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Objective To investigate the clinical characteristics and influencing factors of patients with brain metastases from esophageal squamous cell carcinoma (BM-ESCC) and their impact on survival. Methods We retrospectively analyzed the clinical data of 67 patients with newly diagnosed BM-ESCC from December 2000 to December 2016 in our hospital, to analyze the correlation of brain metastases with clinicopathological characteristics and the survival. Results The number of BM-ESCC was positively correlated with T and N stages (P < 0.05). The median survival time was 9.65 months. Logistic regression analysis showed that N2 and N3 were risk factors for brain metastasis in patients with esophageal squamous cell carcinoma, and the risk of brain metastasis in patients with N0 and N1 was lower (P < 0.05). Log rank survival analysis reveal that patients with one brain metastasis had a significantly longer survial than those with two and three brain metastasis (P < 0.05); patients with N0+N1 stage had better prognosis than those with N2 and N3 (P < 0.05). Cox regression model showed that T3+T4 stage, N3 stage and whole brain radiotherapy were independent prognostic factors for BM-ESCC patients (P < 0.05). Conclusion In esophageal squamous cell carcinoma patients with brain metastases, the number of brain metastases is positively correlated with T and N stages; the higher N stage is, the higher the risk of brain metastases is; the higher T and N stages are, the worse the prognosis is; whole brain radiotherapy could offer better survival benefits.

A series of Polyurethane/Chitosan composite foam with different chitosan content of 5-25 wt% was prepared, and their adsorption performance of malachite green (MG) in aqueous solutions was investigated by pH values, contact time, temperatures and chitosan content. It was observed that Polyurethane/Chitosan composite foam exhibited well-developed open cell structures. Malachite green adsorption capacities of the composite foam increased with the increment of chitosan content in composite foam. Polyurethane/Chitosan composite foam with 20% chitosan content exhibited a maximum removal capacity of 16.67 mg/g and 95.60% MG removal efficiency. In addition, MG adsorption capacities of composite foam increased with pH value increase, 30℃ was the optimum temperature, which affects the adsorption process, and the adsorption maximum was attained within 12 hours. The result of SEM showed the prepared composite foam possess well-developed open cell structures. The MG adsorption kinetics and equilibrium isotherm of the composite foam were well described with the pseudo-second order kinetic model and Langmuir isotherm model, respectively. This work provided an attractive adsorbent for removing of the hazardous materials from water. [ABSTRACT FROM AUTHOR]

Introduction: The aim of this study is to investigate whether the testing time for unstimulated whole salivary flow (UWSF) can be shortened to 5 min in patients with suspected Sjögren's syndrome (SjS); and which SjS patients can use UWSF to evaluate salivary gland (SG) secretory function., Method: A diagnostic cohort comprising suspected SjS patients was conducted to investigate the correlation between UWSF measurements taken at 10 min (UWSF&#95;10 min) and those taken at 5 min (UWSF&#95;5 min). A group of SjS patients was enrolled for a comparison between UWSF and stimulated whole salivary flow (SWSF)., Results: In 734 suspected SjS patients, there was a remarkably high concordance between UWSF&#95;10 min and UWSF&#95;5 min (ICC 0.970, P < 0.001; r 0.973, P < 0.001). Reducing the testing time for UWSF to 5 min resulted in a high PPV of 83.8% and an exceptionally high NPV of 98.7%. In 408 SjS patients, the cut-off values of UWSF&#95;10 min were investigated to classify SG secretory function. Using a threshold of > 0.2 mL/min (36.8%, 150/408) instead of SWSF > 0.7 mL/min (indicating mild secretory hypofunction), the specificity and PPV were found to be 94.2% and 94.0%, respectively; and using a threshold of < 0.05 mL/min (16.9%, 69/408) instead of SWSF ≤ 0.7 mL/min (indicating moderate to severe secretory hypofunction), the specificity was remarkably high at 97.6%, accompanied by a high PPV of 91.3%., Conclusions: This study supports the possibility of reducing UWSF testing time to 5 min; and the SWSF test may be skipped for SjS patients with USWF > 0.2 mL/min, indicating mild secretory hypofunction, or < 0.05 mL/min, indicating moderate to severe secretory hypofunction. Key Points •A diagnostic cohort of 734 patients with clinical suspicion of SjS provides compelling evidence for the potential to reduce the testing time for UWSF from 10 to 5 min. •Our finding challenges the 2019 treatment recommendation for SjS, which does not require SWSF measurement in SjS patients with UWSF ≥ 0.1 mL/min. •We propose that it may be feasible to consider utilizing UWSF instead of SWSF test for objective classification of SG secretory function in over half of SjS patients., Competing Interests: Declarations. Ethics approval and consent to participate: The protocol was approved by the Medical Ethics Committee of Sun Yat-sen Memorial Hospital (SYSEC-KY-KS-2018–012) and the Medical Ethics Committee of Shenshan Medical Center (2023-SSKY-575). This study was conducted in compliance with the Helsinki Declaration. Consent for publication: All participants provided written informed consent forms. Disclosures: None., (© 2024. The Author(s).)

Mitral regurgitation is the most prevalent form of valvular heart disease, impacting over 24 million people globally. Robotic and thoracoscopic minimally invasive mitral valve repair (MIMR) techniques have emerged as viable alternatives to traditional open-heart surgery. However, the comparative effectiveness and safety of these 2 approaches remain underexplored. This systematic review and meta-analysis, conducted according to Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines, aimed to compare robotic and thoracoscopic MIMR outcomes. A literature search was performed across PubMed, Scopus, and Embase databases to identify studies comparing these 2 surgical techniques. Eligible studies included randomized controlled trials and cohort studies. Six propensity score-matched studies and 1 retrospective cohort study, involving 11,823 patients, were included, with 5851 undergoing robotic mitral valve repair and 5972 receiving thoracoscopic MIMR. No significant differences were found in perioperative mortality [risk ratio (RR): 0.97, 95% confidence interval (CI): 0.65-1.45] or pump/clamp times. Robotic surgery was associated with longer operative times (mean difference: 33.01 minutes) and higher intraoperative transfusion rates (RR: 1.53, 95% CI: 1.07-2.18), but a lower risk of atrial fibrillation (RR: 0.89, 95% CI: 0.83-0.95). In conclusion, robotic and thoracoscopic MIMR show comparable mortality and overall safety profiles. However, robotic surgery may require longer operative times and increased transfusion needs, while reducing the risk of atrial fibrillation. Further high-quality, randomized studies are warranted to validate these findings., Competing Interests: Disclosure: The authors declare no conflict of interest., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Purpose: Cells are building blocks for human physiology; consequently, understanding the way cells communicate, co-locate, and interrelate is essential to furthering our understanding of how the body functions in both health and disease. Hematoxylin and eosin (H&E) is the standard stain used in histological analysis of tissues in both clinical and research settings. Although H&E is ubiquitous and reveals tissue microanatomy, the classification and mapping of cell subtypes often require the use of specialized stains. The recent CoNIC Challenge focused on artificial intelligence classification of six types of cells on colon H&E but was unable to classify epithelial subtypes (progenitor, enteroendocrine, goblet), lymphocyte subtypes (B, helper T, cytotoxic T), and connective subtypes (fibroblasts). We propose to use inter-modality learning to label previously un-labelable cell types on H&E., Approach: We took advantage of the cell classification information inherent in multiplexed immunofluorescence (MxIF) histology to create cell-level annotations for 14 subclasses. Then, we performed style transfer on the MxIF to synthesize realistic virtual H&E. We assessed the efficacy of a supervised learning scheme using the virtual H&E and 14 subclass labels. We evaluated our model on virtual H&E and real H&E., Results: On virtual H&E, we were able to classify helper T cells and epithelial progenitors with positive predictive values of 0.34 ± 0.15 (prevalence 0.03 ± 0.01 ) and 0.47 ± 0.1 (prevalence 0.07 ± 0.02 ), respectively, when using ground truth centroid information. On real H&E, we needed to compute bounded metrics instead of direct metrics because our fine-grained virtual H&E predicted classes had to be matched to the closest available parent classes in the coarser labels from the real H&E dataset. For the real H&E, we could classify bounded metrics for the helper T cells and epithelial progenitors with upper bound positive predictive values of 0.43 ± 0.03 (parent class prevalence 0.21) and 0.94 ± 0.02 (parent class prevalence 0.49) when using ground truth centroid information., Conclusions: This is the first work to provide cell type classification for helper T and epithelial progenitor nuclei on H&E., (© 2024 The Authors.)

