTB) disease and positive sputum cultures are the main source of infection. Culture conversion time is inconsistent and defining the length of respiratory isolation is challenging. The objective of this study is to develop a score to predict the length of isolation period.
UNASSIGNED: A retrospective study was carried out to evaluated risk factors associated with persistent positive sputum cultures after 4 weeks of treatment in 229 patients with PTB. A multivariable logistic regression model was used to determinate predictors for positive culture and a scoring system was created based on the coefficients of the final model.
UNASSIGNED: Sputum culture was persistently positive in 40.6%. Fever at consultation (1.87, 95% CI:1.02-3.41), smoking (2.44, 95% CI:1.36-4.37), >2 affected lung lobes (1.95, 95% CI:1.08-3.54), and neutrophil-to-lymphocyte ratio > 3.5 (2.22, 95% CI:1.24-3.99), were significantly associated with delayed culture conversion. Therefore, we assembled a severity score that achieved an area under the curve of 0.71 (95% CI:0.64-0.78).
UNASSIGNED: In patients with smear positive PTB, a score with clinical, radiological and analytical parameters can be used as a supplemental tool to assist clinical decisions in isolation period.

Although immunotherapy has revolutionized cancer management, most patients do not derive benefits from it. Aiming to explore an appropriate strategy for immunotherapy efficacy prediction, we collected 6251 patients\' transcriptome data from multicohort population and analyzed the data using a machine learning algorithm. In this study, we found that patients from three immune gene clusters had different overall survival when treated with immunotherapy (P < 0.001), and that these clusters had differential states of hypoxia scores and metabolism functions. The immune gene score showed good immunotherapy efficacy prediction (AUC was 0.737 at 20 months), which was well validated. The immune gene score, tumor mutation burden, and long non-coding RNA score were further combined to build a tumor immune microenvironment signature, which correlated more strongly with overall survival (AUC, 0.814 at 20 months) than when using a single variable. Thus, we recommend using the characterization of the tumor immune microenvironment associated with immunotherapy efficacy via a multi-omics analysis of cancer.

Mucopolysaccharidosis Type IIIB (MPS IIIB) is an ultrarare, fatal pediatric disease with no approved therapy. It is caused by mutations in the gene encoding for lysosomal enzyme alpha-N-acetylglucosaminidase (NAGLU). Tralesinidase alfa (TA) is a fusion protein comprised of recombinant NAGLU and a modified human insulin-like growth factor 2 that is being developed as an enzyme replacement therapy for MPS IIIB. Since MPS IIIB is a pediatric disease the safety/toxicity, pharmacokinetics and biodistribution of TA were evaluated in juvenile non-human primates that were administered up to 5 weekly intracerebroventricular (ICV) or single intravenous (IV) infusions of TA. TA administered by ICV slow-, ICV isovolumetric bolus- or IV-infusion was well-tolerated, and no effects were observed on clinical observations, electrocardiographic or ophthalmologic parameters, or respiratory rates. The drug-related changes observed were limited to increased cell infiltrates in the CSF and along the ICV catheter track after ICV administration. These findings were not associated with functional changes and are associated with the use of ICV catheters. The CSF PK profiles were consistent across all conditions tested and TA distributed widely in the CNS after ICV administration. Anti-drug antibodies were observed but did not appear to significantly affect the exposure to TA. Correlations between TA concentrations in plasma and brain regions in direct contact with the cisterna magna suggest glymphatic drainage may be responsible for clearance of TA from the CNS. The data support the administration of TA by isovolumetric bolus ICV infusion to pediatric patients with MPS IIIB.

UNASSIGNED: Thyroid cancer (TC) accounts for more than 90% of endocrine tumours and is a typical head and neck tumour in adults. The aim of this study was to develop a predictive tool to predict cancer-specific survival (CSS) in middle-aged patients with papillary thyroid carcinoma (PTC).
UNASSIGNED: The patients from 2004 to 2015 were randomly divided into a training cohort (n = 25,342) and a internal validation cohort (n = 10,725). The patients from 2016 to 2018 were treated as an external validation cohort (n = 11353). COX proportional hazard model was used to screen meaningful independent risk factors. These factors were constructed into a nomogram to predict CSS in middle-aged patients with PTC. The performance and accuracy of the nomogram were then evaluated using the concordance index (C-index), calibration curve and the area under the curve (AUC). The clinical value of nomogram was evaluated by decision curve analysis (DCA).
UNASSIGNED: Age, gender, marriage, tumour grade, T stage, N stage, M stage, surgery, chemotherapy, and tumour size were independent prognostic factors. The C-indexes of the training, internal validation, and external validation cohorts were 0.906, 0.887, and 0.962, respectively. The AUC and calibration curves show good accuracy. DCA shows that the clinical value of the nomogram is higher than that of Tumour, Node and Metastasis (TNM) staging.
UNASSIGNED: We developed a new prediction tool to predict CSS in middle-aged patients with PTC. The model has good performance after internal and external validation, which can be friendly to help doctors and patients predict CSS.

