Hybrid deep learning and optimal graph search method for optical coherence tomography layer segmentation in diseases affecting the optic nerve.

Accurate segmentation of retinal layers in optical coherence tomography (OCT) images is critical for assessing diseases that affect the optic nerve, but existing automated algorithms often fail when pathology causes irregular layer topology, such as extreme thinning of the ganglion cell-inner plexiform layer (GCIPL). Deep LOGISMOS, a hybrid approach that combines the strengths of deep learning and 3D graph search to overcome their limitations, was developed to improve the accuracy, robustness and generalizability of retinal layer segmentation. The method was trained on 124 OCT volumes from both eyes of 31 non-arteritic anterior ischemic optic neuropathy (NAION) patients and tested on three cross-sectional datasets with available reference tracings: Test-NAION (40 volumes from both eyes of 20 NAION subjects), Test-G (29 volumes from 29 glaucoma subjects/eyes), and Test-JHU (35 volumes from 21 multiple sclerosis and 14 control subjects/eyes) and one longitudinal dataset without reference tracings: Test-G-L (155 volumes from 15 glaucoma patients/eyes). In the three test datasets with reference tracings (Test-NAION, Test-G, and Test-JHU), Deep LOGISMOS achieved very high Dice similarity coefficients (%) on GCIPL: 89.97±3.59, 90.63±2.56, and 94.06±1.76, respectively. In the same context, Deep LOGISMOS outperformed the Iowa reference algorithms by improving the Dice score by 17.5, 5.4, and 7.5, and also surpassed the deep learning framework nnU-Net with improvements of 4.4, 3.7, and 1.0. For the 15 severe glaucoma eyes with marked GCIPL thinning (Test-G-L), it demonstrated reliable regional GCIPL thickness measurement over five years. The proposed Deep LOGISMOS approach has potential to enhance precise quantification of retinal structures, aiding diagnosis and treatment management of optic nerve diseases.

Effect of locomotor preference on the evolution of mitochondrial genes in Bovidae.

Locomotor preferences and habitat types may drive animal evolution. In this study, we speculated that locomotor preference and habitat type may have diverse influences on Bovidae mitochondrial genes. We used selection pressure and statistical analysis to explore the evolution of mitochondrial DNA (mtDNA) protein-coding genes (PCGs) from diverse locomotor preferences and habitat types. Our study demonstrates that locomotor preference (energy demand) drives the evolution of Bovidae in mtDNA PCGs. The habitat types had no significant effect on the rate of evolution in Bovidae mitochondrial genes. Our study provides deep insight into the adaptation of Bovidae.

Dorsal raphe nucleus to basolateral amygdala 5-HTergic neural circuit modulates restoration of consciousness during sevoflurane anesthesia.

The advent of general anesthesia (GA) has significant implications for clinical practice. However, the exact mechanisms underlying GA-induced transitions in consciousness remain elusive. Given some similarities between GA and sleep, the sleep-arousal neural nuclei and circuits involved in sleep-arousal, including the 5-HTergic system, could be implicated in GA. Herein, we utilized pharmacology, optogenetics, chemogenetics, fiber photometry, and retrograde tracing to demonstrate that both endogenous and exogenous activation of the 5-HTergic neural circuit between the dorsal raphe nucleus (DR) and basolateral amygdala (BLA) promotes arousal and facilitates recovery of consciousness from sevoflurane anesthesia. Notably, the 5-HT1A receptor within this pathway holds a pivotal role. Our findings will be conducive to substantially expanding our comprehension of the neural circuit mechanisms underlying sevoflurane anesthesia and provide a potential target for modulating consciousness, ultimately leading to a reduction in anesthetic dose requirements and side effects.

Sorption characteristics of phosphorus on marine sediments in the presence of black carbon derived from fly ash.

The sorption behavior of phosphorus on marine sediments in the presence of black carbon derived from fly ash (FC) was studied. For both the FC and sediment samples, the kinetic curves could be described by a two-compartment first order equation, and the isotherms fit the Freundlich and Langmuir models well. The high specific surface area with abundant acidic functional groups of FC promoted the sorption and make this process more irreversible. The effects were more significant with higher amount of FC added. After sorption, more significant increase in Ex-P, Fe/Al-P and CaP was found in the sediment with FC added, while the organic groups in FC rarely react with phosphorus to form OP. The pH of medium influenced the sorption character, and FC promoted the process significantly at pH < pHPZNPC. The sorption was endothermic with an increase in randomness. The presence of FC had little effects on the thermodynamic parameters.

