Difficulty-aware hierarchical convolutional neural networks for deformable registration of brain MR images

The aim of deformable brain image registration is to align anatomical structures, which can potentially vary with large and complex deformations. Anatomical structures vary in size and shape, requiring the registration algorithm to estimate deformation fields at various degrees of complexity. Here, we present a difficulty-aware model based on an attention mechanism to automatically identify hard-to-register regions, allowing better estimation of large complex deformations. The difficulty-aware model is incorporated into a cascaded neural network consisting of three sub-networks to fully leverage both global and local contextual information for effective registration. The first sub-network is trained at the image level to predict a coarse-scale deformation field, which is then used for initializing the subsequent sub-network. The next two sub-networks progressively optimize at the patch level with different resolutions to predict a fine-scale deformation field. Embedding difficulty-aware learning into the hierarchical neural network allows harder patches to be identified in the deeper sub-networks at higher resolutions for refining the deformation field. Experiments conducted on four public datasets validate that our method achieves promising registration accuracy with better preservation of topology, compared with state-of-the-art registration methods

Comparison of the efficacy of thoracolumbosacral and lumbosacral orthosis for adolescent idiopathic scoliosis in patients with major thoracolumbar or lumbar curves: a prospective controlled study

IntroductionThoracolumbosacral orthosis (TLSO) is the most commonly used type of brace for the conservative treatment of adolescent idiopathic scoliosis (AIS). Although lumbosacral orthosis (LSO) is designed to correct single thoracolumbar or lumbar (TL/L) curves, its effectiveness remains underexplored. This novel article aims to compare the effectiveness of LSO with TLSO in treating AIS with main TL/L curves.MethodsThis prospective controlled cohort study enrolled patients with AIS with main TL/L curves and minor thoracic curves who were treated with either TLSO or LSO. Demographic and radiographic data were compared between the two groups. Treatment outcomes were also assessed. Risk factors for minor curve progression were identified, and a cut-off value was determined within the LSO group.ResultsOverall, 82 patients were recruited, including 44 in the TLSO group and 38 in the LSO group. The initial TL/L curves showed no difference between both groups. However, the baseline thoracic curves were significantly larger in the TLSO group compared to the LSO group (25.98° ± 7.47° vs. 18.71° ± 5.95°, P < 0.001). At the last follow-up, LSO demonstrated similar effectiveness to TLSO in treating TL/L curves but was less effective for thoracic curves. The initial magnitude of thoracic curves was identified as a risk factor for minor curve outcomes in the LSO group. The ROC curve analysis determined a cut-off value of 21° for thoracic curves to predict treatment outcomes.DiscussionIn contrast to TLSO, LSO exhibits comparable effectiveness in treating main TL/L curves, making it a viable clinical option; however, it is less effective for thoracic minor curves. The initial magnitude of the minor thoracic curves may guide the selection of the appropriate brace type for patients with AIS with main TL/L curves

Recent Covid-19 infection Is associated With increased Mortality in the ambulatory Surgery Population

BACKGROUND: The effect of COVID-19 infection on post-operative mortality and the optimal timing to perform ambulatory surgery from diagnosis date remains unclear in this population. Our study was to determine whether a history of COVID-19 diagnosis leads to a higher risk of all-cause mortality following ambulatory surgery. METHODS: This cohort constitutes retrospective data obtained from the Optum dataset containing 44,976 US adults who were tested for COVID-19 up to 6 months before surgery and underwent ambulatory surgery between March 2020 to March 2021. The primary outcome was the risk of all-cause mortality between the COVID-19 positive and negative patients grouped according to the time interval from COVID-19 testing to ambulatory surgery, called the Testing to Surgery Interval Mortality (TSIM) of up to 6 months. Secondary outcome included determining all-cause mortality (TSIM) in time intervals of 0-15 days, 16-30 days, 31-45 days, and 46-180 days in COVID-19 positive and negative patients. RESULTS: 44,934 patients (4297 COVID-19 positive, 40,637 COVID-19 negative) were included in our analysis. COVID-19 positive patients undergoing ambulatory surgery had higher risk of all-cause mortality compared to COVID-19 negative patients (OR = 2.51, p \u3c 0.001). The increased risk of mortality in COVID-19 positive patients remained high amongst patients who had surgery 0-45 days from date of COVID-19 testing. In addition, COVID-19 positive patients who underwent colonoscopy (OR = 0.21, p = 0.01) and plastic and orthopedic surgery (OR = 0.27, p = 0.01) had lower mortality than those underwent other surgeries. CONCLUSIONS: A COVID-19 positive diagnosis is associated with significantly higher risk of all-cause mortality following ambulatory surgery. This mortality risk is greatest in patients that undergo ambulatory surgery within 45 days of testing positive for COVID-19. Postponing elective ambulatory surgeries in patients that test positive for COVID-19 infection within 45 days of surgery date should be considered, although prospective studies are needed to assess this

Channel Modeling and Characteristics Analysis under Different 3D Dynamic Trajectories for UAV-Assisted Emergency Communications

