Two-pore channel 2 is required for soluble adenylyl cyclase-dependent regulation of melanosomal pH and melanin synthesis.

Melanosomal pH is important for the synthesis of melanin as the rate-limiting enzyme, tyrosinase, is very pH-sensitive. The soluble adenylyl cyclase (sAC) signaling pathway was recently identified as a regulator of melanosomal pH in melanocytes; however, the melanosomal proteins critical for sAC-dependent regulation of melanosomal pH were undefined. We now systematically examine four well-characterized melanosomal membrane proteins to determine whether any of them are required for sAC-dependent regulation of melanosomal pH. We find that OA1, OCA2, and SLC45A2 are dispensable for sAC-dependent regulation of melanosomal pH. In contrast, TPC2 activity is required for sAC-dependent regulation of melanosomal pH and melanin synthesis. In addition, activation of TPC2 by NAADP-AM rescues melanosomal pH alkalinization and reduces melanin synthesis following pharmacologic or genetic inhibition of sAC signaling. These studies establish TPC2 as a critical melanosomal protein for sAC-dependent regulation of melanosomal pH and pigmentation.

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.

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

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

An immobilized composite microbial material combined with slow release agents enhances oil-contaminated groundwater remediation.

Microbial remediation of oil-contaminated groundwater is often limited by the low temperature and lack of nutrients in the groundwater environment, resulting in low degradation efficiency and a short duration of effectiveness. In order to overcome this problem, an immobilized composite microbial material and two types of slow release agents (SRA) were creatively prepared. Three oil-degrading bacteria, Serratia marcescens X, Serratia sp. BZ-L I1 and Klebsiella pneumoniae M3, were isolated from oil-contaminated groundwater, enriched and compounded, after which the biodegradation rate of the Venezuelan crude oil and diesel in groundwater at 15 °C reached 63 % and 79 %, respectively. The composite microbial agent was immobilized on a mixed material of silver nitrate-modified zeolite and activated carbon with a mass ratio of 1:5, which achieved excellent oil adsorption and water permeability performance. The slow release processes of spherical and tablet SRAs (SSRA, TSRA) all fit well with the Korsmeyer-Peppas kinetic model, and the nitrogen release mechanism of SSRA N2 followed Fick's law of diffusion. The highest oil removal rates by the immobilized microbial material combined with SSRA N2 and oxygen SRA reached 94.9 % (sand column experiment) and 75.1 % (sand tank experiment) during the 45 days of remediation. Moreover, the addition of SRAs promoted the growth of oil-degrading bacteria based on microbial community analysis. This study demonstrates the effectiveness of using immobilized microbial material combined with SRAs to achieve a high efficiency and long-term microbial remediation of oil contaminated shallow groundwater.

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.

Association between adjuvant chemotherapy and survival in stage I gastric cancer patients after curative resection.

Background: The efficacy of adjuvant chemotherapy (AC) on survival outcomes of patients with stage I gastric cancer (GC) after curative resection remains controversial. We aimed to determine whether these patients would benefit from AC. Methods: This retrospective study included patients with pathologically confirmed stage I GC who underwent curative resection between November 2010 and December 2020. Patients were divided into AC and non-AC groups, then a 1:1 propensity score matching (PSM) analysis was performed to minimize the selection bias. Potential risk factors including age, pN stage, pT stage, lymphovascular invasion, perineural invasion, tumor size, histological type, and carcinoembryonic antigen level were used as matching covariates. The recurrence-free survival (RFS) and disease-specific survival (DSS) were compared between groups using the Kaplan-Meier method. Results: A total of 902 consecutive patients were enrolled and 174 (19.3%) patients were treated with AC. PSM created 123 pairs of patients. Before PSM, patients receiving AC had lower 10-year RFS rates (90% vs 94.6%, P = 0.035) than those who did not receive AC; the two groups had similar 10-year DSS rates (93.8% vs 95.0%, P = 0.240). After PSM, there were no statistical differences in the 10-year RFS (90.9% vs 93.0%, P = 0.507) or DSS rates (93.5% vs 93.6%, P = 0.811) between the two groups. Similar results were found in the stage IA and IB subgroups. Moreover, these findings were not affected by AC cycles. Conclusions: The addition of AC could not provide survival benefits for patients with stage I GC after surgery and follow-up is thus recommended. However, large-scale randomized clinical trials are required.