Interference of microplastics on autotrophic microbiome in paddy soils: Shifts in carbon fixation rate, structure, abundance, co-occurrence, and assembly process.

Autotrophic microorganisms play a crucial role in soil CO2 assimilation. Although microplastic pollution is recognized as a significant global concern, its precise impact on carbon sequestration by autotrophic microorganisms in agroecosystem soil remains poorly understood. This study conducted microcosm experiments to explore how conventional polystyrene (PS) and biodegradable poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) microplastics affect carbon fixation rates (CFRs) and the community characteristics of soil autotrophic microorganisms in paddy agroecosystems. The results showed that compared with the control groups, 0.5 % and 1 % microplastic treatments significantly reduced soil CFRs by 11.8 - 24.5 % and 18.7 - 32.3 %, respectively. PS microplastics exerted a stronger inhibition effect on CFRs than PHBV microplastics in bulk soil. However, no significant difference was observed in the inhibition of CFRs by both types of microplastics in rhizosphere soils. Additionally, PS and PHBV microplastics altered the structure of autotrophic microbial communities, resulting in more stochastically dominated assembly and looser, more fragile coexistence networks compared to control groups. Moreover, microplastics drove the changes in autotrophic microbial carbon fixation primarily through their direct interference and the indirect effect by increasing soil organic carbon levels. Our findings enhance the understanding and predictive capabilities regarding the impacts of microplastic pollution on carbon sinks in agricultural soils.

Radical Fluorosulfonamidation: A Facile Access to Sulfamoyl Fluorides.

Recently, the introduction of fluorosulfonyl (-SO2F) groups have attracted considerable research interests, as this moiety could often afford enhanced activities and new functions in the context of chemical biology and drug discovery. Herein, we report the design and synthesis of 1-fluorosulfamoyl-pyridinium (FSAP) salts, which could serve as an effective photoredox-active precursor to fluorosulfamoyl radicals and enable the direct radical C-H fluorosulfonamidation of a variety of (hetero)arenes. This method features mild conditions, visible light, broad substrate scope, good group tolerance, etc., and a metal-free protocol is also viable by using organic photocatalysts. Further, FSAP can also be applied to the radical functionalization of alkenes via 1,2-difunctionalization, radical distal migration, tandem radical-polar crossover reactions, etc. In addition, a formal C-H methylamination of (hetero)arenes by combining this radical C-H fluorosulfonamidation with subsequent hydrolysis as well as product derivatization are also demonstrated.

Nomogram built based on machine learning to predict recurrence in early-stage hepatocellular carcinoma patients treated with ablation.

Introduction: To analyze the risk factors affecting recurrence in early-stage hepatocellular carcinoma (HCC) patients treated with ablation and then establish a nomogram to provide a clear and accessible representation of the patients' recurrence risk. Methods: Collect demographic and clinical data of 898 early-stage HCC patients who underwent ablation treatment at Beijing You'an Hospital, affiliated with Capital Medical University from January 2014 to December 2022. Patients admitted from 2014 to 2018 were included in the training cohort, while 2019 to 2022 were in the validation cohort. Lasso and Cox regression was used to screen independent risk factors for HCC patients recurrence, and a nomogram was then constructed based on the screened factors. Results: Age, gender, Barcelona Clinic Liver Cancer (BCLC) stage, tumor size, globulin (Glob) and γ-glutamyl transpeptidase (γ-GT) were finally incorporated in the nomogram for predicting the recurrence-free survival (RFS) of patients. We further confirmed that the nomogram has optimal discrimination, consistency and clinical utility by the C-index, Receiver Operating Characteristic Curve (ROC), calibration curve and Decision Curve Analysis (DCA). Moreover, we divided the patients into different risk groups and found that the nomogram can effectively identify the high recurrence risk patients by the Kaplan-Meier curves. Conclusion: This study developed a nomogram using Lasso-Cox regression to predict RFS in early-stage HCC patients following ablation, aiding clinicians in identifying high-risk groups for personalized follow-up treatments.

