NRDE2 deficiency impairs homologous recombination repair and sensitizes hepatocellular carcinoma to PARP inhibitors.

To identify novel susceptibility genes for hepatocellular carcinoma (HCC), we performed a rare-variant association study in Chinese populations consisting of 2,750 cases and 4,153 controls. We identified four HCC-associated genes, including NRDE2, RANBP17, RTEL1, and STEAP3. Using NRDE2 (index rs199890497 [p.N377I], p = 1.19 × 10-9) as an exemplary candidate, we demonstrated that it promotes homologous recombination (HR) repair and suppresses HCC. Mechanistically, NRDE2 binds to the subunits of casein kinase 2 (CK2) and facilitates the assembly and activity of the CK2 holoenzyme. This NRDE2-mediated enhancement of CK2 activity increases the phosphorylation of MDC1 and then facilitates the HR repair. These functions are eliminated almost completely by the NRDE2-p.N377I variant, which sensitizes the HCC cells to poly(ADP-ribose) polymerase (PARP) inhibitors, especially when combined with chemotherapy. Collectively, our findings highlight the relevance of the rare variants to genetic susceptibility to HCC, which would be helpful for the precise treatment of this malignancy.

Geochemical behavior of rare earth elements in agricultural soils along the Syr Darya River within the Aral Sea Basin.

The widespread use of rare earth elements (REEs) across various industries makes them a new type of pollutant. Additionally, REEs are powerful indicators of geochemical processes. As one of the two main rivers in the Aral Sea, identifying the geochemical behavior of REEs in agricultural soils of the Syr Darya River is of great significance for subsequent indicative studies. In this study, the geochemical characteristics, influencing factors, and potential application significance of REEs in agricultural soils from three sampling areas along the Syr Darya River were analyzed using soil geography and elemental geochemical analyses. The results showed that the highest total concentration of REEs in the agricultural soil was in Area I, with a mean value of 142.49 µg/g, followed by Area III with a mean value of 124.56 µg/g, and the lowest concentration was in Area II with a mean value of 122.48 µg/g. The agricultural soils in the three regions were enriched in light rare earth elements (LREEs), with mean L/H values of 10.54, 10.13, and 10.24, respectively. The differentiation between light and heavy rare earth elements (HREEs) was also high. The concentration of REEs in agricultural soil along the Syr Darya River was primarily influenced by minerals such as monazite and zircon, rather than human activities (the pollution index of all REEs was less than 1.5). The relationship between Sm and Gd can differentiate soils impacted by agricultural activities from natural background soils. The results of this study can serve as a basis for indicative studies of REEs in Central Asia.

Cu exposure induces liver inflammation via regulating gut microbiota/LPS/liver TLR4 signaling axis.

Copper (Cu) serves as an essential cofactor in all organisms, yet excessive Cu exposure is widely recognized for its role in inducing liver inflammation. However, the precise mechanism by which Cu triggers liver inflammation in ducks, particularly in relation to the interplay in gut microbiota regulation, has remained elusive. In this investigation, we sought to elucidate the impact of Cu exposure on liver inflammation through gut-liver axis in ducks. Our findings revealed that Cu exposure markedly elevated liver AST and ALT levels and induced liver inflammation through upregulating pro-inflammatory cytokines (IL-1ß, IL-6 and TNF-α) and triggering the LPS/TLR4/NF-κB signaling pathway. Simultaneously, Cu exposure induced alterations in the composition of intestinal flora communities, notably increasing the relative abundance of Sphingobacterium, Campylobacter, Acinetobacter and reducing the relative abundance of Lactobacillus. Cu exposure significantly decreased the protein expression related to intestinal barrier (Occludin, Claudin-1 and ZO-1) and promoted the secretion of intestinal pro-inflammatory cytokines. Furthermore, correlation analysis was observed that intestinal microbiome and gut barrier induced by Cu were closely related to liver inflammation. Fecal microbiota transplantation (FMT) experiments further demonstrated the microbiota-depleted ducks transplanting fecal samples from Cu-exposed ducks disturbed the intestinal dysfunction, which lead to impaire liver function and activate the liver inflammation. Our study provided insights into the mechanism by which Cu exposure induced liver inflammation in ducks through the regulation of gut-liver axis. These results enhanced our comprehension of the potential mechanisms driving Cu-induced hepatotoxicity in avian species.

