A Comprehensive Study on Implant Stability Quotient (ISQ) in View of Resonance Frequency and Spectrum Analysis.

PURPOSE: This experimental study investigated how well implant stability quotient (ISQ) represents resonance frequency. Benchtop experiments on standardized samples, mimicking a premolar section of a mandible, were conducted to correlate an ISQ value and a resonance frequency to synthetic bone density and an incremental insertion torque. A frequency spectrum analysis was performed to check the validity of the resonance frequency analysis (RFA). MATERIALS AND METHODS: Branemark Mk III implants with dimensions ∅4 Å~ 11.5 mm were placed in Sawbones test models of five different densities (40, 30, 40/20, 20, 15 PCF). An incremental insertion torque was recorded during implant placement. To perform stability measurements, the test models were clamped partially in a vise (unclamped volume 10 Å~ 20 Å~ 34 mm). A MultiPeg was attached onto the implants, and a Penguin RFA measured ISQ. Simultaneously, motion of the MultiPeg was monitored via a laser Doppler vibrometer and processed by a spectrum analyzer to obtain the resonance frequency. Tightness of the clamp was adjusted to vary the resonance frequency. A statistical analysis produced a linear correlation coefficient 𝑅 among the measured ISQ, resonance frequency, and incremental insertion torque. RESULTS: The resonance frequency had high correlation to the incremental insertion torque (𝑅 = 0.978), confirming the validity of using RFA for this study. Measured ISQ data were scattered and had low correlation to the resonance frequency (𝑅 = 0.214) as well as the incremental insertion torque (𝑅 = -0.386). The spectrum analysis revealed simultaneous presence of multiple resonance frequencies. CONCLUSIONS: For the designed benchtop tests, resonance frequency does indicate implant stability in view of Sawbones density and incremental insertion torque. ISQ measurements, however, do not correlate well to the resonance frequency, and may not reflect the stability when multiple resonance frequencies are present simultaneously.

Diabetic peripheral neuropathy: age-stratified glycemic control.

Background: We explore the effect of suboptimal glycemic control on the incidence of diabetic peripheral neuropathy (DPN) in both non-elderly and elderly patients with type 2 diabetes mellitus (T2DM). Methods: A 6-year follow-up study (2013-2019) enrolled T2DM patients aged >20 without DPN. Participants were classified into two groups: those below 65 years (non-elderly) and those 65 years or older (elderly). Biochemical measurements, including glycated hemoglobin (HbA1C), were recorded regularly. DPN was diagnosed using the Michigan Neuropathy Screening Instrument examination. The outcome was DPN occurrence in 2019. Results: In 552 enrollments (69% non-elderly), DPN occurred in 8.4% non-elderly and 24.0% elderly patients. A higher initial HbA1C level was significantly linked with a higher risk of future DPN in the non-elderly group (adjusted odds ratio [AOR] 1.46, 95% CI 1.13-1.89, p=0.004). In comparison, HbA1c at the end of the study period was not associated with DPN in the non-elderly group (AOR 1.17, 95% CI 0.72-1.90, p=0.526). In the elderly group, no statistical relationship was found between HbA1C levels and DPN, either in 2013 or in 2019. Conclusion: Suboptimal glycemic control at baseline, rather than at the end of the study period, predicts an increased risk of future DPN in individuals with T2DM under age 65. This correlation is not seen in elderly patients. Therefore, we recommend implementing enhanced glycemic control early in middle-aged T2DM patients and propose individualized therapeutic strategies for diabetes in different age groups.

Nerve bypass surgery for spinal cord reconstruction.

OBJECTIVE: Spinal cord injury (SCI) is a devastating condition that significantly decreases the patient's quality of life. Therefore, treatments that can facilitate nerve regeneration, reduce complications, and increase quality of life are valuable for these patients. In this study, we aimed to assess nerve bypass surgery's feasibility and clinical outcomes by transferring the intercostal nerves (ICNs) into the spinal cord. METHODS: Eight patients with complete thoracic SCI and delayed presentation more than a year after the injury were analyzed retrospectively. All patients underwent nerve bypass surgery with the transfer of two pairs of ICNs from proximal to the injury site to the anterolateral spinal cord, followed by duraplasty with fascia grafting to close the dura. RESULTS: Six of the eight (75%) patients demonstrated motor and sensory improvements, based on the American Spinal Cord Injury Association score. Three patients demonstrated a limited recovery of motor function that could be independently triggered without ICN initiation. Five patients demonstrated evidence of cerebrospinal fluid (CSF) leakage after surgery; however, only one patient complained of a headache. CONCLUSION: Spinal cord bypass surgery is a potential reconstruction method to treat chronic complete thoracic SCI with functional improvements, and is worth further investigation.

