[Propensity score matching analysis of the short-term efficacy of Kamikawa versus double- tract reconstruction in laparoscopic proximal gastric cancer surgery]

Objective: To compare the short-term efficacy of Kamikawa anastomosis and double-tract reconstruction (DTR) after proximal gastrectomy. Methods: This was a propensity score matched, retrospective, cohort study. Inclusion criteria comprised age 20-70 years, diagnosis of gastric cancer by pathological examination of preoperative endoscopic biopsies, tumor diameter ≤4 cm, and location in the upper 1/3 of the stomach (including the gastroesophageal junction), and TNM stage IA, IB, or IIA. The study cohort comprised 73 patients who had undergone laparoscopic proximal gastric cancer radical surgery in the Department of Gastroenterology, Tangdu Hospital, Air Force Medical University between June 2020 and February 2023, 19 of whom were in the Kamikawa group and 54 in the DTR group. After using R language to match the baseline characteristics of patients in a ratio of 1:2, there were 17 patients in the Kamikawa group and 34 in the DTR group. Surgery-related conditions, postoperative quality of life, and postoperative complications were compared between the two groups. Results: After propensity score matching, there were no statistically significant differences in baseline data between the two groups (P>0.05). Compared with the DTR group, the Kamikawa group had longer operative times (321.5±15.7 minutes vs. 296.8±26.1 minutes, t=32.056, P<0.001), longer anastomosis times (93.0±6.8 minutes vs. 45.3±7.7 minutes, t=56.303, P<0.001), and less bleeding (76 [54~103] mL vs.112 [82~148) mL, Z=71.536, P<0.001); these differences are statistically significant. There were no statistically significant differences between the two groups in tumor size, time to first postoperative passage of gas, postoperative hospital stay, number of lymph nodes removed, duration of lymph node dissection, or total hospitalization cost (all P>0.05). The median follow-up time was 6.1 ± 1.8 months. As to postoperative quality of life, the Kamikawa group had a lower rate of upper gastrointestinal contrast reflux than did the DTR group (0 vs. 29.4% [10/34], χ2=6.220, P=0.013); this difference is statistically significant. However, differences between the two groups in quality of life score on follow-up of 3 months and 6 months on the Gastroesophageal Reflux Disease (GERD) scale were not statistically significant (all P>0.05). The incidence of postoperative complications was 2/17 in the Kamikawa group, which is significantly lower than the 41.2% (14/34) in the DTR group (χ2=4.554, P=0.033). Conclusion: Kamikawa anastomosis and DTR are equally safe and effective procedures for reconstructing the digestive tract after proximal gastric surgery. Although Kamikawa anastomosis takes slightly longer and places higher demands on the surgical team, it is more effective at preventing postoperative reflux.

Global metabolomics revealed deviations from the metabolic aging clock in colorectal cancer patients

Background: Markers of aging hold promise in the context of colorectal cancer (CRC) care. Utilizing high-resolution metabolomic profiling, we can unveil distinctive age-related patterns that have the potential to predict early CRC development. Our study aims to unearth a panel of aging markers and delve into the metabolomic alterations associated with aging and CRC. Methods: We assembled a serum cohort comprising 5,649 individuals, consisting of 3,002 healthy volunteers, 715 patients diagnosed with colorectal advanced precancerous lesions (APL), and 1,932 CRC patients, to perform a comprehensive metabolomic analysis. Results: We successfully identified unique age-associated patterns across 42 metabolic pathways. Moreover, we established a metabolic aging clock, comprising 9 key metabolites, using an elastic net regularized regression model that accurately estimates chronological age. Notably, we observed significant chronological disparities among the healthy population, APL patients, and CRC patients. By combining the analysis of circulative carcinoembryonic antigen levels with the categorization of individuals into the "hypo" metabolic aging subgroup, our blood test demonstrates the ability to detect APL and CRC with positive predictive values of 68.4% (64.3%, 72.2%) and 21.4% (17.8%, 25.9%), respectively. Conclusions: This innovative approach utilizing our metabolic aging clock holds significant promise for accurately assessing biological age and enhancing our capacity to detect APL and CRC.

DILS: depth incremental learning strategy

There exist various methods for transferring knowledge between neural networks, such as parameter transfer, feature sharing, and knowledge distillation. However, these methods are typically applied when transferring knowledge between networks of equal size or from larger networks to smaller ones. Currently, there is a lack of methods for transferring knowledge from shallower networks to deeper ones, which is crucial in real-world scenarios such as system upgrades where network size increases for better performance. End-to-end training is the commonly used method for network training. However, in this training strategy, the deeper network cannot inherit the knowledge from the existing shallower network. As a result, not only is the flexibility of the network limited but there is also a significant waste of computing power and time. Therefore, it is imperative to develop new methods that enable the transfer of knowledge from shallower to deeper networks. To address the aforementioned issue, we propose an depth incremental learning strategy (DILS). It starts from a shallower net and deepens the net gradually by inserting new layers each time until reaching requested performance. We also derive an analytical method and a network approximation method for training new added parameters to guarantee the new deeper net can inherit the knowledge learned by the old shallower net. It enables knowledge transfer from smaller to larger networks and provides good initialization of layers in the larger network to stabilize the performance of large models and accelerate their training process. Its reasonability can be guaranteed by information projection theory and is verified by a series of synthetic and real-data experiments.

Prevalence and influencing factors of depressive and anxiety symptoms among hospital-based healthcare workers during the surge period of the COVID-19 pandemic in the Chinese mainland: a multicenter cross-sectional study

BACKGROUND

From November 2022 to February 2023, the Chinese mainland experienced a surge in COVID-19 infection and hospitalization, and the hospital-based healthcare workers (HCWs) might suffer serious psychological crisis during this period. This study aims to assess the depressive and anxiety symptoms among HCWs during the surge of COVID-19 pandemic and to provide possible reference on protecting mental health of HCWs in future infectious disease outbreaks.

METHODS

A multicenter cross-sectional study was carried out among hospital-based HCWs in the Chinese mainland from 5 January to 9 February 2023. The PHQ-9 (nine-item Patient Health Questionnaire) and GAD-7 (seven-item Generalized Anxiety Disorder Questionnaire) were used to measure depressive and anxiety symptoms. Ordinal logistic regression analysis was performed to identify influencing factors.

RESULTS

A total of 6522 hospital-based HCWs in the Chinse mainland were included in this survey. The prevalence of depressive symptoms among the HCWs was 70.75%, and anxiety symptoms was 47.87%. The HCWs who perceived higher risk of COVID-19 infection and those who had higher work intensity were more likely to experience depressive and anxiety symptoms. Additionally, higher levels of mindfulness, resilience and perceived social support were negatively associated with depressive and anxiety symptoms.