Stability and reactivity of alkylidene dihydropyridines.

Alkylidene dihydropyridines (ADHPs) are electron-rich nucleophilic intermediates that can be readily prepared by dearomatization of 4-alkylpyridines using chloroformate reagents and mild base. Their stability and reactivity can be tuned with the chloroformate reagent used as evidenced by NMR chemical shifts and oxidation potentials. ADHPs prepared with ethyl, allyl and trichloroethyl chloroformate undergo decomposition under an oxygen atmosphere at different rates (ethyl > allyl > trichloroethyl), predominantly to the corresponding 4-acylpyridine. The ADHPs derived from benzyl chloroformate are stable towards oxidation, and those derived from phenyl chloroformate hydrolyze readily.

Cloning and functional verification of the CmHSP17.9 gene from chrysanthemum.

Small molecular heat shock proteins (sHSPs) belong to the HSP family of molecular chaperones. Under high-temperature stress, they can prevent the aggregation of irreversible proteins and maintain the folding of denatured proteins to enhance heat resistance. In this study, the CmHSP17.9-1 and CmHSP17.9-2 genes, which were cloned from chrysanthemum (Chrysanthemum×morifolium 'Jinba') by homologous cloning, had a complete open reading frame of 480 bp each, encoding 159 amino acids. The protein subcellular localization analysis showed that CmHSP17.9-1 and CmHSP17.9-2 were located in the cytoplasm and mostly aggregated in granules, especially around the nucleus. Real-time quantitative PCR (qRT-PCR) analysis showed that the relative expression level of the CmHSP17.9-1 and CmHSP17.9-2 genes was highest in the terminal buds of the chrysanthemum, followed by the leaves. CmHSP17.9-1 and CmHSP17.9-2 overex-pression vectors were constructed and used to transform the chrysanthemum; overexpression of these genes led to the chrysanthemum phenotypes being less affected by high-temperature, and the antioxidant capacity was enhanced. The results showed that chrysanthemum with overex-pression of the CmHSP17.9-1 and CmHSP17.9-2 genes had stronger tolerance than the wild type chrysanthemum after high-temperature treatment or some degree of heat exercise, and overex-pression of the CmHSP17.9-1 gene led to stronger heat resistance than that of the CmHSP17.9-2 gene, providing an important theoretical basis for the subsequent molecular breeding and pro-duction applications of chrysanthemum.

Construction of a predictive model for acute liver failure after hepatectomy based on neutrophil-to-lymphocyte ratio and albumin-bilirubin score.

