Application of machine learning algorithms to identify people with low bone density.

Background: Osteoporosis is becoming more common worldwide, imposing a substantial burden on individuals and society. The onset of osteoporosis is subtle, early detection is challenging, and population-wide screening is infeasible. Thus, there is a need to develop a method to identify those at high risk for osteoporosis. Objective: This study aimed to develop a machine learning algorithm to effectively identify people with low bone density, using readily available demographic and blood biochemical data. Methods: Using NHANES 2017-2020 data, participants over 50 years old with complete femoral neck BMD data were selected. This cohort was randomly divided into training (70%) and test (30%) sets. Lasso regression selected variables for inclusion in six machine learning models built on the training data: logistic regression (LR), support vector machine (SVM), gradient boosting machine (GBM), naive Bayes (NB), artificial neural network (ANN) and random forest (RF). NHANES data from the 2013-2014 cycle was used as an external validation set input into the models to verify their generalizability. Model discrimination was assessed via AUC, accuracy, sensitivity, specificity, precision and F1 score. Calibration curves evaluated goodness-of-fit. Decision curves determined clinical utility. The SHAP framework analyzed variable importance. Results: A total of 3,545 participants were included in the internal validation set of this study, of whom 1870 had normal bone density and 1,675 had low bone density Lasso regression selected 19 variables. In the test set, AUC was 0.785 (LR), 0.780 (SVM), 0.775 (GBM), 0.729 (NB), 0.771 (ANN), and 0.768 (RF). The LR model has the best discrimination and a better calibration curve fit, the best clinical net benefit for the decision curve, and it also reflects good predictive power in the external validation dataset The top variables in the LR model were: age, BMI, gender, creatine phosphokinase, total cholesterol and alkaline phosphatase. Conclusion: The machine learning model demonstrated effective classification of low BMD using blood biomarkers. This could aid clinical decision making for osteoporosis prevention and management.

Enhancing the understanding of SARS-CoV-2 protein with structure and detection methods: An integrative review.

As the rapid and accurate screening of infectious diseases can provide meaningful information for outbreak prevention and control, as well as owing to the existing limitations of the polymerase chain reaction (PCR), it is imperative to have new and validated detection techniques for SARS-CoV-2. Therefore, the rationale for outlining the techniques used to detect SARS-CoV-2 proteins and performing a comprehensive comparison to serve as a practical benchmark for future identification of similar viral proteins is clear. This review highlights the urgent need to strengthen pandemic preparedness by emphasizing the importance of integrated measures. These include improved tools for pathogen characterization, optimized societal precautions, the establishment of early warning systems, and the deployment of highly sensitive diagnostics for effective surveillance, triage, and resource management. Additionally, with an improved understanding of the virus' protein structure, considerable advances in targeted detection, treatment, and prevention strategies are expected to greatly improve our ability to respond to future outbreaks.

Exciton Localization Modulated by Ultradeep Moiré Potential in Twisted Bilayer γ-Graphdiyne.

Twisted moiré superlattice is featured with its moiré potential energy, the depth of which renders an effective approach to strengthening the exciton-exciton interaction and exciton localization toward high-performance quantum photonic devices. However, it remains as a long-standing challenge to further push the limit of moiré potential depth. Herein, owing to the pz orbital induced band edge states enabled by the unique sp-C in bilayer γ-graphdiyne (GDY), an ultradeep moiré potential of ∼289 meV is yielded. After being twisted into the hole-to-hole layer stacking configuration, the interlayer coupling is substantially intensified to augment the lattice potential of bilayer GDY up to 475%. The presence of lateral constrained moiré potential shifts the spatial distribution of electrons and holes in excitons from the regular alternating mode to their respective separated and localized mode. According to the well-established wave function distribution of electrons contained in excitons, the AA-stacked site is identified to serve for exciton localization. This work extends the materials systems available for moiré superlattice design further to serial carbon allotropes featured with benzene ring-alkyne chain coupling, unlocking tremendous potential for twistronic-based quantum device applications.