Ovarian cancer is a serious threat to women's health and safety. So far, people have discovered more than 130 small molecule compounds of natural origin for anti-tumor, of which approximately 50% are of microbial origin. The Acanthus ilicifolius L. species is primarily distributed in the Guangdong, Hainan, and Guangxi regions of China and grows in tidally accessible coastal areas. Recent studies have revealed that Acanthus ilicifolius L. extracts are endowed with a range of pharmacological properties, including anti-inflammatory, hepatoprotective, antioxidant, and antitumor activities. Endophytic fungi are commonly found in the healthy tissue and organs of medicinal plants. These fungi and the plants they inhabit form mutually beneficial symbiotic relationships. Endophytic fungi produce a series of secondary metabolites, with active substances having shown great economic value and applications prospects in drug research and development as well as for the biological control of plant diseases. Secondary metabolites production by endophytic fungi is regulated by specific gene clusters, and several techniques have been used to stimulate the secondary metabolic processes of fungi, including epigenetic-modification and OSMAC (one strain many compounds) strategies, co-culturing, and gene modification. Among these, epigenetic modification has been shown to be effective; this strategy involves the addition of small-molecule epigenetic modifiers to the culture medium, thereby activating silenced biosynthetic gene clusters without altering the DNA sequences of the fungi. This approach facilitates the expression of silenced genes in endophytic fungi, thereby increasing the number and diversity of secondary metabolites. Furthermore, it assists in overcoming the inhibition of microbial secondary-metabolite synthesis under laboratory conditions, and enhances silenced-gene expressions. The advent of novel analytical techniques and bioinformatics has provided a comprehensive, multifaceted, and holistic understanding of fungal metabolism through the development of metabolomics as a research platform. However, few studies have combined anti-ovarian cancer-activity screening with metabolomic approaches in the search for activity-differentiating metabolites from endophytic fungi under the intervention of epigenetic modifiers. Herein, we investigated the impact of epigenetic modifiers on the secondary metabolites of the endophytic Diaporthe goulteri fungus from Acanthus ilicifolius L. to determine their potential anti-ovarian cancer activities. Crude extracts were obtained by controlling three variables: the number of fermentation days, the type of epigenetic modifier, and its concentration, with activities screened using the CCK-8 (cell counting kit-8) method. Ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was subsequently employed for non-targeted metabolomic analysis. A multivariate statistical analysis model was constructed using principal component analysis and orthogonal partial least squares-discriminant analysis, which combines model and variable importance projection, with qualitative screening performed and significant changes (variable importance in the projection (VIP)≥1; P <0. 05) determined. Fifteen differential metabolites were identified in the fungal and epigenetic modification group, primarily comprising polyketides, amino acids, derivatives, alkaloids, and organic acids, including prenderol, glycine, valine, 2-ethylcaproic acid, rubratoxin B, finasteride, 6-silaspiro[5.5]undecane, 1-(2-nitrophenoxy)octane, heptadecene, 1-pentadecene, 11-ketoetiocholanolone, 3-(1-ethyl-1,3,3-trimethyl-2,3-dihydro-1 H -inden-5-yl)butanal, N 2 -benzoylarginine, tabutrex, (3a R ,6 S ,6a S )-6-(4-hydroxy-2-methoxy-2-butanyl)-4,4-dimethylhexahydro-1(2 H )-pentalenone, and 8-aminoquinoline. The expressions of prenderol, 1-(2-nitrophenoxy)octane, 3-(1-ethyl-1,3,3-trimethyl-2,3-dihydro-1 H -inden-5-yl)butanal, N 2 -benzoylarginine, and 8-aminoquinoline were downregulated, whereas the expressions of the remaining 10 substances were upregulated. Polyketides were the main components that exhibited higher expressions. This study showed that latent active differential metabolites can be searched by combining anti-ovarian cancer-activity screening with metabolomics analysis, thereby providing a reference for the further development of Acanthus ilicifolius L. resources and the subsequent targeted isolation of active compounds.

Smilax glabra Roxb. (SGR) is known for its high nutritional and therapeutic value. However, the frequent appearance of counterfeit products causes confusion and inconsistent quality among SGR varieties. Herein, this study collected the proportion of SGR adulteration and used high-performance liquid chromatography (HPLC) to measure the astilbin content of SGR. Then Fourier-transform near-infrared (FT-NIR) technology, combined with multivariate intelligent algorithms, was used to establish partial least squares regression quantitative models for detecting SGR adulteration and measuring astilbin content, respectively. The method conducted a quantitative analysis of dual indicators through single-spectrum data acquisition (QADS) to comprehensively evaluate the authenticity and superiority of SGR. The coefficients of determination (R 2 ) for both the calibration and prediction sets exceeded 0.96, which successfully leverages FT-NIR combined with multivariate intelligent algorithms to considerably enhance the accuracy and reliability of quantitative models. Overall, this research holds substantial value in the comprehensive quality evaluation in functional health foods., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Purpose: As large analyses merge data across sites, a deeper understanding of variance in statistical assessment across the sources of data becomes critical for valid analyses. Diffusion tensor imaging (DTI) exhibits spatially varying and correlated noise, so care must be taken with distributional assumptions. Here we characterize the role of physiology, subject compliance, and the interaction of subject with the scanner in the understanding of DTI variability, as modeled in spatial variance of derived metrics in homogeneous regions., Approach: We analyze DTI data from 1035 subjects in the Baltimore Longitudinal Study of Aging (BLSA), with ages ranging from 22.4 to 103 years old. For each subject, up to 12 longitudinal sessions were conducted. We assess variance of DTI scalars within regions of interest (ROIs) defined by four segmentation methods and investigate the relationships between the variance and covariates, including baseline age, time from the baseline (referred to as "interval"), motion, sex, and whether it is the first scan or the second scan in the session., Results: Covariate effects are heterogeneous and bilaterally symmetric across ROIs. Inter-session interval is positively related ( p ≪ 0.001 ) to FA variance in the cuneus and occipital gyrus, but negatively ( p ≪ 0.001 ) in the caudate nucleus. Males show significantly ( p ≪ 0.001 ) higher FA variance in the right putamen, thalamus, body of the corpus callosum, and cingulate gyrus. In 62 out of 176 ROIs defined by the Eve type-1 atlas, an increase in motion is associated ( p < 0.05 ) with a decrease in FA variance. Head motion increases during the rescan of DTI ( Δ μ = 0.045 millimeters per volume)., Conclusions: The effects of each covariate on DTI variance, and their relationships across ROIs are complex. Ultimately, we encourage researchers to include estimates of variance when sharing data and consider models of heteroscedasticity in analysis. This work provides a foundation for study planning to account for regional variations in metric variance., Competing Interests: Disclosures No conflict of interest.