CONCLUSION

This study revealed that a high proportion of HCWs in the Chinese mainland suffered from mental health disturbances during the surge of the COVID-19 pandemic. Resilience, mindfulness and perceived social support are important protective factors of HCWs' mental health. Tailored interventions, such as mindfulness practice, should be implemented to alleviate psychological symptoms of HCWs during the COVID-19 pandemic or other similar events in the future.

Exploring the feasibility of using long-term stored newborn dried blood spots to identify metabolic features for congenital heart disease screening

Congenital heart disease (CHD) represents a significant contributor to both morbidity and mortality in neonates and children. There's currently no analogous dried blood spot (DBS) screening for CHD immediately after birth. This study was set to assess the feasibility of using DBS to identify reliable metabolite biomarkers with clinical relevance, with the aim to screen and classify CHD utilizing the DBS. We assembled a cohort of DBS datasets from the California Department of Public Health (CDPH) Biobank, encompassing both normal controls and three pre-defined CHD categories. A DBS-based quantitative metabolomics method was developed using liquid chromatography with tandem mass spectrometry (LC-MS/MS). We conducted a correlation analysis comparing the absolute quantitated metabolite concentration in DBS against the CDPH NBS records to verify the reliability of metabolic profiling. For hydrophilic and hydrophobic metabolites, we executed significant pathway and metabolite analyses respectively. Logistic and LightGBM models were established to aid in CHD discrimination and classification. Consistent and reliable quantification of metabolites were demonstrated in DBS samples stored for up to 15 years. We discerned dysregulated metabolic pathways in CHD patients, including deviations in lipid and energy metabolism, as well as oxidative stress pathways. Furthermore, we identified three metabolites and twelve metabolites as potential biomarkers for CHD assessment and subtypes classifying. This study is the first to confirm the feasibility of validating metabolite profiling results using long-term stored DBS samples. Our findings highlight the potential clinical applications of our DBS-based methods for CHD screening and subtype classification.

High-throughput quantitation of amino acids and acylcarnitine in cerebrospinal fluid: identification of PCNSL biomarkers and potential metabolic messengers

Background: Due to the poor prognosis and rising occurrence, there is a crucial need to improve the diagnosis of Primary Central Nervous System Lymphoma (PCNSL), which is a rare type of non-Hodgkin's lymphoma. This study utilized targeted metabolomics of cerebrospinal fluid (CSF) to identify biomarker panels for the improved diagnosis or differential diagnosis of primary central nervous system lymphoma (PCNSL). Methods: In this study, a cohort of 68 individuals, including patients with primary central nervous system lymphoma (PCNSL), non-malignant disease controls, and patients with other brain tumors, was recruited. Their cerebrospinal fluid samples were analyzed using the Ultra-high performance liquid chromatography - tandem mass spectrometer (UHPLC-MS/MS) technique for targeted metabolomics analysis. Multivariate statistical analysis and logistic regression modeling were employed to identify biomarkers for both diagnosis (Dx) and differential diagnosis (Diff) purposes. The Dx and Diff models were further validated using a separate cohort of 34 subjects through logistic regression modeling. Results: A targeted analysis of 45 metabolites was conducted using UHPLC-MS/MS on cerebrospinal fluid (CSF) samples from a cohort of 68 individuals, including PCNSL patients, non-malignant disease controls, and patients with other brain tumors. Five metabolic features were identified as biomarkers for PCNSL diagnosis, while nine metabolic features were found to be biomarkers for differential diagnosis. Logistic regression modeling was employed to validate the Dx and Diff models using an independent cohort of 34 subjects. The logistic model demonstrated excellent performance, with an AUC of 0.83 for PCNSL vs. non-malignant disease controls and 0.86 for PCNSL vs. other brain tumor patients. Conclusion: Our study has successfully developed two logistic regression models utilizing metabolic markers in cerebrospinal fluid (CSF) for the diagnosis and differential diagnosis of PCNSL. These models provide valuable insights and hold promise for the future development of a non-invasive and reliable diagnostic tool for PCNSL.

[Clinical and genetic analysis of a child with X-linked dominant Alport syndrome]

OBJECTIVE

To investigate the clinical features and genetic variant of a child with X-linked dominant Alport syndrome (XLAS).

METHODS

A child who had presented at the First Affiliated Hospital of Zhengzhou University in May 2019 was selected as the study subject. Clinical data of the child was collected. Next generation sequencing (NGS) was carried out for the child. Candidate variants were validated by Sanger sequencing of his family members.

RESULTS

The child, a 12-year-old boy, had mainly manifested gross hematuria, proteinuria, nephrotic syndrome, and progressive renal impairment in conjunct with hearing loss. Kidney biopsy has revealed uneven glomerular basement membrane thickness. DNA sequencing revealed that the child and his mother have both carried a heterozygous c.2632G>A (p.G878R) variant of the COL4A5 gene, for which his father and brother were of the wild type. This variant was unreported previously. Based on the guidelines from the American College of Medical Genetics and Genomics, the variant was classified as pathogenic (PS1+PM1+PM2_Supporting+PP3).

CONCLUSION

The maternally derived hemizygous c.2632G>A (p.G878R) variant of the COL4A5 gene probably underlay the XLAS in this child. Above finding has enriched the mutational spectrum of the COL4A5 gene.

Effect of Segmental Abutting Esophagus-Sparing Technique to Reduce Severe Esophagitis in Limited-Stage Small-Cell Lung Cancer Patients Treated with Concurrent Hypofractionated Thoracic Radiation and Chemotherapy

PURPOSE/OBJECTIVE(S)

To evaluate the effect of segmental abutting esophagus-sparing (SAES) radiotherapy to reduce severe (G3+) acute esophagitis from 20% to 5% in patients with limited-stage small cell lung cancer (LS-SCLC) treated with concurrent chemoradiotherapy.

MATERIALS/METHODS

Patients with a clinical target volume (CTV) ≤1 cm close to the esophagus were enrolled in the experimental arm (45 Gy in 3 Gy daily fractions in 3 weeks) of an ongoing phase III randomized clinical trial (NCT02675088), which enrolled patients with histologically confirmed SCLC and clinically staged as LS or I-IIIB (AJCC 7th). This trial was designed to determine whether HYPO TRT (45 Gy in 3 Gy QD, experimental arm) has the same efficacy as CF TRT (60 Gy in 2 Gy QD, controlled arm) in patients with LS-SCLC. The whole esophagus was divided into the involved esophagus and abutting esophagus (AE) to receive different dose limitations according to the distance from the edge of the CTV. The primary endpoint was grade ≥ 3 acute esophagitis.

