Generalized sleep decoding with basal ganglia signals in multiple movement disorders.

Sleep disturbances profoundly affect the quality of life in individuals with neurological disorders. Closed-loop deep brain stimulation (DBS) holds promise for alleviating sleep symptoms, however, this technique necessitates automated sleep stage decoding from intracranial signals. We leveraged overnight data from 121 patients with movement disorders (Parkinson's disease, Essential Tremor, Dystonia, Essential Tremor, Huntington's disease, and Tourette's syndrome) in whom synchronized polysomnograms and basal ganglia local field potentials were recorded, to develop a generalized, multi-class, sleep specific decoder - BGOOSE. This generalized model achieved 85% average accuracy across patients and across disease conditions, even in the presence of recordings from different basal ganglia targets. Furthermore, we also investigated the role of electrocorticography on decoding performances and proposed an optimal decoding map, which was shown to facilitate channel selection for optimal model performances. BGOOSE emerges as a powerful tool for generalized sleep decoding, offering exciting potentials for the precision stimulation delivery of DBS and better management of sleep disturbances in movement disorders.

Statistical properties for an optically injected chaotic semiconductor laser with a high-pass filter.

Chaotic waveforms with Gaussian distributions are significant for laser-chaos-based applications such as random number generation. By exploring the injection parameter space of the optical injection semiconductor lasers, we numerically investigate the associated probability density functions of the generated chaotic waveforms when different high-pass filters with different cutoff frequencies are used. Our results demonstrate that the chaotic waveforms with Gaussian probability density functions can be obtained once the cutoff frequency of the high-pass filter is larger than the laser relaxation resonance frequency. Especially, we find that the Gaussian probability density function can reach a superhigh coefficient of determination R2 ≥ 99.5% and an ultralow skewness |S|<0.1 in a large parameter space by jointly controlling the injection parameter and cutoff frequency.

Status and influencing factors of nurses' burnout: A cross-sectional study during COVID-19 regular prevention and control in Jiangsu Province, China.

Background: Chinese nurses working with immense stress may have issues with burnout during COVID-19 regular prevention and control. There were a few studies investigating status of burnout and associated factors among Chinese nurses. However, the relationships remained unclear. Objectives: To investigate status and associated factors of nurses' burnout during COVID-19 regular prevention and control. Methods: 784 nurses completed questionnaires including demographics, Generalized Anxiety Disorder-7, Patient Health Questionnaire-9, Insomnia Severity Index, Impact of Event Scale-revised, Perceived Social Support Scale, Connor-Davidson Resilience Scale, General Self-efficacy Scale and Maslach Burnout Inventory. Results: 310 (39.5%), 393 (50.1%) and 576 (73.5%) of respondents were at high risk of emotional exhaustion (EE), depersonalization (DP) and reduced personal accomplishment (PA). The risk of EE, DP and reduced PA were moderate, high and high. Nurses with intermediate and senior professional rank and title and worked >40 h every week had lower scores in EE. Those worked in low-risk department reported lower scores in PA. Anxiety, post-traumatic stress disorder (PTSD), self-efficacy and social support were influencing factors of EE and DP, while social support and resilience were associated factors of PA. Conclusion: Chinese nurses' burnout during COVID-19 regular prevention and control was serious. Professional rank and title, working unit, weekly working hours, anxiety, PTSD, self-efficacy, social support and resilience were associated factors of burnout.

PyHFO: lightweight deep learning-powered end-to-end high-frequency oscillations analysis application.

OBJECTIVE: This study aims to develop and validate an end-to-end software platform, PyHFO, that streamlines the application of deep learning methodologies in detecting neurophysiological biomarkers for epileptogenic zones from EEG recordings. Methods: We introduced PyHFO, which enables time-efficient HFO detection algorithms like short-term energy (STE) and Montreal Neurological Institute and Hospital (MNI) detectors. It incorporates deep learning models for artifact and HFO with spike classification, designed to operate efficiently on standard computer hardware. Main results: The validation of PyHFO was conducted on three separate datasets: the first comprised solely of grid/strip electrodes, the second a combination of grid/strip and depth electrodes, and the third derived from rodent studies, which sampled the neocortex and hippocampus using depth electrodes. PyHFO demonstrated an ability to handle datasets efficiently, with optimization techniques enabling it to achieve speeds up to 50 times faster than traditional HFO detection applications. Users have the flexibility to employ our pre-trained deep learning model or use their EEG data for custom model training. Significance: PyHFO successfully bridges the computational challenge faced in applying deep learning techniques to EEG data analysis in epilepsy studies, presenting a feasible solution for both clinical and research settings. By offering a user-friendly and computationally efficient platform, PyHFO paves the way for broader adoption of advanced EEG data analysis tools in clinical practice and fosters potential for large-scale research collaborations.