BACKGROUND: Acute liver failure (ALF) is a common cause of postoperative death in patients with hepatocellular carcinoma (HCC) and is a serious threat to patient safety. The neutrophil-to-lymphocyte ratio (NLR) is a common inflammatory indicator that is associated with the prognosis of various diseases, and the albumin-bilirubin score (ALBI) is used to evaluate liver function in liver cancer patients. Therefore, this study aimed to construct a predictive model for postoperative ALF in HCC tumor integrity resection (R0) based on the NLR and ALBI, providing a basis for clinicians to choose appropriate treatment plans. AIM: To construct an ALF prediction model after R0 surgery for HCC based on NLR and ALBI. METHODS: In total, 194 patients with HCC who visited The First People's Hospital of Lianyungang to receive R0 between May 2018 and May 2023 were enrolled and divided into the ALF and non-ALF groups. We compared differences in the NLR and ALBI between the two groups. The risk factors of ALF after R0 surgery for HCC were screened in the univariate analysis. Independent risk factors were analyzed by multifactorial logistic regression. We then constructed a prediction model of ALF after R0 surgery for HCC. A receiver operating characteristic curve, calibration curve, and decision curve analysis (DCA) were used to evaluate the value of the prediction model. RESULTS: Among 194 patients with HCC who met the standard inclusion criteria, 46 cases of ALF occurred after R0 (23.71%). There were significant differences in the NLR and ALBI between the two groups (P < 0.05). The univariate analysis showed that alpha-fetoprotein (AFP) and blood loss volume (BLV) were significantly higher in the ALF group compared with the non-ALF group (P < 0.05). The multifactorial analysis showed that NLR, ALBI, AFP, and BLV were independent risk factors for ALF after R0 surgery in HCC. The predictive efficacy of NLR, ALBI, AFP, and BLV in predicting the occurrence of ALT after R0 surgery for HCC was average [area under the curve (AUC)NLR = 0.767, AUCALBI = 0.755, AUCAFP = 0.599, AUCBLV = 0.718]. The prediction model for ALF after R0 surgery for HCC based on NLR and ALBI had a better predictive efficacy (AUC = 0.916). The calibration curve and actual curve were in good agreement. DCA showed a high net gain and that the model was safer compared to the curve in the extreme case over a wide range of thresholds. CONCLUSION: The prediction model based on NLR and ALBI can effectively predict the risk of developing ALF after HCC R0 surgery, providing a basis for clinical prevention of developing ALF after HCC R0 surgery.

Approaching the Ideal Linearity in Epitaxial Crystalline-Type Memristor by Controlling Filament Growth.

Brain-inspired neuromorphic computing has attracted widespread attention owing to its ability to perform parallel and energy-efficient computation. However, the synaptic weight of amorphous/polycrystalline oxide based memristor usually exhibits large nonlinear behavior with high asymmetry, which aggravates the complexity of peripheral circuit system. Controllable growth of conductive filaments is highly demanded for achieving the highly linear conductance modulation. However, the stochastic behavior of the filament growth in commonly used amorphous/polycrystalline oxide memristor makes it very challenging. Here, the epitaxially grown Hf0.5Zr0.5O2-based memristor with the linearity and symmetry approaching ideal case is reported. A layer of Cu nanoparticles is inserted into epitaxially grown Hf0.5Zr0.5O2 film to form the grain boundaries due to the breaking of the epitaxial growth. By combining with the local electric field enhancement, the growth of filament is confined in the grain boundaries due to the fact that the diffusion of oxygen vacancy in crystalline lattice is more difficult than that in the grain boundaries. Furthermore, the decimal operation and high-accuracy neural network are demonstrated by utilizing the highly linear and multi-level conductance modulation capacity. This method opens an avenue to control the filament growth for the application of resistance random access memory (RRAM) and neuromorphic computing.

NLRP3 inflammasome activation triggers severe inflammatory liver injury in N, N-dimethylformamide-exposed mice.

N,N-dimethylformamide (DMF) is a widely utilized chemical solvent with various industrial applications. Previous studies have indicated that the liver is the most susceptible target to DMF exposure, whereas the underlying mechanisms remain to be elucidated. This study aimed to investigate the role of NLRP3 inflammasome in DMF-induced liver injury in mice by using two NLRP3 inflammasome inhibitors, Nlrp3-/- mice, Nfe2l2-/- mice, and a macrophage-depleting agent. RNA sequencing revealed that endoplasmic reticulum (ER) stress and NLRP3 inflammasome-associated pathways were activated in the mouse liver after acute DMF exposure, which was validated by Western blotting. Interestingly, DMF-induced liver injury was effectively suppressed by two inflammasome inhibitors, MCC950 and Dapansutrile. In addition, knockout of Nlrp3 markedly attenuated DMF-induced liver injury without affecting the metabolism of DMF. Furthermore, silencing Nfe2l2 aggravated the liver injury and the NLRP3 inflammasome activation in mouse liver. Finally, the depletion of hepatic macrophages by clodronate liposomes significantly reduced the liver damage caused by DMF. These results suggest that NLRP3 inflammasome activation is the upstream molecular event in the development of acute liver injury induced by DMF.

Integrated transcriptome and microbiome analyses of residual feed intake in ducks during high production period.