Depression assessment using integrated multi-featured EEG bands deep neural network models: Leveraging ensemble learning techniques.

Mental Status Assessment (MSA) holds significant importance in psychiatry. In recent years, several studies have leveraged Electroencephalogram (EEG) technology to gauge an individual's mental state or level of depression. This study introduces a novel multi-tier ensemble learning approach to integrate multiple EEG bands for conducting mental state or depression assessments. Initially, the EEG signal is divided into eight sub-bands, and then a Long Short-Term Memory (LSTM)-based Deep Neural Network (DNN) model is trained for each band. Subsequently, the integration of multi-band EEG frequency models and the evaluation of mental state or depression level are facilitated through a two-tier ensemble learning approach based on Multiple Linear Regression (MLR). The authors conducted numerous experiments to validate the performance of the proposed method under different evaluation metrics. For clarity and conciseness, the research employs the simplest commercialized one-channel EEG sensor, positioned at FP1, to collect data from 57 subjects (49 depressed and 18 healthy subjects). The obtained results, including an accuracy of 0.897, F1-score of 0.921, precision of 0.935, negative predictive value of 0.829, recall of 0.908, specificity of 0.875, and AUC of 0.8917, provide evidence of the superior performance of the proposed method compared to other ensemble learning techniques. This method not only proves effective but also holds the potential to significantly enhance the accuracy of depression assessment.

Femtosecond laser-assisted astigmatic keratotomy versus toric IOL implantation for correcting astigmatism in cataract patients: a systematic review and meta-analysis with trial sequential analysis.

AIMS: To compare the refractive and visual outcomes of femtosecond laser-assisted astigmatic keratotomy (FSAK) and toric intraocular lens (IOL) implantation for correcting astigmatism in cataract patients. METHODS: Studies were retrieved from the Ovid-Medline, EMBASE, Cochrane Central Register of Controlled Trials and Scopus which compared FSAK and toric IOL for astigmatism correction in cataract patients. Outcome measures included postoperative refractive cylinder, correction index, uncorrected distance visual acuity (UDVA), the proportion of patients achieving a residual refractive cylinder of 1.00 dioptre or less, target-induced astigmatism (TIA) and surgically induced astigmatism (SIA). The trial sequential analysis (TSA) was used to collect firm evidence supporting our conclusion. RESULTS: 9 studies encompassing 590 participants were analysed. The meta-analysis revealed that toric IOLs could result in less postoperative refractive cylinder and provide better UDVA compared with FSAK. The TSA disclosed strong evidence of lower postoperative refractive cylinder in the toric IOL group compared with that of the FSAK group. FSAK showed a smaller correction index and lower mean TIA and SIA compared with toric IOLs. CONCLUSIONS: For cataract patients, both FSAK and toric IOLs are effective methods for correcting astigmatism. However, toric IOLs offer less postoperative astigmatism and result in better postoperative UDVA compared with FSAK. In vector analysis of astigmatism, toric IOLs can also produce higher TIA and SIA. Additionally, neither method is associated with severe untreatable complications. Therefore, the conclusion is that toric IOLs are the preferred choice for astigmatism correction in cataract patients and FSAK serves as a viable alternative when toric IOLs are contraindicated.

OA-GAN: organ-aware generative adversarial network for synthesizing contrast-enhanced medical images.