An Integrated Gather-and-Distribute Mechanism and Attention-Enhanced Deformable Convolution Model for Pig Behavior Recognition.

The behavior of pigs is intricately tied to their health status, highlighting the critical importance of accurately recognizing pig behavior, particularly abnormal behavior, for effective health monitoring and management. This study addresses the challenge of accommodating frequent non-rigid deformations in pig behavior using deformable convolutional networks (DCN) to extract more comprehensive features by incorporating offsets during training. To overcome the inherent limitations of traditional DCN offset weight calculations, the study introduces the multi-path coordinate attention (MPCA) mechanism to enhance the optimization of the DCN offset weight calculation within the designed DCN-MPCA module, further integrated into the cross-scale cross-feature (C2f) module of the backbone network. This optimized C2f-DM module significantly enhances feature extraction capabilities. Additionally, a gather-and-distribute (GD) mechanism is employed in the neck to improve non-adjacent layer feature fusion in the YOLOv8 network. Consequently, the novel DM-GD-YOLO model proposed in this study is evaluated on a self-built dataset comprising 11,999 images obtained from an online monitoring platform focusing on pigs aged between 70 and 150 days. The results show that DM-GD-YOLO can simultaneously recognize four common behaviors and three abnormal behaviors, achieving a precision of 88.2%, recall of 92.2%, and mean average precision (mAP) of 95.3% with 6.0MB Parameters and 10.0G FLOPs. Overall, the model outperforms popular models such as Faster R-CNN, EfficientDet, YOLOv7, and YOLOv8 in monitoring pens with about 30 pigs, providing technical support for the intelligent management and welfare-focused breeding of pigs while advancing the transformation and modernization of the pig industry.

Network Pharmacology Analysis of Liquid-Cultured Armillaria ostoyae Mycelial Metabolites and Their Molecular Mechanism of Action against Gastric Cancer.

Armillaria sp. are traditional edible medicinal mushrooms with various health functions; however, the relationship between their composition and efficacy has not yet been determined. Here, the ethanol extract of liquid-cultured Armillaria ostoyae mycelia (AOME), a pure wild Armillaria sp. strain, was analyzed using UHPLC-QTOF/MS, network pharmacology, and molecular docking techniques. The obtained extract affects various metabolic pathways, such as JAK/STAT and PI3K/AKT. The extract also contains important compounds such as 4-(dimethylamino)-N-[7-(hydroxyamino)-7-oxoheptyl] benzamide, isoliquiritigenin, and 7-hydroxycoumarin. Moreover, the extract targets key proteins, including EGFR, SCR, and IL6, to suppress the progression of gastric cancer, thereby synergistically inhibiting cancer development. The molecular docking analyses indicated that the main compounds stably bind to the target proteins. The final cell culture experimental data showed that the ethanol extract inhibited MGC-803 gastric cancer cells. In summary, our research revealed the beneficial components of AOME for treating gastric cancer and its associated molecular pathways. However, further research is needed to confirm its effectiveness and safety in gastric cancer patients.

Oxidized sodium alginate hydrogel-mouse nerve growth factor sustained release system promotes repair of peripheral nerve injury.

In recent years, mouse nerve growth factor (mNGF) has emerged as an important biological regulator to repair peripheral nerve injury, but its systemic application is restricted by low efficiency and large dosage requirement. These limitations prompted us to search for biomaterials that can be locally loaded. Oxidized sodium alginate hydrogel (OSA) exhibits good biocompatibility and physicochemical properties, and can be loaded with drugs to construct a sustained-release system that can act locally on nerve injury. Here, we constructed a sustained-release system of OSA-mouse nerve growth factor (mNGF), and investigated the loading and release of the drug through Fourier transform infrared spectroscopy and drug release curves. In vitro and in vivo experiments showed that OSA-mNGF significantly promoted the biological activities of RSC-96 cells and facilitated the recovery from sciatic nerve crush injury in rats. This observation may be attributed to the additive effect of OSA on promoting Schwann cell biological activities or its synergistic effect of cross-activating phosphoinositide 3-kinase (PI3K) through extracellular signal regulated kinase (ERK) signaling. Although the specific mechanism of OSA action needs to be explored in the future, the current results provide a valuable preliminary research basis for the clinical application of the OSA-mNGF sustained-release system for nerve repair.