UNASSIGNED: To evaluate diagnostic precision and prove equivalence of 2 devices, Advanced vision analyzer (AVA, Elisar Vision Technology) and Humphrey field analyzer (HFA, Zeiss) for the detection of glaucoma on 10-2 program.
UNASSIGNED: Prospective, cross-sectional, observational study.
UNASSIGNED: Threshold estimates of 1 eye each of 66 patients with glaucoma, 36 control participants, and 10 glaucoma suspects were analyzed on 10-2 test with AVA and HFA.
UNASSIGNED: Mean sensitivity (MS) values of 68 points and central 16 test points were calculated and compared. Intraclass correlation (ICC), Bland-Altman (BA) plots, linear regression of MS, mean deviation (MD), and pattern standard deviation (PSD) were computed to assess the 10-2 threshold estimate of the devices. Receiver operating characteristic curves were generated for MS and MD values, and the area under the curve (AUC) was compared with assessing diagnostic precision.
UNASSIGNED: Mean sensitivity values of 68 points and central 16 points, AUC for MS and MD values, ICC values, BA plots, and linear-regression analysis.
UNASSIGNED: Bland-Altman plot showed significant correlation for MS, MD, and PSD values for both devices. For MS, the overall ICC value was 0.96 (P < 0.001) with a mean bias of 0.0 dB and limits of agreement range of 7.59. The difference in MS values between both devices was -0.4760 ± 1.95 (P > 0.05). The AUC for MS values for AVA was 0.89 and for HFA was 0.92 (P = 0.188); whereas it was similar at 0.88 for MD values (P = 0.799). Advanced vision analyzer and HFA identically discriminated between healthy and patients with glaucoma (P < 0.001), although HFA denoted marginally greater ability (P > 0.05).
UNASSIGNED: Statistical results denote adequate equivalence between AVA and HFA because threshold estimates of AVA strongly correlate with HFA for 10-2 program.
UNASSIGNED: Proprietary or commercial disclosure may be found after the references.

UNASSIGNED: Tongue images (the colour, size and shape of the tongue and the colour, thickness and moisture content of the tongue coating), reflecting the health state of the whole body according to the theory of traditional Chinese medicine (TCM), have been widely used in China for thousands of years. Herein, we investigated the value of tongue images and the tongue coating microbiome in the diagnosis of gastric cancer (GC).
UNASSIGNED: From May 2020 to January 2021, we simultaneously collected tongue images and tongue coating samples from 328 patients with GC (all newly diagnosed with GC) and 304 non-gastric cancer (NGC) participants in China, and 16 S rDNA was used to characterize the microbiome of the tongue coating samples. Then, artificial intelligence (AI) deep learning models were established to evaluate the value of tongue images and the tongue coating microbiome in the diagnosis of GC. Considering that tongue imaging is more convenient and economical as a diagnostic tool, we further conducted a prospective multicentre clinical study from May 2020 to March 2022 in China and recruited 937 patients with GC and 1911 participants with NGC from 10 centres across China to further evaluate the role of tongue images in the diagnosis of GC. Moreover, we verified this approach in another independent external validation cohort that included 294 patients with GC and 521 participants with NGC from 7 centres. This study is registered at ClinicalTrials.gov, NCT01090362.
UNASSIGNED: For the first time, we found that both tongue images and the tongue coating microbiome can be used as tools for the diagnosis of GC, and the area under the curve (AUC) value of the tongue image-based diagnostic model was 0.89. The AUC values of the tongue coating microbiome-based model reached 0.94 using genus data and 0.95 using species data. The results of the prospective multicentre clinical study showed that the AUC values of the three tongue image-based models for GCs reached 0.88-0.92 in the internal verification and 0.83-0.88 in the independent external verification, which were significantly superior to the combination of eight blood biomarkers.
UNASSIGNED: Our results suggest that tongue images can be used as a stable method for GC diagnosis and are significantly superior to conventional blood biomarkers. The three kinds of tongue image-based AI deep learning diagnostic models that we developed can be used to adequately distinguish patients with GC from participants with NGC, even early GC and precancerous lesions, such as atrophic gastritis (AG).
UNASSIGNED: The National Key R&D Program of China (2021YFA0910100), Program of Zhejiang Provincial TCM Sci-tech Plan (2018ZY006), Medical Science and Technology Project of Zhejiang Province (2022KY114, WKJ-ZJ-2104), Zhejiang Provincial Research Center for Upper Gastrointestinal Tract Cancer (JBZX-202006), Natural Science Foundation of Zhejiang Province (HDMY22H160008), Science and Technology Projects of Zhejiang Province (2019C03049), National Natural Science Foundation of China (82074245, 81973634, 82204828), and Chinese Postdoctoral Science Foundation (2022M713203).