Contrast-enhanced computed tomography (CE-CT) images are vital for clinical diagnosis of focal liver lesions (FLLs). However, the use of CE-CT images imposes a significant burden on patients due to the injection of contrast agents and extended shooting. Deep learning-based image synthesis models offer a promising solution that synthesizes CE-CT images from non-contrasted CT (NC-CT) images. Unlike natural images, medical image synthesis requires a specific focus on certain organs or localized regions to ensure accurate diagnosis. Determining how to effectively emphasize target organs poses a challenging issue in medical image synthesis. To solve this challenge, we present a novel CE-CT image synthesis model called, Organ-Aware Generative Adversarial Network (OA-GAN). The OA-GAN comprises an organ-aware (OA) network and a dual decoder-based generator. First, the OA network learns the most discriminative spatial features about the target organ (i.e. liver) by utilizing the ground truth organ mask as localization cues. Subsequently, NC-CT image and captured feature are fed into the dual decoder-based generator, which employs a local and global decoder network to simultaneously synthesize the organ and entire CECT image. Moreover, the semantic information extracted from the local decoder is transferred to the global decoder to facilitate better reconstruction of the organ in entire CE-CT image. The qualitative and quantitative evaluation on a CE-CT dataset demonstrates that the OA-GAN outperforms state-of-the-art approaches for synthesizing two types of CE-CT images such as arterial phase and portal venous phase. Additionally, subjective evaluations by expert radiologists and a deep learning-based FLLs classification also affirm that CE-CT images synthesized from the OA-GAN exhibit a remarkable resemblance to real CE-CT images.

Multitask deep learning for prediction of microvascular invasion and recurrence-free survival in hepatocellular carcinoma based on MRI images.

BACKGROUND AND AIMS: Accurate preoperative prediction of microvascular invasion (MVI) and recurrence-free survival (RFS) is vital for personalised hepatocellular carcinoma (HCC) management. We developed a multitask deep learning model to predict MVI and RFS using preoperative MRI scans. METHODS: Utilising a retrospective dataset of 725 HCC patients from seven institutions, we developed and validated a multitask deep learning model focused on predicting MVI and RFS. The model employs a transformer architecture to extract critical features from preoperative MRI scans. It was trained on a set of 234 patients and internally validated on a set of 58 patients. External validation was performed using three independent sets (n = 212, 111, 110). RESULTS: The multitask deep learning model yielded high MVI prediction accuracy, with AUC values of 0.918 for the training set and 0.800 for the internal test set. In external test sets, AUC values were 0.837, 0.815 and 0.800. Radiologists' sensitivity and inter-rater agreement for MVI prediction improved significantly when integrated with the model. For RFS, the model achieved C-index values of 0.763 in the training set and ranged between 0.628 and 0.728 in external test sets. Notably, PA-TACE improved RFS only in patients predicted to have high MVI risk and low survival scores (p < .001). CONCLUSIONS: Our deep learning model allows accurate MVI and survival prediction in HCC patients. Prospective studies are warranted to assess the clinical utility of this model in guiding personalised treatment in conjunction with clinical criteria.

Doping effect on a two-electron silver nanocluster.

This study investigates the effects of metal addition and doping of a 2-electron silver superatom, [Ag10{S2P(OiPr)2}8] (Ag10). When Ag+ is added to Ag10 in THF solution, [Ag11{S2P(OiPr)2}8(OTf)] (Ag11) is rapidly formed almost quantitatively. When the same method is used with Cu+, a mixture of alloys, [CuxAg11-x{S2P(OiPr)2}8]+ (x = 1-3, CuxAg11-x), is obtained. In contrast, introducing Au+ to Ag10 leads to decomposition. The structural and compositional analysis of Ag11 was characterized by single-crystal X-ray diffraction (SCXRD), ESI-MS, NMR spectroscopy, and DFT calculations. While no crystal structure was obtained for CuxAg11-x, DFT calculations provide insights into potential sites for copper location. The absorption spectrum exhibits a notable blue shift in the low-energy band after copper doping, contrasting with that of the slight shift observed in 8-electron Cu-doped Ag nanoclusters. Ag11 and CuxAg11-x are strongly emissive at room temperature, and solvatochromism across different organic solvents is highlighted. This study underscores the profound influence of metal addition and doping on the structural and optical properties of silver nanoclusters, providing important contributions to understanding the nanoclusters and their photophysical behaviors.

A Silver Nanocluster Assembled by a Superatomic Building Unit.

A unique assembly of a two-electron superatom, [Ag10{S2P(OiPr)2}8], as a primary building unit in the construction of a supramolecule [Ag10{S2P(OiPr)2}8]2(µ-4,4'-bpy) through a 4,4'-bipyridine (4,4'-bpy) linker is reported. This approach is facilitated by an open site in the structure that allows for effective pairing. The assembled structure demonstrates a minimal solvatochromic shift across organic solvents with variable polarities, highlighting the influence of self-assembly on the photophysical properties of silver nanoclusters.