Neurophysiological features of STN LFP underlying sleep fragmentation in Parkinson's disease.

BACKGROUND: Sleep fragmentation is a persistent problem throughout the course of Parkinson's disease (PD). However, the related neurophysiological patterns and the underlying mechanisms remained unclear. METHOD: We recorded subthalamic nucleus (STN) local field potentials (LFPs) using deep brain stimulation (DBS) with real-time wireless recording capacity from 13 patients with PD undergoing a one-night polysomnography recording, 1 month after DBS surgery before initial programming and when the patients were off-medication. The STN LFP features that characterised different sleep stages, correlated with arousal and sleep fragmentation index, and preceded stage transitions during N2 and REM sleep were analysed. RESULTS: Both beta and low gamma oscillations in non-rapid eye movement (NREM) sleep increased with the severity of sleep disturbance (arousal index (ArI)-betaNREM: r=0.9, p=0.0001, sleep fragmentation index (SFI)-betaNREM: r=0.6, p=0.0301; SFI-gammaNREM: r=0.6, p=0.0324). We next examined the low-to-high power ratio (LHPR), which was the power ratio of theta oscillations to beta and low gamma oscillations, and found it to be an indicator of sleep fragmentation (ArI-LHPRNREM: r=-0.8, p=0.0053; ArI-LHPRREM: r=-0.6, p=0.0373; SFI-LHPRNREM: r=-0.7, p=0.0204; SFI-LHPRREM: r=-0.6, p=0.0428). In addition, long beta bursts (>0.25 s) during NREM stage 2 were found preceding the completion of transition to stages with more cortical activities (towards Wake/N1/REM compared with towards N3 (p<0.01)) and negatively correlated with STN spindles, which were detected in STN LFPs with peak frequency distinguishable from long beta bursts (STN spindle: 11.5 Hz, STN long beta bursts: 23.8 Hz), in occupation during NREM sleep (ß=-0.24, p<0.001). CONCLUSION: Features of STN LFPs help explain neurophysiological mechanisms underlying sleep fragmentations in PD, which can inform new intervention for sleep dysfunction. TRIAL REGISTRATION NUMBER: NCT02937727.

Platycodon grandiflorum saponins: Ionic liquid-ultrasound-assisted extraction, antioxidant, whitening, and antiaging activity.

This study explored the use of ionic liquid-ultrasound (ILU)-assisted extraction to enhance the extraction rate of Platycodon grandiflorum saponins (PGSs), and the content, extraction mechanism, antioxidant activity, whitening, and antiaging activity of PGSs prepared using ILU, ultrasound-water, thermal reflux-ethanol, and cellulase hydrolysis were compared. The ILU method particularly disrupted the cell wall, improved PGS extraction efficiency, and yielded a high total saponin content of 1.45 ± 0.02 mg/g. Five monomeric saponins were identified, with platycodin D being the most abundant at 1.357 mg/g. PGSs displayed excellent in vitro antioxidant activity and exhibited inhibitory effects on tyrosinase, elastase, and hyaluronidase. The results suggest that PGSs may have broad antioxidant, skin-whitening, and antiaging potential to a large extent. Overall, this study provided valuable insights into the extraction, identification, and bioactivities of PGSs, which could serve as a reference for future development and application of these compounds in the functional foods industry.

Structural- and DTI- MRI enable automated prediction of IDH Mutation Status in CNS WHO Grade 2-4 glioma patients: a deep Radiomics Approach.