RESULTS

From 1 May 2021 to 30 April 2022, 30 patients were enrolled and completed four cycles of planned chemotherapy and radiotherapy. Our patient population was predominantly male (66.7% men vs. 33.3% women), with a median age of 62 years. A majority of patients presented with Stage N2-3 (90.0%) and T2-4 (76.7%), in which 4 patients had ultracentral-located primary tumors. With the SAES technique, all dosimetric parameters were significantly reduced for the whole esophagus and AE. The maximal and mean dose of the esophagus (47.4±1.9 Gy and 13.5 ± 5.8 Gy, respectively) and AE (42.9±2.3 Gy and 8.6 ± 3.6 Gy, respectively) in the SAES plan were significantly lower than those (esophagus 48.0±1.9 Gy and 14.7± 6.1 Gy, AE 45.1±2.4 Gy and 9.8± 4.2 Gy, respectively) in the non-SAES plan. After the follow-up of more than 7 months (range, 7.0-18.1 months) for all patients, only one patient (3.3%, 95% CI 0.1%-17.2%) experienced grade 3 acute esophagitis and no grade 4-5 acute esophagitis happened (Table 3). For late toxicities, one patient suffered sustained grade 1 late esophagitis and all others had no symptoms of esophagitis. The rate of radiation pneumonitis was very low, with one grade 3 event and no grade 4-5 event. Twelve (40.0%) patients had G3+ hematologic toxic events, including 2 patients with febrile neutropenia. The 1-year OS, LRFS, DMFS and PFS was 96.4%, 88.7%, 78.4% and 64.3%, respectively. No patient developed local recurrence in the abutting esophagus-sparing region.

CONCLUSION

SAES radiotherapy has significant dosimetric advantages compared with standard radiotherapy, which are successfully translated into clinical benefits for patients with LS-SCLC treated with 45 Gy in 3 Gy daily fractions. This may facilitate dose escalation for TRT in LS-SCLC patients.

Altered expression of the L-arginine/nitric oxide pathway in ovarian cancer: metabolic biomarkers and biological implications

MOTIVATION

Ovarian cancer (OC) is a highly lethal gynecological malignancy. Extensive research has shown that OC cells undergo significant metabolic alterations during tumorigenesis. In this study, we aim to leverage these metabolic changes as potential biomarkers for assessing ovarian cancer.

METHODS

A functional module-based approach was utilized to identify key gene expression pathways that distinguish different stages of ovarian cancer (OC) within a tissue biopsy cohort. This cohort consisted of control samples (n = 79), stage I/II samples (n = 280), and stage III/IV samples (n = 1016). To further explore these altered molecular pathways, minimal spanning tree (MST) analysis was applied, leading to the formulation of metabolic biomarker hypotheses for OC liquid biopsy. To validate, a multiple reaction monitoring (MRM) based quantitative LCMS/MS method was developed. This method allowed for the precise quantification of targeted metabolite biomarkers using an OC blood cohort comprising control samples (n = 464), benign samples (n = 3), and OC samples (n = 13).

RESULTS

Eleven functional modules were identified as significant differentiators (false discovery rate, FDR < 0.05) between normal and early-stage, or early-stage and late-stage ovarian cancer (OC) tumor tissues. MST analysis revealed that the metabolic L-arginine/nitric oxide (L-ARG/NO) pathway was reprogrammed, and the modules related to "DNA replication" and "DNA repair and recombination" served as anchor modules connecting the other nine modules. Based on this analysis, symmetric dimethylarginine (SDMA) and arginine were proposed as potential liquid biopsy biomarkers for OC assessment. Our quantitative LCMS/MS analysis on our OC blood cohort provided direct evidence supporting the use of the SDMA-to-arginine ratio as a liquid biopsy panel to distinguish between normal and OC samples, with an area under the ROC curve (AUC) of 98.3%.

CONCLUSION

Our comprehensive analysis of tissue genomics and blood quantitative LC/MSMS metabolic data shed light on the metabolic reprogramming underlying OC pathophysiology. These findings offer new insights into the potential diagnostic utility of the SDMA-to-arginine ratio for OC assessment. Further validation studies using adequately powered OC cohorts are warranted to fully establish the clinical effectiveness of this diagnostic test.

Dual Distillation Discriminator Networks for Domain Adaptive Few-Shot Learning

Domain Adaptive Few-Shot Learning (DA-FSL) aims at accomplishing few-shot classification tasks on a novel domain with the aid of a large number of source-style samples and several target-style samples. It is essential for DA-FSL to transfer task knowledge from the source domain to the target domain and overcome the asymmetry amount of labeled data in both domains. To this end, we propose Dual Distillation Discriminator Networks (D3Net) from the perspective of the lack of labeled target domain style samples in DA-FSL. Specifically, we employ the idea of distillation discrimination to avoid the over-fitting caused by the unequal number of samples in the target and source domains, which trains the student discriminator by the soft labels from the teacher discriminator. Meanwhile, we design the task propagation stage and the mixed domain stage respectively from the level of feature space and instances to generate more target-style samples, which apply the task distributions and the sample diversity of the source domain to enhance the target domain. Our D3Net realizes the distribution alignment between the source domain and the target domain and constraints the FSL task distribution by prototype distributions on the mixed domain. Extensive experiments on three DA-FSL benchmark datasets, i.e., mini-ImageNet, tiered-ImageNet, and DomainNet, demonstrate that our D3Net achieves competitive performance.

[Establishment of hysteroscopic scoring system of chronic endometritis and correlative analysis with pregnancy outcomes of in vitro fertilization-embryo transfer in infertile patients]

Objective: To establish a hysteroscopic chronic endometritis (hCE) scoring system for patients with chronic endometritis, and observe the correlation of hCE score with in vitro fertilization-embryo transfer (IVF-ET) pregnancy outcomes in infertile women. Methods: The study retrospectively investigated the correlation of morphologic features and hCE score with pregnancy outcomes during IVF-ET in infertile women with CE (n=429) at Yantai Yuhuangding Hospital between January 2017 and September 2018. The clinical pregnancy rate and live birth rate with different score levels (1-3,4-7 and 8-14) after IVF-ET treatment were analyzed. Multivariate regression analysis was performed to adjust for confounding factors. The correlation and regression between hCE score and pregnancy outcomes was analyzed by curve fitting. Results: The age of 429 patients [M(Q₁, Q₃)] was 31 (29, 35) years. There were 50.6% (217 cases), 35.4% (152 cases), and 14.0% (60 cases) of patients with hCE score of 1-3, 4-7, and 8-14, respectively. The pregnancy rates of the three groups were 60.8% (132 cases), 44.7% (68 cases) and 16.7% (10 cases), P<0.001; The live birth rates were 51.2% (111 cases), 36.8% (56 cases) and 13.3% (8 cases), respectively (P<0.001). Compared with patients with hCE of 1-3, pregnancy rates in those with hCE of 4-7 and 8-14 were lower, and the OR values were 0.521 (0.342-0.793) and 0.129 (0.062-0.268). The live birth rates in patients with hCE of 4-7 and 8-14 were lower than that in patients with hCE of 1-3, and the OR values were 0.570 (0.372-0.873) and 0.162 (0.073-0.360), all P<0.05. Quadratic curve fitting results showed that clinical pregnancy rate and live birth rate decreased with the increase of hCE score. Conclusions: With the increase of hCE score, the clinical pregnancy rate and live birth rate of patients gradually decrease. hCE 4 is an important cut-off threshold significantly affecting the pregnancy outcome.

