Biomolecular condensates create phospholipid-enriched microenvironments.

Proteins and RNA can phase separate from the aqueous cellular environment to form subcellular compartments called condensates. This process results in a protein-RNA mixture that is chemically different from the surrounding aqueous phase. Here, we use mass spectrometry to characterize the metabolomes of condensates. To test this, we prepared mixtures of phase-separated proteins and extracts of cellular metabolites and identified metabolites enriched in the condensate phase. Among the most condensate-enriched metabolites were phospholipids, due primarily to the hydrophobicity of their fatty acyl moieties. We found that phospholipids can alter the number and size of phase-separated condensates and in some cases alter their morphology. Finally, we found that phospholipids partition into a diverse set of endogenous condensates as well as artificial condensates expressed in cells. Overall, these data show that many condensates are protein-RNA-lipid mixtures with chemical microenvironments that are ideally suited to facilitate phospholipid biology and signaling.

Ocular trauma from Lawn mower accidents: Clinical insights, visual outcomes and microbial profiles.

AIMS: To characterize the clinical features of ocular trauma resulting from lawn mower, identify determinants of unfavorable final visual acuity (FVA), and assess the spectrum of microbial in posttraumatic endophthalmitis. METHODS: This retrospective study enrolled patients who experienced ocular trauma due to lawn mower at Zhongshan Ophthalmic Center from January 2013 to August 2021. Demographics, clinical features, isolated microorganisms, risk factors influencing reduced visual acuity, treatment regimens, and utilization of eyewear were collected. RESULTS: The study included 140 participants (140 eyes) (49.47 ± 12.03 years, 95% male). The predominant injury manifestations were penetrating globe injuries (75.7%) and intraocular foreign bodies (51.4%). Endophthalmitis occurred in 35 cases (25%) and Bacillus cereus (23.5%) was the primary pathogen, followed by Staphylococcus epidermidis (11.8%) and Streptococcus species (11.8%). Following the initial assessment, where 77.9% of patients had initial visual acuity (IVA) at grade IV (ranging from light perception to 4/200) and only 0.7% attained grade I (better than 20/40), post-treatment results revealed that 5.7% achieved FVA at grade I, with a concurrent decrease in patients with grade IV vision to 64.3%. Multivariate logistic regression revealed that injury protection (p < 0.001, OR = 0.237, 95% CI = 0.126-0.446), IVA (p = 0.001, OR = 4.102, 95% CI = 1.730-9.729), and retinal detachment (p = 0.042, OR = 8.105, 95% CI = 1.075-61.111) were significant independent risk factors impacting FVA. CONCLUSION: Lawn mower often cause severe ocular injuries, with high-velocity metal foreign bodies that can lead to infections, most commonly caused by Bacillus cereus. Correct use of protective gear, initial vision assessment, and detecting retinal detachment are crucial for visual prognosis.

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

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

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

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

Reversible methylation of m6Am in the 5' cap controls mRNA stability.

Internal bases in mRNA can be subjected to modifications that influence the fate of mRNA in cells. One of the most prevalent modified bases is found at the 5' end of mRNA, at the first encoded nucleotide adjacent to the 7-methylguanosine cap. Here we show that this nucleotide, N6,2'-O-dimethyladenosine (m6Am), is a reversible modification that influences cellular mRNA fate. Using a transcriptome-wide map of m6Am we find that m6Am-initiated transcripts are markedly more stable than mRNAs that begin with other nucleotides. We show that the enhanced stability of m6Am-initiated transcripts is due to resistance to the mRNA-decapping enzyme DCP2. Moreover, we find that m6Am is selectively demethylated by fat mass and obesity-associated protein (FTO). FTO preferentially demethylates m6Am rather than N6-methyladenosine (m6A), and reduces the stability of m6Am mRNAs. Together, these findings show that the methylation status of m6Am in the 5' cap is a dynamic and reversible epitranscriptomic modification that determines mRNA stability.