Synthesis of Perdeuterated Alkyl Amines/Amides with Pt/C as Catalyst under Mild Conditions.

A convenient method for the synthesis of perdeuterated alkyl amides/amines is disclosed. Perdeuterated acetyl amides can be achieved by a hydrogen-deuterium (H/D) exchange protocol with Pt/C as a catalyst and D2O as a deuterium source under mild conditions. After removal or reduction of the acetyl group, this protocol can provide perdeuterated primary, secondary, and tertiary amines, which are difficult to achieve via other methods.

N-alkane shape distinctive microbial patterns in Kuroshio Extension.

Marine microorganisms are primary drivers of the elemental cycling. The interaction between heterotrophic prokaryotes and biomarker (n-alkane) in Kuroshio Extension (KE) remains unclear. Here, we categorize KE into three characteristic areas based on ocean temperatures and nutrient conditions: Cold Water Area (CWA), Mixed Area (MA), and Warm Water Area (WWA). A total of 49 samples were collected during two-year voyage to identify the source of n-alkane and associated degrading microorganisms. Total n-alkane concentrations (Σn-Alk) in surface water (SW) spanned from 1,308 ng L-1 to 1,890 ng L-1, it was significantly higher (Tukey-Kramer test, p < 0.05) in MA than CWA and WWA. The Σn-Alk in surface sediments (SS) gradually increased from north to south, ranging from 5,982 ng g-1 to 37,857 ng g-1. Bacteria and algae were the primary sources of n-alkane in both SW and SS. Proteobacteria was the most widely distributed among three areas. The presence of Rhodobacteraceae with alkB was the primary reason affecting n-alkane concentrations in SW. The Gammaproteobacteria with alkB and alkR chiefly affected n-alkane concentrations in SS. In summary, n-alkane s serve as an energy source for particular microorganisms, shaping the unique oceanographic patterns.

Mitochondrial Genomic Evidence of Selective Constraints in Small-Bodied Terrestrial Cetartiodactyla.

Body size may drive the molecular evolution of mitochondrial genes in response to changes in energy requirements across species of different sizes. In this study, we perform selection pressure analysis and phylogenetic independent contrasts (PIC) to investigate the association between molecular evolution of mitochondrial genome protein-coding genes (mtDNA PCGs) and body size in terrestrial Cetartiodactyla. Employing selection pressure analysis, we observe that the average non-synonymous/synonymous substitution rate ratio (ω) of mtDNA PCGs is significantly reduced in small-bodied species relative to their medium and large counterparts. PIC analysis further confirms that ω values are positively correlated with body size (R2 = 0.162, p = 0.0016). Our results suggest that mtDNA PCGs of small-bodied species experience much stronger purifying selection as they need to maintain a heightened metabolic rate. On the other hand, larger-bodied species may face less stringent selective pressures on their mtDNA PCGs, potentially due to reduced relative energy expenditure per unit mass. Furthermore, we identify several genes that undergo positive selection, possibly linked to species adaptation to specific environments. Therefore, despite purifying selection being the predominant force in the evolution of mtDNA PCGs, positive selection can also occur during the process of adaptive evolution.

Different Evolutionary Trends of Galloanseres: Mitogenomics Analysis.

The two existing clades of Galloanseres, orders Galliformes (landfowl) and Anseriformes (waterfowl), exhibit dramatically different evolutionary trends. Mitochondria serve as primary sites for energy production in organisms, and numerous studies have revealed their role in biological evolution and ecological adaptation. We assembled the complete mitogenome sequences of two species of the genus Aythya within Anseriformes: Aythya baeri and Aythya marila. A phylogenetic tree was constructed for 142 species within Galloanseres, and their divergence times were inferred. The divergence between Galliformes and Anseriformes occurred ~79.62 million years ago (Mya), followed by rapid evolution and diversification after the Middle Miocene (~13.82 Mya). The analysis of selective pressure indicated that the mitochondrial protein-coding genes (PCGs) of Galloanseres species have predominantly undergone purifying selection. The free-ratio model revealed that the evolutionary rates of COX1 and COX3 were lower than those of the other PCGs, whereas ND2 and ND6 had faster evolutionary rates. The CmC model also indicated that most PCGs in Anseriformes exhibited stronger selective constraints. Our study suggests that the distinct evolutionary trends and energy requirements of Galliformes and Anseriformes drive different evolutionary patterns in the mitogenome.

