Impact of residual antibiotics on microbial decomposition of livestock manures in Eutric Regosol: Implications for sustainable nutrient recycling and soil carbon sequestration.

The land application of livestock manure has been widely acknowledged as a beneficial approach for nutrient recycling and environmental protection. However, the impact of residual antibiotics, a common contaminant of manure, on the degradation of organic compounds and nutrient release in Eutric Regosol is not well understood. Here, we studied, how oxytetracycline (OTC) and ciprofloxacin (CIP) affect the decomposition, microbial community structure, extracellular enzyme activities and nutrient release from cattle and pig manure using litterbag incubation experiments. Results showed that OTC and CIP greatly inhibited livestock manure decomposition, causing a decreased rate of carbon (28%-87%), nitrogen (15%-44%) and phosphorus (26%-43%) release. The relative abundance of gram-negative (G-) bacteria was reduced by 4.0%-13% while fungi increased by 7.0%-71% during a 28-day incubation period. Co-occurrence network analysis showed that antibiotic exposure disrupted microbial interactions, particularly among G- bacteria, G+ bacteria, and actinomycetes. These changes in microbial community structure and function resulted in decreased activity of urease, ß-1,4-N-acetyl-glucosaminidase, alkaline protease, chitinase, and catalase, causing reduced decomposition and nutrient release in cattle and pig manures. These findings advance our understanding of decomposition and nutrient recycling from manure-contaminated antibiotics, which will help facilitate sustainable agricultural production and soil carbon sequestration.

Flow cytometry in acute myeloid leukemia and detection of minimal residual disease.

Acute myeloid leukemia (AML) is a common type of acute leukemia (AL), belonging to malignant tumors of the hematopoietic system with the characteristics of rapid disease development, control with extreme difficulties, easy recurrence, poor prognosis, and incidence rate increasing with age. The traditionally diagnostic standard of French American British (FAB), being based on the morphological examination with high human subjectivity, can no longer meet the demand of clinical diagnosis and treatment of AML. Requirements of objective accuracy and low-dose sample, have become the indispensable method for AML diagnosis and monitoring prognosis. Flow cytometry is a modern technology that can quickly and accurately detect the series, antigen distribution, differentiation stage of AML cells, minimal residual lesions after AML therapy, so as to provide the great significance in guiding clinical diagnosis, hierarchical treatment, and prognosis judgement. This article will systematically elaborate on the application of flow cytometry in the diagnosis and classification of AML, and the detection of minimal residual lesions, thereby providing reference significance for dynamic monitoring and prognostic observation of AML with different immune subtypes of FAB.

The super-resolution reconstruction algorithm of multi-scale dilated convolution residual network.

Aiming at the problems of traditional image super-resolution reconstruction algorithms in the image reconstruction process, such as small receptive field, insufficient multi-scale feature extraction, and easy loss of image feature information, a super-resolution reconstruction algorithm of multi-scale dilated convolution network based on dilated convolution is proposed in this paper. First, the algorithm extracts features from the same input image through the dilated convolution kernels of different receptive fields to obtain feature maps with different scales; then, through the residual attention dense block, further obtain the features of the original low resolution images, local residual connections are added to fuse multi-scale feature information between multiple channels, and residual nested networks and jump connections are used at the same time to speed up deep network convergence and avoid network degradation problems. Finally, deep network extraction features, and it is fused with input features to increase the nonlinear expression ability of the network to enhance the super-resolution reconstruction effect. Experimental results show that compared with Bicubic, SRCNN, ESPCN, VDSR, DRCN, LapSRN, MemNet, and DSRNet algorithms on the Set5, Set14, BSDS100, and Urban100 test sets, the proposed algorithm has improved peak signal-to-noise ratio and structural similarity, and reconstructed images. The visual effect is better.

Characterization of residual microRNAs in AAV vector batches produced in HEK293 mammalian cells and Sf9 insect cells.