Background: Extracorporeal membrane oxygenation (ECMO) has recently emerged as a critical support system for lung function in patients awaiting lung transplantation. This meta-analysis investigates the prognostic factors of lung transplantation following ECMO bridging therapy., Methods: A comprehensive search was conducted in PubMed, Cochrane Library, Embase, CINAHL, Web of Science, Scopus, and ProQuest databases from inception to August 11, 2023. Included were cohort or case-control studies focusing on prognostic factors of lung transplantation with ECMO bridging therapy. Data extraction was performed independently, and study quality was assessed. A meta-analysis was carried out using RevMan 5.4 and Stata17.0 software to aggregate mortality rates and pertinent prognostic factors of ECMO as a bridge to lung transplantation., Results: The search identified eight trials encompassing 1,086 participants. The prognosis of patients undergoing lung transplantation with ECMO bridging was significantly associated with several factors: prolonged ECMO support [odds ratio 1.07, 95% confidence interval (CI): 1.02-1.12, I 2 =77%], deterioration in liver and kidney function (odds ratio 3.62, 95% CI: 2.37-5.54, I 2 =0%), and complications during ECMO (odds ratio 2.24, 95% CI: 1.45-3.44, I 2 =5%)., Conclusions: Prolonged ECMO support, declining liver and kidney functions, and complications during ECMO are vital prognostic factors in lung transplantation following ECMO bridging therapy., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-23-1709/coif). The authors have no conflicts of interest to declare., (2024 Journal of Thoracic Disease. All rights reserved.)

T1-weighted (T1w) MRI has low frequency intensity artifacts due to magnetic field inhomogeneities. Removal of these biases in T1w MRI images is a critical preprocessing step to ensure spatially consistent image interpretation. N4ITK bias field correction, the current state-of-the-art, is implemented in such a way that makes it difficult to port between different pipelines and workflows, thus making it hard to reimplement and reproduce results across local, cloud, and edge platforms. Moreover, N4ITK is opaque to optimization before and after its application, meaning that methodological development must work around the inhomogeneity correction step. Given the importance of bias fields correction in structural preprocessing and flexible implementation, we pursue a deep learning approximation / reinterpretation of the N4ITK bias fields correction to create a method which is portable, flexible, and fully differentiable. In this paper, we trained a deep learning network "DeepN4" on eight independent cohorts from 72 different scanners and age ranges with N4ITK-corrected T1w MRI and bias field for supervision in log space. We found that we can closely approximate N4ITK bias fields correction with naïve networks. We evaluate the peak signal to noise ratio (PSNR) in test dataset against the N4ITK corrected images. The median PSNR of corrected images between N4ITK and DeepN4 was 47.96 dB. In addition, we assess the DeepN4 model on eight additional external datasets and show the generalizability of the approach. This study establishes that incompatible N4ITK preprocessing steps can be closely approximated by naïve deep neural networks, facilitating more flexibility. All code and models are released at https://github.com/MASILab/DeepN4 ., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Purpose: Diffusion tensor imaging (DTI) is a magnetic resonance imaging technique that provides unique information about white matter microstructure in the brain but is susceptible to confounding effects introduced by scanner or acquisition differences. ComBat is a leading approach for addressing these site biases. However, despite its frequent use for harmonization, ComBat's robustness toward site dissimilarities and overall cohort size have not yet been evaluated in terms of DTI., Approach: As a baseline, we match N = 358 participants from two sites to create a "silver standard" that simulates a cohort for multi-site harmonization. Across sites, we harmonize mean fractional anisotropy and mean diffusivity, calculated using participant DTI data, for the regions of interest defined by the JHU EVE-Type III atlas. We bootstrap 10 iterations at 19 levels of total sample size, 10 levels of sample size imbalance between sites, and 6 levels of mean age difference between sites to quantify (i)  β AGE , the linear regression coefficient of the relationship between FA and age; (ii)  γ ^ s f * , the ComBat-estimated site-shift; and (iii)  δ ^ s f * , the ComBat-estimated site-scaling. We characterize the reliability of ComBat by evaluating the root mean squared error in these three metrics and examine if there is a correlation between the reliability of ComBat and a violation of assumptions., Results: ComBat remains well behaved for β AGE when N > 162 and when the mean age difference is less than 4 years. The assumptions of the ComBat model regarding the normality of residual distributions are not violated as the model becomes unstable., Conclusion: Prior to harmonization of DTI data with ComBat, the input cohort should be examined for size and covariate distributions of each site. Direct assessment of residual distributions is less informative on stability than bootstrap analysis. We caution use ComBat of in situations that do not conform to the above thresholds., (© 2024 The Authors.)

Purpose: Two-dimensional single-slice abdominal computed tomography (CT) provides a detailed tissue map with high resolution allowing quantitative characterization of relationships between health conditions and aging. However, longitudinal analysis of body composition changes using these scans is difficult due to positional variation between slices acquired in different years, which leads to different organs/tissues being captured., Approach: To address this issue, we propose C-SliceGen, which takes an arbitrary axial slice in the abdominal region as a condition and generates a pre-defined vertebral level slice by estimating structural changes in the latent space., Results: Our experiments on 2608 volumetric CT data from two in-house datasets and 50 subjects from the 2015 Multi-Atlas Abdomen Labeling Challenge Beyond the Cranial Vault (BTCV) dataset demonstrate that our model can generate high-quality images that are realistic and similar. We further evaluate our method's capability to harmonize longitudinal positional variation on 1033 subjects from the Baltimore longitudinal study of aging dataset, which contains longitudinal single abdominal slices, and confirmed that our method can harmonize the slice positional variance in terms of visceral fat area., Conclusion: This approach provides a promising direction for mapping slices from different vertebral levels to a target slice and reducing positional variance for single-slice longitudinal analysis. The source code is available at: https://github.com/MASILab/C-SliceGen., (© 2024 The Authors.)

Objective: To explore the effect of accurately localized mini anterolateral thigh perforator flap in repairing medium-sized skin and soft tissue defects in fingers. Methods: The study was a retrospective observational study. From December 2019 to September 2022, 15 patients with medium-sized skin and soft tissue defects who met the inclusion criteria in fingers were admitted to the Second Affiliated Hospital of Wenzhou Medical University, including 12 males and 3 females, aged 23 to 62 years. After debridement, the wounds were all accompanied by exposed tendons, bones, vessels and nerves, with an area from 4.0 cm×3.0 cm to 8.0 cm×3.5 cm. Computed tomography angiography and color Doppler ultrasonography examinations were performed on both lower limbs of the patient before surgery to accurately locate the anterolateral thigh perforators. When the flap with area from 6.0 cm×3.0 cm to 11.0 cm×4.0 cm was harvested, the flap was thinned. The artery and vein perforators of the flap were anastomosed respectively with the digital artery and dorsal metacarpal vein. If there was avulsion injury, infection, or burn in the recipient area, the main arterial and veinous vessels carried by the skin flap was anastomosed with the radial artery and accompanying vein. The lateral thigh cutaneous nerve carried by the flap was anastomosed with the stump of the digital nerve. The types of perforators of the lateral thigh artery were observed during operation and compared with the location of the vessels before operation. After operation, the survival and adverse complication of the flap were closely observed. During follow-up, the skin flap color, texture, and shape were observed; the wound healing in donor area was observed. At the last follow-up, the two-point discriminative distance of the affected finger pulp was measured, and the function of the affected finger was evaluated using the trial standard for the evaluation of functions of upper limbs of Hand Surgery Society of Chinese Medical Association, and the interphalangeal joint movement of the affected finger was observed; the patients' complaints about the adverse effects of flap resection on lower limbs were recorded. Results: During the operation, it was observed that the perforators of the flaps in 11 patients were the descending branch of the lateral circumflex thigh artery, in two patients, the perforators of skin flaps were the oblique branch of the lateral thigh artery, and the perforators in another two patients were the transverse branch of the lateral circumflex thigh artery, which were consistent with the preoperative vascular localization. After operation, all flaps survived without vascular crisis and infection. The patients were followed up for 6-12 months, the flaps had excellent color, texture, and appearance; only linear scars remained on the donor wound. At the last follow-up, the two-point discrimination distance in the finger pulp was 7-11 mm; the affected finger function was rated as excellent in 6 cases, good in 6 cases, and fair in 3 cases; the flexion and extension function of the finger was not affected; two patients complained of numbness in the lateral thigh after excision of the skin flap, and the other 13 patients had no complain of adverse complaints. Conclusions: The perforating branch in lateral thigh region can be accurately located by computed tomography angiography and color Doppler ultrasonography, accurate positioning of perforators before operation can reduce the damage to the donor area during the incision of the flap, the appearance and function of the affected finger can be restored to the maximum extent by thinning the transplanted flap and rebuilding the finger sensation. Therefore, it is an effective and reliable way to repair the medium-sized skin and soft tissue defects of fingers with the mini thigh anterolateral perforator flap.

