Experimental evidence to understand mechanical causes of retinal detachment following blunt trauma.

PURPOSE: This study aimed to perform an in vitro experiment to simulate retinal detachment caused by blunt impact, and provide experimental evidence to understand mechanical causes of traumatic retinal detachment. METHODS: The experiment was conducted on twenty-two fresh porcine eyes using a bespoke pendulum testing device at two energy levels (0.1J for low energy and 1.0J for high energy). We examined dynamic forces and mechanical responses to the impact, including global deformations, intraocular pressure changes and the energy absorption. Another set of twenty-two eyes underwent pathological examination immediately after being subjected to blunt impact. Twelve additional intact eyes were examined as controls. All pathological sections were scored to indicate whether retinal detachment had occurred. RESULTS: A dynamic variation in intraocular pressure was detected following impact and exhibited an approximate sinusoidal oscillation-attenuation profile. The peaks of impact force were 12.9 ± 1.9 N at low-energy level and 34.8 ± 9.8 N at high-energy level, showing a significant difference (p < 0.001). The positive and negative peaks of intraocular pressure were 149.4 ± 18.9 kPa and -10.9 ± 7.2 kPa at low-energy level, and 274.5 ± 55.2 kPa and -35.7 ± 23.7 kPa at high-energy level, showing significant differences (p < 0.001 for both levels). Retinal detachments were observed in damaged eyes while few detachments were found in control eyes. The occurrence rate of retinal detachment differed significantly (p < 0.05) between the high- and low-energy impact groups. CONCLUSIONS: This study provided experimental evidence that shockwaves produced by blunt trauma break the force equilibrium and lead to the oscillation and negative pressure, which mainly contribute to traumatic retinal detachment.

Age-related differences in upper limb motor performance and intrinsic motivation during a virtual reality task.

BACKGROUND: In recent years, virtual reality (VR) has evolved from an alternative to a necessity in older adults for health, medical care, and social interaction. Upper limb (UL) motor skill, is an important ability in manipulating VR systems and represents the brain's regulation of movements using the UL muscles. In this study, we used a haptic-feedback Virtual Box and Block Test (VBBT) system and an Intrinsic Motivation Inventory (IMI) to examine age-related differences in UL motor performance and intrinsic motivation in VR use. The findings will be helpful for the development of VR applications for older adults. METHODS: In total, 48 young and 47 older volunteers participated in our study. The parameters including VBBT score, number of velocity peaks, velocity, grasping force and trajectory length were calculated to represent the task performance, manual dexterity, coordination, perceptive ability and cognitive ability in this study. RESULTS: Age-related differences could be found in all the parameters (all p <  0.05) in VR use. Regression analysis revealed that the task performance of young adults was predicted by the velocity and trajectory length (R2 = 64.0%), while that of older adults was predicted by the number of velocity peaks (R2 = 65.6%). Additionally, the scores of understandability, relaxation and tiredness were significantly different between the two groups (all p <  0.05). In older adults, the understandability score showed large correlation with the IMI score (|r| = 0.576, p <  0.001). In young adults, the correlation was medium (|r| = 0.342, p = 0.017). No significant correlation was found between the IMI score and VBBT score (|r| = 0.142, p = 0.342) in older adults, while a medium correlation (|r| = 0.342, p = 0.017) was found in young adults. CONCLUSIONS: The findings demonstrated that decreased smoothness in motor skills dominated the poor VR manipulation in older adults. The experience of understandability is important for older adults' intrinsic motivation in VR use.

Characteristics and Emerging Trends in Research on Rehabilitation Robots from 2001 to 2020: Bibliometric Study.

