Machine Learning and Radiomics of Bone Scintigraphy: Their Role in Predicting Recurrence of Localized or Locally Advanced Prostate Cancer.

Background: Machine-learning (ML) and radiomics features have been utilized for survival outcome analysis in various cancers. This study aims to investigate the application of ML based on patients' clinical features and radiomics features derived from bone scintigraphy (BS) and to evaluate recurrence-free survival in local or locally advanced prostate cancer (PCa) patients after the initial treatment.

Methods: A total of 354 patients who met the eligibility criteria were analyzed and used to train the model.

Enhancing panic disorder treatment with mobile-aided case management: an exploratory study based on a 3-year cohort analysis.

Background: Individuals with panic disorder frequently face ongoing symptoms, suboptimal treatment adherence, and increased relapse rates. Although mobile health interventions have shown promise in improving treatment outcomes for numerous mental health conditions, their effectiveness, specifically for panic disorder, has yet to be determined.

Objective: This study investigates the effects of a mobile-aided case management program on symptom reduction and quality of care among individuals with panic disorder.

Highly accelerated multi-shot intravoxel incoherent motion diffusion-weighted imaging in brain enabled by parametric POCS-based multiplexed sensitivity encoding.

Recently, intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) has also been demonstrated as an imaging tool for applications in neurological and neurovascular diseases. However, the use of single-shot diffusion-weighted echo-planar imaging for IVIM DWI acquisition leads to suboptimal data quality: for instance, geometric distortion and deteriorated image quality at high spatial resolution. Although the recently commercialized multi-shot acquisition methods, such as multiplexed sensitivity encoding (MUSE), can attain high-resolution and high-quality DWI with signal-to-noise ratio (SNR) performance superior to that of the conventional parallel imaging method, the prolonged scan time associated with multi-shot acquisition is impractical for routine IVIM DWI.

Interpretable modeling of time-resolved single-cell gene-protein expression with CrossmodalNet.

Cell-surface proteins play a critical role in cell function and are primary targets for therapeutics. CITE-seq is a single-cell technique that enables simultaneous measurement of gene and surface protein expression. It is powerful but costly and technically challenging.

A novel method for synthesis of carbon dots and their applications in reactive oxygen species (ROS) and glucose detections.

A simple and novel method is proposed for preparation of water-soluble fluorescent carbon dots (C-dots), which have potential to be applied in detecting reactive oxygen species (ROS). The C-dots with high fluorescence quantum yield were created by hydrothermal methods with lactose as the carbon source and tris(hydroxylmethyl)aminomethane (Tris) as the surface passivation reagent. The C-dots have some unique characteristics such as excellent biocompatibility with a broad pH working range of 5-11 and high fluorescence, which makes them especially useful in the bio-detection field.

Imaging prediction of mutation in patients with rectal cancer through deep metric learning using pretreatment [F]Fluorodeoxyglucose positron emission tomography/computed tomography.

Objectives: To predict KRAS mutation in rectal cancer (RC) through computer vision of [18F]fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT) by using metric learning (ML).

Methods: This study included 160 patients with RC who had undergone preoperative PET/CT. KRAS mutation was identified through polymerase chain reaction analysis.

Orthosiphon aristatus (Blume) Miq. Extracts attenuate Alzheimer-like pathology through anti-inflammatory, anti-oxidative, and β-amyloid inhibitory activities.

Ethnopharmacological Relevance: Orthosiphon aristatus (Blume) Miq. (OA) is a traditional folk-herb, which is usually used to treat acute and chronic nephritis, epilepsy, cystitis, and other diseases. Phenols and flavonoids are the main active compound compounds of OA, with proven anti-inflammatory and antioxidant activities.

The effect of uveitis and undiagnosed spondyloarthritis: a systematic review and meta-analysis.

Delay diagnosis of spondyloarthritis (SpA) is associated with poor functional ability and quality of life. Uveitis is the most frequent extraarticular manifestation in SpA, and its prevalence increases with longer disease duration. This study examines the effect of uveitis on the disease activity and functional outcome of undiagnosed SpA.

Homoharringtonine as a PHGDH inhibitor: Unraveling metabolic dependencies and developing a potent therapeutic strategy for high-risk neuroblastoma.

Neuroblastoma, a childhood cancer affecting the sympathetic nervous system, continues to challenge the development of potent treatments due to the limited availability of druggable targets for this aggressive illness. Recent investigations have uncovered that phosphoglycerate dehydrogenase (PHGDH), an essential enzyme for de novo serine synthesis, serves as a non-oncogene dependency in high-risk neuroblastoma. In this study, we show that homoharringtonine (HHT) acts as a PHGDH inhibitor, inducing intricate alterations in cellular metabolism, and thus providing an efficient treatment for neuroblastoma.

Outcomes of pediatric community-acquired pneumonia before and after national pneumococcal immunization in Taiwan.

Objective: In Taiwan, the incidence of invasive pneumococcal disease (IPD) in children declined after the catch-up primary vaccination programs and the full national immunization program (NIP) with PCV13. The objective of the study was to investigate the clinical outcomes of pediatric community-acquired pneumonia (CAP) before and after the NIP.