BACKGROUND: The role of isocitrate dehydrogenase (IDH) mutation status for glioma stratification and prognosis is established. While structural magnetic resonance image (MRI) is a promising biomarker, it may not be sufficient for non-invasive characterisation of IDH mutation status. We investigated the diagnostic value of combined diffusion tensor imaging (DTI) and structural MRI enhanced by a deep radiomics approach based on convolutional neural networks (CNNs) and support vector machine (SVM), to determine the IDH mutation status in Central Nervous System World Health Organization (CNS WHO) grade 2-4 gliomas. METHODS: This retrospective study analyzed the DTI-derived fractional anisotropy (FA) and mean diffusivity (MD) images and structural images including fluid attenuated inversion recovery (FLAIR), non-enhanced T1-, and T2-weighted images of 206 treatment-naïve gliomas, including 146 IDH mutant and 60 IDH-wildtype ones. The lesions were manually segmented by experienced neuroradiologists and the masks were applied to the FA and MD maps. Deep radiomics features were extracted from each subject by applying a pre-trained CNN and statistical description. An SVM classifier was applied to predict IDH status using imaging features in combination with demographic data. RESULTS: We comparatively assessed the CNN-SVM classifier performance in predicting IDH mutation status using standalone and combined structural and DTI-based imaging features. Combined imaging features surpassed stand-alone modalities for the prediction of IDH mutation status [area under the curve (AUC) = 0.846; sensitivity = 0.925; and specificity = 0.567]. Importantly, optimal model performance was noted following the addition of demographic data (patients' age) to structural and DTI imaging features [area under the curve (AUC) = 0.847; sensitivity = 0.911; and specificity = 0.617]. CONCLUSIONS: Imaging features derived from DTI-based FA and MD maps combined with structural MRI, have superior diagnostic value to that provided by standalone structural or DTI sequences. In combination with demographic information, this CNN-SVM model offers a further enhanced non-invasive prediction of IDH mutation status in gliomas.

Non-invasive prediction of preeclampsia using the maternal plasma cell-free DNA profile and clinical risk factors.

Background: Preeclampsia (PE) is a pregnancy complication defined by new onset hypertension and proteinuria or other maternal organ damage after 20 weeks of gestation. Although non-invasive prenatal testing (NIPT) has been widely used to detect fetal chromosomal abnormalities during pregnancy, its performance in combination with maternal risk factors to screen for PE has not been extensively validated. Our aim was to develop and validate classifiers that predict early- or late-onset PE using the maternal plasma cell-free DNA (cfDNA) profile and clinical risk factors. Methods: We retrospectively collected and analyzed NIPT data of 2,727 pregnant women aged 24-45 years from four hospitals in China, which had previously been used to screen for fetal aneuploidy at 12 + 0 ~ 22 + 6 weeks of gestation. According to the diagnostic criteria for PE and the time of diagnosis (34 weeks of gestation), a total of 143 early-, 580 late-onset PE samples and 2,004 healthy controls were included. The wilcoxon rank sum test was used to identify the cfDNA profile for PE prediction. The Fisher's exact test and Mann-Whitney U-test were used to compare categorical and continuous variables of clinical risk factors between PE samples and healthy controls, respectively. Machine learning methods were performed to develop and validate PE classifiers based on the cfDNA profile and clinical risk factors. Results: By using NIPT data to analyze cfDNA coverages in promoter regions, we found the cfDNA profile, which was differential cfDNA coverages in gene promoter regions between PE and healthy controls, could be used to predict early- and late-onset PE. Maternal age, body mass index, parity, past medical histories and method of conception were significantly differential between PE and healthy pregnant women. With a false positive rate of 10%, the classifiers based on the combination of the cfDNA profile and clinical risk factors predicted early- and late-onset PE in four datasets with an average accuracy of 89 and 80% and an average sensitivity of 63 and 48%, respectively. Conclusion: Incorporating cfDNA profiles in classifiers might reduce performance variations in PE models based only on clinical risk factors, potentially expanding the application of NIPT in PE screening in the future.

LncRNA HMOX1 alleviates renal ischemia-reperfusion-induced ferroptotic injury via the miR-3587/HMOX1 axis.