BACKGROUND: The past 2 decades have seen rapid development in the use of robots for rehabilitation. Research on rehabilitation robots involves interdisciplinary activities, making it a great challenge to obtain comprehensive insights in this research field. OBJECTIVE: We performed a bibliometric study to understand the characteristics of research on rehabilitation robots and emerging trends in this field in the last 2 decades. METHODS: Reports on the topic of rehabilitation robots published from January 1, 2001, to December 31, 2020, were retrieved from the Web of Science Core Collection on July 28, 2022. Document types were limited to "article" and "meeting" (excluding the "review" type), to ensure that our analysis of the evolution over time of this research had high validity. We used CiteSpace to conduct a co-occurrence and co-citation analysis and to visualize the characteristics of this research field and emerging trends. Landmark publications were identified using metrics such as betweenness centrality and burst strength. RESULTS: Through data retrieval, cleaning, and deduplication, we retrieved 9287 publications and 110,619 references cited in these publications that were on the topic of rehabilitation robots and were published between 2001 and 2020. Results of the Mann-Kendall test indicated that the numbers of both publications (P<.001; St=175.0) and citations (P<.001; St=188.0) related to rehabilitation robots exhibited a significantly increasing yearly trend. The co-occurrence results revealed 120 categories connected with research on rehabilitation robots; we used these categories to determine research relationships. The co-citation results identified 169 co-citation clusters characterizing this research field and emerging trends in it. The most prominent label was "soft robotic technology" (the burst strength was 79.07), which has become a topic of great interest in rehabilitative recovery for both the upper and lower limbs. Additionally, task-oriented upper-limb training, control strategies for robot-assisted lower limb rehabilitation, and power in exoskeleton robots were topics of great interest in current research. CONCLUSIONS: Our work provides insights into research on rehabilitation robots, including its characteristics and emerging trends during the last 2 decades, providing a comprehensive understanding of this research field.

Parental presence during induction of anesthesia in children undergoing tonsillectomy and adenoidectomy.

OBJECTIVE: To investigate the effect of parental presence during induction of anesthesia (PPIA) in relieving preoperative anxiety of children undergoing tonsillectomy and adenoidectomy. METHODS: One hundred and sixty children undergoing tonsillectomy and adenoidectomy were divided into the control group and the trial group. The control group received routine nursing in the operation room, while anesthesia was induced in the trial group children in the presence of their parents as part of the routine nursing. The differences in heart rate and mean dynamic pressure during pre-operative visit and anesthesia induction between the two groups were observed and recorded. The Modified Yale Preoperative Anxiety Scale (m-YPAS) and the Induction Compliance Checklist (ICC) were scored. The anxiety status of the children and their family members in the two groups was scored at different times, and the psychological stress of anesthesiologists during anesthesia induction was scored by a visual analogue scale. The differences in each index between the two groups were compared. Operation time and costs in-hospital were also compared. RESULTS: Compared with the control group, the heart rate and blood pressure scores as well as the ICC in the trial group were lower than those in the control group (P < 0.01). On comparing the scores of m-YPAS between the two groups, we observed that the scores of the children in the trial group were lower than those in the control group before entering the induction room and anesthesia induction (P < 0.01). There was no statistical difference between the scores of the children in the trial group and the control group on the day of operation and on the way to the operating room (P > 0.05). The nursing satisfaction scores of the family members in the trial group were significantly superior to those in the control group (P < 0.01). The scores of the visual analogue scale for psychological pressure of anesthesiologists during anesthesia induction were higher in the trial group than in the control group (P < 0.05). The operation time and costs in study group were both significantly higher than those of control group (P < 0.05). CONCLUSION: PPIA can significantly reduce preoperative anxiety and surgical physiological stress response in children undergoing tonsillectomy and adenoidectomy, and it is worth being encouraged.

The role of low-frequency oscillations in three-dimensional perception with depth cues in virtual reality.

Currently, vision-related neuroscience studies are undergoing a trend from simplified image stimuli toward more naturalistic stimuli. Virtual reality (VR), as an emerging technology for visual immersion, provides more depth cues for three-dimensional (3D) presentation than two-dimensional (2D) image. It is still unclear whether the depth cues used to create 3D visual perception modulate specific cortical activation. Here, we constructed two visual stimuli presented by stereoscopic vision in VR and graphical projection with 2D image, respectively, and used electroencephalography to examine neural oscillations and their functional connectivity during 3D perception. We find that neural oscillations are specific to delta and theta bands in stereoscopic vision and the functional connectivity in the two bands increase in cortical areas related to visual pathways. These findings indicate that low-frequency oscillations play an important role in 3D perception with depth cues.

Photocatalytic Oxo-Amination of Aryl Cyclopropanes through an Unusual SN2-Like Ring-Opening Pathway: Won >99% ee.