Residual feed intake (RFI) is a crucial parameter for assessing the feeding efficiency of poultry. Minimizing RFI can enhance feed utilization and reduce costs. In this study, 315 healthy female ducks were individually housed in cages. Growth performance was monitored during the high laying period, from 290 to 325 d of age. The cecal transcriptome and microbiome of 12 ducks with high RFI and 12 with low residual feed intake (LRFI) were analyzed. Regarding growth performance, the LRFI group exhibited significantly lower RFI, feed conversion ratio (FCR), and feed intake (Fi) compared to the HRFI group (p < 0.01). However, there were no significant differences observed in body weight (BW), body weight gain (BWG), and egg mass (EML) between the groups (p > 0.05). Microbiome analysis demonstrated that RFI impacted gut microbial abundance, particularly affecting metabolism and disease-related microorganisms such as Romboutsia, Enterococcus, and Megamonas funiformis. Transcriptome analysis revealed that varying RFI changed the expression of genes related to glucose metabolism and lipid metabolism, including APOA1, G6PC1, PCK1, and PLIN1. The integrated analysis indicated that host genes were closely linked to the microbiota and primarily function in lipid metabolism, which may enhance feeding efficiency by influencing metabolism and maintaining gut homeostasis.

PPARß/δ as a promising molecular drug target for liver diseases: A focused review.

Various liver diseases pose great threats to humans. Although the etiologies of these liver diseases are quite diverse, they share similar pathologic phenotypes and molecular mechanisms such as oxidative stress, lipid and glucose metabolism disturbance, hepatic Kupffer cell (KC) proinflammatory polarization and inflammation, insulin resistance, and hepatic stellate cell (HSC) activation and proliferation. Peroxisome proliferator-activated receptor ß/δ (PPARß/δ) is expressed in various types of liver cells with relatively higher expression in KCs and HSCs. Accumulating evidence has revealed the versatile functions of PPARß/δ such as controlling lipid homeostasis, inhibiting inflammation, regulating glucose metabolism, and restoring insulin sensitivity, suggesting that PPARß/δ may serve as a potential molecular drug target for various liver diseases. This article aims to provide a concise review of the structure, expression pattern and biological functions of PPARß/δ in the liver and its roles in various liver diseases, and to discuss potential future research perspectives.

Effective Activation of Peroxymonosulfate by Oxygen Vacancy Induced Musa Basjoo Biochar to Degrade Sulfamethoxazole: Efficiency and Mechanism.

Biochar materials have garnered attention as potential catalysts for peroxymonosulfate (PMS) activation due to their cost-effectiveness, notable specific surface area, and advantageous structural properties. In this study, a suite of plantain-derived biochar (MBB-400, MBB-600, and MBB-800), possessing a well-defined pore structure and a substantial number of uniformly distributed active sites (oxygen vacancy, OVs), was synthesized through a facile calcination process at varying temperatures (400, 600, and 800 °C). These materials were designed for the activation of PMS in the degradation of sulfamethoxazole (SMX). Experimental investigations revealed that OVs not only functioned as enriched sites for pollutants, enhancing the opportunities for free radicals (•OH/SO4•-) and surface-bound radicals (SBRs) to attack pollutants, but also served as channels for intramolecular charge transfer leaps. This role contributed to a reduction in interfacial charge transfer resistance, expediting electron transfer rates with PMS, thereby accelerating the decomposition of pollutants. Capitalizing on these merits, the MBB-800/PMS system displayed a 61-fold enhancement in the conversion rate for SMX degradation compared to inactivated MBB/PMS system. Furthermore, the MBB-800 exhibited less cytotoxicity towards rat pheochromocytoma (PC12) cells. Hence, the straightforward calcination synthesis of MBB-800 emerges as a promising biochar catalyst with vast potential for sustainable and efficient wastewater treatment and environmental remediation.

Effects of coated sodium butyrate on the growth performance, serum biochemistry, antioxidant capacity, intestinal morphology, and intestinal microbiota of broiler chickens.