Emerging evidence suggests that long non-coding RNAs (lncRNAs) play significant roles in renal ischemia reperfusion (RIR) injury. However, the specific mechanisms by which lncRNAs regulate ferroptosis in renal tubular epithelial cells remain largely unknown. The objective of this study was to investigate the biological function of lncRNA heme oxygenase 1 (lnc-HMOX1) in RIR and its potential molecular mechanism. Our findings demonstrated that the expression of HMOX1-related lnc-HMOX1 was reduced in renal tubular epithelial cells treated with hypoxia-reoxygenation (HR). Furthermore, the over-expression of lnc-HMOX1 mitigated ferroptotic injury in renal tubular epithelial cells in vivo and in vitro. Mechanistically, lnc-HMOX1, as a competitive endogenous RNA (ceRNA), promoted the expression of HMOX1 by sponging miR-3587. Furthermore, the inhibition of HMOX1 effectively impeded the aforementioned effects exerted by lnc-HMOX1. Ultimately, the inhibitory or mimic action of miR-3587 reversed the promoting or refraining influence of silenced or over-expressed lnc-HMOX1 on ferroptotic injury during HR. In summary, our findings contribute to a comprehensive comprehension of the mechanism underlying ferroptotic injury mediated by lnc-HMOX1 during RIR. Significantly, we identified a novel lnc-HMOX1-miR-3587-HMOX1 axis, which holds promise as a potential therapeutic target for RIR injury.

Effect of angled abutments in the posterior maxillary region on tilted implants: a 3D finite element analysis.

The aim of this study was to evaluate the bone tissue effects under dynamic loading using finite element analysis (FEA) for four angled abutments with different deviated palatal lateral tilt angles. A three-dimensional model of the posterior maxillary region and an implant crown model were reconstructed and assembled with a three-dimensional model of the implant, angled abutment, and central screw to create a total of 10 three-dimensional finite element models tilted at 15 ∘ , 20 ∘ , 25 ∘ , and 30 ∘ in three groups, and the dynamic loads simulating oral mastication were loaded on the implant crown to analyze the equivalent stresses and strains in the peri-implant bone tissues. Under the dynamic loading, the cortical bone on the buccal side of the implant neck showed different degrees of stress concentration, and the cortical bone stress was much higher than the cancellous bone, and the strain concentration area of each model was located in the bone tissue around the implant neck and base. For the use of angular abutment, under the premise that the cortical bone stresses and strains of the 10 models meet the requirements for use, the peak stresses of 2.907 MPa, 3.018 MPa, and 2.164 MPa were achieved by using the 20 ∘ angular abutment to achieve the tilt angles of 20 ∘ , 25 ∘ , and 30 ∘ implantation, which is more advantageous compared with other models.

Effects of tillage and maturity stage on the yield, nutritive composition, and silage fermentation quality of whole-crop wheat.

Whole-crop wheat (Triticum aestivum, WCW) has a high nutritional value and digestibility. No-tillage (NT) can reduces energy and labor inputs in the agricultural production process, thus decreasing production costs. There are many studies on planting techniques of WCW at present, few being on no-tillage planting. This study aimed to compare the effects of different tillage methods and maturity stages on the yield, nutritive value, and silage fermentation quality of WCW. The experiment included two tillage methods (NT; conventional tillage, CT), two maturity stages (flowering stage; milk stage), and three years (2016-2017; 2017-2018; 2018-2019). Years had a strong influence on the yield and nutritional composition of WCW. This was mainly related to the amount of rainfall, as it affects the seedling emergence rate of wheat. Although tillage methods showed no significant effects on the yield, plant height, and stem number per plant of WCW (P > 0.05), compared to CT, the dry matter (DM) and crude protein (CP) yields of NT decreased by 0.74 t/ha and 0.13 t/ha. Tillage methods showed no significant effects on the nutritive composition of WCW (P > 0.05). The WCW at the milk stage had greater DM (5.25 t/ha) and CP (0.60 t/ha) yields than that at the flowering stage (3.19 t/ha and 0.39 t/ha) (P< 0.05). The acid detergent fiber concentration of WCW decreased by 34.5% from the flowering to the milk stage, whereas water-soluble carbohydrates concentration increased by 50.6%. The CP concentration at the milk stage was lower than that at the flowering stage (P< 0.05). The lactic acid concentration of NT (17.1 g/kg DM) silage was lower than that of CT (26.6 g/kg DM) silage (P< 0.05). The WCW silage at the milk stage had a lower NH3-N concentration (125 g/kg TN) than that at the flowering stage (169 g/kg TN) (P< 0.05). Wheat sown by NT and CT was of similar yield and nutritional value, irrespective of harvest stages. WCW harvested at the milk stage had greater yield and better nutritional composition and silage fermentation quality than that at the flowering stage. Based upon the results of the membership function analysis, no-tillage sowing of wheat was feasible and harvesting at milk stage was recommended.