Preparation and Performance of Ceramic Tiles with Steel Slag and Waste Clay Bricks.

Steel slag and waste clay bricks are two prevalent solid waste materials generated during industrial production. The complex chemical compositions of these materials present challenges to their utilization in conventional alumina silicate ceramics manufacturing. A new type of ceramic tile, which utilizes steel slag and waste clay brick as raw materials, has been successfully developed in order to effectively utilize these solid wastes. The optimal composition of the ceramic material was determined through orthogonal experimentation, during which the effects of the sample molding pressure, the soaking time, and the sintering temperature on the ceramic properties were studied. The results show that the optimal ceramic tile formula was 45% steel slag, 35% waste clay bricks, and 25% talc. The optimal process parameters for this composition included a molding pressure of 25 MPa, a sintering temperature of 1190 °C, and a soaking time of 60 min. The prepared ceramic tile samples had compositions in which solid waste accounted for more than 76% of the total material. Additionally, they possessed a modulus of rupture of more than 73.2 MPa and a corresponding water absorption rate of less than 0.05%.

Real-time soft body dissection simulation with parallelized graph-based shape matching on GPU.

BACKGROUND AND OBJECTIVE: Interactive soft tissue dissection has been a fundamental procedure in virtual surgery systems. Existing cutting algorithms involve complex topology changes of simulation meshes, which can increase simulation overhead and produce visual artifacts. In this paper, we proposed a novel graph-based shape-matching method that allows for real-time, flexible, progressive, and discontinuous cuts on soft tissue. METHODS: We employed shape-matching constraints within the position-based dynamics (PBD) framework, a widely adopted approach for real-time simulation applications. The soft tissue was effectively modeled using overlapping clusters, each governed by shape-matching constraints. The dissection process was bifurcated into two distinct stages. In the first stage, the surgical scalpel presses the surface of the soft tissue. The soft tissue is cut apart when the surface pressure exceeds a threshold, entering the second stage. To address the discrepancy between the visual mesh and the simulation model during cluster separation, we developed an Aggregate Finding Connected Components (AFCC) algorithm, optimized for GPU computation and integrated with a background grid. This approach also avoids ghost forces and fragmentation artifacts. To control the increase in the number of clusters, we also propose a merging strategy that can run in parallel. RESULTS: Our simulation outcomes demonstrated that the AFCC dissection algorithm effectively manages cluster separation and expansion with robustness. There were no ghost forces between the cutting surface and unrealistic fragments. Our simulation capability extended to supporting intricate and discontinuous cutting routes. Our dissection simulation maintained real-time performance even with over 100,000 particles constituting the soft tissue. CONCLUSIONS: Our real-time and robust surgical dissection simulation technique enables the performance of complex cuts in various surgical scenarios, demonstrating its potential in virtual surgery applications.

Methylation of microRNA genes and its effect on secondary xylem development of stem in poplar.

MicroRNAs (miRNAs) and DNA methylation are both vital regulators of gene expression. DNA methylation can affect the transcription of miRNAs, just like coding genes, through methylating the CpG islands in the gene regions of miRNAs. Although previous studies have shown that DNA methylation and miRNAs can each be involved in the process of wood formation, the relationship between the two has been relatively little studied in plant wood formation. Studies have shown that the second internode (IN2) (from top to bottom) of 3-month-old poplar trees can represent the primary stage of poplar stem development and IN8 can represent the secondary stage. There were also significant differences in DNA methylation patterns and miRNA expression patterns obtained from PS and SS. In this study, we first interactively analyzed methylation and miRNA sequencing data to identify 43 differentially expressed miRNAs regulated by differential methylation from the primary stage and secondary stage, which were found to be involved in multiple biological processes related to wood formation by enrichment analysis. In addition, six miRNA/target gene modules were finally identified as potentially involved in secondary xylem development of poplar stems through degradome sequencing and functional analysis. In conclusion, this study provides important reference information on the mechanism of interaction between different regulatory pathways of wood formation.