Introduction: This study examined the impact of adding coated sodium butyrate (CSB) to the diet on the growth performance, serum biochemistry, antioxidant capacity, intestinal morphology, and cecal microbiota of yellow-feathered broiler chickens. Methods: In this study, 240 yellow-feathered broiler chickens at 26 days old were divided into two groups: the control group (CON group) received a standard diet, and the experimental group (CSB group) received a diet with 0.5 g/kg of a supplement called CSB. Each group had 6 replicates, with 20 chickens in each replicate, and the experiment lasted for 36 days. Results: Compared to the CON group, the CSB group showed a slight but insignificant increase in average daily weight gain during the 26-62 day period, while feed intake significantly decreased. The CSB group exhibited significant increases in serum superoxide dismutase, catalase, and total antioxidant capacity. Additionally, the CSB group had significant increases in total protein and albumin content, as well as a significant decrease in blood ammonia levels. Compared to the CON group, the CSB group had significantly increased small intestine villus height and significantly decreased jejunal crypt depth. The abundance of Bacteroidetes and Bacteroides in the cecal microbiota of the CSB group was significantly higher than that of the CON group, while the abundance of Proteobacteria, Deferribacteres, and Epsilonbacteraeota was significantly lower than that of the CON group. Conclusion: These results suggest that adding CSB to the diet can improve the growth performance and antioxidant capacity of yellow-feathered broiler chickens while maintaining intestinal health.

New evidence for gut-muscle axis: Lactic acid bacteria-induced gut microbiota regulates duck meat flavor.

The interaction between gut microbiota and muscles through the gut-muscle axis has received increasing attention. This study attempted to address existing research gaps by investigating the effects of gut microbiota on meat flavor. Specifically, lactic acid bacteria were administered to ducks, and the results of e-nose and e-tongue showed significantly enhanced meat flavor in the treatment group. Further analyses using GC-MS revealed an increase in 6 characteristic volatile flavor compounds, including pentanal, hexanal, heptanal, 1-octen-3-ol, 2,3-octanedione, and 2-pentylfuran. Linoleic acid was identified as the key fatty acid that influences meat flavor. Metagenomic and transcriptomic results further confirmed that cecal microbiota affects the duck meat flavor by regulating the metabolic pathways of fatty acids and amino acids, especially ACACB was related to fatty acid biosynthesis and ACAT2, ALDH1A1 with fatty acid degradation. This study sheds light on a novel approach to improving the flavor of animal-derived food.

Develop a radiomics-based machine learning model to predict the stone-free rate post-percutaneous nephrolithotomy.

Radiomics and machine learning have been extensively utilized in the realm of urinary stones, particularly in forecasting stone treatment outcomes. The objective of this study was to integrate clinical variables and radiomic features to develop a machine learning model for predicting the stone-free rate (SFR) following percutaneous nephrolithotomy (PCNL). A total of 212 eligible patients who underwent PCNL surgery at the Second Affiliated Hospital of Nanchang University were included in a retrospective analysis. Preoperative clinical variables and non-contrast-enhanced CT images of all patients were collected, and radiomic features were extracted after delineating the stone ROI. Univariate analysis was conducted to identify clinical variables strongly correlated with the stone-free rate after PCNL, and the least absolute shrinkage and selection operator algorithm (lasso regression) was utilized to screen radiomic features. Four supervised machine learning algorithms, including Logistic Regression, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Gradient Boosting Decision Tree (GBDT), were employed. The clinical variables with strong correlation and screened radiomic features were integrated into the four machine learning algorithms to construct a prediction model, and the receiver operating curve was plotted. The area under the receiver operating curve (AUC), the accuracy rate, the specificity, etc., were used to evaluate the predictive performance of the four models. After analyzing postoperative statistics, the stone-free rate following the procedure was found to be 70.3% (n = 149). Among the various clinical variables examined, factors, such as stone number, stone diameter, stone CT value, stone location, and history of stone surgery, were identified as statistically significant in relation to the stone-free rate after PCNL. A total of 121 radiomic features were extracted, and through lasso regression, 7 features most closely associated with the stone-free rate post-PCNL were identified. The predictive accuracy of different models (Logistic Regression, RF, XGBoost, and GBDT) for determining the stone-free rate after PCNL was evaluated, yielding accuracies of 78.1%, 76.6%, 75.0%, and 73.4%, respectively. The corresponding area under the curve AUC (95%CI) were 0.85 (0.83-0.89), 0.81 (0.76-0.85), 0.82 (0.78-0.85), and 0.77 (0.73-0.81), positioning these models among the top performers in logistic regression prediction. In terms of predictive importance scores, the key factors identified by the logistic regression model were number of stone, zone percentage, stone diameter, and surface area. Similarly, the RF model highlighted number of stone, stone CT value, stone diameter, and surface area as the top predictors. Among the four machine learning models, the logistic regression model demonstrated the highest accuracy and discrimination ability in predicting the stone-free rate following PCNL. In comparison to XGBoost and GBDT, RF also exhibited superior accuracy and a certain level of discrimination ability. However, based on the performance of all four models, logistic regression is more likely to aid in clinical decision-making by assisting clinicians in diagnosing PCNL in patients. This enables us to effectively predict the presence of residual stones post-surgery and ultimately select patients who are suitable candidates for PCNL.

