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This meta-analysis examined roles of several metabolites in differentiating recurrent tumor from necrosis in patients with brain tumors using MR perfusion and spectroscopy. Medline, Cochrane, EMBASE, and Google Scholar were searched for studies using perfusion MRI and/or MR spectroscopy published up to March 4, 2015 which differentiated between recurrent tumor vs. necrosis in patients with primary brain tumors or brain metastasis. Only two-armed, prospective or retrospective studies were included. A meta-analysis was performed on the difference in relative cerebral blood volume (rCBV), ratios of choline/creatine (Cho/Cr) and/or choline/N-acetyl aspartate (Cho/NAA) between participants undergoing MRI evaluation. A χ2-based test of homogeneity was performed using Cochran's Q statistic and I2. Of 397 patients in 13 studies who were analyzed, the majority had tumor recurrence. As there was evidence of heterogeneity among 10 of the studies which used rCBV for evaluation (Q statistic = 31.634, I2 = 97.11%, P < 0.0001) a random-effects analysis was applied. The pooled difference in means (2.18, 95%CI = 0.85 to 3.50) indicated that the average rCBV in a contrast-enhancing lesion was significantly higher in tumor recurrence compared with radiation injury (P = 0.001). Based on a fixed-effect model of analysis encompassing the six studies which used Cho/Cr ratios for evaluation (Q statistic = 8.388, I2 = 40.39%, P = 0.137), the pooled difference in means (0.77, 95%CI = 0.57 to 0.98) of the average Cho/Cr ratio was significantly higher in tumor recurrence than in tumor necrosis (P = 0.001). There was significant difference in ratios of Cho to NAA between recurrent tumor and necrosis (1.02, 95%CI = 0.03 to 2.00, P = 0.044). MR spectroscopy and MR perfusion using Cho/NAA and Cho/Cr ratios and rCBV may increase the accuracy of differentiating necrosis from recurrent tumor in patients with primary brain tumors or metastases.

It has been unclear to which antimicrobial activities (e.g., anti-gram-positive bacterial, anti-gram-negative bacterial, antifungal, antiparasitic, and antiviral activities) of antimicrobial peptides (AMPs) a given physiochemical property matters most. This is the first computational study using large-scale AMPs to examine the relationships between antimicrobial activities and two major physiochemical properties of AMPs—amphipathicity and net charge. The results showed that among all kinds of antimicrobial activities, amphipathicity and net charge best differentiated between AMPs with and without anti-gram-negative bacterial activities. In terms of amphipathicity and charge, all the AMPs whose activities were significantly associated with amphipathicity and net charge were alike except those with anti-gram-positive bacterial activities. Furthermore, the higher the amphipathic value, the greater the proportion of AMPs possessing both antibacterial and antifungal activities. This dose–response-like pattern suggests a possible causal relationship—dual antibacterial and antifungal activities of AMPs may be attributable to amphipathicity. These novel findings could be useful for identifying potent AMPs computationally.

The aim of the study was to use a previously proposed mask region–based convolutional neural network (Mask R-CNN) for automatic abnormal liver density detection and segmentation based on hepatocellular carcinoma (HCC) computed tomography (CT) datasets from a radiological perspective. Training and testing datasets were acquired retrospectively from two hospitals of Taiwan. The training dataset contained 10,130 images of liver tumor densities of 11,258 regions of interest (ROIs). The positive testing dataset contained 1,833 images of liver tumor densities with 1,874 ROIs, and negative testing data comprised 20,283 images without abnormal densities in liver parenchyma. The Mask R-CNN was used to generate a medical model, and areas under the curve, true positive rates, false positive rates, and Dice coefficients were evaluated. For abnormal liver CT density detection, in each image, we identified the mean area under the curve, true positive rate, and false positive rate, which were 0.9490, 91.99%, and 13.68%, respectively. For segmentation ability, the highest mean Dice coefficient obtained was 0.8041. This study trained a Mask R-CNN on various HCC images to construct a medical model that serves as an auxiliary tool for alerting radiologists to abnormal CT density in liver scans; this model can simultaneously detect liver lesions and perform automatic instance segmentation.