Understanding the way cells communicate, co-locate, and interrelate is essential to understanding human physiology. Hematoxylin and eosin (H&E) staining is ubiquitously available both for clinical studies and research. The Colon Nucleus Identification and Classification (CoNIC) Challenge has recently innovated on robust artificial intelligence labeling of six cell types on H&E stains of the colon. However, this is a very small fraction of the number of potential cell classification types. Specifically, the CoNIC Challenge is unable to classify epithelial subtypes (progenitor, endocrine, goblet), lymphocyte subtypes (B, helper T, cytotoxic T), or connective subtypes (fibroblasts, stromal). In this paper, we propose to use inter-modality learning to label previously un-labelable cell types on virtual H&E. We leveraged multiplexed immunofluorescence (MxIF) histology imaging to identify 14 subclasses of cell types. We performed style transfer to synthesize virtual H&E from MxIF and transferred the higher density labels from MxIF to these virtual H&E images. We then evaluated the efficacy of learning in this approach. We identified helper T and progenitor nuclei with positive predictive values of 0.34 ± 0.15 (prevalence 0.03 ± 0.01) and 0.47 ± 0.1 (prevalence 0.07 ± 0.02) respectively on virtual H&E. This approach represents a promising step towards automating annotation in digital pathology.

Diffusion MRI (dMRI) streamline tractography, the gold-standard for in vivo estimation of white matter (WM) pathways in the brain, has long been considered as a product of WM microstructure. However, recent advances in tractography demonstrated that convolutional recurrent neural networks (CoRNN) trained with a teacher-student framework have the ability to learn to propagate streamlines directly from T1 and anatomical context. Training for this network has previously relied on high resolution dMRI. In this paper, we generalize the training mechanism to traditional clinical resolution data, which allows generalizability across sensitive and susceptible study populations. We train CoRNN on a small subset of the Baltimore Longitudinal Study of Aging (BLSA), which better resembles clinical scans. We define a metric, termed the epsilon ball seeding method, to compare T1 tractography and traditional diffusion tractography at the streamline level. We show that under this metric T1 tractography generated by CoRNN reproduces diffusion tractography with approximately three millimeters of error.

Gradient nonlinearities not only induce spatial distortion in magnetic resonance imaging (MRI), but also introduce discrepancies between intended and acquired diffusion sensitization in diffusion weighted (DW) MRI. Advances in scanner performance have increased the importance of correcting gradient nonlinearities. The most common approaches for gradient nonlinear field estimations rely on phantom calibration field maps which are not always feasible, especially on retrospective data. Here, we derive a quadratic minimization problem for the complete gradient nonlinear field (L(r)). This approach starts with corrupt diffusion signal and estimates the L(r) in two scenarios: (1) the true diffusion tensor known and (2) the true diffusion tensor unknown (i.e., diffusion tensor is estimated). We show the validity of this mathematical approach, both theoretically and through tensor simulation. The estimated field is assessed through diffusion tensor metrics: mean diffusivity (MD), fractional anisotropy (FA), and principal eigenvector (V1). In simulation with 300 diffusion tensors, the study shows the mathematical model is not ill-posed and remains stable. We find when the true diffusion tensor is known (1) the change in determinant of the estimated L(r) field and the true field is near zero and (2) the median difference in estimated L(r) corrected diffusion metrics to true values is near zero. We find the results of L(r) estimation are dependent on the level of L(r) corruption. This work provides an approach to estimate gradient field without the need for additional calibration scans. To the best of our knowledge, the mathematical derivation presented here is novel.

Magnetic resonance spectroscopy (MRS) is one of the few non-invasive imaging modalities capable of making neurochemical and metabolic measurements in vivo . Traditionally, the clinical utility of MRS has been narrow. The most common use has been the "single-voxel spectroscopy" variant to discern the presence of a lactate peak in the spectra in one location in the brain, typically to evaluate for ischemia in neonates. Thus, the reduction of rich spectral data to a binary variable has not classically necessitated much signal processing. However, scanners have become more powerful and MRS sequences more advanced, increasing data complexity and adding 2 to 3 spatial dimensions in addition to the spectral one. The result is a spatially- and spectrally-variant MRS image ripe for image processing innovation. Despite this potential, the logistics for robustly accessing and manipulating MRS data across different scanners, data formats, and software standards remain unclear. Thus, as research into MRS advances, there is a clear need to better characterize its image processing considerations to facilitate innovation from scientists and engineers. Building on established neuroimaging standards, we describe a framework for manipulating these images that generalizes to the voxel, spectral, and metabolite level across space and multiple imaging sites while integrating with LCModel, a widely used quantitative MRS peak-fitting platform. In doing so, we provide examples to demonstrate the advantages of such a workflow in relation to recent publications and with new data. Overall, we hope our characterizations will lower the barrier of entry to MRS processing for neuroimaging researchers.

This study investigates the prevalence and risk factors associated with venous thrombotic events in patients receiving (ECMO) support. Systematic review and meta-analysis of case-control and cohort studies. PubMed, Cochrane Library, Embase, CINAHL, Web of Science, Scopus, and ProQuest databases from inception through November 25, 2023.Case-control and cohort studies focusing on the prevalence and risk factors for venous thrombotic events in patients supported by ECMO. Identification of risk factors and calculation of incidence rates. Nineteen studies encompassing 10,767 participants were identified and included in the analysis. The pooled prevalence of venous thrombotic events among patients receiving ECMO support was 48% [95% confidence interval (CI) 0.37-0.60, I 2  = 97.18%]. Factors associated with increased incidence rates included longer duration of ECMO support (odds ratio [OR] 1.08, 95% CI 1.07-1.09, I 2  = 49%), abnormal anti-coagulation monitoring indicators (OR 1.02, 95% CI 1.00-1.04, I 2 =  84%), and type of ECMO cannulation (OR 1.77, 95% CI 1.14-3.34, I 2  = 64%). The pooled prevalence of venous thrombotic events in patients with ECMO support is high. Increased risk is associated with extended duration of ECMO support, abnormal anti-coagulation monitoring, and specific types of ECMO cannulation., Competing Interests: Declaration of Conflicting InterestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Multiplex immunofluorescence (MxIF) is an advanced molecular imaging technique that can simultaneously provide biologists with multiple (i.e., more than 20) molecular markers on a single histological tissue section. Unfortunately, due to imaging restrictions, the more routinely used hematoxylin and eosin (H&E) stain is typically unavailable with MxIF on the same tissue section. As biological H&E staining is not feasible, previous efforts have been made to obtain H&E whole slide image (WSI) from MxIF via deep learning empowered virtual staining. However, the tiling effect is a long-lasting problem in high-resolution WSI-wise synthesis. The MxIF to H&E synthesis is no exception. Limited by computational resources, the cross-stain image synthesis is typically performed at the patch-level. Thus, discontinuous intensities might be visually identified along with the patch boundaries assembling all individual patches back to a WSI. In this work, we propose a deep learning based unpaired high-resolution image synthesis method to obtain virtual H&E WSIs from MxIF WSIs (each with 27 markers/stains) with reduced tiling effects. Briefly, we first extend the CycleGAN framework by adding simultaneous nuclei and mucin segmentation supervision as spatial constraints. Then, we introduce a random walk sliding window shifting strategy during the optimized inference stage, to alleviate the tiling effects. The validation results show that our spatially constrained synthesis method achieves a 56% performance gain for the downstream cell segmentation task. The proposed inference method reduces the tiling effects by using 50% fewer computation resources without compromising performance. The proposed random sliding window inference method is a plug-and-play module, which can be generalized for other high-resolution WSI image synthesis applications. The source code with our proposed model are available at https://github.com/MASILab/RandomWalkSlidingWindow.git.