Role of ubiquitin-specific protease 5 in the inflammatory response of chronic periodontitis.

BACKGROUND: The systemic inflammatory response caused by chronic periodontitis is a risk factor for multiple diseases. Ubiquitin-specific protease 5 (USP5) is a kind of deubiquitinase which mainly responsible for dissociating unanchored polyubiquitin. However, the functions of USP5 in chronic periodontitis have not been reported. METHODS: Chronic periodontitis patients were recruited, and their periodontal samples were collected. The levels of USP5, tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), and interleukin-1ß (IL-1ß) in gingival crevicular fluid were evaluated by ELISA. The expression of USP5, TNF-α, IL-6, and IL-1ß in human periodontal ligament stem cells (PDLSCs) was estimated by qRT-PCR assay. The activation of STAT3 signaling was examined by Western blot. RESULTS: USP5 was upregulated in the gingival crevicular fluid and gingival tissues of chronic periodontitis patients. USP5 expression was positively correlated with the expression of proinflammatory factors. USP5 knockdown and deubiquitinase inhibitor inhibited LPS-induced inflammatory responses in PDLSCs. Suppressing USP5 inhibited STAT3 signaling in PDLSCs. CONCLUSION: Suppression deubiquitinase USP5 inhibits the inflammatory response of chronic periodontitis by suppressing STAT3 signaling.

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

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

Transcriptional and metabolic remodeling in clear cell renal cell carcinoma caused by ATF4 activation and the integrated stress response (ISR).

Research has shown extensive metabolic remodeling in clear cell renal cell carcinoma (ccRCC), with increased glutathione (GSH) levels. We hypothesized that activating transcription factor-4 (ATF4) and the integrated stress response (ISR) induce a metabolic shift, including increased GSH accumulation, and that Vitamin A deficiency (VAD), found in ccRCCs, can also activate ATF4 signaling in the kidney. To determine the role of ATF4, we used publicly available RNA sequencing (RNA-seq) data sets from The Cancer Genomics Atlas. Subsequently, we performed RNA-seq and liquid chromatography-mass spectrometry-based metabolomics analysis of the murine TRAnsgenic Cancer of the Kidney (TRACK) model for early-stage ccRCC. To validate our findings, we generated RCC4 cell lines with ATF4 gene edits (ATF4-knockout [KO]) and subjected these cells to metabolic isotope tracing. Analysis of variance, the two-sided Student's t test, and gene set enrichment analysis were used (p < 0.05) to determine statistical significance. Here we show that most human ccRCC tumors exhibit activation of the transcription factor ATF4. Activation of ATF4 is concomitant with enrichment of the ATF4 gene set and elevated expression of ATF4 target genes ASNS, ALDH1L2, MTHFD2, DDIT3 (CHOP), DDIT4, TRIB3, EIF4EBP1, SLC7A11, and PPP1R15A (GADD34). Transcript profiling and metabolomics analyses show that activated hypoxia-inducible factor-1α (HIF1α) signaling in our TRACK ccRCC murine model also induces an ATF4-mediated ISR. Notably, both normoxic HIF1α signaling in TRACK kidneys and VAD in wild-type kidneys diminish amino acid levels, increase ASNS, TRIB3, and MTHFD2 messenger RNA levels, and increase levels of lipids and GSH. By metabolic isotope tracing in human RCC4 kidney cancer parental and ATF4 gene-edited (ATF4-KO) cell lines, we show that ATF4 increases GSH accumulation in part via activation of the mitochondrial one-carbon metabolism pathway. Our results demonstrate for the first time that activation of ATF4 enhances GSH accumulation, increases purine and pyrimidine biosynthesis, and contributes to transcriptional and metabolic remodeling in ccRCC. Moreover, constitutive HIF1α expressed only in murine kidney proximal tubules activates ATF4, leading to the metabolic changes associated with the ISR. Our data indicate that HIF1α can promote ccRCC via ATF4 activation. Moreover, lack of Vitamin A in the kidney recapitulates aspects of the ISR.