Methods: The study included patients aged 3 months to 17 years who were diagnosed with CAP and treated at the National Taiwan University Hospital between 2007 and 2019.

Stratifying High-Risk Thyroid Nodules Using a Novel Deep Learning System.

Introduction: The current ultrasound scan classification system for thyroid nodules is time-consuming, labor-intensive, and subjective. Artificial intelligence (AI) has been shown to increase the accuracy of predicting the malignancy rate of thyroid nodules. This study aims to demonstrate the state-of-the-art Swin Transformer to classify thyroid nodules.

Evaluating the Potential of Machine Learning and Wearable Devices in End-of-Life Care in Predicting 7-Day Death Events Among Patients With Terminal Cancer: Cohort Study.

Background: An accurate prediction of mortality in end-of-life care is crucial but presents challenges. Existing prognostic tools demonstrate moderate performance in predicting survival across various time frames, primarily in in-hospital settings and single-time evaluations. However, these tools may fail to capture the individualized and diverse trajectories of patients.

Biopharma innovation trends during COVID-19 and beyond: an evidence from global partnerships and fundraising activities, 2011-2022.

Background: Co-development alliances and capital-raising activities are essential supports for biopharmaceutical innovation. During the initial outbreak of the COVID-19, the level of these business activities has increased greatly. Yet the magnitude, direction, and duration of the trend remain ambiguous.

Prediction of antidepressant responses to non-invasive brain stimulation using frontal electroencephalogram signals: Cross-dataset comparisons and validation.

Background: 10-Hz repetitive transcranial magnetic stimulation(rTMS) and intermittent theta-burst stimulation(iTBS) over left prefrontal cortex are FDA-approved, effective options for treatment-resistant depression (TRD). Optimal prediction models for iTBS and rTMS remain elusive. Therefore, our primary objective was to compare prediction accuracy between classification by frontal theta activity alone and machine learning(ML) models by linear and non-linear frontal signals.

Exploiting Machine Learning Technologies to Study the Compound Effects of Serum Creatinine and Electrolytes on the Risk of Acute Kidney Injury in Intensive Care Units.

Assessing the risk of acute kidney injury (AKI) has been a challenging issue for clinicians in intensive care units (ICUs). In recent years, a number of studies have been conducted to investigate the associations between several serum electrolytes and AKI. Nevertheless, the compound effects of serum creatinine, blood urea nitrogen (BUN), and clinically relevant serum electrolytes have yet to be comprehensively investigated.

Applying a Smartwatch to Predict Work-related Fatigue for Emergency Healthcare Professionals: Machine Learning Method.

Introduction: Healthcare professionals frequently experience work-related fatigue, which may jeopardize their health and put patient safety at risk. In this study, we applied a machine learning (ML) approach based on data collected from a smartwatch to construct prediction models of work-related fatigue for emergency clinicians.

Methods: We conducted this prospective study at the emergency department (ED) of a tertiary teaching hospital from March 10-June 20, 2021, where we recruited physicians, nurses, and nurse practitioners.

Galectin-12 modulates Kupffer cell polarization to alter the progression of nonalcoholic fatty liver disease.

Nonalcoholic fatty liver disease is caused by an imbalance in lipid metabolism and immune response to pose a risk factor for liver fibrosis. Recent evidence indicates that M2 macrophages secrete transforming growth factor-β1, which contributes to liver fibrosis. Galectin-12 has been demonstrated to regulate lipid metabolism and macrophage polarization.

Prognostic Value of Pace Variability, a Novel 6MWT-Derived Feature, in Patients with Chronic Obstructive Pulmonary Disease.

Purpose: The 6-minute walk test (6MWT) is often used to evaluate chronic obstructive pulmonary disease (COPD) patients' functional capacity, with 6-minute walk distance (6MWD) and related measures being linked to mortality and hospitalizations. This study investigates the prognostic value of pace variability, a significant indicator in sports medicine, during the 6MWT for COPD patients.

Patients And Methods: We retrospectively screened consecutive COPD patients who had been prospectively enrolled in a pay-for-performance program from January 2019 to May 2020 to determine their eligibility.

Reducing primary cesarean delivery rate through implementation of a smart intrapartum surveillance system.

The rapid changes in clinical maternity situations that occur in a labor and delivery unit can lead to unpredictable maternal and newborn morbidities. Cesarean section (CS) rate is a key indicator of the accessibility and quality of a labor and delivery unit. This retrospective cross-sectional study assesses the nulliparous, term, singleton, vertex (NTSV) cesarean delivery rates before and after the implementation of a smart intrapartum surveillance system.

Diagnostic accuracy of algorithms to define incident and second hip fractures: A Taiwan validation study.

Background: Previous epidemiological researchers have used various algorithms to identify a second hip fracture; however, there has been no validation of these algorithms to date. This study aimed to verify existing algorithms for identifying second hip fracture under the International Classification of Diseases diagnostic coding systems. Furthermore, we examined the validity of two newly proposed algorithms that integrated the concept of periprosthetic fractures and laterality of the ICD-10 coding system.