Paranasal sinusitis is widespread and can lead to orbital complications, blindness, and death. However, the correlation between ophthalmological findings and disease staging remains unclear. This study aimed to investigate the staging, acute ophthalmological manifestations, diagnosis, management, and outcomes of orbital complications of paranasal sinusitis during a 27-year period. We retrospectively reviewed the medical records of all patients with orbital complications of paranasal sinusitis hospitalized at the National Cheng Kung University Hospital, a medical center in Taiwan during 1988-2015. Sex, age, symptoms, history, ophthalmological findings, laboratory and imaging findings, treatments, and outcomes were analyzed by staging. Eighty-three patients aged 9 days to 80 years had stage I (preseptal cellulitis, n = 39 patients), II (postseptal orbital cellulitis, n = 8), III (subperiosteal abscess, n = 16), IV (orbital abscess, n = 8), or V (intracranial involvement, n = 12) complications. Peak incidences occurred in patients aged 0-19 and 60-69 years. Chronic sinusitis and diabetes mellitus were common preexisting diseases. Extraocular movement limitation and proptosis predicted postseptal (stage II or more) involvement. The likelihood of elevated intraocular pressure increased with stage. Reduced visual acuity and presence of relative afferent pupillary defect indicated consideration of magnetic resonance imaging to investigate possible intracranial extension. Ipsilateral maxillary (81.7%) and ethmoidal (75.6%) sinuses were the most common sources of infection, and the most frequently implicated pathogens were coagulase-negative Staphylococcus spp. (25.3%) and Staphylococcus aureus (20.5%). All patients received intravenous antimicrobial therapy (multi-drug therapy in 88.0%), and 55.4% underwent surgery, most commonly endoscopic sinus surgery. One (1.2%) diabetic man with stage V complications died of fungal sinusitis with intracranial invasion. Five (6.0%) patients, all stage V, lost vision despite intensive treatment. The average length of hospital stay was 13.8 days (range 2-72 days), and significantly longer stays were associated with stages II-V as compared to stage I. Orbital infection originating from paranasal sinusitis can cause vision loss and death due to intracranial extension. Acute ophthalmological findings predict staging and prognosis. Cooperative consultation between ophthalmologists, otorhinolaryngologists, and neurologists is essential. Urgent diagnostic studies and aggressive antimicrobial therapy are indicated, and surgery should be considered.

Objectives This study aimed to evaluate the feasibility of automatic Stanford classification of classic aortic dissection (AD) using a 2-step hierarchical neural network. Methods Between 2015 and 2019, 130 arterial phase series (57 type A, 43 type B, and 30 negative cases) in aortic CTA were collected for the training and validation. A 2-step hierarchical model was built including the first step detecting AD and the second step predicting the probability (0–1) of Stanford types. The model’s performance was evaluated with an off-line prospective test in 2020. The sensitivity and specificity for Stanford type A, type B, and no AD (Sens A, B, N and Spec A, B, N , respectively) and Cohen’s kappa were reported. Results Of 298 cases (22 with type A, 29 with type B, and 247 without AD) in the off-line prospective test, the Sens A , Sens B , and Sens N were 95.45% (95% confidence interval [CI], 77.16–99.88%), 79.31% (95% CI, 60.28–92.01%), and 93.52% (95% CI, 89.69–96.25%), respectively. The Spec A , Spec B , and Spec N were 98.55% (95% CI, 96.33–99.60%), 94.05% (95% CI, 90.52–96.56%), and 94.12% (95% CI, 83.76–98.77%), respectively. The classification rate achieved 92.28% (95% CI, 88.64–95.04%). The Cohen’s kappa was 0.766 (95% CI, 0.68–0.85; p  < 0.001). Conclusions Stanford classification of classic AD can be determined by a 2-step hierarchical neural network with high sensitivity and specificity of type A and high specificity in type B and no AD. Key Points • The Stanford classification for aortic dissection is widely adopted and divides it into Stanford type A and type B based on the ascending thoracic aorta dissected or not . • The 2-step hierarchical neural network for Stanford classification of classic aortic dissection achieved high sensitivity (95.45%) and specificity (98.55%) of type A and high specificity in type B and no aortic dissection (94.05% and 94.12%, respectively) in 298 test cases . • The 2-step hierarchical neural network demonstrated moderate agreement (Cohen’s kappa: 0.766, p < 0.001) with cardiovascular radiologists in detection and Stanford classification of classic aortic dissection in 298 test cases .

Background Currently, no standard of care or therapies have been established for patients with advanced HCC. We evaluated the efficacy and safety of conventional transarterial chemoembolization using gelatin sponges or microspheres plus lipiodol-doxorubicin (cTACE) and TACE with doxorubicin-loaded drug eluting beads (DEB-TACE). Methods This retrospective study included 273 patients who received cTACE ( n  = 201) or DEB-TACE. Tumor response, survival, and adverse events were evaluated over a 5-year follow-up period. Results During 5-year follow-up, a greater percentage of patients treated with cTACE died than those treated with DEB-TACE (76.1% vs. 66.7%) ( P  = 0.045). At the last evaluation, all surviving patients had disease progression and no differences were seen between treatment groups. However, the time to disease progression differed between groups; median time to disease progression was 11.0 months for cTACE and 16.0 months for DEB-TACE ( P  = 0.019). The median survival time was 37 months in both treatment groups. No significant differences were observed between cTACE and DEB-TACE therapies in subgroups of patients with BCLC stage A or stage B + C either in survival time or time to disease progression ( P values > 0.05). No significant differences were observed in survival status or disease progression between cTACE and DEB-TACE in patient subgroups with either tumor number > 5 or with the sum of the diameter of largest five HCC tumors being > 7 cm. Conclusions DEB-TACE demonstrates greater long-term benefits than cTACE in treating treatment-naïve patients with HCC. Results of this long-term study support the use of DEB-TACE in treating HCC.