Tuning the magnetic properties of double transition-metal carbide CoMC (M = Ti, V, Cr, Mn, Fe, Ni) monolayers.

The intrinsic ferromagnetism of two-dimensional transition metal carbide Co2C is remarkable. However, its practical application in spintronic devices is encumbered by a low Curie temperature (TC). To surmount this constraint, double transition-metal carbide CoMC (M = Ti, V, Cr, Mn, Fe, Ni) monolayers are constructed with the aim of improving the magnetic properties and Curie temperature of Co2C. The magnetic properties of CoMC monolayers are comprehensively investigated by first-principles calculations and the effects of hole doping and biaxial strain on the magnetic properties of CoMC (M = V, Cr, Mn) monolayers are also studied. The ground states of CoTiC, CoMnC and CoNiC monolayers all favor ferromagnetic ordering, whereas the CoVC and CoCrC monolayers favor antiferromagnetic ordering and the CoFeC monolayer is non-magnetic. Excitedly, the CoMnC monolayer displays a high total magnetic moment of 4.024µB and a TC of 1366 K. Moreover, the control of hole doping can effectively improve the TC of CoVC, CoCrC, and CoMnC monolayers to 680, 1317, 3044 K, respectively. Finally, applying the in-plain biaxial strain, the CoVC monolayer can be transformed into a ferromagnetic semiconductor under a tensile strain of 6%. The TC values of CoVC, CoCrC, and CoMnC monolayers are tuned by biaxial strain to 440, 1334 and 2390 K, respectively. Their TC above room temperature demonstrates that these monolayers have potential applications in spintronic devices. These theoretical investigations provide valuable insights into guiding experimental synthesis endeavors.

Bacterial and fungal community structures in Hulun Lake are regulated by both stochastic processes and environmental factors.

Microorganisms are a crucial component of lake ecosystems and significant contributors to biogeochemical cycles. However, the understanding of how primary microorganism groups (e.g., bacteria and fungi) are distributed and constructed within different lake habitats is lacking. We investigated the bacterial and fungal communities of Hulun Lake using high-throughput sequencing techniques targeting 16S rRNA and Internal Transcribed Spacer 2 genes, including a range of ecological and statistical methodologies. Our findings reveal that environmental factors have high spatial and temporal variability. The composition and community structures vary significantly depending on differences in habitats. Variance partitioning analysis showed that environmental and geographical factors accounted for <20% of the community variation. Canonical correlation analysis showed that among the environmental factors, temperature, pH, and dissolved oxygen had strong control over microbial communities. However, the microbial communities (bacterial and fungal) were primarily controlled by the dispersal limitations of stochastic processes. This study offers fresh perspectives regarding the maintenance mechanism of bacterial and fungal biodiversity in lake ecosystems, especially regarding the responses of microbial communities under identical environmental stress.IMPORTANCELake ecosystems are an important part of the freshwater ecosystem. Lake microorganisms play an important role in material circulation and energy flow owing to their unique enzymatic and metabolic capacity. In this study, we observed that bacterial and fungal communities varied widely in the water and sediments of Hulun Lake. The primary factor affecting their formation was identified as dispersal limitation during stochastic processes. Environmental and geographical factors accounted for <20% of the variation in bacterial and fungal communities, with pH, temperature, and dissolved oxygen being important environmental factors. Our findings provide new insights into the responses of bacteria and fungi to the environment, shed light on the ecological processes of community building, and deepen our understanding of lake ecosystems. The results of this study provide a reference for lake management and conservation, particularly with respect to monitoring and understanding microbial communities in response to environmental changes.

METTL16 promotes liver cancer stem cell self-renewal via controlling ribosome biogenesis and mRNA translation.