Tunable High-Sensitivity Four-Frequency Refractive Index Sensor Based on Graphene Metamaterial.

As graphene-related technology advances, the benefits of graphene metamaterials become more apparent. In this study, a surface-isolated exciton-based absorber is built by running relevant simulations on graphene, which can achieve more than 98% perfect absorption at multiple frequencies in the MWIR (MediumWavelength Infra-Red (MWIR) band as compared to the typical absorber. The absorber consists of three layers: the bottom layer is gold, the middle layer is dielectric, and the top layer is patterned with graphene. Tunability was achieved by electrically altering graphene's Fermi energy, hence the position of the absorption peak. The influence of graphene's relaxation time on the sensor is discussed. Due to the symmetry of its structure, different angles of light source incidence have little effect on the absorption rate, leading to polarization insensitivity, especially for TE waves, and this absorber has polarization insensitivity at ultra-wide-angle degrees. The sensor is characterized by its tunability, polarisation insensitivity, and high sensitivity, with a sensitivity of up to 21.60 THz/refractive index unit (RIU). This paper demonstrates the feasibility of the multi-frequency sensor and provides a theoretical basis for the realization of the multi-frequency sensor. This makes it possible to apply it to high-sensitivity sensors.

The 3-Dimensional Intelligent Structured Light Technique: A New Registration Method in Stereotactic Neurosurgery.

BACKGROUND AND OBJECTIVES: Surface-based facial scanning registration emerged as an essential registration method in the robot-assisted neuronavigation surgery, providing a marker-free way to align a patient's facial surface with the imaging data. The 3-dimensional (3D) structured light was developed as an advanced registration method based on surface-based facial scanning registration. We aspire to introduce the 3D structured light as a new registration method in the procedure of the robot-assisted neurosurgery and assess the accuracy, efficiency, and safety of this method by analyzing the relative operative results. METHODS: We analyzed the results of 47 patients who underwent Ommaya reservoir implantation (n = 17) and stereotactic biopsy (n = 30) assisted by 3D structured light at our hospital from January 2022 to May 2023. The accuracy and additional operative results were analyzed. RESULTS: For the Ommaya reservoir implantation, the target point error was 3.2 ± 2.2 mm and the entry point error was 3.3 ± 2.4 mm, while the operation duration was 35.8 ± 8.3 minutes. For the stereotactic biopsy, the target point error was 2.3 ± 1.3 mm and the entry point error was 2.7 ± 1.2 mm, while the operation duration was 24.5 ± 6.3 minutes. CONCLUSION: The 3D structured light technique reduces the patients' discomfort and offers the advantage of a simpler procedure, which can improve the clinical efficiency with the sufficient accuracy and safety to meet the clinical requirements of the puncture and navigation.

Chromosome-level genome assembly of Hippophae tibetana provides insights into high-altitude adaptation and flavonoid biosynthesis.