Morphologic and Molecular Heterogeneity of High-grade Serous Carcinoma Precursor Lesions.

Serous tubal intraepithelial carcinoma (STIC) is the fallopian tube precursor lesion for most cases of pelvic high-grade serous carcinoma (HGSC). To date, the morphologic, molecular, and clinical heterogeneity of STIC and a less atypical putative precursor lesion, termed serous tubal intraepithelial lesion, has not been well characterized. Better understanding of precursor heterogeneity could impact the clinical management of women with incidental STICs (without concurrent carcinoma) identified in cases of prophylactic or opportunistic salpingectomy. This study analyzed morphologic and molecular features of 171 STICs and 21 serous tubal intraepithelial lesions. We assessed their histologic features, Ki-67 and p53 staining patterns, and genome-wide DNA copy number alterations. We classified all precursor lesions into 2 morphologic subtypes, one with a flat surface (Flat) and the other characterized by budding, loosely adherent, or detached (BLAD) morphology. On the basis of pathology review by a panel of 8 gynecologic pathologists, we found 87 BLAD, 96 Flat, and 9 indeterminate lesions. As compared with Flat lesions, BLAD lesions were more frequently diagnostic of STIC ( P <0.0001) and were found concurrently with HGSC ( P <0.0001). BLAD morphology was also characterized by higher Ki-67 proliferation index ( P <0.0001), presence of epithelial stratification ( P <0.0001), and increased lymphocyte density ( P <0.0001). BLAD lesions also exhibited more frequent DNA copy number gain/amplification at the CCNE1 or CMYC loci canonical to HGSCs ( P <0.0001). Both BLAD morphology and STIC diagnoses are independent risk factors for an elevated Ki-67 proliferation index. No correlation was observed between BLAD and Flat lesions with respect to patient age, presence of germline BRCA1/2 mutation, or p53 staining pattern. These findings suggest that tubal precursor lesions are morphologically and molecularly heterogeneous, laying the foundation for further studies on the pathogenesis of HGSC initiation and identifying histologic features predictive of poor patient outcomes.

Memory Guided Transformer With Spatio-Semantic Visual Extractor for Medical Report Generation.

Medicalimaging-based report writing for effective diagnosis in radiology is time-consuming and can be error-prone by inexperienced radiologists. Automatic reporting helps radiologists avoid missed diagnoses and saves valuable time. Recently, transformer-based medical report generation has become prominent in capturing long-term dependencies of sequential data with its attention mechanism. Nevertheless, input features obtained from traditional visual extractor of conventional transformers do not capture spatial and semantic information of an image. So, the transformer is unable to capture fine-grained details and may not produce detailed descriptive reports of radiology images. Therefore, we propose a spatio-semantic visual extractor (SSVE) to capture multi-scale spatial and semantic information from radiology images. Here, we incorporate two types of networks in ResNet 101 backbone architecture, i.e. (i) deformable network at the intermediate layer of ResNet 101 that utilizes deformable convolutions in order to obtain spatially invariant features, and (ii) semantic network at the final layer of backbone architecture which uses dilated convolutions to extract rich multi-scale semantic information. Further, these network representations are fused to encode fine-grained details of radiology images. The performance of our proposed model outperforms existing works on two radiology report datasets, i.e., IU X-ray and MIMIC-CXR.

MSPA-DLA++: A Multi-Scale Phase Attention Deep Layer Aggregation for Lesion Detection in Multi-Phase CT Images.

Object detection using convolutional neural networks (CNNs) has achieved high performance and achieved state-of-the-art results with natural images. Compared to natural images, medical images present several challenges for lesion detection. First, the sizes of lesions vary tremendously, from several millimeters to several centimeters. Scale variations significantly affect lesion detection accuracy, especially for the detection of small lesions. Moreover, the effective extraction of temporal and spatial features from multi-phase CT images is also an important issue. In this paper, we propose a group-based deep layer aggregation method with multiphase attention for liver lesion detection in multi-phase CT images. The method, which is called MSPA-DLA++, is a backbone feature extraction network for anchor-free liver lesion detection in multi-phase CT images that addresses scale variations and extracts hidden features from such images. The effectiveness of the proposed method is demonstrated on public datasets (LiTS2017) and our private multiphase dataset. The results of the experiments show that MSPA-DLA++ can improve upon the performance of state-of-the-art networks by approximately 3.7%.