Radiation therapy promotes unsaturated fatty acids to maintain survival of glioblastoma.

Radiation therapy (RT) is essential for the management of glioblastoma (GBM). However, GBM frequently relapses within the irradiated margins, thus suggesting that RT might stimulate mechanisms of resistance that limits its efficacy. GBM is recognized for its metabolic plasticity, but whether RT-induced resistance relies on metabolic adaptation remains unclear. Here, we show in vitro and in vivo that irradiated GBM tumors switch their metabolic program to accumulate lipids, especially unsaturated fatty acids. This resulted in an increased formation of lipid droplets to prevent endoplasmic reticulum (ER) stress. The reduction of lipid accumulation with genetic suppression and pharmacological inhibition of the fatty acid synthase (FASN), one of the main lipogenic enzymes, leads to mitochondrial dysfunction and increased apoptosis of irradiated GBM cells. Combination of FASN inhibition with focal RT improved the median survival of GBM-bearing mice. Supporting the translational value of these findings, retrospective analysis of the GLASS consortium dataset of matched GBM patients revealed an enrichment in lipid metabolism signature in recurrent GBM compared to primary. Overall, these results demonstrate that RT drives GBM resistance by generating a lipogenic environment permissive to GBM survival. Targeting lipid metabolism might be required to develop more effective anti-GBM strategies.

Novel genetically engineered mouse models for clear cell renal cell carcinoma.

Genetically engineered mouse models (GEMMs) are important immunocompetent models for research into the roles of individual genes in cancer and the development of novel therapies. Here we use inducible CRISPR-Cas9 systems to develop two GEMMs which aim to model the extensive chromosome p3 deletion frequently observed in clear cell renal cell carcinoma (ccRCC). We cloned paired guide RNAs targeting early exons of Bap1, Pbrm1, and Setd2 in a construct containing a Cas9D10A (nickase, hSpCsn1n) driven by tetracycline (tet)-responsive elements (TRE3G) to develop our first GEMM. The founder mouse was crossed with two previously established transgenic lines, one carrying the tet-transactivator (tTA, Tet-Off) and one with a triple-mutant stabilized HIF1A-M3 (TRAnsgenic Cancer of the Kidney, TRACK), both driven by a truncated, proximal tubule-specific γ-glutamyltransferase 1 (ggt or γGT) promoter, to create triple-transgenic animals. Our results indicate that this model (BPS-TA) induces low numbers of somatic mutations in Bap1 and Pbrm1 (but not in Setd2), known tumor suppressor genes in human ccRCC. These mutations, largely restricted to kidneys and testis, induced no detectable tissue transformation in a cohort of 13 month old mice (N = 10). To gain insights into the low frequencies of insertions and deletions (indels) in BPS-TA mice we analyzed wild type (WT, N = 7) and BPS-TA (N = 4) kidneys by RNAseq. This showed activation of both DNA damage and immune response, suggesting activation of tumor suppressive mechanisms in response to genome editing. We then modified our approach by generating a second model in which a ggt-driven, cre-regulated Cas9WT(hSpCsn1) was employed to introduce Bap1, Pbrm1, and Setd2 genome edits in the TRACK line (BPS-Cre). The BPS-TA and BPS-Cre lines are both tightly controlled in a spatiotemporal manner with doxycycline (dox) and tamoxifen (tam), respectively. In addition, whereas the BPS-TA line relies on paired guide RNAs (gRNAs), the BPS-Cre line requires only single gRNAs for gene perturbation. In the BPS-Cre we identified increased Pbrm1 gene-editing frequencies compared to the BPS-TA model. Whereas we did not detect Setd2 edits in the BPS-TA kidneys, we found extensive editing of Setd2 in the BPS-Cre model. Bap1 editing efficiencies were comparable between the two models. Although no gross malignancies were observed in our study, this is the first reported GEMM which models the extensive chromosome 3p deletion frequently observed in kidney cancer patients. Further studies are required (1) to model more extensive 3p deletions, e.g. impacting additional genes, and (2) to increase the cellular resolution, e.g. by employing single-cell RNAseq to ascertain the effects of specific combinatorial gene inactivation.