Purpose: Diffusion-weighted magnetic resonance imaging (DW-MRI) is a critical imaging method for capturing and modeling tissue microarchitecture at a millimeter scale. A common practice to model the measured DW-MRI signal is via fiber orientation distribution function (fODF). This function is the essential first step for the downstream tractography and connectivity analyses. With recent advantages in data sharing, large-scale multisite DW-MRI datasets are being made available for multisite studies. However, measurement variabilities (e.g., inter- and intrasite variability, hardware performance, and sequence design) are inevitable during the acquisition of DW-MRI. Most existing model-based methods [e.g., constrained spherical deconvolution (CSD)] and learning-based methods (e.g., deep learning) do not explicitly consider such variabilities in fODF modeling, which consequently leads to inferior performance on multisite and/or longitudinal diffusion studies., Approach: In this paper, we propose a data-driven deep CSD method to explicitly constrain the scan-rescan variabilities for a more reproducible and robust estimation of brain microstructure from repeated DW-MRI scans. Specifically, the proposed method introduces a three-dimensional volumetric scanner-invariant regularization scheme during the fODF estimation. We study the Human Connectome Project (HCP) young adults test-retest group as well as the MASiVar dataset (with inter- and intrasite scan/rescan data). The Baltimore Longitudinal Study of Aging dataset is employed for external validation., Results: From the experimental results, the proposed data-driven framework outperforms the existing benchmarks in repeated fODF estimation. By introducing the contrastive loss with scan/rescan data, the proposed method achieved a higher consistency while maintaining higher angular correlation coefficients with the CSD modeling. The proposed method is assessing the downstream connectivity analysis and shows increased performance in distinguishing subjects with different biomarkers., Conclusion: We propose a deep CSD method to explicitly reduce the scan-rescan variabilities, so as to model a more reproducible and robust brain microstructure from repeated DW-MRI scans. The plug-and-play design of the proposed approach is potentially applicable to a wider range of data harmonization problems in neuroimaging., (© 2024 Society of Photo-Optical Instrumentation Engineers (SPIE).)

Transformer-based models, capable of learning better global dependencies, have recently demonstrated exceptional representation learning capabilities in computer vision and medical image analysis. Transformer reformats the image into separate patches and realizes global communication via the self-attention mechanism. However, positional information between patches is hard to preserve in such 1D sequences, and loss of it can lead to sub-optimal performance when dealing with large amounts of heterogeneous tissues of various sizes in 3D medical image segmentation. Additionally, current methods are not robust and efficient for heavy-duty medical segmentation tasks such as predicting a large number of tissue classes or modeling globally inter-connected tissue structures. To address such challenges and inspired by the nested hierarchical structures in vision transformer, we proposed a novel 3D medical image segmentation method (UNesT), employing a simplified and faster-converging transformer encoder design that achieves local communication among spatially adjacent patch sequences by aggregating them hierarchically. We extensively validate our method on multiple challenging datasets, consisting of multiple modalities, anatomies, and a wide range of tissue classes, including 133 structures in the brain, 14 organs in the abdomen, 4 hierarchical components in the kidneys, inter-connected kidney tumors and brain tumors. We show that UNesT consistently achieves state-of-the-art performance and evaluate its generalizability and data efficiency. Particularly, the model achieves whole brain segmentation task complete ROI with 133 tissue classes in a single network, outperforming prior state-of-the-art method SLANT27 ensembled with 27 networks. Our model performance increases the mean DSC score of the publicly available Colin and CANDI dataset from 0.7264 to 0.7444 and from 0.6968 to 0.7025, respectively. Code, pre-trained models, and use case pipeline are available at: https://github.com/MASILab/UNesT., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Yucheng Tang reports financial support was provided by Vanderbilt University. Yucheng Tang reports a relationship with Vanderbilt University that includes: employment., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Functional magnetic resonance images (fMRI) acquired using echo planar sequences typically suffer from spatial distortions due to susceptibility induced off-resonance fields, which may cause geometric mismatch with structural images and affect subsequent quantification and localization of brain function. State-of-the art distortion correction methods (for example, using FSL's topup or AFNI's 3dQwarp algorithms) require the collection of additional scans - either field maps or images with reverse phase encoding directions (i.e., blip-up/blip-down acquisitions) - to estimate and correct distortions. However, not all imaging protocols acquire these additional data and thus cannot take advantage of these post-acquisition corrections. In this study, we aim to enable state-of-the art processing of historical or limited datasets that do not include specific sequences for distortion correction by using only the acquired functional data and a single commonly acquired structural image. To achieve this, we synthesize an undistorted image with contrast similar to the fMRI data and use the non-distorted synthetic image as an anatomical target for distortion correction. We evaluate the efficacy of this approach, named SynBOLD-DisCo (Synthetic BOLD contrast for Distortion Correction), and show that this distortion correction process yields fMRI data that are geometrically similar to non-distorted structural images, with distortion correction virtually equivalent to acquisitions that do contain both blip-up/blip-down images. Our method is available as a Singularity container, source code, and an executable trained model to facilitate evaluation and integration into existing fMRI preprocessing pipelines., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Under the catalysis of Rh 2 (OAc) 4 (10 mol%) and binapbisphosphine ligand (±)- L3 (20 mol%) in DCE at 80 °C, the cascade cyclization of diazoimides with alkylidenepyrazolones underwent stereoselectively (dr > 20:1), affording pyrazole-fused oxa-bridged oxazocines in reasonable chemical yields. The chemical structure and relative configuration of title products were firmly identified by X-ray diffraction analysis.

The accuracy of predictive models for solitary pulmonary nodule (SPN) diagnosis can be greatly increased by incorporating repeat imaging and medical context, such as electronic health records (EHRs). However, clinically routine modalities such as imaging and diagnostic codes can be asynchronous and irregularly sampled over different time scales which are obstacles to longitudinal multimodal learning. In this work, we propose a transformer-based multimodal strategy to integrate repeat imaging with longitudinal clinical signatures from routinely collected EHRs for SPN classification. We perform unsupervised disentanglement of latent clinical signatures and leverage time-distance scaled self-attention to jointly learn from clinical signatures expressions and chest computed tomography (CT) scans. Our classifier is pretrained on 2,668 scans from a public dataset and 1,149 subjects with longitudinal chest CTs, billing codes, medications, and laboratory tests from EHRs of our home institution. Evaluation on 227 subjects with challenging SPNs revealed a significant AUC improvement over a longitudinal multimodal baseline (0.824 vs 0.752 AUC), as well as improvements over a single cross-section multimodal scenario (0.809 AUC) and a longitudinal imaging-only scenario (0.741 AUC). This work demonstrates significant advantages with a novel approach for co-learning longitudinal imaging and non-imaging phenotypes with transformers. Code available at https://github.com/MASILab/lmsignatures.