Overexpression of the PtrNF-YA6 gene inhibits secondary cell wall thickening in poplar.

The NF-Y gene family in plants plays a crucial role in numerous biological processes, encompassing hormone response, stress response, as well as growth and development. In this study, we first used bioinformatics techniques to identify members of the NF-YA family that may function in wood formation. We then used molecular biology techniques to investigate the role and molecular mechanism of PtrNF-YA6 in secondary cell wall (SCW) formation in Populus trichocarpa. We found that PtrNF-YA6 protein was localized in the nucleus and had no transcriptional activating activity. Overexpression of PtrNF-YA6 had an inhibitory effect on plant growth and development and significantly suppressed hemicellulose synthesis and SCW thickening in transgenic plants. Yeast one-hybrid and ChIP-PCR assays revealed that PtrNF-YA6 directly regulated the expression of hemicellulose synthesis genes (PtrGT47A-1, PtrGT8C, PtrGT8F, PtrGT43B, PtrGT47C, PtrGT8A and PtrGT8B). In conclusion, PtrNF-YA6 can inhibit plant hemicellulose synthesis and SCW thickening by regulating the expression of downstream SCW formation-related target genes.

Identification, Characterization, Cloning, and Cross-Reactivity of Zan b 2, a Novel Pepper Allergen of 11S Legumin.

Protein from Sichuan peppers can elicit mild to severe allergic reactions. However, little is known about their allergenic proteins. We aimed to isolate, identify, clone, and characterize Sichuan pepper allergens and to determine its allergenicity and cross-reactivities. Sichuan pepper seed proteins were extracted and then analyzed by SDS-PAGE. Western blotting was performed with sera from Sichuan pepper-allergic individuals. Proteins of interest were purified using hydrophobic interaction chromatography and gel filtration and further analyzed by analytical ultracentrifugation, circular dichroism spectroscopy, and mass spectrometry (MS). Their coding region was amplified in the genome. IgE reactivity and cross-reactivity of allergens were evaluated by dot blot, enzyme-linked immunosorbent assay (ELISA), and competitive ELISA. Western blot showed IgE binding to a 55 kDa protein. This protein was homologous to the citrus proteins and has high stability and a sheet structure. Four DNA sequences were cloned. Six patients' sera (60%) showed specific IgE reactivity to this purified 11S protein, which was proved to have cross-reactivation with extracts of cashew nuts, pistachios, and citrus seeds. A novel allergen in Sichuan pepper seeds, Zan b 2, which belongs to the 11S globulin family, was isolated and identified. Its cross-reactivity with cashew nuts, pistachios, and citrus seeds was demonstrated.

Ultralow Thermal Conductivity of a Chalcogenide System Pt3Bi4Q9 (Q = S, Se) Driven by the Hierarchy of Rigid [Pt6Q12]12- Clusters Embedded in Soft Bi-Q Sublattice.

Knowledge of structure-property relationships in solids with intrinsic low thermal conductivity is crucial for fields such as thermoelectrics, thermal barrier coatings, and refractories. Herein, we propose a new "rigidness in softness" structural scheme for intrinsic low lattice thermal conductivity (κL), which embeds rigid clusters into the soft matrix to induce large lattice anharmonicity, and accordingly discover a new series of chalcogenides Pt3Bi4Q9 (Q = S, Se). Pt3Bi4S9-xSex (x = 3, 6) achieved an intrinsic ultralow κL down to 0.39 W/(m K) at 773 K, which is considerably low among the Bi chalcogenide thermoelectric materials. Pt3Bi4Q9 contains the rigid cubic [Pt6Q12]12- clusters embedded in the soft Bi-Q sublattice, involving multiple bonding interactions and vibration hierarchy. The hierarchical structure yields a large lattice anharmonicity with high Grüneisen parameters (γ) 1.97 of Pt3Bi4Q9, as verified by the effective scatter of low-lying optical phonons toward heat-carrying acoustic phonons. Consequently, the rigid-soft coupling significantly inhibits heat propagation, exhibiting low acoustic phonon frequencies (∼25 cm-1) and Debye temperatures (ΘD = 170.4 K) in Pt3Bi4Se9. Owing to the suppressed κL and considerable power factor (PF), the ZT value of Pt3Bi4S6Se3 can reach 0.56 at 773 K without heavy carrier doping, which is competitive among the pristine Bi chalcogenides. Theoretical calculations predicted a large potential for performance improvement via proper doping, indicating the great potential of this structure type for promising thermoelectric materials.