Development of a Urine Metabolomics Biomarker-Based Prediction Model for Preeclampsia during Early Pregnancy

Preeclampsia (PE) is a condition that poses a significant risk of maternal mortality and multiple organ failure during pregnancy. Early prediction of PE can enable timely surveillance and interventions, such as low-dose aspirin administration. In this study, conducted at Stanford Health Care, we examined a cohort of 60 pregnant women and collected 478 urine samples between gestational weeks 8 and 20 for comprehensive metabolomic profiling. By employing liquid chromatography mass spectrometry (LCMS/MS), we identified the structures of seven out of 26 metabolomics biomarkers detected. Utilizing the XGBoost algorithm, we developed a predictive model based on these seven metabolomics biomarkers to identify individuals at risk of developing PE. The performance of the model was evaluated using 10-fold cross-validation, yielding an area under the receiver operating characteristic curve of 0.856. Our findings suggest that measuring urinary metabolomics biomarkers offers a noninvasive approach to assess the risk of PE prior to its onset.

A Novel Strategy for Accelerating Pumpable Ice Slurry Production with Ozone Micro-Nano Bubbles and Extending the Shelf Life of Larimichthys polyactis

In this study, a novel strategy for accelerating the production of pumpable ice slurry (PIS) by using ozone micro-nano bubbles (O₃-MNBs) was proposed. The effect of PIS containing sodium alginate (SA) and O₃-MNBs on the preservation of small yellow croaker (Larimichthys polyactis) was investigated. The results indicate that using SA solution containing O₃-MNBs instead of only SA solution resulted in quicker production of PIS by promoting ice nucleation and eliminating supercooling. The distribution and positive effect of O₃-MNBs as a nucleation agent on freezing characteristics were discussed. Microbial concentrations, pH, total volatile basic nitrogen, and thiobarbituric acid reactive substance content were also examined. Storage in novel PIS (containing O₃-MNBs) had higher performance than storage in flake ice or conventional PIS due to the strong bacteriostatic ability of O₃. Therefore, O₃-MNBs injection can be used as a novel method for PIS production and the preservation of fresh marine products.

Aneurysmal bone cyst of the temporal bone presenting with reversible vestibular impairment

BACKGROUND

Aneurysmal bone cysts are expansile benign lesions associated with compressive destruction and obscure pathogenesis. The most common sites of temporal bone involvement are the petrous apex, squamous portions and mastoid.

CASE REPORT

This paper reports a right temporal aneurysmal bone cyst in a 51-year-old man who presented clinically with facial palsy, and hearing loss and impaired vestibular function. Magnetic resonance imaging and computed tomography findings were consistent with a diagnosis of aneurysmal bone cyst. Inter-operative findings showed that the lesion had caused compressive damage to the internal auditory canal. Following surgical excision, the patient experienced vertigo, indicating recovery of vestibular function. Follow-up imaging revealed complete resection without clinical recurrence.

CONCLUSION

To our knowledge, this is the first report of aneurysmal bone cyst invasion of the inner auditory canal. Our clinical experience indicates that vestibular nerve damage recovery is relatively uncommon. This case report will hopefully inform future studies.

Environmental-friendly regenerated lignocellulose functionalized cotton fabric to prepare multi-functional degradable membrane for efficient oil-water separation and solar seawater desalination

Freshwater pollution and shortage have become an imminent problem. Therefore, it is necessary to develop a multi-functional membrane for the production of fresh water. In this work, the regenerated lignocellulose modified cotton fabric was developed as a novel, multi-functional and degradable membrane (LCPT@CF) for efficient oil-water separation and solar steam generation for the first time. The fabrication method has the merits of simple, environmentally friendly and cost effective. The regenerated lignocellulose was adhered on the surface of cotton fabric by tannic acid and polyvinyl alcohol complexes tightly, and the multilayered structures of the LCPT@CF can be formed, which endowed the membranes with underwater superoleophobic property and durability. The underwater superoleophobic property enabled LCPT@CF to purify various kinds of oil-in-water emulsions with a separation efficiency of more than 99.90%. Moreover, benefiting from the excellent photothermal conversion capacity of regenerated lignocellulose, the LCPT@CF achieved high evaporation rate of 1.39 kg m^-2 h^-1 and favorable evaporation efficiency of 84% under 1 sun illumination, and the LCPT@CF also presented excellent salt-resistance for evaporating seawater for 20 cycles, without salt accumulation. More importantly, the LCPT@CF could be naturally degradable by microorganisms in the natural condition within 3 months, which had outstanding environmental friendliness. These above results demonstrated that the green and efficient LCPT@CF could play great potential in oil-water separation and sewage purification.

Foliar application of nanoceria attenuated cadmium stress in okra (Abelmoschus esculentus L.)

Foliar application of nanoparticles (NPs) as a means for ameliorating abiotic stress is increasingly employed in crop production. In this study, the potential of CeO₂-NPs as stress suppressants for cadmium (Cd)-stressed okra (Abelmoschus esculentus) plants was investigated, using two cycles of foliar application of CeO₂-NPs at 200, 400, and 600 mg/l. Compared to untreated stressed plants, Cd-stressed plants treated with CeO₂-NPs presented higher pigments (chlorophyll a and carotenoids). In contrast, foliar applications did not alter Cd root uptake and leaf bioaccumulation. Foliar CeO₂-NPs application modulated stress enzymes (APX, SOD, and GPx) in both roots and leaves of Cd-stressed plants, and led to decreases in Cd toxicity in plant's tissues. In addition, foliar application of CeO₂-NPs in Cd-stressed okra plants decreased fruit Cd contents, and improved fruit mineral elements and bioactive compounds. The infrared spectroscopic analysis of fruit tissues showed that foliar-applied CeO₂-NPs treatments did not induce chemical changes but induced conformational changes in fruit macromolecules. Additionally, CeO₂-NPs applications did not alter the eating quality indicator (Mg/K ratio) of okra fruits. Conclusively, the present study demonstrated that foliar application of CeO₂-NPs has the potential to ameliorate Cd toxicity in tissues and improve fruits of okra plants.