BACKGROUND: As an endemic shrub of the Qinghai-Tibetan Plateau (QTP), the distribution of Hippophae tibetana Schlecht. ranges between 2800 and 5200 m above sea level. As the most basal branch of the Hippophae genus, H. tibetana has an extensive evolutionary history. The H. tibetana is a valuable tree for studying the ecological evolution of species under extreme conditions. RESULTS: Here, we generated a high-quality chromosome-level genome of H. tibetana. The total size of the assembly genome is 917 Mb. The phylogenomic analysis of 1064 single-copy genes showed a divergence between 3.4 and 12.8 Mya for H. tibetana. Multiple gene families associated with DNA repair and disease resistance were significantly expanded in H. tibetana. We also identified many genes related to DNA repair with signs of positive selection. These results showed expansion and positive selection likely play important roles in H. tibetana's adaptation to comprehensive extreme environments in the QTP. A comprehensive genomic and transcriptomic analysis identified 49 genes involved in the flavonoid biosynthesis pathway in H. tibetana. We generated transgenic sea buckthorn hairy root producing high levels of flavonoid. CONCLUSIONS: Taken together, this H. tibetana high-quality genome provides insights into the plant adaptation mechanisms of plant under extreme environments and lay foundation for the functional genomic research and molecular breeding of H. tibetana.

Riboflavin ameliorates intestinal inflammation via immune modulation and alterations of gut microbiota homeostasis in DSS-colitis C57BL/6 mice.

While there have been advancements in understanding the direct and indirect impact of riboflavin (B2) on intestinal inflammation, the precise mechanisms are still unknown. This study focuses on evaluating the effects of riboflavin (B2) supplementation on a colitis mouse model induced with 3% dextran sodium sulphate (DSS). We administered three different doses of oral B2 (VB2L, VB2M, and VB2H) and assessed its impact on various physiological and biochemical parameters associated with colitis. Mice given any of the three doses exhibited relative improvement in the symptoms and intestinal damage. This was evidenced by the inhibition of the pro-inflammatory cytokines TNF-α, IL-1ß, and CALP, along with an increase in the anti-inflammatory cytokine IL-10. B2 supplementation also led to a restoration of oxidative homeostasis, as indicated by a decrease in myeloperoxidase (MPO) and malondialdehyde (MDA) levels and an increase in reduced glutathione (GSH) and catalase (CAT) activities. B2 intervention showed positive effects on intestinal barrier function, confirmed by increased expression of tight junction proteins (occludin and ZO-1). B2 was linked to an elevated relative abundance of Actinobacteriota, Desulfobacterota, and Verrucomicrobiota. Notably, Verrucomicrobiota showed a significant increase in the VB2H group, reaching 15.03% relative abundance. Akkermansia exhibited a negative correlation with colitis and might be linked to anti-inflammatory function. Additionally, a remarkable increase in n-butyric acid, i-butyric acid, and i-valeric acid was reported in the VB2H group. The ameliorating role of B2 in gut inflammation can be attributed to immune system modulation as well as alterations in the gut microbiota composition, along with elevated levels of fecal SCFAs.

Synthesis and Study of Steering of Azido-tetrazole Behavior in Tetrazolo[1,5-c]pyrimidin-5-amine-Based Energetic Materials.

Tetrazoles and their derivatives are essential for compound synthesis due to their versatility, effectiveness, stability in air, and cost-efficiency. This has stimulated interest in developing techniques for their production. In this work, four compounds, tetrazolo[1,5-c]pyrimidin-5-amine (1), N-(4-azidopyrimidin-2-yl)nitramide (2), tetrazolo[1,5-c]pyrimidin-5(6H)-one (3), and tetrazolo[1,5-a]pyrimidin-5-amine (4), were obtained from commercially available reagents and straightforward synthetic methodologies. These new compounds were characterized by infrared (IR), 13C, and 1H NMR spectroscopy, differential scanning calorimetry (DSC), and single-crystal X-ray diffraction. The solvent, temperature, and electron-donating group (EDG) factors that were responsible for the steering of azido-tetrazole equilibrium in all compounds were also studied. In addition, the detonation performance of the target compounds was calculated by using heats of formation (HOFs) and crystal densities. Hirshfeld surface analysis was used to examine the intermolecular interactions of the four synthesized compounds. The results show that the excellent properties of 1-4 are triggered by ionic bonds, hydrogen bonds, and π-π stacking interactions, indicating that these compounds have the potential to be used in the development of high-performance energetic materials. Additionally, DFT analysis is in support of experimental results, which proved the effect of different factors that can influence the azido-tetrazole equilibrium in the synthesized pyrimidine derivatives in the solution.

TT-type resonator-based differential photoacoustic spectroscopy for trace gas detection.