With more than 130 clinical trials and 8 approved gene therapy products, adeno-associated virus (AAV) stands as one of the most popular vehicles to deliver therapeutic DNA in vivo. One critical quality attribute analyzed in AAV batches is the presence of residual DNA, as it could pose genotoxic risks or induce immune responses. Surprisingly, the presence of small cell-derived RNAs, such as microRNAs (miRNAs), has not been investigated previously. In this study, we examined the presence of miRNAs in purified AAV batches produced in mammalian or in insect cells. Our findings revealed that miRNAs were present in all batches, regardless of the production cell line or capsid serotype (2 and 8). Quantitative assays indicated that miRNAs were co-purified with the recombinant AAV particles in a proportion correlated with their abundance in the production cells. The level of residual miRNAs was reduced via an immunoaffinity chromatography purification process including a tangential flow filtration step or by RNase treatment, suggesting that most miRNA contaminants are likely non-encapsidated. In summary, we demonstrate, for the first time, that miRNAs are co-purified with AAV particles. Further investigations are required to determine whether these miRNAs could interfere with the safety or efficacy of AAV-mediated gene therapy.

The role of acute stress recovery in emotional resilience.

Background: Resilience refers to the process of demonstrating better outcomes than would be expected based on the adversity one experienced. Resilience is increasingly measured using a residual approach, which typically assesses adversity and mental health outcomes over a longitudinal timeframe. It remains unknown to what extent such a residual-based measurement of resilience is sensitive to variation in acute stress resilience, a candidate resilience factor. Methods: Fifty-seven emerging adults enrolled in tertiary education completed measures of adversity and emotional experiences. To assess stress recovery, participants were exposed to a lab-based adverse event from which a Laboratory Stress Resilience Index was derived. Results: We derived a residual-based measure of emotional resilience from regressing emotional experience scores onto adversity scores. This residual-based measure of emotional resilience predicted variance in the Laboratory Stress Resilience Index over and above that predicted by both a traditional resilience measure and the emotional experiences measure. These findings suggest that acute stress resilience may be a factor underpinning variation in emotional resilience, and that the residual-based approach to measuring resilience is sensitive to such variation in stress resilience.

Reviving Sodium Tunnel Oxide Cathodes Based on Structural Modulation and Sodium Compensation Strategy Toward Practical Sodium-Ion Cylindrical Battery.

As a typical tunnel oxide, Na0.44MnO2 features excellent electrochemical performance and outstanding structural stability, making it a promising cathode for sodium-ion batteries (SIBs). However, it suffers from undesirable challenges such as surface residual alkali, multiple voltage plateaus, and low initial charge specific capacity. Herein, an internal and external synergistic modulation strategy is adopted by replacing part of the Mn with Ti to optimize the bulk phase and construct a Ti-containing epitaxial stabilization layer, resulting in reduced surface residual alkali, excellent Na+ transport kinetics and improved water/air stability. Specifically, the Na0.44Mn0.85Ti0.15O2 using water-soluble carboxymethyl cellulose as a binder can realize a capacity retention rate of 94.30% after 1,000 cycles at 2C, and excellent stability is further verified in kilogram large-up applications. In addition, taking advantage of the rich Na content in Prussian blue analog (PBA), PBA-Na0.44Mn1-xTixO2 composites are designed to compensate for the insufficient Na in the tunnel oxide and are matched with hard carbon to achieve the preparation of coin full cell and 18650 cylindrical battery with satisfactory electrochemical performance. This work enables the application of tunnel oxides cathode for SIBs in 18650 cylindrical batteries for the first time and promotes the commercialization of SIBs.

Reconstruction residual network with a fused spatial-channel attention mechanism for automatically classifying diabetic foot ulcer.