Chronic musculoskeletal pain imposes immense suffering and diminishes the quality of life for millions of patients worldwide; the pain persists despite the use of standard conservative treatments. Increases in our understanding of the pathophysiological mechanisms underlying musculoskeletal disorders indicate the involvement of inappropriate angiogenesis. Accordingly, the resulting neovessels are the target of emerging treatments for chronic musculoskeletal pain, including transarterial embolization. The use of this noninvasive procedure to treat pain refractory to standard therapy in a variety of musculoskeletal conditions is the focus of numerous recent investigations. Here, we describe the pathophysiological indications for the use of transarterial embolization and summarize the findings of studies investigating its use in a variety of histopathological conditions and anatomical sites.

Antimicrobial peptides (AMPs) are potent drug candidates against microbes such as bacteria, fungi, parasites, and viruses. The size of AMPs ranges from less than ten to hundreds of amino acids. Often only a few amino acids or the critical regions of antimicrobial proteins matter the functionality. Accurately predicting the AMP critical regions could benefit the experimental designs. However, no extensive analyses have been done specifically on the AMP critical regions and computational modeling on them is either non-existent or settled to other problems. With a focus on the AMP critical regions, we thus develop a computational model AMPcore by introducing a state-of-the-art machine learning method, conditional random fields. We generate a comprehensive dataset of 798 AMPs cores and a low similarity dataset of 510 representative AMP cores. AMPcore could reach a maximal accuracy of 90% and 0.79 Matthew's correlation coefficient on the comprehensive dataset and a maximal accuracy of 83% and 0.66 MCC on the low similarity dataset. Our analyses of AMP cores follow what we know about AMPs: High in glycine and lysine, but low in aspartic acid, glutamic acid, and methionine; the abundance of α-helical structures; the dominance of positive net charges; the peculiarity of amphipathicity. Two amphipathic sequence motifs within the AMP cores, an amphipathic α-helix and an amphipathic π-helix, are revealed. In addition, a short sequence motif at the N-terminal boundary of AMP cores is reported for the first time: arginine at the P(-1) coupling with glycine at the P1 of AMP cores occurs the most, which might link to microbial cell adhesion.

To assess the effect of transarterial embolization (TAE) for adhesive capsulitis (AC) by evaluating clinical outcomes and changes in inflammation using magnetic resonance imaging (MRI). Patients who had undergone TAE between August 2020 and August 2023 for AC refractory to conservative treatments without any invasive procedures for more than 3 months, and had undergone baseline and 3-month post-AC follow-up contrast-enhanced MRI evaluations, were included. A suspension mixture of 500 mg imipenem/cilastatin in 10 mL of iodinated contrast agent was used for TAE. MRI results were analyzed to assess periarticular capsule/ligament inflammation. Clinical assessments included pain scores using the numeric rating scale (NRS) and functional scores using the quick disabilities of the arm, shoulder, and hand (Quick DASH) questionnaire. Twenty-five patients (female:male, 14:11; age, 54.9 ± 7.1 years) were included. Significant reductions in average NRS pain scores as well as improvements in Quick DASH scores and range of motion, including anterior flexion and abduction, were observed at 1, 3, and 6 months after TAE (all < 0.001). MRI analyses revealed that TAE significantly decreased the grades of axillary recess capsule enhancement, rotator interval (RI) capsule T2 signal intensity, and RI capsule enhancement (all ≤ 0.004). TAE may be an effective and safe therapeutic approach for AC refractory to conservative treatments, alleviating pain and supporting functional recovery. The observed MRI findings suggest that the effectiveness of TAE for AC may be attributed to the reduction of inflammation and the elimination of angiogenesis.

In this study, we modified the previously proposed X2CT-GAN to build a 2Dto3D-GAN of the spine. This study also incorporated the radiologist’s perspective in the adjustment of input signals to prove the feasibility of the automatic production of three-dimensional (3D) structures of the spine from simulated bi-planar two-dimensional (2D) X-ray images. Data from 1012 computed tomography (CT) studies of 984 patients were retrospectively collected. We tested this model under different dataset sizes (333, 666, and 1012) with different bone signal conditions to observe the training performance. A 10-fold cross-validation and five metrics—Dice similarity coefficient (DSC) value, Jaccard similarity coefficient (JSC), overlap volume (OV), and structural similarity index (SSIM)—were applied for model evaluation. The optimal mean values for DSC, JSC, OV, SSIM_anteroposterior (AP), and SSIM_Lateral (Lat) were 0.8192, 0.6984, 0.8624, 0.9261, and 0.9242, respectively. There was a significant improvement in the training performance under empirically enhanced bone signal conditions and with increasing training dataset sizes. These results demonstrate the potential of the clinical implantation of GAN for automatic production of 3D spine images from 2D images. This prototype model can serve as a foundation in future studies applying transfer learning for the development of advanced medical diagnostic techniques.