Comparative Study of C-TIRADS, ACR-TIRADS, and EU-TIRADS for Diagnosis and Management of Thyroid Nodules.

RATIONALE AND OBJECTIVES: Chinese Thyroid Imaging Reporting and Data Systems (C-TIRADS) was developed to provide a more simplified tool for stratifying thyroid nodules. Here we aimed to validate the efficacy of C-TIRADS in distinguishing benign from malignant and in guiding fine-needle aspiration biopsies in comparison with the American College of Radiology TIRADS (ACR-TIRADS) and European TIRADS (EU-TIRADS). MATERIALS AND METHODS: This study retrospectively included 3438 thyroid nodules (≥10 mm) in 3013 patients (mean age, 47.1 years ± 12.9) diagnosed between January 2013 and November 2019. Ultrasound features of the nodules were evaluated and categorized according to the lexicons of the three TIRADS. We compared these TIRADS by using the area under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve (AUPRC), sensitivity, specificity, net reclassification improvement (NRI), and unnecessary fine-needle aspiration biopsy (FNAB) rate. RESULTS: Of the 3438 thyroid nodules, 707 (20.6%) were malignant. C-TIRADS showed higher discrimination performance (AUROC, 0.857; AUPRC, 0.605) than ACR-TIRADS (AUROC, 0.844; AUPRC, 0.567) and EU-TIRADS (AUROC, 0.802; AUPRC, 0.455). The sensitivity of C-TIRADS (85.3%) was lower than that of ACR-TIRADS (89.1%) but higher than that of EU-TIRADS (78.4%). The specificity of C-TIRADS (76.9%) was similar to that of EU-TIRADS (78.9%) and higher than that of ACR-TIRADS (69.5%). The unnecessary FNAB rate was lowest with C-TIRADS (21.2%), followed by ACR-TIRADS (41.7%) and EU-TIRADS (58.3%). C-TIRADS obtained significant NRI for recommending FNAB over ACR-TIRADS (19.0%, P < 0.001) and EU-TIRADS (25.5%, P < 0.001). CONCLUSION: C-TIRADS may be a clinically applicable tool to manage thyroid nodules, which warrants thorough tests in other geographic settings.

Integrative Scoring System for Survival Prediction in Patients With Locally Advanced Nasopharyngeal Carcinoma: A Retrospective Multicenter Study.

PURPOSE: Tumor stage is crucial for prognostic evaluation and therapeutic decisions in locally advanced nasopharyngeal carcinoma (NPC) but is imprecise. We aimed to propose a new prognostic system by integrating quantitative imaging features and clinical factors. MATERIALS AND METHODS: This retrospective study included 1,319 patients with stage III-IVa NPC between April 1, 2010, and July 31, 2019, who underwent pretherapy magnetic resonance imaging (MRI) and received concurrent chemoradiotherapy with or without induction chemotherapy. The hand-crafted and deep-learned features were extracted from MRI for each patient. After feature selection, the clinical score, radiomic score, deep score, and integrative scores were constructed via Cox regression analysis. The scores were validated in two external cohorts. The predictive accuracy and discrimination were measured by the area under the curve (AUC) and risk group stratification. The end points were progression-free survival (PFS), overall survival (OS), and distant metastasis-free survival (DMFS). RESULTS: Both radiomics and deep learning were complementary to clinical variables (age, T stage, and N stage; all P < .05). The clinical-deep score was superior or equivalent to clinical-radiomic score, whereas it was noninferior to clinical-radiomic-deep score (all P > .05). These findings were also verified in the evaluation of OS and DMFS. The clinical-deep score yielded an AUC of 0.713 (95% CI, 0.697 to 0.729) and 0.712 (95% CI, 0.693 to 0.731) in the two external validation cohorts for predicting PFS with good calibration. This scoring system could stratify patients into high- and low-risk groups with distinct survivals (all P < .05). CONCLUSION: We established and validated a prognostic system integrating clinical data and deep learning to provide an individual prediction of survival for patients with locally advanced NPC, which might inform clinicians in treatment decision making.