Diabetic foot ulcer (DFU) is a common chronic complication of diabetes. This complication is characterized by the formation of ulcers that are difficult to heal on the skin of the foot. Ulcers can negatively affect patients' quality of life, and improperly treated lesions can result in amputation and even death. Traditionally, the severity and type of foot ulcers are determined by doctors through visual observations and on the basis of their clinical experience; however, this subjective evaluation can lead to misjudgments. In addition, quantitative methods have been developed for classifying and scoring are therefore time-consuming and labor-intensive. In this paper, we propose a reconstruction residual network with a fused spatial-channel attention mechanism (FARRNet) for automatically classifying DFUs. The use of pseudo-labeling and Data augmentation as a pre-processing technique can overcome problems caused by data imbalance and small sample size. The developed model's attention was enhanced using a spatial channel attention (SPCA) module that incorporates spatial and channel attention mechanisms. A reconstruction mechanism was incorporated into the developed residual network to improve its feature extraction ability for achieving better classification. The performance of the proposed model was compared with that of state-of-the-art models and those in the DFUC Grand Challenge. When applied to the DFUC Grand Challenge, the proposed method outperforms other state-of-the-art schemes in terms of accuracy, as evaluated using 5-fold cross-validation and the following metrics: macro-average F1-score, AUC, Recall, and Precision. FARRNet achieved the F1-score of 60.81%, AUC of 87.37%, Recall of 61.04%, and Precision of 61.56%. Therefore, the proposed model is more suitable for use in medical diagnosis environments with embedded devices and limited computing resources. The proposed model can assist patients in initial identifications of ulcer wounds, thereby helping them to obtain timely treatment.

A Novel Network for Low-Dose CT Denoising Based on Dual-Branch Structure and Multi-Scale Residual Attention.

Deep learning-based denoising of low-dose medical CT images has received great attention both from academic researchers and physicians in recent years, and has shown important application value in clinical practice. In this work, a novel two-branch and multi-scale residual attention-based network for low-dose CT image denoising is proposed. It adopts a two-branch framework structure, to extract and fuse image features at shallow and deep levels respectively, to recover image texture and structure information as much as possible. We propose the adaptive dynamic convolution block (ADCB) in the local information extraction layer. It can effectively extract the detailed information of low-dose CT denoising and enables the network to better capture the local details and texture features of the image, thereby improving the denoising effect and image quality. Multi-scale edge enhancement attention block (MEAB) is proposed in the global information extraction layer, to perform feature fusion through dilated convolution and a multi-dimensional attention mechanism. A multi-scale residual convolution block (MRCB) is proposed to integrate feature information and improve the robustness and generalization of the network. To demonstrate the effectiveness of our method, extensive comparison experiments are conducted and the performances evaluated on two publicly available datasets. Our model achieves 29.3004 PSNR, 0.8659 SSIM, and 14.0284 RMSE on the AAPM-Mayo dataset. It is evaluated by adding four different noise levels σ = 15, 30, 45, and 60 on the Qin_LUNG_CT dataset and achieves the best results. Ablation studies show that the proposed ADCB, MEAB, and MRCB modules improve the denoising performances significantly. The source code is available at https://github.com/Ye111-cmd/LDMANet .

Well-Being Therapy for Depression.

Well-being therapy (WBT) is a short-term psychotherapeutic strategy, based on the technique of self-observation via the use of a structured diary and the guide of a therapist, with the goal of increasing psychological well-being, thus reaching euthymia and a balance among psychic forces. WBT showed to be suitable for application in residual symptoms of unipolar and bipolar depression, since the sequential combination with cognitive-behavioural therapy (CBT) led to a decrease in the relapse rate of recurrent depression. WBT also showed clinical utility in the treatment of cyclothymia, which represents one of the stages of bipolar disorder. Further, WBT seems to have efficacy in treatment-resistant depression and in case of withdrawal syndromes (in particular the so-called persistent post-withdrawal disorder) following antidepressant decrease, switch or discontinuation. In brief, WBT is a rather new but promising therapeutic strategy in the management of unipolar and bipolar depression. This chapter offers an overview of WBT possible applications.

Acute treatment residual depression symptoms and functional impairment among depressive patients of different age groups and education levels in China: A prospective, multicenter, randomized study.