CMIR: A Unified Cross-Modality Framework for Preoperative Accurate Prediction of Microvascular Invasion in Hepatocellular Carcinoma.

Microvascular invasion of HCC is an important factor affecting postoperative recurrence and prognosis of patients. Preoperative diagnosis of MVI is greatly significant to improve the prognosis of HCC. Currently, the diagnosis of MVI is mainly based on the histopathological examination after surgery, which is difficult to meet the requirement of preoperative diagnosis. Also, the sensitivity, specificity and accuracy of MVI diagnosis based on a single imaging feature are low. In this paper, a robust, high-precision cross-modality unified framework for clinical diagnosis is proposed for the prediction of microvascular invasion of hepatocellular carcinoma. It can effectively extract, fuse and locate multi-phase MR Images and clinical data, enrich the semantic context, and comprehensively improve the prediction indicators in different hospitals. The state-of-the-art performance of the approach was validated on a dataset of HCC patients with confirmed pathological types. Moreover, CMIR provides a possible solution for related multimodality tasks in the medical field.

Efficacy and safety of intravitreal faricimab for neovascular age-related macular degeneration: a systematic review and meta-analysis.

We conducted a systematic review and meta-analysis to evaluate the visual, anatomical, and safety outcomes of the intravitreal faricimab, a novel vascular endothelial growth factor (VEGF)/angiopoietin-2 (Ang-2) bispecific agent, in neovascular age-related macular degeneration (nAMD) patients. The follow-up times in the included studies ranged from a minimum of 36 weeks to a maximum of 52 weeks. EMBASE, Ovid-Medline, Cochrane Central Register of Controlled Trials (CENTRAL), Web of Science, Scopus, the WHO ICTRP, ClinicalTrial.gov, the EU Clinical Trials Register, and Chinese Clinical Trial Registry (ChiCTR) were searched (The last literature search was performed on August 17, 2023) for randomized controlled trials (RCTs) comparing faricimab with control groups for neovascular age-related macular degeneration (nAMD). The risk of bias for eligible RCTs was independently assessed using the Cochrane Risk of Bias Tool by two authors (W.-T.Y. and C.-S.W.). The meta-analysis was conducted using Review Manager 5.4 software. The mean best corrected visual acuity (BCVA), central subfield thickness (CST), total choroidal neovascularization (CNV) area, and total lesion leakage were analyzed as continuous variables and the outcome measurements were reported as the weighted mean difference (WMD) with a 95% confidence interval (CI). The ocular adverse events and ocular serious adverse events were analyzed as dichotomous variables and the outcome measurements were analyzed as the odds ratios (ORs) with a 95% CI. Random-effects model was used in our study for all outcome synthesizing due to different clinical characteristics. Four RCTs with 1,486 patients were eligible for quantitative analysis. There was no statistically significant difference between intravitreal faricimab and anti-VEGF in BCVA [weighted mean difference (WMD) = 0.47; 95% CI: (- 0.17, 1.11)]. The intravitreal faricimab group showed numerically lower CST [WMD = - 5.96; 95% CI = (- 7.11, - 4.82)], total CNV area [WMD = - 0.49; 95% CI = (- 0.68, - 0.30)], and total lesion leakage [WMD = - 0.88; 95% CI = (- 1.08, - 0.69)] after intravitreal therapy compared with the intravitreal anti-VEGF group. There were no statistically significant differences between intravitreal faricimab and anti-VEGF in ocular adverse events (AEs) [pooled odds ratio (OR) = 1.10; 95% CI = (0.81, 1.49)] and serious adverse events (SAEs) [pooled OR = 0.84; 95% CI = (0.37, 1.90)]. The intravitreal bispecific anti-VEGF/angiopoietin 2 (Ang2) antibody faricimab with a extended injection interval was non-inferior to first-line anti-VEGF agents in BCVA. It was safe and had better anatomical recovery. Large, well-designed RCTs are needed to explore the potential benefit of extended faricimab for nAMD. This systematic review was registered in the International Prospective Register of Systematic Reviews (PROSPERO) database (CRD42022327450).

Lightweight neural network for Alzheimer's disease classification using multi-slice sMRI.