Catalytic hairpin self-assembly amplification fluorescence detection of chloramphenicol based on cross-shaped DNA and UiO-66.

A catalytic hairpin self-assembly (CHA) amplification method was developed for CAP detection based on cross-shaped DNA and UiO-66. MOF was used to quench the fluorescent signal of FAM labeled DNA. Cross-shaped DNA with four fluorophore group (FAM) was utilized to enhance the fluorescent intensity. CAP could open hairpin structure of H-apt and induce CHA reaction. The product of CHA hybridized with cross-shaped DNA, resulting its leaving from the surface of UiO-66 and recovery of fluorescent signal. The limit of detection (LOD) was low to 0.87 pM. This method had been successfully applied for the detection of CAP in actual samples. Importantly, the high sensitivity was attributed to the great amplification efficiency of CHA, strong fluorescent intensity of cross-shaped DNA structure and great fluorescent quenched efficiency of UiO-66.

Atom-based optical polarization modulator.

In this work, we employ 87Rb atoms as rotation media to manipulate the polarization of optical fields in both magnetic and magnetic-free environments. Employing the nonlinear magneto-optical rotation mechanism, we achieve a state-of-the-art magneto-optical rotation coefficient of 1.74×108 rad⋅T-1⋅m-1 which is four orders of magnitude higher than commonly employed materials. Additionally, in a magnetic-free environment, we achieve all-optical cross-polarization modulation between the pump and probe light via Rb atoms. The nonlinear magneto-optical rotation configuration introduces inventive techniques for a new type of magneto-optical modulator while the all-optical configuration paves the way for exploring photonic integrated circuit (PIC) devices free from disruptions caused by electrical or magnetic crosstalk.

Comparison of the therapeutic effects of medication therapy, specific immunotherapy and anti-IgE (Omalizumab) in patients with hay fever.

Background: Hay fever, characterized by seasonal allergic reactions, poses a significant health challenge. Existing therapies encompass standard drug regimens, biological agents, and specific immunotherapy. This study aims to assess and compare the effectiveness of anti-IgE (omalizumab), medication therapy, and subcutaneous immunotherapy (SCIT) for hay fever. Methods: Conducted as a retrospective cohort study, this research involved 98 outpatient hay fever patients who underwent routine medication, omalizumab treatment, or SCIT before the onset of the spring pollen season. A follow-up was performed one month after the start of the pollen season. The comprehensive symptoms and drug scores were used to evaluate patients with different intervention methods, facilitating a comparative analysis of therapeutic outcomes. Results: Compared with before treatment, the symptoms of patients treated with the three methods were all significantly relieved, and the medication score were significantly reduced. Patients treated with omalizumab demonstrated higher symptoms and medication scores than SCIT group before treatment, but similar scores after treatment, which were both lower than medicine treatment group. After treatment with omalizumab or SCIT, patients in both groups had significantly lower medication scores than the medication group and were close to no longer using medication for symptom relief. The mountain juniper-sIgE was significantly higher after treatment than before treatment in both medicine treatment group and omalizumab treatment group. Conclusion: Omalizumab and SCIT offer superior effects than medication therapy in hay fever patients.

Development and Validation of Deep Learning Model for Intermediate-Stage Hepatocellular Carcinoma Survival with Transarterial Chemoembolization (MC-hccAI 002): a Retrospective, Multicenter, Cohort Study.