OBJECTIVE: A prospective, multicenter, randomized study evaluated the efficacy of major depressive disorder (MDD) patients after 2-3 months of acute treatment based on the dual factors of education and age. METHODS: This study classified the included patients into four groups using two classification parameters: age (≤45 years, vs. >45 years) and education years (≤12 vs. >12). We analyzed age, gender, marital status, personal income, depression onset history, medication use, and follow-up across various groups. We evaluated residual somatic symptoms and social functioning in depression patients was conducted using the 16-item Quick Inventory of Depressive Symptomatology Self-report (QIDS-SR16), the Patient Health Questionnaire-15 (PHQ15), and the Sheehan Disability Scale (SDS). RESULTS: In China, 16 hospitals, 553 depression patients, and 428 fulfilled the inclusion criteria. Baseline patient data revealed significant differences among the different age groups in gender, marital status, income, first onset age, physical illness, combination of antipsychotics, and benzodiazepines use (all p < .05). Statistically significant differences were observed in overall comparisons among the four groups, encompassing the QIDS-SR16 score, PHQ15 score, and various SDS parameters (all p < .05). However, no statistically significant differences (all p > .05) were found in residual somatic symptoms and social functioning parameters between different education levels (≤12 years vs. >12 years) at baseline, 3 months, and 6 months, based on total scores on the scale. Repeated measures mixed model indicates that the QIDS-SR16 assessment indicates statistical differences among various marital statuses, income levels, medical histories, and antipsychotic medication use (p < .05). Furthermore, PHQ-15 and SDS assessments reveal statistical differences between single and married/cohabiting statuses, physical comorbidities, 3 and 6 months follow-ups compared to baseline (p < .05). CONCLUSION: This study indicates that compared to depressive patients >45 years old, those ≤45 years old often exhibit more residual depression, somatic symptoms, and severe social functional impairment; patients' education levels less influence this trend.

Determining the debris flow yield strength of weathered soils: a case study of the Miryang debris flow in the Republic of Korea.

Debris flow hazards are often interpreted through back-calculated simulation analysis or empirical methods. The mobility of a debris flow is greatly influenced by mechanical and hydrological parameters. The strength parameters play important roles in the debris flow initiation and flow stages. In particular, the rheological parameters of yield strength and plastic viscosity directly affect the debris flow runout distance and velocity. One of the most important parameters to consider when evaluating debris flow hazards is the shear strength. This strength is called the residual shear strength in the failure stage and the yield strength in the post-failure stage. The residual shear strength obtained from ring shear tests can be related to the initiation of mass movements; the yield strength obtained from rheological tests can be related to the mobilization of debris flows. The residual shear stresses obtained from ring shear tests of weathered soils typically range between 10 and 100 kPa and strongly depend on the normal stress and shear velocity. When progressive slope failure (i.e., strain-softening behavior) occurs at a relatively shallow slope depth (e.g., < 1 m), the soil strength ranges from approximately 5-10 kPa. If the liquid limit state (i.e., solid‒liquid transition) is reached, the shear strength of the soil is approximately 2 kPa. Once the soil fails and mixes with ambient water along the slip surface, the yield strength decreases dramatically, resulting in high mobilization. A suggestion on how strength parameters can be applied to estimate debris flow mobility is presented by considering the 2011 Miryang debris flow, which occurred in weathered soil deposits in Miryang city, Republic of Korea. The best approach for debris flow yield strength estimation would be to consider the residual shear strength in the initiation stage, the yield strength in the flow stage, and the reduction in yield strength with the entrainment effect of the flow in the rapid fluidization stage.

iVAC-XS15-CLL01: personalized multi-peptide vaccination in combination with the TLR1/2 ligand XS15 in CLL patients undergoing BTK-inhibitor-based regimens.

Chronic lymphocytic leukemia (CLL) is the most common form of leukemia among adults in Western countries. Despite the introduction of targeted therapies, including first-line Bruton's tyrosine kinase inhibitor (BTKi) treatment, CLL remains largely incurable. Frequent disease relapses occur due to remaining treatment-resistant CLL cells, calling for novel therapies to eliminate minimal residual disease (MRD). Peptide-based vaccination targeting human leucocyte antigen (HLA)-presented CLL-associated antigens represents a promising, low-side-effect therapeutic option to optimize treatment responses and eliminate residual tumor cells by inducing an anti-leukemic immune response. The iVAC-XS15-CLL01 trial is an open-label, first-in-human (FIH) Phase I trial, evaluating the CLL-VAC-XS15 vaccine in CLL patients undergoing BTKi-based therapy. The vaccine was developed from HLA-presented CLL-associated antigen peptides, identified through comparative mass-spectrometry-based immunopeptidome analyses of CLL versus healthy samples in a previous study. To facilitate rapid and cost-effective deployment, vaccine peptides are selected for each patient from a pre-manufactured "peptide warehouse" based on the patient's individual HLA allotype and CLL immunopeptidome. The trial enrolls 20 CLL patients, who receive up to three doses of the vaccine, adjuvanted with the toll-like-receptor (TLR) 1/2 ligand XS15 and emulsified in Montanide ISA 51 VG. The primary objective of the iVAC-XS15-CLL01 trial is to assess the safety and immunogenicity of the CLL-VAC-XS15 vaccine. Secondary objectives are to evaluate the vaccine impact on MRD, progression-free survival, and overall survival, as well as comprehensive immunophenotyping to characterize vaccine-induced T-cell responses. This Phase I trial aims to advance CLL treatment by enhancing immune-mediated disease clearance and guiding the design of subsequent Phase II/III trials to implement a new therapeutic strategy for CLL patients.