UNASSIGNED: Transit time flow measurement (TTFM) can detect critical anastomotic stenosis during coronary artery bypass grafting. However, the identification of subcritical stenosis remains challenging. We hypothesized that diastolic resistance index (DRI), a novel TTFM metric, is more effective in evaluating subcritical stenosis than the currently available TTFM metrics. DRI is used to measure changes in the diastolic versus systolic resistance of distal anastomosis.
UNASSIGNED: A total of 123 coronary bypass anastomoses in 35 patients were prospectively analyzed. During coronary artery bypass grafting, the mean graft flow (Qmean), pulsatility index, and diastolic filling were obtained. DRI was calculated using the intraoperative recordings of TTFM and arterial pressure. Postoperatively, stenosis of anastomoses was categorized into successful (<50%), subcritical (50%-74%), and critical (≥75%) via multidetector computed tomography scan.
UNASSIGNED: In total, 93 (76%), 13 (10%), and 17 (14%) anastomoses were graded as successful, subcritical, and critical, respectively. DRI and diastolic filling could distinguish subcritical from successful anastomoses (P < .01 and < .01, respectively), whereas Qmean and pulsatility index could not (P = .12 and .39, respectively). The receiver operating characteristic curves were established to evaluate the diagnostic ability for detecting ≥50% stenosis. In left anterior descending artery grafting (n = 55), DRI had the highest area under the curve (0.91), followed by diastolic filling (0.87), Qmean (0.74), and pulsatility index (0.65).
UNASSIGNED: DRI and diastolic filling had a reliable diagnostic ability for detecting ≥50% stenosis during coronary artery bypass grafting. In left anterior descending artery grafting, DRI had a more satisfactory detection capability than other TTFM metrics.

UNASSIGNED: Complex anterior skull base defects produced by resection of mass lesions vary in size and configuration and may be extensive. We analyzed the largest single-center series of midline craniofacial lesions extending intra- and extracranially. The study aims at the development of a predictive model for preoperative measurement of the risk of the postoperative cerebrospinal fluid (CSF) leak based on patients\' characteristics and surgical plans.
UNASSIGNED: 166 male and 149 female patients with mean age 40,5 years (1 year and - 81 years) operated for benign and tumor-like midline craniofacial mass lesions were retrospectively analyzed using logistic regression method (Ridge regression algorithm was selected). The overall CSF leak rate was 9.6%. The ROSE algorithm and \'glmnet\' software suite in R were used to overcome the cohort\'s disbalance and avoid overtraining the model.
UNASSIGNED: The most influential modifiable negative predictor of the postoperative CSF leak was the use of extracranial and combined approaches. Use of transbasal approaches, gross total resection, utilization of one or two vascularized flaps for skull base reconstruction were the foremost modifiable predictors of a good outcome. Criterium of elevated risk was established at 50% with a specificity of the model as high as 0.83.
UNASSIGNED: The performed study has allowed for identifying the most significant predictors of postoperative CSF leak and developing an effective formula to estimate the risk of this complication using data known for each patient. We believe that the suggested web-based online calculator can be helpful for decision making support in off-pattern clinical situations.

Treatment of patients with oesophageal and gastric cancer (OeGC) is guided by disease stage, patient performance status and preferences. Lymph node (LN) status is one of the strongest prognostic factors for OeGC patients. However, survival varies between patients with the same disease stage and LN status. We recently showed that LN size from patients with OeGC might also have prognostic value, thus making delineations of LNs essential for size estimation and the extraction of other imaging biomarkers. We hypothesized that a machine learning workflow is able to: (1) find digital H&E stained slides containing LNs, (2) create a scoring system providing degrees of certainty for the results, and (3) delineate LNs in those images. To train and validate the pipeline, we used 1695 H&E slides from the OE02 trial. The dataset was divided into training (80%) and validation (20%). The model was tested on an external dataset of 826 H&E slides from the OE05 trial. U-Net architecture was used to generate prediction maps from which predefined features were extracted. These features were subsequently used to train an XGBoost model to determine if a region truly contained a LN. With our innovative method, the balanced accuracies of the LN detection were 0.93 on the validation dataset (0.83 on the test dataset) compared to 0.81 (0.81) on the validation (test) datasets when using the standard method of thresholding U-Net predictions to arrive at a binary mask. Our method allowed for the creation of an \"uncertain\" category, and partly limited false-positive predictions on the external dataset. The mean Dice score was 0.73 (0.60) per-image and 0.66 (0.48) per-LN for the validation (test) datasets. Our pipeline detects images with LNs more accurately than conventional methods, and high-throughput delineation of LNs can facilitate future LN content analyses of large datasets.