BACKGROUND: While liver cancer stem cells (CSCs) play a crucial role in hepatocellular carcinoma (HCC) initiation, progression, recurrence, and treatment resistance, the mechanism underlying liver CSC self-renewal remains elusive. We aim to characterize the role of Methyltransferase 16 (METTL16), a recently identified RNA N6-methyladenosine (m6A) methyltransferase, in HCC development/maintenance, CSC stemness, as well as normal hepatogenesis. METHODS: Liver-specific Mettl16 conditional KO (cKO) mice were generated to assess its role in HCC pathogenesis and normal hepatogenesis. Hydrodynamic tail-vein injection (HDTVi)-induced de novo hepatocarcinogenesis and xenograft models were utilized to determine the role of METTL16 in HCC initiation and progression. A limiting dilution assay was utilized to evaluate CSC frequency. Functionally essential targets were revealed via integrative analysis of multi-omics data, including RNA-seq, RNA immunoprecipitation (RIP)-seq, and ribosome profiling. RESULTS: METTL16 is highly expressed in liver CSCs and its depletion dramatically decreased CSC frequency in vitro and in vivo. Mettl16 KO significantly attenuated HCC initiation and progression, yet only slightly influenced normal hepatogenesis. Mechanistic studies, including high-throughput sequencing, unveiled METTL16 as a key regulator of ribosomal RNA (rRNA) maturation and mRNA translation and identified eukaryotic translation initiation factor 3 subunit a (eIF3a) transcript as a bona-fide target of METTL16 in HCC. In addition, the functionally essential regions of METTL16 were revealed by CRISPR gene tiling scan, which will pave the way for the development of potential inhibitor(s). CONCLUSIONS: Our findings highlight the crucial oncogenic role of METTL16 in promoting HCC pathogenesis and enhancing liver CSC self-renewal through augmenting mRNA translation efficiency.

A novel nomogram to predict the overall survival of early-stage hepatocellular carcinoma patients following ablation therapy.

Introduction: This study aimed to assess factors affecting the prognosis of early-stage hepatocellular carcinoma (HCC) patients undergoing ablation therapy and create a nomogram for predicting their 3-, 5-, and 8-year overall survival (OS). Methods: The research included 881 early-stage HCC patients treated at Beijing You'an Hospital, affiliated with Capital Medical University, from 2014 to 2022. A nomogram was developed using independent prognostic factors identified by Lasso and multivariate Cox regression analyses. Its predictive performance was evaluated with concordance index (C-index), receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA). Results: The study identified age, tumor number, tumor size, gamma-glutamyl transpeptidase (GGT), international normalized ratio (INR), and prealbumin (Palb) as independent prognostic risk factors. The nomogram achieved C-indices of 0.683 (primary cohort) and 0.652 (validation cohort), with Area Under the Curve (AUC) values of 0.776, 0.779, and 0.822 (3-year, 5-year, and 8-year OS, primary cohort) and 0.658, 0.724, and 0.792 (validation cohort), indicating that the nomogram possessed strong discriminative ability. Calibration and DCA curves further confirmed the nomogram's predictive accuracy and clinical utility. The nomogram can effectively stratify patients into low-, intermediate-, and high-risk groups, particularly identifying high-risk patients. Conclusions: The established nomogram in our study can provide precise prognostic information for HCC patients following ablation treatment and enable physicians to accurately identify high-risk individuals and facilitate timely intervention.

Revealing the Marine Cycles of Volatile Sulfur Compounds and Their Biogeochemical Controls: A Case of the Western North Pacific.

Volatile sulfur compounds, such as dimethyl sulfide (DMS), carbonyl sulfide (OCS), and carbon disulfide (CS2), have significant implications for both atmospheric chemistry and climate change. Despite the crucial role of oceans in regulating their atmospheric budgets, our comprehension of their cycles in seawater remains insufficient. To address this gap, a field investigation was conducted in the western North Pacific to clarify the sources, sinks, and biogeochemical controls of these gases in two different marine environments, including relatively eutrophic Kuroshio-Oyashio extension (KOE) and oligotrophic North Pacific subtropical gyre. Our findings revealed higher concentrations of these gases in both seawater and the atmosphere in the KOE compared to the subtropical gyre. In the KOE, nutrient-rich upwelling stimulated rapid DMS biological production, while reduced seawater temperatures hindered the removal of OCS and CS2, leading to their accumulation. Furthermore, we have quantitatively evaluated the relative contribution of each pathway to the source and sink of DMS, OCS, and CS2 within the mixed layer and identified vertical exchange as a potential sink in most cases, transporting substantial amounts of these gases from the mixed layer to deeper waters. This research advances our understanding of sulfur gas source-sink dynamics in seawater, contributing to the assessment of their marine emissions and atmospheric budgets.