Alzheimer's disease (AD) is a progressive neurodegenerative disease. Early detection and intervention are crucial in preventing the progression of AD. To achieve efficient and scalable AD auto-detection based on structural Magnetic Resonance Imaging (sMRI), a lightweight neural network using multi-slice sMRI is proposed in this paper. The backbone for feature extraction is based on ShuffleNet V1 architecture, which is effective for overcoming the limitations posed by limited sMRI data and resource-restricted devices. In addition, we incorporate Efficient Channel Attention (ECA) to capture cross-channel interaction information, enabling us to effectively enhance features of disease associated brain regions. To optimize the model, we employ both cross entropy loss and triplet loss functions to constrain the predicted probabilities to the ground-truth labels, and to ensure appropriate representation of distances between different classes in the learned features. Experimental results show that the classification accuracies of our method for AD vs. CN, AD vs. MCI, and MCI vs. CN classification tasks are 95.00%, 87.50%, and 85.62% respectively. Our method utilizes only 3.42 M parameters and 6.08G FLOPs, while maintaining a comparable level of performance compared to the other 5 latest lightweight methods. This model design is computationally efficient, allowing it to process large amounts of data quickly and accurately in a timely manner. Additionally, it has the potential to advance the intelligent detection of Alzheimer's disease on devices with limited computing capabilities.

First 100 total hip arthroplasties performed by a young surgeon using the direct anterior approach: learning curve and complications.

INTRODUCTION: Most of the reported discussions about the learning curve for the direct anterior approach (DAA) in total hip arthroplasty (THA) have been by experienced surgeons. The study's aim was to describe the learning curve, short-term outcomes, complications, and adaptations to the DAA used in the first 100 THA cases experienced by a young surgeon who had received DAA training for trauma surgeries. MATERIALS AND METHODS: This retrospective study summarizes the first 100 consecutive cases experienced by a young surgeon who performed the unilateral DAA for THA between 2019 and 2021. Cumulative sum (CUSUM) analysis was performed to evaluate the learning curve on the basis of operative time and overall complications. The demographics data, short-term outcomes, and complications of the first 50 and second 50 cases were compared. RESULTS: The CUSUM curve declined after 49 and 55 cases, measured by operative time and overall complications, respectively. The median operative time (104 vs. 80 min) and intraoperative fluoroscopic time (38 vs. 12 s) increased significantly in the first 50 cases compared with the times in the second 50 cases. Complications tended to occur in the first 50 cases (12% vs. 6%), and the overall rate was 9%. Major complications all occurred in the first 50 cases, with a rate of 4%. Only one case, which involved a complicated periprosthetic fracture around the stem that extended to the tip, required the intervention of a senior surgeon. CONCLUSIONS: Even after receiving training on the DAA for trauma surgeries, the young surgeon experienced a steep learning curve and more complications in the first 50 cases. The DAA for THA is a technically demanding procedure and may require guidance from an experienced surgeon to manage unexpected complications.

Aneuploidy Landscape in Precursors of Ovarian Cancer.

PURPOSE: Serous tubal intraepithelial carcinoma (STIC) is now recognized as the main precursor of ovarian high-grade serous carcinoma (HGSC). Other potential tubal lesions include p53 signatures and tubal intraepithelial lesions. We aimed to investigate the extent and pattern of aneuploidy in these epithelial lesions and HGSC to define the features that characterize stages of tumor initiation and progression. EXPERIMENTAL DESIGN: We applied RealSeqS to compare genome-wide aneuploidy patterns among the precursors, HGSC (cases, n = 85), and histologically unremarkable fallopian tube epithelium (HU-FTE; control, n = 65). On the basis of a discovery set (n = 67), we developed an aneuploidy-based algorithm, REAL-FAST (Repetitive Element AneupLoidy Sequencing Fallopian Tube Aneuploidy in STIC), to correlate the molecular data with pathology diagnoses. We validated the result in an independent validation set (n = 83) to determine its performance. We correlated the molecularly defined precursor subgroups with proliferative activity and histology. RESULTS: We found that nearly all p53 signatures lost the entire Chr17, offering a "two-hit" mechanism involving both TP53 and BRCA1 in BRCA1 germline mutation carriers. Proliferatively active STICs harbor gains of 19q12 (CCNE1), 19q13.2, 8q24 (MYC), or 8q arm, whereas proliferatively dormant STICs show 22q loss. REAL-FAST classified HU-FTE and STICs into 5 clusters and identified a STIC subgroup harboring unique aneuploidy that is associated with increased proliferation and discohesive growth. On the basis of a validation set, REAL-FAST showed 95.8% sensitivity and 97.1% specificity in detecting STIC/HGSC. CONCLUSIONS: Morphologically similar STICs are molecularly distinct. The REAL-FAST assay identifies a potentially "aggressive" STIC subgroup harboring unique DNA aneuploidy that is associated with increased cellular proliferation and discohesive growth. REAL-FAST offers a highly reproducible adjunct technique to assist the diagnosis of STIC lesions.