In diffusion weighted MRI (DW-MRI), hardware nonlinearities lead to spatial variations in the orientation and magnitude of diffusion weighting. While the correction of these spatial distortions has been well established for analyses of DW-MRI, the existing voxel-wise empirical correction for gradient nonlinearities requires reimplementation of existing models, as the resultant gradients vary by voxel. Herein, we propose a two-step signal approximation after voxel-wise correction of gradient nonlinearity effects in DW-MRI. The proposed technique (1) scales the diffusion signal and (2) resamples the gradient orientations. This results in uniform gradients across the corrected image and provides the key advantage of seamless integration into current diffusion workflows. We investigated the validity of our technique by fitting a multi-compartment neurite orientation dispersion and density imaging (NODDI) model to the empirical correction and proposed approximation in five subjects from the MASiVar pediatric dataset. We evaluated intra-cellular volume fraction (iVF), CSF volume fraction (cVF), and orientation dispersion index (ODI) from NODDI. The Cohen's d of iVF, cVF and ODI between the techniques was <0.2 indicating the proposed technique does not exhibit significant differences from the voxel-wise correction technique. Our two-step signal approximation is an efficient representation of the voxel-wise gradient table correction. Using this approximation, correction of gradient nonlinearities can be easily incorporated into existing diffusion preprocessing pipelines and is implemented in "PreQual: An automated pipeline for integrated preprocessing and quality assurance of diffusion weighted MRI images"., Competing Interests: Declaration of Competing Interest The authors declare that they have no conflict of interest., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Medical image segmentation, or computing voxel-wise semantic masks, is a fundamental yet challenging task in medical imaging domain. To increase the ability of encoder-decoder neural networks to perform this task across large clinical cohorts, contrastive learning provides an opportunity to stabilize model initialization and enhances downstream tasks performance without ground-truth voxel-wise labels. However, multiple target objects with different semantic meanings and contrast level may exist in a single image, which poses a problem for adapting traditional contrastive learning methods from prevalent "image-level classification" to "pixel-level segmentation". In this article, we propose a simple semantic-aware contrastive learning approach leveraging attention masks and image-wise labels to advance multi-object semantic segmentation. Briefly, we embed different semantic objects to different clusters rather than the traditional image-level embeddings. We evaluate our proposed method on a multi-organ medical image segmentation task with both in-house data and MICCAI Challenge 2015 BTCV datasets. Compared with current state-of-the-art training strategies, our proposed pipeline yields a substantial improvement of 5.53% and 6.09% on Dice score for both medical image segmentation cohorts respectively (p-value 0.01). The performance of the proposed method is further assessed on external medical image cohort via MICCAI Challenge FLARE 2021 dataset, and achieves a substantial improvement from Dice 0.922 to 0.933 (p-value 0.01).

Under the catalysis of Pd(OAc) 2 /dppf/Na 2 CO 3 , the decarboxylative 1,4-addition reaction of benzofuran-based azadienes with allyl phenyl carbonates took place easily and delivered the desired products in reasonable chemical yields. The chemical structure of the target compounds was clearly identified by single crystal X-ray structural analysis.

Cerebral ischemia-reperfusion injury (CIRI) is a series of cascade reactions that occur after blood flow recanalization in the ischemic zone in patients with cerebral infarction, causing an imbalance in intracellular homeostasis through multiple pathologies such as increased oxygen free radicals, inflammatory response, calcium overload, and impaired energy metabolism, leading to mitochondrial dysfunction and ultimately apoptosis. Rescue of reversibly damaged neurons in the ischemic hemispheric zone is the key to saving brain infarction and reducing neurological deficits. Complex and active neurological functions are highly dependent on an adequate energy supply from mitochondria. Mitochondrial biogenesis (MB), a process that generates new functional mitochondria and restores normal mitochondrial function by replacing damaged mitochondria, is a major mechanism for maintaining intra-mitochondrial homeostasis and is involved in mitochondrial quality control to ameliorate mitochondrial dysfunction and thus protects against CIRI. The main regulator of MB is peroxisome proliferator-activated receptor gamma coactivator-1α (PGC-1α), which improves mitochondrial function to protect against CIRI by activating its downstream nuclear respiratory factor 1 (NRF1) and mitochondrial transcription factor A (TFAM) to promote mitochondrial genome replication and transcription. This paper provides a theoretical reference for the treatment of neurological impairment caused by CIRI by discussing the mechanisms of mitochondrial biogenesis during cerebral ischemia-reperfusion injury., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher., (Copyright © 2023 Yuan, Tian, Jiang, Cai, Song, Peng and Zhang.)

Why are people with focal epilepsy not continuously having seizures? Previous neuronal signalling work has implicated gamma-aminobutyric acid balance as integral to seizure generation and termination, but is a high-level distributed brain network involved in suppressing seizures? Recent intracranial electrographic evidence has suggested that seizure-onset zones have increased inward connectivity that could be associated with interictal suppression of seizure activity. Accordingly, we hypothesize that seizure-onset zones are actively suppressed by the rest of the brain network during interictal states. Full testing of this hypothesis would require collaboration across multiple domains of neuroscience. We focused on partially testing this hypothesis at the electrographic network level within 81 individuals with drug-resistant focal epilepsy undergoing presurgical evaluation. We used intracranial electrographic resting-state and neurostimulation recordings to evaluate the network connectivity of seizure onset, early propagation and non-involved zones. We then used diffusion imaging to acquire estimates of white-matter connectivity to evaluate structure-function coupling effects on connectivity findings. Finally, we generated a resting-state classification model to assist clinicians in detecting seizure-onset and propagation zones without the need for multiple ictal recordings. Our findings indicate that seizure onset and early propagation zones demonstrate markedly increased inwards connectivity and decreased outwards connectivity using both resting-state (one-way ANOVA, P-value = 3.13 × 10-13) and neurostimulation analyses to evaluate evoked responses (one-way ANOVA, P-value = 2.5 × 10-3). When controlling for the distance between regions, the difference between inwards and outwards connectivity remained stable up to 80 mm between brain connections (two-way repeated measures ANOVA, group effect P-value of 2.6 × 10-12). Structure-function coupling analyses revealed that seizure-onset zones exhibit abnormally enhanced coupling (hypercoupling) of surrounding regions compared to presumably healthy tissue (two-way repeated measures ANOVA, interaction effect P-value of 9.76 × 10-21). Using these observations, our support vector classification models achieved a maximum held-out testing set accuracy of 92.0 ± 2.2% to classify early propagation and seizure-onset zones. These results suggest that seizure-onset zones are actively segregated and suppressed by a widespread brain network. Furthermore, this electrographically observed functional suppression is disproportionate to any observed structural connectivity alterations of the seizure-onset zones. These findings have implications for the identification of seizure-onset zones using only brief electrographic recordings to reduce patient morbidity and augment the presurgical evaluation of drug-resistant epilepsy. Further testing of the interictal suppression hypothesis can provide insight into potential new resective, ablative and neuromodulation approaches to improve surgical success rates in those suffering from drug-resistant focal epilepsy., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Purpose: Thigh muscle group segmentation is important for assessing muscle anatomy, metabolic disease, and aging. Many efforts have been put into quantifying muscle tissues with magnetic resonance (MR) imaging, including manual annotation of individual muscles. However, leveraging publicly available annotations in MR images to achieve muscle group segmentation on single-slice computed tomography (CT) thigh images is challenging., Approach: We propose an unsupervised domain adaptation pipeline with self-training to transfer labels from three-dimensional MR to single CT slices. First, we transform the image appearance from MR to CT with CycleGAN and feed the synthesized CT images to a segmenter simultaneously. Single CT slices are divided into hard and easy cohorts based on the entropy of pseudo-labels predicted by the segmenter. After refining easy cohort pseudo-labels based on anatomical assumption, self-training with easy and hard splits is applied to fine-tune the segmenter., Results: On 152 withheld single CT thigh images, the proposed pipeline achieved a mean Dice of 0.888 (0.041) across all muscle groups, including gracilis, hamstrings, quadriceps femoris, and sartorius muscle., Conclusions: To our best knowledge, this is the first pipeline to achieve domain adaptation from MR to CT for thigh images. The proposed pipeline effectively and robustly extracts muscle groups on two-dimensional single-slice CT thigh images. The container is available for public use in GitHub repository available at: https://github.com/MASILab/DA&#95;CT&#95;muscle&#95;seg., (© 2023 Society of Photo-Optical Instrumentation Engineers (SPIE).)