Artificial intelligence reveals features associated with breast cancer neoadjuvant chemotherapy responses from multi-stain histopathologic images

Advances in computational algorithms and tools have made the prediction of cancer patient outcomes using computational pathology feasible. However, predicting clinical outcomes from pre-treatment histopathologic images remains a challenging task, limited by the poor understanding of tumor immune micro-environments. In this study, an automatic, accurate, comprehensive, interpretable, and reproducible whole slide image (WSI) feature extraction pipeline known as, IMage-based Pathological REgistration and Segmentation Statistics (IMPRESS), is described. We used both H&E and multiplex IHC (PD-L1, CD8+, and CD163+) images, investigated whether artificial intelligence (AI)-based algorithms using automatic feature extraction methods can predict neoadjuvant chemotherapy (NAC) outcomes in HER2-positive (HER2+) and triple-negative breast cancer (TNBC) patients. Features are derived from tumor immune micro-environment and clinical data and used to train machine learning models to accurately predict the response to NAC in breast cancer patients (HER2+ AUC = 0.8975; TNBC AUC = 0.7674). The results demonstrate that this method outperforms the results trained from features that were manually generated by pathologists. The developed image features and algorithms were further externally validated by independent cohorts, yielding encouraging results, especially for the HER2+ subtype.

Single center blind testing of a US multi-center validated diagnostic algorithm for Kawasaki disease in Taiwan

Background

Kawasaki disease (KD) is the leading cause of acquired heart disease in children. The major challenge in KD diagnosis is that it shares clinical signs with other childhood febrile control (FC) subjects. We sought to determine if our algorithmic approach applied to a Taiwan cohort.

Methods

A single center (Chang Gung Memorial Hospital in Taiwan) cohort of patients suspected with acute KD were prospectively enrolled by local KD specialists for KD analysis. Our previously single-center developed computer-based two-step algorithm was further tested by a five-center validation in US. This first blinded multi-center trial validated our approach, with sufficient sensitivity and positive predictive value, to identify most patients with KD diagnosed at centers across the US. This study involved 418 KDs and 259 FCs from the Chang Gung Memorial Hospital in Taiwan.

Findings

Our diagnostic algorithm retained sensitivity (379 of 418; 90.7%), specificity (223 of 259; 86.1%), PPV (379 of 409; 92.7%), and NPV (223 of 247; 90.3%) comparable to previous US 2016 single center and US 2020 fiver center results. Only 4.7% (15 of 418) of KD and 2.3% (6 of 259) of FC patients were identified as indeterminate. The algorithm identified 18 of 50 (36%) KD patients who presented 2 or 3 principal criteria. Of 418 KD patients, 157 were infants younger than one year and 89.2% (140 of 157) were classified correctly. Of the 44 patients with KD who had coronary artery abnormalities, our diagnostic algorithm correctly identified 43 (97.7%) including all patients with dilated coronary artery but one who found to resolve in 8 weeks.

Interpretation

This work demonstrates the applicability of our algorithmic approach and diagnostic portability in Taiwan.

Oxygen Vacancy-Induced Construction of CoO/h-TiO2 Z-Scheme Heterostructures for Enhanced Photocatalytic Hydrogen Evolution

Environmentally friendly catalysts with excellent performance and low cost are critical for photocatalysis. Herein, using hydrogenated TiO₂ (h-TiO₂) nanosheets with enriched oxygen vacancies as the support, two-dimensional CoO/h-TiO₂ Z-scheme heterostructures are fabricated for hydrogen production through photocatalytic water splitting. It is revealed that the oxygen vacancies in h-TiO₂ can inhibit the oxidation of Co²⁺ into high-valence Co³⁺ during the hydrothermal reaction and thermal treatment processes. A CoO/h-TiO₂ Z-scheme heterostructure possesses a space charge region and a built-in electric field at the interface, and oxygen vacancies in h-TiO₂ can provide more reactive sites, which synergistically improve the separation and transportation of photogenerated carriers. As a result, the photocatalytic hydrogen evolution rate achieves 129.75 μmol·h^-1 (with 50 mg of photocatalysts) on the optimized CoO/h-TiO₂ heterostructures. This work provides a new design idea for the preparation of excellent TiO₂-based photocatalysts.

Improvements in chitosan-based slurry ice production and its application in precooling and storage of Pampus argenteus

The effects of microbubbles in chitosan-based slurry ice production were investigated, and the efficiency of chitosan-based slurry ice was evaluated for silver pomfret (Pampus argenteus) precooling and storage at 0 °C. Microbubbles generated though agitation accelerated slurry ice production by promoting ice nucleation and eliminating supercooling. Higher bubble counts improved freezing, but overly large bubbles reduced the performance. The rheological properties of chitosan solutions were also investigaed, and solutions with higher viscosity formed more bubbles. Experiments investigating precooling rates, microbial concentrations, pH, thiobarbituric-acid-reactive substances, and total volatile basic nitrogen all confirmed that chitosan-based slurry ice had higher performance than flake ice or conventional slurry ice. Chitosan-based slurry ice can be used for precooling in the fish industry to reduce energy consumption, accelerate precooling, reduce microbial growth, and improve shelf life.

TSAFinder: exhaustive tumor-specific antigen detection with RNAseq

MOTIVATION

Tumor-specific antigen (TSA) identification in human cancer predicts response to immunotherapy and provides targets for cancer vaccine and adoptive T-cell therapies with curative potential, and TSAs that are highly expressed at the RNA level are more likely to be presented on major histocompatibility complex (MHC)-I. Direct measurements of the RNA expression of peptides would allow for generalized prediction of TSAs. Human leukocyte antigen (HLA)-I genotypes were predicted with seq2HLA. RNA sequencing (RNAseq) fastq files were translated into all possible peptides of length 8-11, and peptides with high and low expressions in the tumor and control samples, respectively, were tested for their MHC-I binding potential with netMHCpan-4.0.

RESULTS

A novel pipeline for TSA prediction from RNAseq was used to predict all possible unique peptides size 8-11 on previously published murine and human lung and lymphoma tumors and validated on matched tumor and control lung adenocarcinoma (LUAD) samples. We show that neoantigens predicted by exomeSeq are typically poorly expressed at the RNA level, and a fraction is expressed in matched normal samples. TSAs presented in the proteomics data have higher RNA abundance and lower MHC-I binding percentile, and these attributes are used to discover high confidence TSAs within the validation cohort. Finally, a subset of these high confidence TSAs is expressed in a majority of LUAD tumors and represents attractive vaccine targets.