One-pot oxo-amination of unactivated cyclopropanes with safe, green dioxygen as an oxidant and low-cost amines as nitrogen sources has generated interest since this can directly result in uniform ß-located difunctional units. Formation of the three-electron cation radical followed by the nucleophilic attack of amines to open the strained ring of cyclopropanes catalyzed by classic noble-complex photocatalysts was a promising strategy. However, this ring-opening pathway could not maintain the entire second-order nucleophilic substitution (SN2) conversion, which generally led to unsatisfactory enantioselectivity (enantiomeric excess (ee) value ∼60%). Here, we demonstrate that for such a one-step oxo-amination of cyclopropanes with benign dioxygen and pyrazoles, a highly uniform inversion of configuration could be first accomplished through a TiO2 photocatalyst. This strategy features low-cost, semiheterogeneous photocatalysis and environmentally friendly reaction conditions, without using any sacrificial reagent or additive. Importantly, our protocol not only provides a relatively broad substrate scope tolerant to a certain range of substituted cyclopropanes and pyrazoles, resulting in various ß-amino ketone products (∼50 examples) with excellent conversions and yields, but also retains excellent enantioselectivity (ee value ∼99%). A concerted SN2 ring opening raised from an oxetane cation intermediate rather than a conventional three-electron cation radical prior to attaching to dioxygen was proposed.

Variations in disease burden of laryngeal cancer attributable to alcohol use and smoking in 204 countries or territories, 1990-2019.

BACKGROUND: Alcohol consumption and smoking are the leading risk factors for laryngeal cancer (LC). Understanding the variations in disease burden of LC attributable to alcohol use and smoking is critical for LC prevention. METHODS: Disease burden data of LC were retrieved from the Global Burden of Disease Study 2019. We used estimated average percentage change (EAPC) to measure the temporal trends of the age-standardized mortality rate (ASMR) of LC. RESULTS: Globally, while the ASMR of LC decreased by 1.49% (95% CI, 1.41-1.57%) per year between 1990 and 2019, the number of deaths from LC has increased 41.0% to 123.4 thousand in 2019. In 2019, 19.4 and 63.5% of total LC-related deaths were attributable to alcohol use and smoking worldwide, respectively. The ASMR of alcohol- and smoking-related LC decreased by 1.78 and 1.93% per year, whereas the corresponding death number has increased 29.2 and 25.1% during this period, respectively. The decreasing trend was more pronounced in developed countries. In some developing countries, such as Guinea and Mongolia, the LC mortality has shown an unfavorable trend. CONCLUSION: The ubiquitous decrease in LC mortality was largely attributed to the smoking control and highlighted the importance of smoking control policies. However, the disease burden of LC remained in increase and more effective strategies are needed to combat the global increase of alcohol consumption.

Cervical vestibular evoked myogenic potential testing for assessment of vestibular functions in cochlear implant patients.

This study aims to investigate the vestibular function status of cochlear implant patients using cervical vestibular evoked myogenic potential (cVEMP) testing and estimate the effects of cochlear implants on vestibular function. The cVEMPs of 50 cochlear implant patients were measured preoperatively, and at one and six months postoperatively. Then, implanted ears and non-implanted ears were compared in terms of p13/n23 wave response rates, latency, amplitude and threshold. Preoperatively, the binaural cVEMP response rate was 92%, while the cVEMP response rates of implanted ears vs. non-implanted ears at postoperative one and six months were 24% vs. 80% and 52% vs. 82%, respectively. No significant difference between implanted and non-implanted ears was found preoperatively, in terms of latent period, amplitude, or threshold. However, significant changes were found in amplitude and threshold for implanted ears after the operation, but not in latency. No significant postoperative change was found in amplitude, latent period, or threshold for non-implanted ears. Significant differences between implanted and non-implanted ears were found in both amplitude and threshold. Cochlear implants affect vestibular function, especially saccular function, and reduce the cVEMP amplitude and threshold of implanted ears.

Neutrophil-to-Lymphocyte Ratio and Platelet-to-Lymphocyte Ratio in Patients with Sudden Sensorineural Hearing Loss.