Background: There are few effective prediction models for intermediate-stage hepatocellular carcinoma (IM-HCC) patients treated with transarterial chemoembolization (TACE) to predict overall survival (OS) is available. The learning survival neural network (DeepSurv) was developed to showed a better performance than cox proportional hazards model in prediction of OS. This study aimed to develop a deep learning-based prediction model to predict individual OS. Methods: This multicenter, retrospective, cohort study examined data from the electronic medical record system of four hospitals in China between January 1, 2007, to December 31, 2016. Patients were divided into a training set(n=1075) and a test set(n=269) at a ratio of 8:2 to develop a deep learning-based algorithm (deepHAP IV). The deepHAP IV model was externally validated on an independent cohort(n=414) from the other three centers. The concordance index, the area under the receiver operator characteristic curves, and the calibration curve were used to assess the performance of the models. Results: The deepHAP IV model had a c-index of 0.74, whereas AUROC for predicting survival outcomes of 1-, 3-, and 5-year reached 0.80, 0.76, and 0.74 in the training set. Calibration graphs showed good consistency between the actual and predicted OS in the training set and the validation cohort. Compared to the other five Cox proportional-hazards models, the model this study conducted had a better performance. Patients were finally classified into three groups by X-tile plots with predicted 3-year OS rate (low: ≤ 0.11; middle: > 0.11 and ≤ 0.35; high: >0.35). Conclusion: The deepHAP IV model can effectively predict the OS of patients with IM-HCC, showing a better performance than previous Cox proportional hazards models.

Machine Learning Method to Explore the Correlation between Fly Ash Content and Chloride Resistance.

Chloride ion corrosion has been considered to be one of the main reasons for durability deterioration of reinforced concrete structures in marine or chlorine-containing deicing salt environments. This paper studies the relationship between the amount of fly ash and the durability of concrete, especially the resistance to chloride ion erosion. The heat trend map of total chloride ion factor correlation displayed that the ranking of factor correlations was as follows: sampling depth > cement dosage > fly ash dosage. In order to verify the effect of fly ash dosage on chloride ion resistance, three different machine learning algorithms (RF, GBR, DT) are employed to predict the total chloride content of fly ash proportioned concrete with varying admixture ratios, which are evaluated based on R2, MSE, RMSE, and MAE. The results predicted by the RF model show that the threshold of fly ash admixture in chlorinated salt environments is 30-40%. Replacing part of cement with fly ash in the mixture of concrete below this threshold of fly ash, it could change the phase structure and pore structure, which could improve the permeability of fly ash concrete and reduce the content of free chloride ions in the system. Machine learning modeling using sample data can accurately predict concrete properties, which effectively reduce engineering tests. The development of machine learning models is essential for the decarbonization and intelligence of engineering.

Model construction and drug therapy of primary ovarian insufficiency by ultrasound-guided injection.

BACKGROUND: Clinically, hormone replacement therapy (HRT) is the main treatment for primary ovarian insufficiency (POI). However, HRT may increase the risk of both breast cancer and cardiovascular disease. Exosomes derived from human umbilical cord mesenchymal stem cell (hUC-MSC) have been gradually applied to the therapy of a variety of diseases through inflammation inhibition, immune regulation, and tissue repair functions. However, the application and study of hUC-MSC exosomes in POI remain limited. METHODS: Here, we first constructed four rat animal models: the POI-C model (the "cyclophosphamide-induced" POI model via intraperitoneal injection), the POI-B model (the "busulfan-induced" POI model), the POI-U model (the "cyclophosphamide-induced" POI model under ultrasonic guidance), and MS model (the "maternal separation model"). Second, we compared the body weight, ovarian index, status, Rat Grimace Scale, complications, and mortality rate of different POI rat models. Finally, a transabdominal ultrasound-guided injection of hUC-MSC exosomes was performed, and its therapeuticy effects on the POI animal models were evaluated, including changes in hormone levels, oestrous cycles, ovarian apoptosis levels, and fertility. In addition, we performed RNA-seq to explore the possible mechanism of hUC-MSC exosomes function. RESULTS: Compared with the POI-C, POI-B, and MS animal models, the POI-U model showed less fluctuation in weight, a lower ovarian index, fewer complications, a lower mortality rate, and a higher model success rate. Second, we successfully identified hUC-MSCs and their exosomes, and performed ultrasound-guided intraovarian hUC-MSCs exosomes injection. Finally, we confirmed that the ultrasound-guided exosome injection (termed POI-e) effectively improved ovarian hormone levels, the oestrous cycle, ovarian function, and fertility. Mechanically, hUC-MSCs may play a therapeutic role by regulating ovarian immune and metabolic functions. CONCLUSIONS: In our study, we innovatively constructed an ultrasound-guided ovarian drug injection method to construct POI-U animal models and hUC-MSC exosomes injection. And we confirmed the therapeutic efficacy of hUC-MSC exosomes on the POI-U animal models. Our study will offer a better choice for new animal models of POI in the future and provides certain guidance for the hUC-MSCs exosome therapy in POI patients.

