A supervised machine learning model for identifying predictive factors for recommending head and neck cancer surgery.

Background: New patient referrals are often processed by practice coordinators with little-to-no medical background. Treatment delays due to incorrect referral processing, however, have detrimental consequences. Identifying variables that are associated with a higher likelihood of surgical oncological resection may improve patient referral processing and expedite the time to treatment.

On Laws of Thought-A Quantum-like Machine Learning Approach.

Incorporating insights from quantum theory, we propose a machine learning-based decision-making model, including a logic tree and a value tree; a genetic programming algorithm is applied to optimize both the logic tree and value tree. The logic tree and value tree together depict the entire decision-making process of a decision-maker. We applied this framework to the financial market, and a "machine economist" is developed to study a time series of the Dow Jones index.

Limited generalizability of deep learning algorithm for pediatric pneumonia classification on external data.

Purpose: (1) Develop a deep learning system (DLS) to identify pneumonia in pediatric chest radiographs, and (2) evaluate its generalizability by comparing its performance on internal versus external test datasets.

Methods: Radiographs of patients between 1 and 5 years old from the Guangzhou Women and Children's Medical Center (Guangzhou dataset) and NIH ChestXray14 dataset were included. We utilized 5232 radiographs from the Guangzhou dataset to train a ResNet-50 deep convolutional neural network (DCNN) to identify pediatric pneumonia.

Antimicrobial Susceptibility, Biotypes and Phylotypes of Clinical Cutibacterium (Formerly Propionibacterium) acnes Strains Isolated from Acne Patients: An Observational Study.

Introduction: The aim of this study was to investigate the distribution of antimicrobial susceptibility, biotypes and phylotypes of clinical Cutibacterium acnes (C. acnes, formerly Propionibacterium acnes) isolates as well as the relationship among demographic factors, C. acnes biotypes and phylotypes.

Selective serotonin reuptake inhibitors may lead to improved cataract surgery outcomes in patients with amblyopia.

Purpose: To evaluate the effects of selective serotonin reuptake inhibitor (SSRI)/serotonin norepinephrine reuptake inhibitor (SNRI) medications in combination with cataract surgery in treating amblyopia in adult patients.

Patients And Methods: A retrospective chart review study was conducted on patients who had undergone cataract surgery at the Johns Hopkins Hospital Wilmer Eye Institute. Six inclusion criteria were used to assess patient eligibility: 1) >18 years of age, 2) diagnosis of amblyopia, 3) diagnosis of cataract and treatment with surgery, 4) electronic medical record contains pre-surgery and post-surgery visual acuity (VA) measurements, 5) electronic medical record contains information on whether the patient was ever prescribed a SSRI/SNRI and the treatment duration, and 6) interocular VA difference of two lines or more on Snellen chart prior to cataract surgery.

Poly(ε-Caprolactone) Nanofiber Wrap Improves Nerve Regeneration and Functional Outcomes after Delayed Nerve Repair.

Background: The purpose of this study was to assess the efficacy of biodegradable, electrospun poly(ε-caprolactone) nanofiber nerve conduits in improving nerve regeneration.

Methods: The authors used a rat forelimb chronic denervation model to assess the effects of poly(ε-caprolactone) conduits on improving nerve regeneration and upper extremity function. Three groups of rats were examined: (1) negative-control animals (n = 5), which underwent 8 weeks of median nerve chronic denervation injury followed by repair with no conduit; (2) experimental animals (n = 5), which underwent 8 weeks of median nerve chronic denervation followed by repair and poly(ε-caprolactone) nerve conduit wrapping of the nerve coaptation site; and (3) positive-control animals (n = 5), which were naive controls.

Interconnectedness of global hemostasis assay parameters in simultaneously evaluated thrombin generation, fibrin generation and clot lysis in normal plasma.

Background: Fluorogenic thrombin generation (TG) assays and turbidity-based fibrin generation (FG)- and fibrinolysis (FL)-resistance assays have been sought to assess bleeding and clotting disorders. Theoretically, TG, FG and FL tests should provide overlapping information because thrombin is responsible for FG and induces protection from FL. The relationships between TG, FG and FL parameters remain poorly investigated, partly because existing experimental systems do not permit simultaneous detection of both TG and FG in the same sample of plasma, and are instead tested in separate experiments.