OBJECTIVE: Sudden sensorineural hearing loss (SSNHL) is a common acute disease with an incidence of 0.5-2/10,000. This study aimed to determine whether the neutrophil-to-lymphocyte ratio (NLR) and the platelet-to-lymphocyte ratio (PLR) could be indicators for SSNHL. METHODS: A total of 60 confirmed cases of SSNHL and 60 healthy volunteers were included in this study. Peripheral blood NLRs and PLRs were compared between these groups. The SSNHL patients were divided into two groups, according to therapeutic effect: an effective group and an ineffective group. Peripheral blood NLRs and PLRs before and after treatment were compared between these two groups. RESULTS: The average NLRs and PLRs of these patients were both significantly higher than in controls. The average NLRs and PLRs of the ineffective group were both significantly higher than those of the effective group. CONCLUSION: Peripheral blood NLR and PLR could be used as a convenient, reliable, and cost-effective indicator to predict the prognosis of SSNHL.

Correlation between Preoperative Auditory Steady-State Response and Postoperative Electrically Evoked Auditory Brainstem Response and T Level in Cochlear Implantation for Child Patients with Inner-Ear Malformations.

OBJECTIVE: This study aims to investigate the correlation between thresholds of preoperative multiple auditory steady-state response (ASSR) and electrically evoked auditory brainstem response (EABR) and the behavioral threshold. METHODS: A total of 72 patients were elected to receive a multichannel cochlear implant. According to the residual hearing determined in a preoperative test using high-, moderate-, and low-frequency ASSR, these patients were divided into the following 2 groups: residual hearing and hearing loss. The EABR and behavioral thresholds 1 year after implantation were assayed, and differences between these 2 parameters were compared. RESULTS: Among the high-, moderate-, and low-frequency residual hearing groups, the EABR and behavioral thresholds of patients 1 year after implantation were significantly lower than those in the hearing loss group, and the differences were statistically significant (p < 0.01). CONCLUSION: Before the operation, ASSR results can be used to predict the efficacy of cochlear implantation in patients, and they serve as one of the reference conditions for choosing the ear for implantation. However, the threshold of ASSR is not equivalent to the actual auditory threshold of patients after implantation, and the deviation between these 2 thresholds is more significant at low frequencies.

[Analysis of inducement of benign paroxysmal positional vertigo].

Objective:To analyze the related factors that may affect the onset of benign paroxysmal positional vertigo（BPPV）. Methods:Fifty BPPV patients treated in Department of Otolaryngology Head and Neck Surgery, Shanxi Provincial People's Hospital from May to September 2023 were selected as the case group, and 50 healthy adults were selected as the control group. Relevant information was collected by means of questionnaire survey and medical history inquiry. The two groups were compared in terms of sleep time, night sleep duration, wake times, underlying diseases（hypertension, diabetes, coronary heart disease, etc.） and negative emotional impact. Results:The proportion of male and female in the case group was 16% and 84%, and that in the control group was 20% and 80%. The mean age of the case group was（54.66±13.39） years old, and the mean age of the control group was（54.42±12.55） years old, ranging from 27 to 80 years old. The sleeping time of the case group was significantly later than that of the healthy group, and the difference was statistically significant（P<0.05）. The night sleep duration of the case group was shorter than that of the healthy group, the difference was statistically significant（P<0.05）. There was no significant difference in awakening times between the case group and the healthy group（P>0.05）. There were more patients in the case group with underlying diseases（54%） and affected by negative emotions（70%） than in the healthy group, and the difference was statistically significant（P<0.05）. Conclusion:Late sleep time, short sleep duration at night, accompanied by underlying diseases and negative emotions can affect the onset of BPPV.

Radiomic-based machine learning model for the accurate prediction of prostate cancer risk stratification.