Development and Validation of a Prognostic Nomogram for Patients with AFP and DCP Double-Negative Hepatocellular Carcinoma After Local Ablation.

Purpose: Although alpha-fetoprotein (AFP) and des-gamma-carboxyprothrombin (DCP) have a certain predictive ability for the prognosis of hepatocellular carcinoma (HCC), there are still some cases of aggressive recurrence among patients with AFP and DCP double-negative HCC (DNHC) after local ablation. However, prediction models to forecast the prognosis of DNHC patients are still lacking. Thus, this retrospective study aims to explore the prognostic factors in DNHC patients and develop a nomogram to predict recurrence. Patients and methods: 493 DNHC patients who underwent the local ablation at Beijing You'an Hospital between January 1, 2014, and December 31, 2022, were enrolled. A part that was admitted from January 1, 2014, to December 31, 2018, was designated to the training cohort (n = 307); others from January 1, 2019, to December 31, 2022, were allocated to the validation cohort (n = 186). Lasso regression and Cox regression were employed with the aim of screening risk factors and developing the nomogram. The nomogram outcome was assessed by discrimination, calibration, and decision curve analysis (DCA). Results: Independent prognostic factors selected by Lasso-Cox analysis included age, tumor size, tumor number, and gamma-glutamyl transferase. The area under the receiver operating characteristic (ROC) curves (AUCs) of the training and validation groups (0.738, 0.742, 0.836, and 0.758, 0.821) exhibited the excellent predicted outcome of the nomogram. Calibration plots and DCA plots suggest desirable calibration performance and clinical utility. Patients were stratified into three risk groups by means of the nomogram: low-risk, intermediate-risk, and high-risk, respectively. There exists an obvious distinction in recurrence-free survival (RFS) among three groups (p<0.0001). Conclusion: In conclusion, we established and validated a nomogram for DNHC patients who received local ablation. The nomogram showed excellent predictive power for the recurrence of HCC and could contribute to guiding clinical decisions.

A Technique to Enable Efficient Adaptive Radiation Therapy: Automated Contouring of Prostate and Adjacent Organs.

Purpose: The purpose of this work was to investigate the use of a segmentation approach that could potentially improve the speed and reproducibility of contouring during magnetic resonance-guided adaptive radiation therapy. Methods and Materials: The segmentation algorithm was based on a hybrid deep neural network and graph optimization approach that also allows rapid user intervention (Deep layered optimal graph image segmentation of multiple objects and surfaces [LOGISMOS] + just enough interaction [JEI]). A total of 115 magnetic resonance-data sets were used for training and quantitative assessment. Expert segmentations were used as the independent standard for the prostate, seminal vesicles, bladder, rectum, and femoral heads for all 115 data sets. In addition, 3 independent radiation oncologists contoured the prostate, seminal vesicles, and rectum for a subset of patients such that the interobserver variability could be quantified. Consensus contours were then generated from these independent contours using a simultaneous truth and performance level estimation approach, and the deviation of Deep LOGISMOS + JEI contours to the consensus contours was evaluated and compared with the interobserver variability. Results: The absolute accuracy of Deep LOGISMOS + JEI generated contours was evaluated using median absolute surface-to-surface distance which ranged from a minimum of 0.20 mm for the bladder to a maximum of 0.93 mm for the prostate compared with the independent standard across all data sets. The median relative surface-to-surface distance was less than 0.17 mm for all organs, indicating that the Deep LOGISMOS + JEI algorithm did not exhibit a systematic under- or oversegmentation. Interobserver variability testing yielded a mean absolute surface-to-surface distance of 0.93, 1.04, and 0.81 mm for the prostate, seminal vesicles, and rectum, respectively. In comparison, the deviation of Deep LOGISMOS + JEI from consensus simultaneous truth and performance level estimation contours was 0.57, 0.64, and 0.55 mm for the same organs. On average, the Deep LOGISMOS algorithm took less than 26 seconds for contour segmentation. Conclusions: Deep LOGISMOS + JEI segmentation efficiently generated clinically acceptable prostate and normal tissue contours, potentially limiting the need for time intensive manual contouring with each fraction.