Comparison of cold snare endoscopic mucosal resection and hot snare endoscopic mucosal resection for small colorectal polyps: a randomized controlled trial.

Incomplete resection rates vary among endoscopists performing cold snare polypectomy. Cold snare endoscopic mucosal resection (CS-EMR) is the technique of cold resection after submucosal injection to reduce incomplete resection. This study aimed to evaluate the efficacy and safety of CS-EMR for small colorectal polyps compared to hot snare endoscopic mucosal resection (HS-EMR). Preplanned sample size required 70 polyps to CS-EMR group or HS-EMR group, respectively. Patients with polyps sized 6-9 mm were randomly allocated to either the CS-EMR or the HS-EMR group. The primary outcome was residual or recurrent adenoma (RAA) rate. A total of 70 and 68 polyps were resected using CS-EMR and HS-EMR, respectively. In the intention-to-treat population, the RAA rate was 0% in the CS-EMR group and 1.5% in the HS-EMR group (risk difference [RD], - 1.47; 95% confidence interval [CI] - 4.34 to 1.39). En bloc resection rate was 98.6% and 98.5% (RD, - 0.04; 95% CI - 4.12 to 4.02); the R0 resection rate was 55.7% and 82.4% (RD, - 27.80; 95% CI - 42.50 to - 13.10). The total procedure time was 172 s (IQR, 158-189) in the CS-EMR group and 186 s (IQR, 147-216) in the HS-EMR group (median difference, - 14; 95% CI - 32 to 2). Delayed bleeding was 2.9% vs 1.5% (RD, 1.37; 95% CI - 3.47 to 6.21) in both groups, respectively. CS-EMR was non-inferior to HS-EMR for the treatment of small colorectal polyps. CS-EMR can be considered one of the standard methods for the removal of colorectal polyps sized 6-9 mm.

[A deep transfer learning approach for cross-subject recognition of mental tasks based on functional near-infrared spectroscopy].

In the field of brain-computer interfaces (BCIs) based on functional near-infrared spectroscopy (fNIRS), traditional subject-specific decoding methods suffer from the limitations of long calibration time and low cross-subject generalizability, which restricts the promotion and application of BCI systems in daily life and clinic. To address the above dilemma, this study proposes a novel deep transfer learning approach that combines the revised inception-residual network (rIRN) model and the model-based transfer learning (TL) strategy, referred to as TL-rIRN. This study performed cross-subject recognition experiments on mental arithmetic (MA) and mental singing (MS) tasks to validate the effectiveness and superiority of the TL-rIRN approach. The results show that the TL-rIRN significantly shortens the calibration time, reduces the training time of the target model and the consumption of computational resources, and dramatically enhances the cross-subject decoding performance compared to subject-specific decoding methods and other deep transfer learning methods. To sum up, this study provides a basis for the selection of cross-subject, cross-task, and real-time decoding algorithms for fNIRS-BCI systems, which has potential applications in constructing a convenient and universal BCI system.

Lesion region inpainting: an approach for pseudo-healthy image synthesis in intracranial infection imaging.