OBJECTIVES: To precisely predict prostate cancer (PCa) risk stratification, we constructed a machine learning (ML) model based on magnetic resonance imaging (MRI) radiomic features. METHODS: Between August 2016 and May 2021, patients with histologically proven PCa who underwent pre-operative MRI and prostate-specific antigen screening were included. The patients were grouped into different risk categories as defined by the European Association of Urology-European Association of Nuclear Medicine-European Society for Radiotherapy and Oncology-European Society of Urogenital Radiology-International Society of Geriatric Oncology guidelines. Using Artificial Intelligence Kit software, PCa regions of interest were delineated and radiomic features were extracted. Subsequently, predictable models were built by utilising five traditional ML approaches: support vector machine, logistic regression, gradient boosting decision tree, k-nearest neighbour and random forest (RF) classifiers. The classification capacity of the developed models was assessed by area under the receiver operating characteristic curve (AUC) analysis. RESULTS: A total of 213 patients were enrolled, including 16 low-risk, 65 intermediate-risk, and 132 high-risk PCa patients. The risk stratification of PCa could be revealed by MRI radiomic features, and second-order features accounted for most of the selected features. Among the five established ML models, the RF model showed the best overall predictive performance (AUC = 0.87). After further analysis of the subgroups based on the RF model, the prediction of the high-risk group was the best (AUC = 0.89). CONCLUSION: This study demonstrated that the MR radiomics-based ML method could be a promising tool for predicting PCa risk stratification precisely. ADVANCES IN KNOWLEDGE: The ML models have valuable prospect for accurate PCa risk assessment, which might contribute to customize treatment and surveillance strategies.

Peritumoral Radiomics Strategy Based on Ensemble Learning for the Prediction of Gleason Grade Group of Prostate Cancer.

RATIONALE AND OBJECTIVES: To develop and evaluate a peritumoral radiomic-based machine learning model to differentiate low-Gleason grade group (L-GGG) and high-GGG (H-GGG) prostate lesions. MATERIALS AND METHODS: In this retrospective study, a total of 175 patients with prostate cancer (PCa) confirmed by puncture biopsy were recruited and included 59 patients with L-GGG and 116 patients with H-GGG. The original PCa regions of interest (ROIs) were delineated on T2-weighted (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) maps, and then centra-tumoral and peritumoral ROIs were defined. Features were meticulously extracted from each ROI to establish radiomics models, employing distinct sequence datasets. Peritumoral radiomics models were specifically developed for both the peripheral zone (PZ) and transitional zone (TZ), utilizing dedicated PZ and TZ datasets, respectively. The performances of the models were evaluated by using the receiver operating characteristic (ROC) curve and precision-recall curve. RESULTS: The classification model with combined peritumoral features based on T2 + DWI + ADC sequence dataset demonstrated superior performance compared to the original tumor and centra-tumoral classification models. It achieved an area under the ROC curve (AUC) of 0.850 [95% confidence interval, 0.849, 0.860] and an average accuracy of 0.950. The combined peritumoral model outperformed the regional peritumoral models with AUC of 0.85 versus 0.75 for PZ lesions and 0.88 versus 0.69 for TZ lesions, respectively. The peritumoral classification models exhibit greater efficacy in predicting PZ lesions as opposed to TZ lesions. CONCLUSION: The peritumoral radiomics features showed excellent performance in predicting GGG in PCa patients and might be a valuable addition to the non-invasive assessment of PCa aggressiveness.

Interhemispheric multisensory perception and Bayesian causal inference.

In daily life, our brain needs to eliminate irrelevant signals and integrate relevant signals to facilitate natural interactions with the surrounding. Previous study focused on paradigms without effect of dominant laterality and found that human observers process multisensory signals consistent with Bayesian causal inference (BCI). However, most human activities are of bilateral interaction involved in processing of interhemispheric sensory signals. It remains unclear whether the BCI framework also fits to such activities. Here, we presented a bilateral hand-matching task to understand the causal structure of interhemispheric sensory signals. In this task, participants were asked to match ipsilateral visual or proprioceptive cues with the contralateral hand. Our results suggest that interhemispheric causal inference is most derived from the BCI framework. The interhemispheric perceptual bias may vary strategy models to estimate the contralateral multisensory signals. The findings help to understand how the brain processes the uncertainty information coming from interhemispheric sensory signals.

MRI Radiomics-Based Machine Learning Models for Ki67 Expression and Gleason Grade Group Prediction in Prostate Cancer.