Systematic review and network meta-analysis of different non-steroidal anti-inflammatory drugs for juvenile idiopathic arthritis.

BACKGROUND: Various non-steroidal anti-inflammatory drugs (NSAIDs) have been used for juvenile idiopathic arthritis (JIA). However, the optimal method for JIA has not yet been developed. AIM: To perform a systematic review and network meta-analysis to determine the optimal instructions. METHODS: We searched for randomized controlled trials (RCTs) from PubMed, EMBASE, Google Scholar, CNKI, and Wanfang without restriction for publication date or language at August, 2023. Any RCTs that comparing the effectiveness of NSAIDs with each other or placebo for JIA were included in this network meta-analysis. The surface under the cumulative ranking curve (SUCRA) analysis was used to rank the treatments. P value less than 0.05 was identified as statistically significant. RESULTS: We included 8 RCTs (1127 patients) comparing 8 different instructions including meloxicam (0.125 qd and 0.250 qd), Celecoxib (3 mg/kg bid and 6 mg/kg bid), piroxicam, Naproxen (5.0 mg/kg/d, 7.5 mg/kg/d and 12.5 mg/kg/d), inuprofen (30-40 mg/kg/d), Aspirin (60-80 mg/kg/d, 75 mg/kg/d, and 55 mg/kg/d), Tolmetin (15 mg/kg/d), Rofecoxib, and placebo. There were no significant differences between any two NSAIDs regarding ACR Pedi 30 response. The SUCRA shows that celecoxib (6 mg/kg bid) ranked first (SUCRA, 88.9%), rofecoxib ranked second (SUCRA, 68.1%), Celecoxib (3 mg/kg bid) ranked third (SUCRA, 51.0%). There were no significant differences between any two NSAIDs regarding adverse events. The SUCRA shows that placebo ranked first (SUCRA, 88.2%), piroxicam ranked second (SUCRA, 60.5%), rofecoxib (0.6 mg/kg qd) ranked third (SUCRA, 56.1%), meloxicam (0.125 mg/kg qd) ranked fourth (SUCRA, 56.1%), and rofecoxib (0.3 mg/kg qd) ranked fifth (SUCRA, 56.1%). CONCLUSION: In summary, celecoxib (6 mg/kg bid) was found to be the most effective NSAID for treating JIA. Rofecoxib, piroxicam, and meloxicam may be safer options, but further research is needed to confirm these findings in larger trials with higher quality studies.

NIMO: A Natural Product-Inspired Molecular Generative Model Based on Conditional Transformer.