AVAILABILITY AND IMPLEMENTATION

The datasets were derived from sources in the public domain as follows: TSAFinder is open-source software written in python and R. It is licensed under CC-BY-NC-SA and can be downloaded at https://github.com/RNAseqTSA.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Nonconvex Nonlocal Tucker Decomposition for 3D Medical Image Super-Resolution

Limited by hardware conditions, imaging devices, transmission efficiency, and other factors, high-resolution (HR) images cannot be obtained directly in clinical settings. It is expected to obtain HR images from low-resolution (LR) images for more detailed information. In this article, we propose a novel super-resolution model for single 3D medical images. In our model, nonlocal low-rank tensor Tucker decomposition is applied to exploit the nonlocal self-similarity prior knowledge of data. Different from the existing methods that use a convex optimization for tensor Tucker decomposition, we use a tensor folded-concave penalty to approximate a nonlocal low-rank tensor. Weighted 3D total variation (TV) is used to maintain the local smoothness across different dimensions. Extensive experiments show that our method outperforms some state-of-the-art (SOTA) methods on different kinds of medical images, including MRI data of the brain and prostate and CT data of the abdominal and dental.

Application of unsupervised deep learning algorithms for identification of specific clusters of chronic cough patients from EMR data

BACKGROUND

Chronic cough affects approximately 10% of adults. The lack of ICD codes for chronic cough makes it challenging to apply supervised learning methods to predict the characteristics of chronic cough patients, thereby requiring the identification of chronic cough patients by other mechanisms. We developed a deep clustering algorithm with auto-encoder embedding (DCAE) to identify clusters of chronic cough patients based on data from a large cohort of 264,146 patients from the Electronic Medical Records (EMR) system. We constructed features using the diagnosis within the EMR, then built a clustering-oriented loss function directly on embedded features of the deep autoencoder to jointly perform feature refinement and cluster assignment. Lastly, we performed statistical analysis on the identified clusters to characterize the chronic cough patients compared to the non-chronic cough patients.

RESULTS

The experimental results show that the DCAE model generated three chronic cough clusters and one non-chronic cough patient cluster. We found various diagnoses, medications, and lab tests highly associated with chronic cough patients by comparing the chronic cough cluster with the non-chronic cough cluster. Comparison of chronic cough clusters demonstrated that certain combinations of medications and diagnoses characterize some chronic cough clusters.

CONCLUSIONS

To the best of our knowledge, this study is the first to test the potential of unsupervised deep learning methods for chronic cough investigation, which also shows a great advantage over existing algorithms for patient data clustering.

Serological Phenotyping Analysis Uncovers a Unique Metabolomic Pattern Associated With Early Onset of Type 2 Diabetes Mellitus

Background: Type 2 diabetes mellitus (T2DM) is a multifaceted disorder affecting epidemic proportion at global scope. Defective insulin secretion by pancreatic β-cells and the inability of insulin-sensitive tissues to respond effectively to insulin are the underlying biology of T2DM. However, circulating biomarkers indicative of early diabetic onset at the asymptomatic stage have not been well described. We hypothesized that global and targeted mass spectrometry (MS) based metabolomic discovery can identify novel serological metabolic biomarkers specifically associated with T2DM. We further hypothesized that these markers can have a unique pattern associated with latent or early asymptomatic stage, promising an effective liquid biopsy approach for population T2DM risk stratification and screening.

Methods: Four independent cohorts were assembled for the study. The T2DM cohort included sera from 25 patients with T2DM and 25 healthy individuals for the biomarker discovery and sera from 15 patients with T2DM and 15 healthy controls for the testing. The Pre-T2DM cohort included sera from 76 with prediabetes and 62 healthy controls for the model training and sera from 35 patients with prediabetes and 27 healthy controls for the model testing. Both global and targeted (amino acid, acylcarnitine, and fatty acid) approaches were used to deep phenotype the serological metabolome by high performance liquid chromatography-high resolution mass spectrometry. Different machine learning approaches (Random Forest, XGBoost, and ElasticNet) were applied to model the unique T2DM/Pre-T2DM metabolic patterns and contrasted with their effectiness to differentiate T2DM/Pre-T2DM from controls.

Results: The univariate analysis identified unique panel of metabolites (n = 22) significantly associated with T2DM. Global metabolomics and subsequent structure determination led to the identification of 8 T2DM biomarkers while targeted LCMS profiling discovered 14 T2DM biomarkers. Our panel can effectively differentiate T2DM (ROC AUC = 1.00) or Pre-T2DM (ROC AUC = 0.84) from the controls in the respective testing cohort.

Conclusion: Our serological metabolite panel can be utilized to identifiy asymptomatic population at risk of T2DM, which may provide utility in identifying population at risk at an early stage of diabetic development to allow for clinical intervention. This early detection would guide ehanced levels of care and accelerate development of clinical strategies to prevent T2DM.

Depth Selection for Deep ReLU Nets in Feature Extraction and Generalization

Deep learning is recognized to be capable of discovering deep features for representation learning and pattern recognition without requiring elegant feature engineering techniques by taking advantages of human ingenuity and prior knowledge. Thus it has triggered enormous research activities in machine learning and pattern recognition. One of the most important challenges of deep learning is to figure out relations between a feature and the depth of deep neural networks (deep nets for short) to reflect the necessity of depth. Our purpose is to quantify this feature-depth correspondence in feature extraction and generalization. We present the adaptivity of features to depths and vice-verse via showing a depth-parameter trade-off in extracting both single feature and composite features. Based on these results, we prove that implementing the classical empirical risk minimization on deep nets can achieve the optimal generalization performance for numerous learning tasks. Our theoretical results are verified by a series of numerical experiments including toy simulations and a real application of earthquake seismic intensity prediction.

Supplemented phase diagrams for vitrification CPA cocktails: DP6, VS55 and M22

DP6, VS55 and M22 are the most commonly used cryoprotective agent (CPA) cocktails for vitrification experiments in tissues and organs. However, complete phase diagrams for the three CPAs are often unavailable or incomplete (only available for full strength CPAs) thereby hampering optimization of vitrification and rewarming procedures. In this paper, we used differential scanning calorimetry (DSC) to measure the transition temperatures including heterogeneous nucleation temperatures (T_het), glass transition temperatures (T_g), rewarming phase crystallization (devitrification and/or recrystallization) temperatures (T_d) and melting temperatures (T_m) while cooling or warming the CPA sample at 5 °C/min and plotted the obtained transition temperatures for different concentrations of CPAs into the phase diagrams. We also used cryomicroscopy cooling or warming the sample at the same rate to record the ice crystallization during the whole process, and we presented the cryomicroscopic images at the transition temperatures, which agreed with the DSC presented phenomena.