PURPOSE: The Ki67 index and the Gleason grade group (GGG) are vital prognostic indicators of prostate cancer (PCa). This study investigated the value of biparametric magnetic resonance imaging (bpMRI) radiomics feature-based machine learning (ML) models in predicting the Ki67 index and GGG of PCa. METHODS: A total of 122 patients with pathologically proven PCa who had undergone preoperative MRI were retrospectively included. Radiomics features were extracted from T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) maps. Then, recursive feature elimination (RFE) was applied to remove redundant features. ML models for predicting Ki67 expression and GGG were constructed based on bpMRI and different algorithms, including logistic regression (LR), support vector machine (SVM), random forest (RF), and K-nearest neighbor (KNN). The performances of different models were evaluated with receiver operating characteristic (ROC) analysis. In addition, a joint analysis of Ki67 expression and GGG was performed by assessing their Spearman correlation and calculating the diagnostic accuracy for both indices. RESULTS: The ML model based on LR and ADC + T2 (LR_ADC + T2, AUC = 0.8882) performed best in predicting Ki67 expression, and ADC_wavelet-LHH_firstorder_Maximum had the highest feature weighting. The SVM_DWI + T2 (AUC = 0.9248) performed best in predicting GGG, and DWI_wavelet HLL_glcm_SumAverage had the highest feature weighting. The Ki67 and GGG exhibited a weak positive correlation (r = 0.382, p < 0.001), and LR_ADC + DWI had the highest diagnostic accuracy in predicting both (0.6230). CONCLUSION: The proposed ML models are suitable for predicting both Ki67 expression and GGG in PCa. This algorithm could be used to identify indolent or invasive PCa with a noninvasive, repeatable, and accurate diagnostic method.

Eye movement characteristics in a mental rotation task presented in virtual reality.

Introduction: Eye-tracking technology provides a reliable and cost-effective approach to characterize mental representation according to specific patterns. Mental rotation tasks, referring to the mental representation and transformation of visual information, have been widely used to examine visuospatial ability. In these tasks, participants visually perceive three-dimensional (3D) objects and mentally rotate them until they identify whether the paired objects are identical or mirrored. In most studies, 3D objects are presented using two-dimensional (2D) images on a computer screen. Currently, visual neuroscience tends to investigate visual behavior responding to naturalistic stimuli rather than image stimuli. Virtual reality (VR) is an emerging technology used to provide naturalistic stimuli, allowing the investigation of behavioral features in an immersive environment similar to the real world. However, mental rotation tasks using 3D objects in immersive VR have been rarely reported. Methods: Here, we designed a VR mental rotation task using 3D stimuli presented in a head-mounted display (HMD). An eye tracker incorporated into the HMD was used to examine eye movement characteristics during the task synchronically. The stimuli were virtual paired objects oriented at specific angular disparities (0, 60, 120, and 180°). We recruited thirty-three participants who were required to determine whether the paired 3D objects were identical or mirrored. Results: Behavioral results demonstrated that the response times when comparing mirrored objects were longer than identical objects. Eye-movement results showed that the percent fixation time, the number of within-object fixations, and the number of saccades for the mirrored objects were significantly lower than that for the identical objects, providing further explanations for the behavioral results. Discussion: In the present work, we examined behavioral and eye movement characteristics during a VR mental rotation task using 3D stimuli. Significant differences were observed in response times and eye movement metrics between identical and mirrored objects. The eye movement data provided further explanation for the behavioral results in the VR mental rotation task.

MRI-Based Radiomics Models to Discriminate Hepatocellular Carcinoma and Non-Hepatocellular Carcinoma in LR-M According to LI-RADS Version 2018.

Differentiating hepatocellular carcinoma (HCC) from other primary liver malignancies in the Liver Imaging Reporting and Data System (LI-RADS) M (LR-M) tumours noninvasively is critical for patient treatment options, but visual evaluation based on medical images is a very challenging task. This study aimed to evaluate whether magnetic resonance imaging (MRI) models based on radiomics features could further improve the ability to classify LR-M tumour subtypes. A total of 102 liver tumours were defined as LR-M by two radiologists based on LI-RADS and were confirmed to be HCC (n = 31) and non-HCC (n = 71) by surgery. A radiomics signature was constructed based on reproducible features using the max-relevance and min-redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) logistic regression algorithms with tenfold cross-validation. Logistic regression modelling was applied to establish different models based on T2-weighted imaging (T2WI), arterial phase (AP), portal vein phase (PVP), and combined models. These models were verified independently in the validation cohort. The area under the curve (AUC) of the models based on T2WI, AP, PVP, T2WI + AP, T2WI + PVP, AP + PVP, and T2WI + AP + PVP were 0.768, 0.838, 0.778, 0.880, 0.818, 0.832, and 0.884, respectively. The combined model based on T2WI + AP + PVP showed the best performance in the training cohort and validation cohort. The discrimination efficiency of each radiomics model was significantly better than that of junior radiologists' visual assessment (p < 0.05; Delong). Therefore, the MRI-based radiomics models had a good ability to discriminate between HCC and non-HCC in LR-M tumours, providing more options to improve the accuracy of LI-RADS classification.