LKB1-Dependent Regulation of TPI1 Creates a Divergent Metabolic Liability between Human and Mouse Lung Adenocarcinoma.

KRAS is the most frequently mutated oncogene in human lung adenocarcinomas (hLUAD), and activating mutations frequently co-occur with loss-of-function mutations in TP53 or STK11/LKB1. However, mutation of all three genes is rarely observed in hLUAD, even though engineered comutation is highly aggressive in mouse lung adenocarcinoma (mLUAD). Here, we provide a mechanistic explanation for this difference by uncovering an evolutionary divergence in the regulation of triosephosphate isomerase (TPI1). In hLUAD, TPI1 activity is regulated via phosphorylation at Ser21 by the salt inducible kinases (SIK) in an LKB1-dependent manner, modulating flux between the completion of glycolysis and production of glycerol lipids. In mice, Ser21 of TPI1 is a Cys residue that can be oxidized to alter TPI1 activity without a need for SIKs or LKB1. Our findings suggest this metabolic flexibility is critical in rapidly growing cells with KRAS and TP53 mutations, explaining why the loss of LKB1 creates a liability in these tumors. SIGNIFICANCE: Utilizing phosphoproteomics and metabolomics in genetically engineered human cell lines and genetically engineered mouse models (GEMM), we uncover an evolutionary divergence in metabolic regulation within a clinically relevant genotype of human LUAD with therapeutic implications. Our data provide a cautionary example of the limits of GEMMs as tools to study human diseases such as cancers. This article is highlighted in the In This Issue feature, p. 799.

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.

Relationship Between Paravascular Abnormalities and Choroidal Thickness in Young Highly Myopic Adults.

Purpose: The purpose of this study was to investigate the clinical characteristics of paravascular abnormalities (PVAs) and retinoschisis, and their associations with choroidal thickness (ChT) in young highly myopic (HM) adults. Methods: A total number of 645 eyes were included. Paravascular microfolds (PMs), paravascular cystoid spaces (PCs), paravascular lamellar holes (PLHs), and retinoschisis were detected using swept-source optical coherence tomography. Their associations with macular ChT and risk factors were analyzed. Results: PMs, PCs, and PLHs were detected in 203 (31.5%), 141 (21.9%), and 30 (4.7%) eyes, respectively. Retinoschisis was found in 50 (7.8%) eyes, 43 (86.0%) of which were located around the retinal vessels surrounding the optic disc. A decreasing trend of macular ChT (P < 0.001) was observed in the eyes with PMs only, with both PCs and PMs, and with PLHs, PCs, and PMs. After adjustments for age, sex, and axial length (AL), the presence of PCs, PLHs, or retinoschisis around the optic disc was negatively associated with macular ChT (all P < 0.05). Eyes with longer AL, incomplete posterior vitreous detachment (PVD), and myopic atrophic maculopathy (MAM) were more likely to have PCs (all P < 0.01) and retinoschisis around the optic disc (all P < 0.05). Conclusions: PVAs were observed in approximately one third of the young HM adults in this study. The presence of PCs, PLHs, or retinoschisis around the optic disc was associated with thinner macular ChT. Eyes with longer AL, incomplete PVD, and MAM may be at risk of developing PVAs and retinoschisis around the optic disc. Translational Relevance: PCs, PLHs, and retinoschisis around the optic disc could serve as early indicators for myopia progression.

Real-time automatic prediction of treatment response to transcatheter arterial chemoembolization in patients with hepatocellular carcinoma using deep learning based on digital subtraction angiography videos.