Deviation from the precisely timed age-associated patterns revealed by blood metabolomics to find CRC patients at risk of relapse at the CRC diagnosis

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Background: Human serum metabolome profiles have been analyzed to explore the molecular changes that occur with aging. We hypothesized that deep metabolic profiling of sera with different ages would allow the identification of distinct metabolic chronologic patterns as a normal biological baseline to study personal aging. We further hypothesized that metabolic assessment of this chronologic deviation, resulting from advanced precancerous lesion (APL) and stage I/II/III CRC, from the normal reference baseline, would be instrumental for prognosis of relapse revealing underlying pathophysiology. Methods: A cohort of normal (n=3,616, training; n=1,170, testing), 631 advanced adenoma, 1,019 stage I, 404 stage II and 417 stage III serum samples were assembled. Innovative global LCMS metabolomic production were applied to deep profile these subjects. Identification of the age-associated molecular patterns in normal subjects, modeled with an elastic net algorithm, established the reference baseline to mirror a metabolic clock. CRC associated deviation from the precise chronologically paced metabolic patterns was quantified to associate the clinical endpoints of relapse, OSF and PFS, and to identify the tightly associated metabolic pathways. Results: We observed that for those CRCs, the predicted metabolic age can differ from the chronological age with consistent variations, resulting “older” or “younger” metabolic age subgroup in reference to the chronological age. Significant disruptions from the normal baseline were observed in CRCs patients, and consistent stage specific patterns were observed. Outlier, “Older” or “younger” metabolic age subgroup, CRC patients were found with significant future relapse enrichment. Predictive models were derived to case find the patients at risk of future relapse at the CRC diagnosis timepoint. Conclusions: Deviations from the meticulously timed metabolic aging patterns may provide utility to allow prognosis of future clinical endpoints of relapse and overall survival. Close examination of the underlying metabolic pathways, associated with CRC stage specific metabolic patterns, disrupting the baseline ageotypes, not only may improve the sensitivity and specificity of prognostic tests of CRC relapse, but also shed new insights into CRC therapeutics.

Ultrathin tungsten-doped hydrogenated titanium dioxide nanosheets for solar-driven hydrogen evolution

Ultrathin tungsten-doped hydrogenated TiO2 (W-h-TiO2) nanosheets are highly efficient for photocatalytic hydrogen production by water splitting without a noble metal cocatalyst.

Weakly Supervised Deep Ordinal Cox Model for Survival Prediction From Whole-Slide Pathological Images

Whole-Slide Histopathology Image (WSI) is generally considered the gold standard for cancer diagnosis and prognosis. Given the large inter-operator variation among pathologists, there is an imperative need to develop machine learning models based on WSIs for consistently predicting patient prognosis. The existing WSI-based prediction methods do not utilize the ordinal ranking loss to train the prognosis model, and thus cannot model the strong ordinal information among different patients in an efficient way. Another challenge is that a WSI is of large size (e.g., 100,000-by-100,000 pixels) with heterogeneous patterns but often only annotated with a single WSI-level label, which further complicates the training process. To address these challenges, we consider the ordinal characteristic of the survival process by adding a ranking-based regularization term on the Cox model and propose a weakly supervised deep ordinal Cox model (BDOCOX) for survival prediction from WSIs. Here, we generate amounts of bags from WSIs, and each bag is comprised of the image patches representing the heterogeneous patterns of WSIs, which is assumed to match the WSI-level labels for training the proposed model. The effectiveness of the proposed method is well validated by theoretical analysis as well as the prognosis and patient stratification results on three cancer datasets from The Cancer Genome Atlas (TCGA).

A Computational Framework to Analyze the Associations Between Symptoms and Cancer Patient Attributes Post Chemotherapy Using EHR Data

Patients with cancer, such as breast and colorectal cancer, often experience different symptoms post-chemotherapy. The symptoms could be fatigue, gastrointestinal (nausea, vomiting, lack of appetite), psychoneurological symptoms (depressive symptoms, anxiety), or other types. Previous research focused on understanding the symptoms using survey data. In this research, we propose to utilize the data within the Electronic Health Record (EHR). A computational framework is developed to use a natural language processing (NLP) pipeline to extract the clinician-documented symptoms from clinical notes. Then, a patient clustering method is based on the symptom severity levels to group the patient in clusters. The association rule mining is used to analyze the associations between symptoms and patient attributes (smoking history, number of comorbidities, diabetes status, age at diagnosis) in the patient clusters. The results show that the various symptom types and severity levels have different associations between breast and colorectal cancers and different timeframes post-chemotherapy. The results also show that patients with breast or colorectal cancers, who smoke and have severe fatigue, likely have severe gastrointestinal symptoms six months after the chemotherapy. Our framework can be generalized to analyze symptoms or symptom clusters of other chronic diseases where symptom management is critical.

Applying interpretable deep learning models to identify chronic cough patients using EHR data

BACKGROUND AND OBJECTIVE

Chronic cough (CC) affects approximately 10% of adults. Many disease states are associated with chronic cough, such as asthma, upper airway cough syndrome, bronchitis, and gastroesophageal reflux disease. The lack of an ICD code specific for chronic cough makes it challenging to identify such patients from electronic health records (EHRs). For clinical and research purposes, computational methods using EHR data are urgently needed to identify chronic cough cases. This research aims to investigate the data representations and deep learning algorithms for chronic cough prediction.

METHODS

Utilizing real-world EHR data from a large academic healthcare system from October 2005 to September 2015, we investigated Natural Language Representation of the EHR data and systematically evaluated deep learning and traditional machine learning models to predict chronic cough patients. We built these machine learning models using structured data (medication and diagnosis) and unstructured data (clinical notes).

RESULTS

The sensitivity and specificity of a transformer-based deep learning algorithm, specifically BERT with attention model, was 0.856 and 0.866, respectively, using structured data (medication and diagnosis). Sensitivity and specificity improved to 0.952 and 0.930 when we combined structured data with symptoms extracted from clinical notes. We further found that the attention mechanism of deep learning models can be used to extract important features that drive the prediction decisions. Compared with our previously published rule-based algorithm, the deep learning algorithm can identify more chronic cough patients with structured data.

CONCLUSIONS

By applying deep learning models, chronic cough patients can be reliably identified for prospective or retrospective research through medication and diagnosis data, widely available in EHR and electronic claims data, thus improving the generalizability of the patient identification algorithm. Deep learning models can identify chronic cough patients with even higher sensitivity and specificity when structured and unstructured EHR data are utilized. We anticipate language-based data representation and deep learning models developed in this research could also be productively used for other disease prediction and case identification.