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

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

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

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

Long-term surgical outcomes and prognostic factors of foveal detachment in pathologic myopia: based on the ATN classification.

BACKGROUND: To investigate the long-term surgical outcomes and prognostic factors of foveal detachment (FD) in pathological myopia. METHODS: This retrospective observational study included 59 patients with FD (61 eyes) who underwent pars plana vitrectomy at Shanghai General Hospital between June 2017 and July 2018 with follow-up for at least 24 months. Comprehensive ophthalmic examinations, including best-corrected visual acuity (BCVA) and swept-source optical coherence tomography, were assessed. Preoperative myopic maculopathy was evaluated according to the ATN classification. RESULTS: FD completely resolved in 59 of 61 eyes (96.7%). Mean duration of retinal reattachment was 12.10 ± 8.10 months. Mean logMAR BCVA improved from 1.34 ± 0.52 to 0.83 ± 0.43 at 24 months postoperatively (P < 0.001). Secondary macular hole occurred in 8 eyes (13.1%) with a mean period of 3.4 ± 4.1 weeks after primary surgery. In regression analyses, baseline myopic atrophy maculopathy (MAM) (B = 0.213, P = 0.005) and vitreomacular traction (VMT) (B = 0.292, P = 0.007) were adverse prognostic factors for postoperative BCVA. A more severe MAM revealed a delay in retinal reattachment (B = 5.670, P = 0.002). FD eyes with VMT (OR = 1.309, P = 0.003) or outer lamellar macular hole (O-LMH) (OR = 1.369, P < 0.001) were risk factors for postoperative secondary macular hole. CONCLUSIONS: Vitrectomy was effective in the long-term for treating FD. Careful consideration is needed for those with VMT or O-LMH due to the high risk of secondary macular hole after vitrectomy. FD eyes with more severe MAM tended to have poorer postoperative BCVA and extended periods of retinal reattachment.

Automatic identification of myopic maculopathy related imaging features in optic disc region via machine learning methods.

BACKGROUND: Myopic maculopathy (MM) is the most serious and irreversible complication of pathologic myopia, which is a major cause of visual impairment and blindness. Clinic proposed limited number of factors related to MM. To explore additional features strongly related with MM from optic disc region, we employ a machine learning based radiomics analysis method, which could explore and quantify more hidden or imperceptible MM-related features to the naked eyes and contribute to a more comprehensive understanding of MM and therefore may assist to distinguish the high-risk population in an early stage. METHODS: A total of 457 eyes (313 patients) were enrolled and were divided into severe MM group and without severe MM group. Radiomics analysis was applied to depict features significantly correlated with severe MM from optic disc region. Receiver Operating Characteristic were used to evaluate these features' performance of classifying severe MM. RESULTS: Eight new MM-related image features were discovered from the optic disc region, which described the shapes, textural patterns and intensity distributions of optic disc region. Compared with clinically reported MM-related features, these newly discovered features exhibited better abilities on severe MM classification. And the mean values of most features were markedly changed between patients with peripapillary diffuse chorioretinal atrophy (PDCA) and macular diffuse chorioretinal atrophy (MDCA). CONCLUSIONS: Machine learning and radiomics method are useful tools for mining more MM-related features from the optic disc region, by which complex or even hidden MM-related features can be discovered and decoded. In this paper, eight new MM-related image features were found, which would be useful for further quantitative study of MM-progression. As a nontrivial byproduct, marked changes between PDCA and MDCA was discovered by both new image features and clinic features.

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

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

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

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

Morphological differences between two types of Bruch's membrane defects in pathologic myopia.