Background and Purpose: Type 1 diabetes affects over 200,000 children in the United States and is associated with an increased risk of cognitive dysfunction. Prior single-site, single-voxel MRS case reports and studies have identified associations between reduced NAA/Cr, a marker of neuroaxonal loss, and type 1 diabetes. However, NAA/Cr differences among children with various disease complications or across different brain tissues remain unclear. To better understand this phenomenon and the role of MRS in characterizing it, we conducted a multisite pilot study., Materials and Methods: In 25 children, 6-14 years of age, with type 1 diabetes across 3 sites, we acquired T1WI and axial 2D MRSI along with phantom studies to calibrate scanner effects. We quantified tissue-weighted NAA/Cr in WM and deep GM and modeled them against study covariates., Results: We found that MRSI differentiated WM and deep GM by NAA/Cr on the individual level. On the population level, we found significant negative associations of WM NAA/Cr with chronic hyperglycemia quantified by hemoglobin A1c ( P  < .005) and a history of diabetic ketoacidosis at disease onset ( P  < .05). We found a statistical interaction ( P  < .05) between A1c and ketoacidosis, suggesting that neuroaxonal loss from ketoacidosis may outweigh that from poor glucose control. These associations were not present in deep GM., Conclusions: Our pilot study suggests that MRSI differentiates GM and WM by NAA/Cr in this population, disease complications may lead to neuroaxonal loss in WM in children, and deeper investigation is warranted to further untangle how diabetic ketoacidosis and chronic hyperglycemia affect brain health and cognition in type 1 diabetes., (© 2023 by American Journal of Neuroradiology.)

In diffusion MRI, gradient nonlinearities cause spatial variations in the magnitude and direction of diffusion gradients. Studies have shown artifacts from these distortions can results in biased diffusion tensor information and tractography. Here, we investigate the impact of gradient nonlinearity correction in the presence of noise. We introduced empirically derived gradient nonlinear fields at different signal-to-noise ratio (SNR) levels in two experiments: tensor field simulation and simulation of the brain. For each experiment, this work compares two techniques empirically: voxel-wise gradient table correction and approximate correction by scaling the signal directly. The impact was assessed through diffusion metrics including mean diffusivity (MD), fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD), and principal eigen vector (V1). The study shows (1) the correction of gradient nonlinearities will not lead to substantively incorrect estimation of diffusion metrics in a linear system, (2) gradient nonlinearity correction does not interact adversely with noise, (3) nonlinearity correction suppresses the impact of nonlinearities in typical SNR data, (4) for SNR below 30, the performance of both the gradient nonlinearity correction techniques were similar, and (5) larger impacts are seen in regions where the gradient nonlinearities are distinct. Thus, this study suggests that there were greater beneficial effects than adverse effects due to the correction of nonlinearities. Additionally, correction of nonlinearities is recommended when region of interests are in areas with pronounced nonlinearities., Competing Interests: Declaration of Competing Interest The authors declare that they have no conflict of interest., (Copyright © 2023. Published by Elsevier Inc.)

Superficial white matter (SWM) represents a significantly understudied part of the human brain, despite comprising a large portion of brain volume and making up a majority of cortico-cortical white matter connections. Using multiple, high-quality datasets with large sample sizes (N = 2421, age range 5-100) in combination with methodological advances in tractography, we quantified features of SWM volume and thickness across the brain and across development, young adulthood, and aging. We had four primary aims: (1) characterize SWM thickness across brain regions (2) describe associations between SWM volume and age (3) describe associations between SWM thickness and age, and (4) quantify relationships between SWM thickness and cortical features. Our main findings are that (1) SWM thickness varies across the brain, with patterns robust across individuals and across the population at the region-level and vertex-level; (2) SWM volume shows unique volumetric trajectories with age that are distinct from gray matter and other white matter trajectories; (3) SWM thickness shows nonlinear cross-sectional changes across the lifespan that vary across regions; and (4) SWM thickness is associated with features of cortical thickness and curvature. For the first time, we show that SWM volume follows a similar trend as overall white matter volume, peaking at a similar time in adolescence, leveling off throughout adulthood, and decreasing with age thereafter. Notably, the relative fraction of total brain volume of SWM continuously increases with age, and consequently takes up a larger proportion of total white matter volume, unlike the other tissue types that decrease with respect to total brain volume. This study represents the first characterization of SWM features across the large portion of the lifespan and provides the background for characterizing normal aging and insight into the mechanisms associated with SWM development and decline., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The blood oxygen level dependent (BOLD) signal from functional magnetic resonance imaging (fMRI) is a noninvasive technique that has been widely used in research to study brain function. However, fMRI suffers from susceptibility-induced off resonance fields which may cause geometric distortions and mismatches with anatomical images. State-of-the-art correction methods require acquiring reverse phase encoded images or additional field maps to enable distortion correction. However, not all imaging protocols include these additional scans and thus cannot take advantage of these susceptibility correction capabilities. As such, in this study we aim to enable state-of-the-art distortion correction with FSL's topup algorithm of historical and/or limited fMRI data that include only a structural image and single phase encoded fMRI. To do this, we use 3D U-net models to synthesize undistorted fMRI BOLD contrast images from the structural image and use this undistorted synthetic image as an anatomical target for distortion correction with topup . We evaluate the efficacy of this approach, named SynBOLD-DisCo (synthetic BOLD images for distortion correction), and show that BOLD images corrected using our approach are geometrically more similar to structural images than the distorted BOLD data and are practically equivalent to state-of-the-art correction methods which require reverse phase encoded data. Future directions include additional validation studies, integration with other preprocessing operations, retraining with broader pathologies, and investigating the effects of spin echo versus gradient echo images for training and distortion correction. In summary, we demonstrate SynBOLD-DisCo corrects distortion of fMRI when reverse phase encoding scans or field maps are not available.

Complex graph theory measures of brain structural connectomes derived from diffusion weighted images (DWI) provide insight into the network structure of the brain. Further, as the number of available DWI datasets grows, so does the ability to investigate associations in these measures with major biological factors, like age. However, one key hurdle that remains is the presence of scanner effects that can arise from different DWI datasets and confound multisite analyses. Two common approaches to correct these effects are voxel-wise and feature-wise harmonization. However, it is still unclear how to best leverage them for graph-theory analysis of an aging population. Thus, there is a need to better characterize the impact of each harmonization method and their ability to preserve age related features. We investigate this by characterizing four complex graph theory measures (modularity, characteristic path length, global efficiency, and betweenness centrality) in 48 participants aged 55 to 86 from Baltimore Longitudinal Study of Aging (BLSA) and Vanderbilt Memory and Aging Project (VMAP) before and after voxel- and feature-wise harmonization with the Null Space Deep Network (NSDN) and ComBat, respectively. First, we characterize across dataset coefficients of variation (CoV) and find the combination of NSDN and ComBat causes the greatest reduction in CoV followed by ComBat alone then NSDN alone. Second, we reproduce published associations of modularity with age after correcting for other covariates with linear models. We find that harmonization with ComBat or ComBat and NSDN together improves the significance of existing age effects, reduces model residuals, and qualitatively reduces separation between datasets. These results reinforce the efficiency of statistical harmonization on the feature-level with ComBat and suggest that harmonization on the voxel-level is synergistic but may have reduced effect after running through the multiple layers of the connectomics pipeline. Thus, we conclude that feature-wise harmonization improves statistical results, but the addition of biologically informed voxel-based harmonization offers further improvement.