Systematic transcriptome-wide meta-analysis across endocrine disrupting chemicals reveals shared and unique liver pathways, gene networks, and disease associations.

Cardiometabolic disorders (CMD) are a growing public health problem across the world. Among the known cardiometabolic risk factors are compounds that induce endocrine and metabolic dysfunctions, such as endocrine disrupting chemicals (EDCs). To date, how EDCs influence molecular programs and cardiometabolic risks has yet to be fully elucidated, especially considering the complexity contributed by species-, chemical-, and dose-specific effects. Moreover, different experimental and analytical methodologies employed by different studies pose challenges when comparing findings across studies. To explore the molecular mechanisms of EDCs in a systematic manner, we established a data-driven computational approach to meta-analyze 30 human, mouse, and rat liver transcriptomic datasets for 4 EDCs, namely bisphenol A (BPA), bis(2-ethylhexyl) phthalate (DEHP), tributyltin (TBT), and perfluorooctanoic acid (PFOA). Our computational pipeline uniformly re-analyzed pre-processed quality-controlled microarray data and raw RNAseq data, derived differentially expressed genes (DEGs) and biological pathways, modeled gene regulatory networks and regulators, and determined CMD associations based on gene overlap analysis. Our approach revealed that DEHP and PFOA shared stable transcriptomic signatures that are enriched for genes associated with CMDs, suggesting similar mechanisms of action such as perturbations of peroxisome proliferator-activated receptor gamma (PPARγ) signaling and liver gene network regulators VNN1 and ACOT2. In contrast, TBT exhibited highly divergent gene signatures, pathways, network regulators, and disease associations from the other EDCs. In addition, we found that the rat, mouse, and human BPA studies showed highly variable transcriptomic patterns, providing molecular support for the variability in BPA responses. Our work offers insights into the commonality and differences in the molecular mechanisms of various EDCs and establishes a streamlined data-driven workflow to compare molecular mechanisms of environmental substances to elucidate the underlying connections between chemical exposure and disease risks.

Surgical reconstructions for adult brachial plexus injuries. Part II: Treatments for total arm type.

Brachial plexus injuries (BPI) contribute not only to physical dysfunction but also to socioeconomic aspects and psychological disability. Patients with total arm-type BPI will lose not only the shoulder and elbow function but also the hand function, making reconstruction particularly challenging. Reconstructive procedures commonly include nerve repair, grafting, neurotization (nerve transfer), tendon transfer and free functional muscle transfer (FFMT). Although it is difficult to achieve prehensile hand function, most of patients with total arm-type BPI can be treated with satisfied outcomes. In addition to surgical techniques, comprehensive rehabilitation is another important factor for successful outcomes, and efficient communication can help to boost patient morale and eliminate uncertainty.

Recommendations and guidelines for endoscope reprocessing: Current position statement of digestive endoscopic society of Taiwan.

Reprocessing of gastrointestinal (GI) endoscopes and accessories is an essential part of patient safety and quality control in GI endoscopy centers. However, current endoscopic reprocessing guidelines or procedures are not adequate to ensure patient-safe endoscopy. Approximately 5.4 % of the clinically used duodenoscopes remain contaminated with high-concern microorganisms. Thus, the Digestive Endoscopy Society of Taiwan (DEST) sets standards for the reprocessing of GI endoscopes and accessories in endoscopy centers. DEST organized a task force working group using the guideline-revision process. These guidelines contain principles and instructions of step-by-step for endoscope reprocessing. The updated guidelines were established after a thorough review of the existing global and local guidelines, systematic reviews, and health technology assessments of clinical effectiveness. This guideline aims to provide detailed recommendations for endoscope reprocessing to ensure adequate quality control in endoscopy centers.