Natural products (NPs) have diverse biological activity and significant medicinal value. The structural diversity of NPs is the mainstay of drug discovery. Expanding the chemical space of NPs is an urgent need. Inspired by the concept of fragment-assembled pseudo-natural products, we developed a computational tool called NIMO, which is based on the transformer neural network model. NIMO employs two tailor-made motif extraction methods to map a molecular graph into a semantic motif sequence. All these generated motif sequences are used to train our molecular generative models. Various NIMO models were trained under different task scenarios by recognizing syntactic patterns and structure-property relationships. We further explored the performance of NIMO in structure-guided, activity-oriented, and pocket-based molecule generation tasks. Our results show that NIMO had excellent performance for molecule generation from scratch and structure optimization from a scaffold.

Adiabat-to-Diabat Angle in Seam Space: Renner-Teller-Type and Pseudo-Jahn-Teller-Type Problems.

In this study, we examine the adiabat-to-diabat (ATD) angles for trajectories in 2-dimensional vibrational subspace of the seam space of two degenerate states. In circulating around the tangential touching degeneracy center, the ATD angle is changed by 2 π ${2\pi }$ or 0, similar to the Renner-Teller problem and the pseudo-Jahn-Teller problem, respectively. These ATD angle profiles may be indistinguishable from those of circulating multiple conical intersections or a pseudo-Jahn-Teller center. Methods to discern those seemingly indistinguishable cases are proposed. A sharp zigzag variation of the ATD angle is seen as a feature for trajectories that graze a pseudo-Jahn-Teller-type tangential touching center, in contrast to the monotonic steep variation for grazing a conical intersection or a Renner-Teller-type tangential touching center.

Multi-modal molecular determinants of clinically relevant osteoporosis subtypes.

Due to a rapidly aging global population, osteoporosis and the associated risk of bone fractures have become a wide-spread public health problem. However, osteoporosis is very heterogeneous, and the existing standard diagnostic measure is not sufficient to accurately identify all patients at risk of osteoporotic fractures and to guide therapy. Here, we constructed the first prospective multi-omics atlas of the largest osteoporosis cohort to date (longitudinal data from 366 participants at three time points), and also implemented an explainable data-intensive analysis framework (DLSF: Deep Latent Space Fusion) for an omnigenic model based on a multi-modal approach that can capture the multi-modal molecular signatures (M3S) as explicit functional representations of hidden genotypes. Accordingly, through DLSF, we identified two subtypes of the osteoporosis population in Chinese individuals with corresponding molecular phenotypes, i.e., clinical intervention relevant subtypes (CISs), in which bone mineral density benefits response to calcium supplements in 2-year follow-up samples. Many snpGenes associated with these molecular phenotypes reveal diverse candidate biological mechanisms underlying osteoporosis, with xQTL preferences of osteoporosis and its subtypes indicating an omnigenic effect on different biological domains. Finally, these two subtypes were found to have different relevance to prior fracture and different fracture risk according to 4-year follow-up data. Thus, in clinical application, M3S could help us further develop improved diagnostic and treatment strategies for osteoporosis and identify a new composite index for fracture prediction, which were remarkably validated in an independent cohort (166 participants).

Feasibility and safety of robotic liver resection for huge (≥10 cm) hepatocellular carcinoma in a single centre: A propensity score-matched single-surgeon study.

BACKGROUND: The applicability of robot-assisted resection for huge hepatocellular carcinoma (HCC) of ≥10 cm remains contentious with limited available data. METHODS: This retrospective analysis involved 337 patients who underwent robotic liver resection for HCC by a single surgeon. Propensity score matching (PSM) was employed to compare perioperative indicators between patients with regular and huge HCC. RESULTS: The regular HCC group exhibited a shorter median operative duration than the huge HCC group. The IWATE criteria revealed higher scores in the huge HCC group than in the regular HCC group. No significant differences were observed between the two groups in Pringle time, drainage tube removal, duration of hospital stays, blood loss volume, blood product transfusion, margin status, conversion rate to open surgery, bile leakage, in-hospital mortality, and reoperation rate. CONCLUSION: Robotic liver resection is feasible for huge HCC, with effective perioperative risk management potentially improving outcomes for subsequent minimally invasive surgeries.