An Automatic Needle Puncture Path-Planning Method for Thermal Ablation of Lung Tumors.

Computed tomography (CT)-guided thermal ablation is an emerging treatment method for lung tumors. Ablation needle path planning in preoperative diagnosis is of critical importance. In this work, we proposed an automatic needle path-planning method for thermal lung tumor ablation. First, based on the improved cube mapping algorithm, binary classification was performed on the surface of the bounding box of the patient's CT image to obtain a feasible puncture area that satisfied all hard constraints. Then, for different clinical soft constraint conditions, corresponding grayscale constraint maps were generated, respectively, and the multi-objective optimization problem was solved by combining Pareto optimization and weighted product algorithms. Finally, several optimal puncture paths were planned within the feasible puncture area obtained for the clinicians to choose. The proposed method was evaluated with 18 tumors of varying sizes (482.79 mm3 to 9313.81 mm3) and the automatically planned paths were compared and evaluated with manually planned puncture paths by two clinicians. The results showed that over 82% of the paths (74 of 90) were considered reasonable, with clinician A finding the automated planning path superior in 7 of 18 cases, and clinician B in 9 cases. Additionally, the time efficiency of the algorithm (35 s) was much higher than that of manual planning. The proposed method is expected to aid clinicians in preoperative path planning for thermal ablation of lung tumors. By providing a valuable reference for the puncture path during preoperative diagnosis, it may reduce the clinicians' workload and enhance the objectivity and rationality of the planning process, which in turn improves the effectiveness of treatment.

Prognostic significance of globulin/low-density lipoprotein ratio in patients with hepatocellular carcinoma after local ablative therapy: a retrospective cohort study.

Background: Low-density lipoprotein (LDL) and globulin (GLOB) have been found to be predictors for some malignant tumors, but their predictive value in hepatocellular carcinoma (HCC) has hardly been elucidated. This study assessed the prognostic significance of GLOB to LDL ratio (GLR) in HCC patients before ablation. Methods: This study analyzed 312 early-stage HCC patients who were hospitalized and underwent ablative treatment in Beijing You'an Hospital, Capital Medical University, from 1 January 2014 to 1 January 2019. The primary endpoint was the recurrence-free survival (RFS), calculated from treatment initiation to cancer recurrence, whereas the overall survival (OS) was measured from treatment initiation to death or last follow-up. Cox regression analysis was used to assess the GLR independently associated with recurrence and survival. OS and RFS were calculated by Kaplan-Meier analysis and compared between groups using the log rank test. The optimal cut-off value and prognostic role of GLR and other markers were evaluated via the receiver operating characteristic (ROC) curves and the Youden index. Results: Univariate and multivariate analysis found that the tumor number, tumor size, and GLR were independent risk factors of relapse, whereas etiology, tumor number, tumor size, fibrinogen (Fib), and GLR were related to OS. We classified the patients into groups with high and low levels of GLR based on the optimal cut-off value of GLR identified by ROC curve. The cumulative 1-, 3-, and 5-year RFS rates in the low GLR group were 76.4%, 53.8%, and 43.4% respectively, whereas those in the high GLR group were 71%, 31%, and 22%, respectively (P<0.001). In terms of OS, the low GLR group showed a 1-, 3-, and 5-year OS of 99.5%, 92.0%, and 80.2% respectively, and 98%, 73%, and 63% respectively for the high GLR group (P<0.001). Finally, patients were stratified by GLR and tumor size. The outcomes revealed that patients in group A (GLR <16.54 and tumor size ≤30 mm) showed better prognosis than those in group B (GLR ≥16.54 and tumor size ≤30 mm or GLR <16.54 and tumor size >30 mm) and group C (GLR ≥16.54 and tumor size >30 mm) (P<0.001). Conclusions: Preoperative GLR ratio has predictive value for patients with HCC who have undergone complete ablation.