The synthesis of pseudo-healthy images, involving the generation of healthy counterparts for pathological images, is crucial for data augmentation, clinical disease diagnosis, and understanding pathology-induced changes. Recently, Generative Adversarial Networks (GANs) have shown substantial promise in this domain. However, the heterogeneity of intracranial infection symptoms caused by various infections complicates the model's ability to accurately differentiate between pathological and healthy regions, leading to the loss of critical information in healthy areas and impairing the precise preservation of the subject's identity. Moreover, for images with extensive lesion areas, the pseudo-healthy images generated by these methods often lack distinct organ and tissue structures. To address these challenges, we propose a three-stage method (localization, inpainting, synthesis) that achieves nearly perfect preservation of the subject's identity through precise pseudo-healthy synthesis of the lesion region and its surroundings. The process begins with a Segmentor, which identifies the lesion areas and differentiates them from healthy regions. Subsequently, a Vague-Filler fills the lesion areas to construct a healthy outline, thereby preventing structural loss in cases of extensive lesions. Finally, leveraging this healthy outline, a Generative Adversarial Network integrated with a contextual residual attention module generates a more realistic and clearer image. Our method was validated through extensive experiments across different modalities within the BraTS2021 dataset, achieving a healthiness score of 0.957. The visual quality of the generated images markedly exceeded those produced by competing methods, with enhanced capabilities in repairing large lesion areas. Further testing on the COVID-19-20 dataset showed that our model could effectively partially reconstruct images of other organs.

ResNet diagnosis of rotor faults in oil transfer pumps.

To address rotor imbalance and misalignment in oil transfer pumps, an innovative diagnostic framework using Residual Network (ResNet) is proposed. The model incorporates advanced signal processing algorithms and strategic sensor placement to enhance diagnostic efficacy. A fault simulation test rig captured vibration signals from eight key measurement points on the pump. One-dimensional and multi-dimensional signal processing techniques generated comprehensive datasets for training and validating the model. Sensor placement optimization, focusing on the bearing seat's axial direction, inlet flange's vertical direction, and outlet flange's axial direction, increased rotor fault sensitivity. Time-frequency data processed via Short-Time Fourier Transform (STFT) achieved the highest diagnostic accuracy, surpassing 98 %. This study highlights the importance of optimal signal processing and precise sensor placement in improving the accuracy of diagnosing rotor faults in oil transfer pumps, thus enhancing the operational reliability and efficiency of energy transportation systems.

Application of the bicharacteristic attention residual pyramid for the treatment of brain tumors.

Currently, surgery remains the primary treatment for craniocerebral tumors. Before doctors perform surgeries, they need to determine the surgical plan according to the shape, location, and size of the tumor; however, various conditions of different patients make the tumor segmentation task challenging. To improve the accuracy of determining tumor shape and realizing edge segmentation, a U-shaped network combining a residual pyramid module and a dual feature attention module is proposed. The residual pyramid module can enlarge the receptive field, extract multiscale features, and fuse original information, which solves the problem caused by the feature pyramid pooling where the local information is not related to the remote information. In addition, the dual feature attention module is proposed to replace the skip connection in the original U-Net network, enrich the features, and improve the attention of the model to space and channel features with large amounts of information to be used for more accurate brain tumor segmentation. To evaluate the performance of the proposed model, experiments were conducted on the public datasets Kaggle_3M and BraTS2021. Because the model proposed in this study is applicable to two-dimensional image segmentation, it is necessary to obtain the crosscutting images of fair class in the BraTS2021 dataset in advance. Results show that the model accuracy, Jaccard similarity coefficient, Dice similarity coefficient, and false negative rate (FNR) on the Kaggle_3M dataset are 0.9395, 0.8812, 0.8958, and 0.007, respectively. The model accuracy, Jaccard similarity coefficient, Dice similarity coefficient, and FNR on the BraTS2021 dataset were 0.9375, 0.9072, 0.8981, and 0.0087, respectively. Compared with existing algorithms, all the indicators of the proposed algorithm have been improved, but the proposed model still has certain limitations and has not been applied to actual clinical trials. For specific datasets, the generalization ability of the model needs to be further improved. In the future work, the model will be further improved to address the aforementioned limitations.

Evaluation of sunobinop for next-day residual effects in healthy participants.