BACKGROUND: Transcatheter arterial chemoembolization (TACE) is the mainstay of therapy for intermediate-stage hepatocellular carcinoma (HCC); yet its efficacy varies between patients with the same tumor stage. Accurate prediction of TACE response remains a major concern to avoid overtreatment. Thus, we aimed to develop and validate an artificial intelligence system for real-time automatic prediction of TACE response in HCC patients based on digital subtraction angiography (DSA) videos via a deep learning approach. METHODS: This retrospective cohort study included a total of 605 patients with intermediate-stage HCC who received TACE as their initial therapy. A fully automated framework (i.e., DSA-Net) contained a U-net model for automatic tumor segmentation (Model 1) and a ResNet model for the prediction of treatment response to the first TACE (Model 2). The two models were trained in 360 patients, internally validated in 124 patients, and externally validated in 121 patients. Dice coefficient and receiver operating characteristic curves were used to evaluate the performance of Models 1 and 2, respectively. RESULTS: Model 1 yielded a Dice coefficient of 0.75 (95% confidence interval [CI]: 0.73-0.78) and 0.73 (95% CI: 0.71-0.75) for the internal validation and external validation cohorts, respectively. Integrating the DSA videos, segmentation results, and clinical variables (mainly demographics and liver function parameters), Model 2 predicted treatment response to first TACE with an accuracy of 78.2% (95%CI: 74.2-82.3), sensitivity of 77.6% (95%CI: 70.7-84.0), and specificity of 78.7% (95%CI: 72.9-84.1) for the internal validation cohort, and accuracy of 75.1% (95% CI: 73.1-81.7), sensitivity of 50.5% (95%CI: 40.0-61.5), and specificity of 83.5% (95%CI: 79.2-87.7) for the external validation cohort. Kaplan-Meier curves showed a significant difference in progression-free survival between the responders and non-responders divided by Model 2 (p = 0.002). CONCLUSIONS: Our multi-task deep learning framework provided a real-time effective approach for decoding DSA videos and can offer clinical-decision support for TACE treatment in intermediate-stage HCC patients in real-world settings.

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.

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.

Embryonic Hypotaurine Levels Contribute to Strain-Dependent Susceptibility in Mouse Models of Valproate-Induced Neural Tube Defects.

Valproic acid (VPA, valproate, Depakote) is a commonly used anti-seizure medication (ASM) in the treatment of epilepsy and a variety of other neurological disorders. While VPA and other ASMs are efficacious for management of seizures, they also increase the risk for adverse pregnancy outcomes, including neural tube defects (NTDs). Thus, the utility of these drugs during pregnancy and in women of childbearing potential presents a continuing public health challenge. Elucidating the underlying genetic or metabolic risk factors for VPA-affected pregnancies may lead to development of non-teratogenic ASMs, novel prevention strategies, or more targeted methods for managing epileptic pregnancies. To address this challenge, we performed unbiased, whole embryo metabolomic screening of E8.5 mouse embryos from two inbred strains with differential susceptibility to VPA-induced NTDs. We identified metabolites of differential abundance between the two strains, both in response to VPA exposure and in the vehicle controls. Notable enriched pathways included lipid metabolism, carnitine metabolism, and several amino acid pathways, especially cysteine and methionine metabolism. There also was increased abundance of ω-oxidation products of VPA in the more NTD-sensitive strain, suggesting differential metabolism of the drug. Finally, we found significantly reduced levels of hypotaurine in the susceptible strain regardless of VPA status. Based on this information, we hypothesized that maternal supplementation with L-carnitine (400 mg/kg), coenzyme A (200 mg/kg), or hypotaurine (350 mg/kg) would reduce VPA-induced NTDs in the sensitive strain and found that administration of hypotaurine prior to VPA exposure significantly reduced the occurrence of NTDs by close to one-third compared to controls. L-carnitine and coenzyme A reduced resorption rates but did not significantly reduce NTD risk in the sensitive strain. These results suggest that genetic variants or environmental exposures influencing embryonic hypotaurine status may be factors in determining risk for adverse pregnancy outcomes when managing the health care needs of pregnant women exposed to VPA or other ASMs.