Translation and validation of a simplified Chinese version of the psychosocial assessment tool.

BACKGROUND: The Psychosocial Assessment Tool (PAT2.0) is widely used to assess psychosocial risk in families of children with cancer. Our study aims to apply PAT2.0 to Chinese patients and assess the reliability, content validity, and construct validity of the Chinese version. METHODS: A total of 161 participants completed the study, each with only one child diagnosed with cancer. Psychometric evaluations, including internal consistency, score distribution, test-retest reliability, and construct validity, were conducted. RESULTS: Cronbach's alpha values ranged from 0.732 to 0.843, indicating good internal consistency. Additionally, intraclass correlation coefficient values ranged from 0.869 to 0.984, indicating excellent test-retest reliability. The Simplified Chinese version of PAT2.0 demonstrated high construct validity in factor analyses and correlations with the General Functioning Subscale of the Family Assessment Device. CONCLUSION: The translation process of the Chinese version of PAT2.0 was successful, proving its applicability for psychosocial evaluation and interventions in families of children with cancer in China.

Nomogram for predicting the surgical difficulty of laparoscopic total mesorectal excision and exploring the technical advantages of robotic surgery.

Background: Total mesorectal excision (TME), represents a key technique in radical surgery for rectal cancer. This study aimed to construct a preoperative nomogram for predicting the surgical difficulty of laparoscopic total mesorectal excision (L-TME) and to investigate whether there were potential benefits of robotic TME (R-TME) for patients with technically challenging rectal cancer. Methods: Consecutive mid-low rectal cancer patients receiving total mesorectal excision were included. A preoperative nomogram to predict the surgical difficulty of L-TME was established and validated. Patients with technically challenging rectal cancer were screened by calculating the prediction score of the nomogram. Then patients with technically challenging rectal cancer who underwent different types of surgery, R-TME or L-TME, were analyzed for comparison. Results: A total of 533 consecutive patients with mid-low rectal cancer who underwent TME at a single tertiary medical center between January 2018 and January 2021 were retrospectively enrolled. Multivariable analysis demonstrated that mesorectal fat area, intertuberous distance, tumor size, and tumor height were independent risk factors for surgical difficulty. Subsequently, these variables were used to construct the nomogram model to predict the surgical difficulty of L-TME. The area under the receiver operating characteristic curve of the nomogram was 0.827 (95% CI 0.745 - 0.909) and 0.809 (95% CI 0.674- 0.944) in the training and validation cohort, respectively. For patients with technically challenging rectal cancer, R-TME was associated with a lower diverting ileostomy rate (p = 0.003), less estimated blood loss (p < 0.043), shorter procedure time (p = 0.009) and shorter postoperative hospital stay (p = 0.037). Conclusion: In this study, we established a preoperative nomogram to predict the surgical difficulty of L-TME. Furthermore, this study also indicated that R-TME has potential technical advantages for patients with technically challenging rectal cancer.