PURPOSE: To investigate morphological differences between two types of Bruch's membrane (BM) defects-patchy atrophy (PA) and CNV-related macular atrophy (CNV-MA) METHODS: Eyes presenting with PA or CNV-MA were included. Scleral thickness (ST), choroidal thickness (CT), and scleral morphological characteristics were obtained by swept-source optical coherence tomography (SS-OCT). Fundus photographs were performed to measure the size of PA and CNV-MA lesions. RESULTS: Among a total of 167 eyes evaluated, 106 eyes had PA and 61 eyes had CNV-MA. In addition, dome-shaped macula (DSM) was identified in 20 (18.87%) and 10 (16.39%) eyes among PA and CNV-MA, respectively. The eyes of CNV-MA without DSM showed a thicker subfoveal ST (278.61 ± 56.17 vs 231.58 ± 66.09 mm, P < 0.001), a thinner subfoveal CT, and a higher rate of scleral perforating vessels (70.6% vs 50.0%, P = 0.021) when compared with those of PA without DSM. The size of PA/CNV-MA lesions was associated with CT in eyes without DSM. However, it was only associated with bulge height in eyes with DSM (r = 0.5, P = 0.013). CONCLUSIONS: The eyes with CNV-MA had a thicker sclera than those with PA, which add another evidence to indicate the absence of the progressive relationship between two types of BM defects. The enlargement of lesions in BM defects between eyes with and without DSM may be caused by different mechanical forces. SS-OCT, which focuses on scleral and choroid morphology, may be necessary for more accurate classification of pathologic myopia.

Dynamic and driving evolution of lake basin pressure in cold and arid regions based on a new method: A case study of three lakes in Inner Mongolia, China.

Static single-path assessment methods usually underestimate the lake basin pressure (LBP). Considering the cumulative transmission of multiple paths, how to assess the dynamic changes of LBP and reveal the driving evolution is not clear. Here, taking Hulun, Daihai and Wuliangsuhai lake basins in the cold and arid region as the study case, we established an LBP assessment method that coupled multiple driving paths. For the first time, this study reveals the dynamic and driving evolution of LBP and found that rapid economic development and accelerated urbanization dramatically increased the LBPs in the three lakes. Specifically, the LBPs in the three lake basins has increased by 90%-270 % in the past 32 years, and they experienced a stable stage driven by climate (1987-1992), followed by a slight increasing stage driven by agriculture (1993-2004) and finally a significant increase stage (2005-2018) driven by industry and urbanization. Different degrees of warming, populations and development intensities of agriculture and livestock were the main factors driving the spatial differences in LBPs in the three lake basins. The LBPs in the Hulun, Daihai and Wuliangsuhai Lake Basins exhibited phase driving, continuous driving and ineffective driving characteristics to the water environment changes, respectively, which were related to lake basin governance. Compared with the driving paths of water quality and water quantity, the LBP was most strongly transmitted through the water ecological path. Rapid economic development and accelerated urbanization will bring greater LBPs to the lake basins. Lake management should promote the construction of water environmental protection mechanisms that correspond to urbanization, such as land use, and continue to strengthen watershed governance to alleviate the impact of LBP, especially the impact on the service functions of water ecosystem. Our method quantified the LBPs that were transmitted from different driving paths and provided action priorities for watershed management.

Accelerated transsulfuration metabolically defines a discrete subclass of amyotrophic lateral sclerosis patients.

Amyotrophic lateral sclerosis is a disease characterized by progressive paralysis and death. Most ALS-cases are sporadic (sALS) and patient heterogeneity poses challenges for effective therapies. Applying metabolite profiling on 77-sALS patient-derived-fibroblasts and 43-controls, we found ~25% of sALS cases (termed sALS-1) are characterized by transsulfuration pathway upregulation, where methionine-derived-homocysteine is channeled into cysteine for glutathione synthesis. sALS-1 fibroblasts selectively exhibited a growth defect under oxidative conditions, fully-rescued by N-acetylcysteine (NAC). [U13C]-glucose tracing showed transsulfuration pathway activation with accelerated glucose flux into the Krebs cycle. We established a four-metabolite support vector machine model predicting sALS-1 metabotype with 97.5% accuracy. Both sALS-1 metabotype and growth phenotype were validated in an independent cohort of sALS cases. Importantly, plasma metabolite profiling identified a system-wide cysteine metabolism perturbation as a hallmark of sALS-1. Findings reveal that sALS patients can be stratified into distinct metabotypes with differential sensitivity to metabolic stress, providing novel insights for personalized therapy.