A novel TT-type resonator was proposed for the first time, to our knowledge, to realize differential photoacoustic (PA) detection for trace gas measurement. The special design of the TT-type resonator allows us to install the microphone at the resonant center of the acoustic field to maximize the use of the absorption-induced PA signal. To meet the requirement of low gas consumption and easy integration, the TT-type resonator-based PA cell was fabricated as a fiber-coupled module with an inner volume of only 1.1 ml. For validation, the TT-type PA cell was integrated to a photoacoustic spectroscopy (PAS) system for acetylene detection. As a result, a linearity of 0.99999 was achieved in a concentration range from 0 to 5000 ppm with a noise equivalent sensitivity of 101 ppb. The proposed TT-type resonator contributes a new style of PA cell structure to the field of PAS gas detection, combining the advantages of easy integration, low gas consumption, differential detection, and photoacoustic enhancement together.

Preoperative evaluation and orthopedic surgical strategies for tumor-induced osteomalacia.

Tumor-induced osteomalacia (TIO), also known as oncogenic osteomalacia, is very rare, with about 1000 reported cases globally. Removing most TIO culprit tumors requires the evaluation and intervention of orthopedic doctors. However, orthopedic doctors often have a poor understanding of the optical treatment of TIO due to its rarity. In addition, most TIO patients lack specific clinical manifestations. Also, the clinical localization and qualitative diagnosis of TIO are difficult and thus can easily be misdiagnosed and mistreated. Furthermore, the true incidence rate of TIO may be underestimated. Although many breakthroughs have been made in exploring the pathogenesis, clinical diagnosis, and treatment of TIO, rational and standardized orthopedic surgical treatment experience summary and sorting for TIO patients are lacking. In this article, the recent experience and progress in the field of orthopedic surgical treatment for TIO globally have been summarized, providing a theoretical basis and new clinical practice guidance for the rational treatment of TIO patients.

A Lesion-Fusion Neural Network for Multi-View Diabetic Retinopathy Grading.

As the most common complication of diabetes, diabetic retinopathy (DR) is one of the main causes of irreversible blindness. Automatic DR grading plays a crucial role in early diagnosis and intervention, reducing the risk of vision loss in people with diabetes. In these years, various deep-learning approaches for DR grading have been proposed. Most previous DR grading models are trained using the dataset of single-field fundus images, but the entire retina cannot be fully visualized in a single field of view. There are also problems of scattered location and great differences in the appearance of lesions in fundus images. To address the limitations caused by incomplete fundus features, and the difficulty in obtaining lesion information. This work introduces a novel multi-view DR grading framework, which solves the problem of incomplete fundus features by jointly learning fundus images from multiple fields of view. Furthermore, the proposed model combines multi-view inputs such as fundus images and lesion snapshots. It utilizes heterogeneous convolution blocks (HCB) and scalable self-attention classes (SSAC), which enhance the ability of the model to obtain lesion information. The experimental results show that our proposed method performs better than the benchmark methods on the large-scale dataset.

Trinitromethyl-Substituted 1H-1,2,4-Triazole Bridging Nitropyrazole: A Strategy of Utterly Manipulable Nitration Achieving High-Energy Density Material.

Nitro groups have been demonstrated to play a decisive role in the development of the most powerful known energetic materials. Two trinitromethyl-substituted 1H-1,2,4-triazole bridging nitropyrazoles were first synthesized by straightforward routes and were characterized by chemical (MS, NMR, IR spectroscopy, and single-crystal X-ray diffraction) and experimental analysis (sensitivity toward friction, impact, and differential scanning calorimetry-thermogravimetric analysis test). Their detonation properties (detonation pressure, detonation velocity, etc.) were predicted by the EXPLO5 package based on the crystal density and calculated heat of formation with Gaussian 09. These new trinitromethyl triazoles were found to show suitable sensitivities, high density, and highly positive heat of formation. The combination of exceedingly high performances superior to those of HMX (1,3,5,7-tetranitrotetraazacyclooctane), and its straightforward preparation highlights compound 8 as a promising high-energy density material (HEDM). This work supports the effectivity of utterly manipulable nitration and provides a generalizable design synthesis strategy for developing new HEDMs.