BrcaSeg: A Deep Learning Approach for Tissue Quantification and Genomic Correlations of Histopathological Images

Epithelial and stromal tissues are components of the tumor microenvironment and play a major role in tumor initiation and progression. Distinguishing stroma from epithelial tissues is critically important for spatial characterization of the tumor microenvironment. Here, we propose BrcaSeg, an image analysis pipeline based on a convolutional neural network (CNN) model to classify epithelial and stromal regions in whole-slide hematoxylin and eosin (H&E) stained histopathological images. The CNN model is trained using well-annotated breast cancer tissue microarrays and validated with images from The Cancer Genome Atlas (TCGA) Program. BrcaSeg achieves a classification accuracy of 91.02%, which outperforms other state-of-the-art methods. Using this model, we generate pixel-level epithelial/stromal tissue maps for 1000 TCGA breast cancer slide images that are paired with gene expression data. We subsequently estimate the epithelial and stromal ratios and perform correlation analysis to model the relationship between gene expression and tissue ratios. Gene Ontology (GO) enrichment analyses of genes that are highly correlated with tissue ratios suggest that the same tissue is associated with similar biological processes in different breast cancer subtypes, whereas each subtype also has its own idiosyncratic biological processes governing the development of these tissues. Taken all together, our approach can lead to new insights in exploring relationships between image-based phenotypes and their underlying genomic events and biological processes for all types of solid tumors. BrcaSeg can be accessed at https://github.com/Serian1992/ImgBio.

[Japan narrow-band imaging Expert Team type 2B colorectal cancer: consistency between endoscopic prediction and pathological diagnosis]

OBJECTIVE

To explore the potential factors that affect the accuracy of endoscopic diagnosis for Japan narrow-band imaging (NBI) Expert Team (JNET) type 2B colorectal lesions.

OBJECTIVE

The clinical data were collected from 261 patients with JNET type 2B colorectal lesions diagnosed in Nanfang Hospital between July, 2018 and July, 2021. We analyzed the macroscopic type, size, location or pit pattern classification of the lesions for their potential influence of the diagnostic accuracy of JNET type 2B lesions.

OBJECTIVE

The 261 lesions included 91 low-grade intramucosal neoplasia lesions (34.9%), 132 high-grade intramucosal neoplasia lesions (50.6%), 13 submucosal invasive cancer lesions (5.0%), and 25 deep submucosal invasive cancer lesions (9.6%). The coincidence rate between endoscopic prediction and pathological diagnosis of these lesions was 55.6% (145/ 261). The macroscopic type and size of the lesions were significantly associated with the diagnostic accuracy of JNET type 2B lesions (P < 0.001). There was a significant difference in the diagnostic accuracy among the lesions with different pit pattern types (P < 0.001).

OBJECTIVE

Both the macroscopic type and size affect the accuracy of endoscopic diagnosis of JNET type 2B colorectal lesions. JNET classification combined with pit pattern types can have better accuracy in predicting the pathological diagnosis of these lesions.

Abstract 238: Exhaustive tumor specific antigen detection with RNAseq

Background: Tumor specific antigen (TSA) identification in human cancer predicts response to immunotherapy and provides vaccine targets for precision medicine. In addition to neoantigens from somatic coding mutations, numerous non-mutated TSAs can elicit T-cell responses but are often overlooked by current methods. We present a method that accurately and comprehensively predict TSAs from RNAseq data regardless of mutation status.

Methods: HLA-I genotypes were predicted with seq2HLA. RNAseq fastq files were translated into all possible peptides of length 8-11, and peptides with high expression in the tumor and comparatively low expression in normal were tested for their MHC-I binding potential with netMHCpan-4.0. We defined our predicted TSA by i) high expression in tumor samples, ii) low expression in normal samples, and iii) high predicted patient-specific MHC-I binding affinity.

Results: We developed a novel pipeline for TSA prediction from RNAseq that is not limited to mutation-derived TSAs. This pipeline was used to predict all possible unique peptides size 8-11 on previously published murine and human lung and lymphoma tumors then validated on matched tumor and control lung adenocarcinoma (LUAD) samples. This pipeline is able to predict TSAs in MHC-I ligand-purified proteomics data with favorable performance to existing methods. Furthermore, neoantigens predicted by exomeSeq are typically poorly expressed at the RNA level, (28% of predicted neoantigens with &gt;0 expression, mean of 15.6 reads/sample) and a fraction of them (47/6,928, 0.68%) are expressed in matched normal samples. Finally, a set of 6 TSAs are expressed in 22/39 (56%) of LUAD tumors and represent attractive vaccine targets.

Conclusion: Direct quantification of RNAseq evidence of the potential peptidome in matched tumor and control RNAseq samples, via our novel pipeline, allows for exhaustive detection of TSAs.

Citation Format: Michael Sharpnack, Travis Johnson, Robert Chalkley, Zhi Han, David Carbone, Kun Huang, Kai He. Exhaustive tumor specific antigen detection with RNAseq [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 238.

SGN nerve filaments develop synapses with IHCs earlier than with OHCs in C57BL/6 mouse inner ear

OBJECTIVE

To explore the connections between hair cells and spiral ganglion neurons (SGNs) during the development of the C57BL/6 mouse inner ear.

MATERIALS AND METHODS

The specimens of C57BL/6 mouse inner ear, from E15 (embryo day 15) to adult mouse, were collected; immunohistochemistry was employed to explore the frozen sections of specimens.

RESULTS

The development of cochlea starts sequentially from the basal turn to the apex turn. Morphological development of SGNs occurs mainly from E16 to P12 (postnatal day 12). Hair cells appear from E18 to P12, and inner hair cells (IHCs) develop earlier than outer hair cells (OHCs). The connections between hair cells and SGNs begin to develop during E18-P1, morphologically resemble mature synapses during P8-P12, and completely mature in adult mice.

CONCLUSIONS

The genesis of auditory ribbon synapse occurs from E18 to P1. Synchronized with the development of SGNs and hair cells, the functional filaments remain connected to hair cells, while the spare ones get disconnected from the surface of hair cells. Connections between SGN nerve filaments and IHCs occur earlier than those between SGN nerve filaments and OHCs.

A mathematical method for calibrating the signal drift in liquid chromatography - mass spectrometry analysis

Liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) has become the most versatile analytical tool for profiling small-molecule compounds and increasingly been applied in many fields. Nevertheless, LC-MS based quantification still face some challenges, such as signal drift in LC-MS, which may affect the validity of the obtained data and lead to misinterpretation of biological results. Here, we established a calibration method known as "RIM" to compensate the signal drift of LC-MS. To this end, a mixture of d₄-2-dimethylaminoethylamine (d₄-DMED)-coded normal fatty acids (C5-C23) was used as calibrants to construct RIM calibration. With the addition of calibrants, not only the MS signal drift, but also the mass accuracy and LC retention time can be calibrated, thereby improving the reliability of quantitative data. The effectiveness of RIM was carefully validated using a human serum extract spiked with 34 standards and then RIM was applied for rat brain untargeted metabolome research. In addition, to expand the functionality and flexibility of RIM for data handling, we generated a MATLAB-based RIM program, which implements the above concepts and allows automatic data process. Taken together, the proposed RIM method has potential application in large-scale quantitative study of complex samples.