Development and validation of a Web-based malignancy risk-stratification system of thyroid nodules.

OBJECTIVES: Previous publications on risk-stratification systems for malignant thyroid nodules were based on conventional ultrasound only. We aimed to develop a practical and simplified prediction model for categorizing the malignancy risk of thyroid nodules based on clinical data, biochemical data, conventional ultrasound and real-time elastography. DESIGN: Retrospective cohort study. PATIENTS: A total of 2818 patients (1890 female, mean age, 45.5 ± 13.2 years) with 2850 thyroid nodules were retrospectively evaluated between April 2011 and October 2016. 26.8% nodules were malignant. MEASUREMENTS: We used a randomly divided sample of 80% of the nodules to perform a multivariate logistic regression analysis. Cut-points were determined to create a risk-stratification scoring system. Patients were classified as having low, moderate and high probability of malignancy according to their scores. We validated the models to the remaining 20% of the nodules. The area under the curve (AUC) was used to evaluate the discrimination ability of the systems. RESULTS: Ten variables were selected as predictors of malignancy. The point-based scoring systems with and without elasticity score achieved similar AUCs of 0.916 (95% confidence interval [CI]: 0.885-0.948) and 0.906 (95% CI: 0.872-0.941) when validated. Malignancy risk was segmented from 0% to 100.0% and was positively associated with an increase in risk scores. We then developed a Web-based risk-stratification system of thyroid nodules (http: thynodscore.com). CONCLUSION: A simple and reliable Web-based risk-stratification system could be practically used in stratifying the risk of malignancy in thyroid nodules.

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

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

In vivo three-dimensional evaluation of tumour hypoxia in nasopharyngeal carcinomas using FMT-CT and MSOT.

PURPOSE: Accurate evaluation of hypoxia is particularly important in patients with nasopharyngeal carcinoma (NPC) undergoing radiotherapy. The aim of this study was to propose a novel imaging strategy for quantitative three-dimensional (3D) evaluation of hypoxia in a small animal model of NPC. METHODS: A carbonic anhydrase IX (CAIX)-specific molecular probe (CAIX-800) was developed for imaging of hypoxia. Mouse models of subcutaneous, orthotopic, and spontaneous lymph node metastasis from NPC (5 mice per group) were established to assess the imaging strategy. A multi-modality imaging method that consisted of a hybrid combination of fluorescence molecular tomography-computed tomography (FMT-CT) and multispectral optoacoustic tomography (MSOT) was used for 3D quantitative evaluation of tumour hypoxia. Magnetic resonance imaging, histological examination, and immunohistochemical analysis were used as references for comparison and validation. RESULTS: In the early stage of NPC (2 weeks after implantation), FMT-CT enabled precise 3D localisation of the hypoxia biomarker with high sensitivity. At the advanced stage (6 weeks after implantation), MSOT allowed multispectral analysis of the biomarker and haemoglobin molecules with high resolution. The combination of high sensitivity and high resolution from FMT-CT and MSOT could not only detect hypoxia in small-sized NPCs but also visualise the heterogeneity of hypoxia in 3D. CONCLUSIONS: Integration of FMT-CT and MSOT could allow comprehensive and quantifiable evaluation of hypoxia in NPC. These findings may potentially benefit patients with NPC undergoing radiotherapy in the future. Graphical abstract A novel multimodality imaging strategy for three-dimensional evaluation of tumour hypoxia in an orthotopic model of nasopharyngeal carcinoma.