Diffusion weighted magnetic resonance imaging (DW-MRI) captures tissue microarchitecture at millimeter scale. With recent advantages in data sharing, large-scale multi-site DW-MRI datasets are being made available for multi-site studies. However, DW-MRI suffers from measurement variability (e.g., inter- and intra-site variability, hardware performance, and sequence design), which consequently yields inferior performance on multi-site and/or longitudinal diffusion studies. In this study, we propose a novel, deep learning-based method to harmonize DW-MRI signals for a more reproducible and robust estimation of microstructure. Our method introduces a data-driven scanner-invariant regularization scheme to model a more robust fiber orientation distribution function (FODF) estimation. We study the Human Connectome Project (HCP) young adults test-retest group as well as the MASiVar dataset (with inter- and intra-site scan/rescan data). The 8 th order spherical harmonics coefficients are employed as data representation. The results show that the proposed harmonization approach maintains higher angular correlation coefficients (ACC) with the ground truth signals (0.954 versus 0.942), while achieves higher consistency of FODF signals for intra-scanner data (0.891 versus 0.826), as compared with the baseline supervised deep learning scheme. Furthermore, the proposed data-driven framework is flexible and potentially applicable to a wider range of data harmonization problems in neuroimaging.

Batch size is a key hyperparameter in training deep learning models. Conventional wisdom suggests larger batches produce improved model performance. Here we present evidence to the contrary, particularly when using autoencoders to derive meaningful latent spaces from data with spatially global similarities and local differences, such as electronic health records (EHR) and medical imaging. We investigate batch size effects in both EHR data from the Baltimore Longitudinal Study of Aging and medical imaging data from the multimodal brain tumor segmentation (BraTS) challenge. We train fully connected and convolutional autoencoders to compress the EHR and imaging input spaces, respectively, into 32-dimensional latent spaces via reconstruction losses for various batch sizes between 1 and 100. Under the same hyperparameter configurations, smaller batches improve loss performance for both datasets. Additionally, latent spaces derived by autoencoders with smaller batches capture more biologically meaningful information. Qualitatively, we visualize 2-dimensional projections of the latent spaces and find that with smaller batches the EHR network better separates the sex of the individuals, and the imaging network better captures the right-left laterality of tumors. Quantitatively, the analogous sex classification and laterality regressions using the latent spaces demonstrate statistically significant improvements in performance at smaller batch sizes. Finally, we find improved individual variation locally in visualizations of representative data reconstructions at lower batch sizes. Taken together, these results suggest that smaller batch sizes should be considered when designing autoencoders to extract meaningful latent spaces among EHR and medical imaging data driven by global similarities and local variation.

Nonlinear gradients impact diffusion weighted MRI by introducing spatial variation in estimated diffusion tensors. Recent studies have shown that increasing signal-to-noise ratios and the use of ultra-strong gradients may lead to clinically significant impacts on analyses due to these nonlinear gradients in microstructural measures. These effects can potentially bias tractography results and cause misinterpretation of data. Herein, we characterize the impact of an "approximate" gradient nonlinearity correction technique in tractography using empirically derived gradient nonlinear fields. This technique scales the diffusion signal by the change in magnitude due to the gradient nonlinearities, without concomitant correction of gradient direction errors. The impact of this correction on tractography is assessed through white matter bundle segmentation and connectomics via bundle-wise volume, fractional anisotropy, mean diffusivity, radial diffusivity, axial diffusivity, primary eigenvector, and length; as well as the modularity, global efficiency, and characteristic path length connectomics graph measures. We investigate the differences between (1) these measures directly and (2) the within session variability of these measures before and after approximate correction in 61 subjects from the MASiVar pediatric reproducibility dataset. We find approximate correction results is little to no differences on the population level, but large differences on the subject-specific level for both the measures directly and their within session variability. Thus, this study suggests though approximate correction of gradient nonlinearities may not change tractography findings on the population level, subject-specific interpretations may exhibit large fluctuations. A limitation is the lack of comparison with the empirical voxel-wise gradient table correction.

7T magnetic resonance imaging (MRI) has the potential to drive our understanding of human brain function through new contrast and enhanced resolution. Whole brain segmentation is a key neuroimaging technique that allows for region-by-region analysis of the brain. Segmentation is also an important preliminary step that provides spatial and volumetric information for running other neuroimaging pipelines. Spatially localized atlas network tiles (SLANT) is a popular 3D convolutional neural network (CNN) tool that breaks the whole brain segmentation task into localized sub-tasks. Each sub-task involves a specific spatial location handled by an independent 3D convolutional network to provide high resolution whole brain segmentation results. SLANT has been widely used to generate whole brain segmentations from structural scans acquired on 3T MRI. However, the use of SLANT for whole brain segmentation from structural 7T MRI scans has not been successful due to the inhomogeneous image contrast usually seen across the brain in 7T MRI. For instance, we demonstrate the mean percent difference of SLANT label volumes between a 3T scan-rescan is approximately 1.73%, whereas its 3T-7T scan-rescan counterpart has higher differences around 15.13%. Our approach to address this problem is to register the whole brain segmentation performed on 3T MRI to 7T MRI and use this information to finetune SLANT for structural 7T MRI. With the finetuned SLANT pipeline, we observe a lower mean relative difference in the label volumes of ~8.43% acquired from structural 7T MRI data. Dice similarity coefficient between SLANT segmentation on the 3T MRI scan and the after finetuning SLANT segmentation on the 7T MRI increased from 0.79 to 0.83 with p<0.01. These results suggest finetuning of SLANT is a viable solution for improving whole brain segmentation on high resolution 7T structural imaging.

Crohn's disease (CD) is a debilitating inflammatory bowel disease with no known cure. Computational analysis of hematoxylin and eosin (H&E) stained colon biopsy whole slide images (WSIs) from CD patients provides the opportunity to discover unknown and complex relationships between tissue cellular features and disease severity. While there have been works using cell nuclei-derived features for predicting slide-level traits, this has not been performed on CD H&E WSIs for classifying normal tissue from CD patients vs active CD and assessing slide label-predictive performance while using both separate and combined information from pseudo-segmentation labels of nuclei from neutrophils, eosinophils, epithelial cells, lymphocytes, plasma cells, and connective cells. We used 413 WSIs of CD patient biopsies and calculated normalized histograms of nucleus density for the six cell classes for each WSI. We used a support vector machine to classify the truncated singular value decomposition representations of the normalized histograms as normal or active CD with four-fold cross-validation in rounds where nucleus types were first compared individually, the best was selected, and further types were added each round. We found that neutrophils were the most predictive individual nucleus type, with an AUC of 0.92 ± 0.0003 on the withheld test set. Adding information improved cross-validation performance for the first two rounds and on the withheld test set for the first three rounds, though performance metrics did not increase substantially beyond when neutrophils were used alone.

Abstract Background/purpose: Recurrent aphthous ulcer (RAU) is one of the most common diseases of oral mucosa, which is generally believed to be related to immunity, though the etiology is still unclear. It is suspected that allergies are directly related to RAU. So we sought to explore the relationship between hypersensitivity and RAU., Materials and Methods: 40 RAU patients who were in ulceration period and 40 people who were in good health were selected from Jan 2016 to Feb 2017, matched in age and sex. The peripheral blood antigens of 40 RAU patients and 40 healthy people was tested, and serum specific IgE (sIgE) with 6 groups of antigens and total IgE (tIgE) analysis was performed to identify IgE-mediated allergic reaction possibly affecting RAU. We then investigated the food intolerance and IgG levels to discover the correlation between non-IgE mediated allergic reaction and RAU., Results: The positive cases and rate of sIgE in RAU group was higher than that of control, but the difference was not statistically significant ( P ＞0.05). Positive grade of animal fur scraps (EX1), house dust mixed (HX2) and the serum tIgE concentration of the RAU group were significantly higher than the control group ( P ＜0.05).The number of food intolerance in RAU group was significantly higher than that in control group ( P ＜0.05)., Conclusion: Our findings suggested certain correlation between RAU and anaphylaxis. Daily contact allergens and food intolerance may be one of the causes of RAU. Moreover, this provides reference value for clinical diagnosis and treatment., Competing Interests: The authors have no conflicts of interest relevant to this article., (© 2022 Association for Dental Sciences of the Republic of China. Publishing services by Elsevier B.V.)