Sunobinop is a novel, potent, selective partial agonist at nociceptin/orphanin FQ peptide (NOP) receptors. The primary objective of this randomized, double-blind, placebo-controlled study was to assess the next-day residual effects of an evening dose of sunobinop in healthy participants. Participants were randomized into 1 of 5 treatment sequences. Treatment consisted of 1 dose each of sunobinop 0.2, 0.6, 2, and 6 mg suspension and placebo suspension. Key pharmacodynamic (PD) measures included the digit symbol substitution test (DSST), Karolinska sleepiness scale (KSS), and body sway. The randomized safety population consisted of 25 participants. The DSST, KSS, and body sway showed dose-dependent effects following the administration of sunobinop, with no significant differences versus placebo at sunobinop doses <2 mg. At sunobinop 2 mg, PD effects were relatively small in magnitude and inconsistent. The last timepoint where significant differences between sunobinop 2 mg and placebo on the DSST, KSS, and body sway were observed was at 12 h, 16.5 h, and 13.5 h postdose, respectively. Sunobinop 6 mg resulted in larger and consistent PD effects, with significant differences from placebo at all timepoints up to 16.5-18 h postdose. Somnolence was the most frequently reported adverse event (AE), and all AEs were mild-to-moderate. No deaths occurred during the study or discontinuations due to an AE. Overall, a nighttime oral dose of sunobinop up to 2 mg was safe and generally well tolerated in healthy participants with limited next-day residual effects that were consistent with other sedative/hypnotic drugs.

Salvage hysterectomy for persistent residual cervical cancer: assessment of prognostic factors.

In this multicenter retrospective cohort study of 99 patients who underwent salvage hysterectomy for residual disease in the uterine cervix following the completion of definitive radiotherapy for cervical cancer across 25 Japan Clinical Oncology Group-affiliated centers from 2005-2014, (i) time duration from the completion of definitive radiotherapy to the diagnosis of residual disease in the uterine cervix, (ii) salvage hysterectomy surgical margin status, and (iii) extent of residual disease, were independently associated with progression-free survival (PFS). Specifically, (i) time duration to identify residual disease of >62 days was associated with decreased PFS compared to ≤62 days (4-year rates 21.8% vs. 55.0%, adjusted-hazard ratio [aHR]=2.69, 95% confidence interval [CI]=1.55-4.67); (ii) presence of tumor in the surgical margin of hysterectomy specimen was associated with 4 times increased risk of disease progression compared to tumor-free surgical margin (4-year PFS rates 0% vs. 45.3%, aHR=4.27, 95% CI=2.20-8.29); and (iii) hazards of disease progression was 4.5-fold increased when the residual disease extended beyond the uterine cervix compared to residual disease within the uterine cervix only (4-year PFS rates 11.1% vs. 50.6%, aHR=4.54, 95% CI=2.60-7.95). In the absence of these 3 prognostic factors, 4-year PFS rate reached nearly 80% (78.6%, SAL-HYS criteria). In sum, these data suggested that early detection of persistent, residual disease following definitive radiotherapy for cervical cancer may be the key to improve survival if salvage hysterectomy is considered as a tailored treatment option. Ideal surgical candidate would be uterine cervix-contained disease and assurance of adequate tumor-free surgical margin.

Anomaly Detection and Correction in Dense Functional Data Within Electronic Medical Records.

In medical research, the accuracy of data from electronic medical records (EMRs) is critical, particularly when analyzing dense functional data, where anomalies can severely compromise research integrity. Anomalies in EMRs often arise from human errors in data measurement and entry, and increase in frequency with the volume of data. Despite the established methods in computer science, anomaly detection in medical applications remains underdeveloped. We address this deficiency by introducing a novel tool for identifying and correcting anomalies specifically in dense functional EMR data. Our approach utilizes studentized residuals from a mean-shift model, and therefore assumes that the data adheres to a smooth functional trajectory. Additionally, our method is tailored to be conservative, focusing on anomalies that signify actual errors in the data collection process while controlling for false discovery rates and type II errors. To support widespread implementation, we provide a comprehensive R package, ensuring that our methods can be applied in diverse settings. Our methodology's efficacy has been validated through rigorous simulation studies and real-world applications, confirming its ability to accurately identify and correct errors, thus enhancing the reliability and quality of medical data analysis.