Radiomics-Based Machine Learning Models for Predicting P504s/P63 Immunohistochemical Expression: A Noninvasive Diagnostic Tool for Prostate Cancer.

Objective: To develop and validate a noninvasive radiomic-based machine learning (ML) model to identify P504s/P63 status and further achieve the diagnosis of prostate cancer (PCa). Methods: A retrospective dataset of patients with preoperative prostate MRI examination and P504s/P63 pathological immunohistochemical results between June 2016 and February 2021 was conducted. As indicated by P504s/P63 expression, the patients were divided into label 0 (atypical prostatic hyperplasia), label 1 (benign prostatic hyperplasia, BPH) and label 2 (PCa) groups. This study employed T2WI, DWI and ADC sequences to assess prostate diseases and manually segmented regions of interest (ROIs) with Artificial Intelligence Kit software for radiomics feature acquisition. Feature dimensionality reduction and selection were performed by using a mutual information algorithm. Based on screened features, P504s/P63 prediction models were established by random forest (RF), gradient boosting decision tree (GBDT), logistic regression (LR), adaptive boosting (AdaBoost) and k-nearest neighbor (KNN) algorithms. The performance was evaluated by the area under the ROC curve (AUC) and accuracy. Results: A total of 315 patients were enrolled. Among the 851 radiomic features, the 32 top features were derived from T2WI, in which the gray-level run length matrix (GLRLM) and gray-level cooccurrence matrix (GLCM) features accounted for the largest proportion. Among the five models, the RF algorithm performed best in general evaluations (microaverage AUC=0.920, macroaverage AUC=0.870) and provided the most accurate result in further sublabel prediction (the accuracies of label 0, 1, and 2 were 0.831, 0.831, and 0.932, respectively). In comparative sequence analyses, T2WI was the best single-sequence candidate (microaverage AUC=0.94 and macroaverage AUC=0.78). The merged datasets of T2WI, DWI, and ADC yielded optimal AUCs (microaverage AUC=0.930 and macroaverage AUC=0.900). Conclusions: The radiomic-based RF classifier has the potential to be used to evaluate the presurgical P504s/P63 status and further diagnose PCa noninvasively and accurately.

Expression of long-chain noncoding RNA GAS5 in osteoarthritis and its effect on apoptosis and autophagy of osteoarthritis chondrocytes.

OBJECTIVE: To investigate the expression of long-chain noncoding RNA GAS5 in osteoarthritis (OA) and the effect of silencing GAS5 on autophagy of osteoarthritis chondrocytes (OACs). METHODS: OA rat models were constructed by cutting the anterior cruciate ligament, and the expressions of GAS5 in rat cartilage tissues at 4 weeks (early OA) and 12 weeks (late OA) after modeling were detected. The rat chondrocytes were isolated, cultured and transfected with si-GAS5 to silencing GAS5. Then, the changes of apoptosis and autophagy levels of OA chondrocytes were detected by transfection of GFP-LC3 and flow cytometry. Bioinformatic tools were used to analyze the miRNA binding to GAS5 and the downstream target genes, then luciferase reporter assay and GDC-0349 (inhibitor of mTOR) were used to verify their relationships. RESULTS: The expression of GAS5 in cartilage tissue of OA rats was higher than control, which was higher in late OA than that in early OA. After silencing the GAS5, the autophagy ability of OACs was increased and the apoptosis rate was decreased. GAS5 was able to bind to miR-144 and regulate the expressin of mTOR. mTOR inhibitor GDC-0349 could reverse the inhibition of GAS5 on autophagy but could not reverse its effect on apoptosis. CONCLUSION: GAS5 expresses highly in OA cartilage tissues and increases with the progression of OA. GAS5 inhibits autophagy and promotes the apoptosis of OACs, and the inhibition of autophagy may be related to its regulation of mTOR.