This study involved channel modeling and characteristics analysis of unmanned aerial vehicles (UAVs) according to different operating trajectories. Based on the idea of standardized channel modeling, air-to-ground (AG) channel modeling of a UAV was carried out, taking into consideration that both the receiver (Rx) and the transmitter (Tx) ran along different types of trajectories. In addition, based on Markov chains and a smooth-turn (ST) mobility model, the influences of different operation trajectories on typical channel characteristics—including time-variant power delay profile (PDP), stationary interval, temporal autocorrelation function (ACF), root mean square (RMS) delay spread (DS), and spatial cross-correlation function (CCF)—were studied. The multi-mobility multi-trajectory UAV channel model matched well with actual operation scenarios, and the characteristics of the UAV AG channel could be analyzed more accurately, thus providing a reference for future system design and sensor network deployment of sixth-generation (6G) UAV-assisted emergency communications

RBMX deficiency increases telomere replication stress in U2OS cells.

(A) C-circle increases in RBMX-depleted U2OS cells. Cells were transfected with siRNAs for 72 h, and total DNA was extracted. C-circle was amplified by Φ29 and determined by slot blot using telomeric C-probe, with genomic DNA as loading control, and the group without Φ29 as negative control. (B) Quantification of (A). The amount of C-circle was calculated as C-circle intensity/G-DNA intensity, and then normalized to the siNC group. (C) C-rich ssDNA increases in RBMX-depleted U2OS cells. Genomic DNA was purified from RBMX-depleted U2OS cells and subjected to 2D gel electrophoresis and hybridization using 32P-G probe under native or denaturing conditions. The arrow indicates ssDNA signal. (D) Quantification of (C). The amount of relative ssDNA was calculated, and then normalized to the siNC group. (E) C-rich ssDNA increases in RBMX-depleted U2OS cells. RBMX-depleted U2OS cells were collected and analyzed by contrast-field gel electrophoresis (CFGE). Telomeric fragments were detected by hybridization of 32P-G probe under native or denaturing conditions. (F) Quantification of (E). The amount of relative ssDNA was calculated, and then normalized to the siNC group. Error bars, standard deviations from ≥3 biological replicates. p values, two-tailed Student’s t test (*p<0.05, **p <0.01, ***p <0.001, ****P <0.0001).</p

RBMX deficiency stimulates R-loop formation at telomeres.

(A) TERRA foci at telomeres increases in RBMX-depleted U2OS cells. TERRA and TRF2 were detected by IF-FISH using C probe and anti-TRF2 antibody, respectively. Scale bars, 10 μm. Arrows indicate co-localization events. (B) Quantification of (A). The mean numbers of TERRA foci per cell were counted (n ≥100 cells × 3 repeats). (C) Quantification of (A). The percentage of cells with more than 5 TERRA foci co-localized with telomeres per nucleus were calculated (n ≥100 cells × 3 repeats). (D) R-loop foci at telomeres increases in RBMX-depleted U2OS cells. R-loops and telomeres were detected by anti-S9.6 antibody and C probe, respectively. Scale bars, 10 μm. Arrows indicate co-localization events. (E) Quantification of (D). The mean numbers of R-loop foci co-localized with telomeres were counted (n ≥100 cells × 3 repeats). (F) DRIP analysis of R-loops at telomeres in control and RBMX deficient cells. S9.6 antibody was used to pull-down R-loops containing DNA, which was then subjected to slot blot using G probe. Samples digested with RNase H was used as a control. (G) Quantification of (F). The relative amount of telomeric R-loops was determined. (H) TERRA foci at telomeres increases in ZCCHC8-depleted U2OS cells. TERRA and TRF2 were detected by IF-FISH using C probe and anti-TRF2 antibody, respectively. Scale bars, 10μm. Arrows indicate co-localization events. (I) Quantification of (H). The mean numbers of TERRA foci per cell were counted (n ≥100 cells × 3 repeats). (J) Quantification of (H). The percentage of cells with more than 7 TERRA foci co-localized with telomeres per nucleus were calculated (n ≥100 cells × 3 repeats). (K) R-loop foci at telomeres increases in ZCCHC8-depleted U2OS cells. R-loops and telomeres were detected by IF-FISH using anti-S9.6 antibody and C probe, respectively. Scale bars, 10 μm. Arrows indicate co-localization events. (L) Quantification of (K). The mean numbers of R-loop foci co-localized with telomeres were counted (n ≥100 cells × 3 repeats). p values, two-tailed Student’s t test (*P <0.05, **P <0.01, ****P <0.0001).</p

Metaphase telomere FISH detection of fragile telomere signals at the end of chromosomes in the indicated cells.

(A) Metaphase telomere FISH detection of fragile telomere signals at the end of chromosomes in RBMX-deficient HeLa cells. The fragile telomere signals increase in RBMX-depleted HeLa cells. (B) Quantification of (A). The percentage of chromosomes with multiple telomeres signals were calculated. For each group, 500 or more chromosomes were examined. (C) Metaphase telomere FISH detection of fragile telomere signals at the end of chromosomes in AZD6738 treated and RBMX depleted U2OS cells. (D) Quantification of (C). The percentage of chromosomes with fragile telomere signals were calculated. For each group, 150 or more chromosomes were examined. For panel B, two-tailed Student’s t test was used to determine the statistical significance (*p (TIF)</p