Stereo-Differentiating Asymmetric Rh(I)-Catalyzed Pauson-Khand Reaction: A DFT-Informed Approach to Thapsigargin Stereoisomers.

We report a stereo-differentiating dynamic kinetic asymmetric Rh(I)-catalyzed Pauson-Khand reaction, which provides access to an array of thapsigargin stereoisomers. Using catalyst-control, a consistent stereochemical outcome is achieved at C2─for both matched and mismatched cases─regardless of the allene-yne C8 stereochemistry. The stereochemical configuration for all stereoisomers was assigned by comparing experimental vibrational circular dichroism (VCD) and C NMR to DFT-computed spectra.

Sexual health counseling improves the sexual satisfaction of breast cancer survivors: a randomized controlled trial.

Purpose: Breast cancer and its treatments can cause sexual problems both physically and psychologically by the changes it brings. This study aimed to investigate the effect of sexual health counseling based on acceptance and commitment therapy (ACT) on the sexual satisfaction of women with chemotherapy-induced menopause (CIM) in breast cancer survivors.

Methods: Seventy women with CIM were randomly divided into two intervention (N = 34) and control (N = 36) groups.

Association of Blast Exposure in Military Breaching with Intestinal Permeability Blood Biomarkers Associated with Leaky Gut.

Injuries and subclinical effects from exposure to blasts are of significant concern in military operational settings, including tactical training, and are associated with self-reported concussion-like symptomology and physiological changes such as increased intestinal permeability (IP), which was investigated in this study. Time-series gene expression and IP biomarker data were generated from "breachers" exposed to controlled, low-level explosive blast during training. Samples from 30 male participants at pre-, post-, and follow-up blast exposure the next day were assayed via RNA-seq and ELISA.

Self-supervised learning for medical image analysis: Discriminative, restorative, or adversarial?

Discriminative, restorative, and adversarial learning have proven beneficial for self-supervised learning schemes in computer vision and medical imaging. Existing efforts, however, fail to capitalize on the potentially synergistic effects these methods may offer in a ternary setup, which, we envision can significantly benefit deep semantic representation learning. Towards this end, we developed DiRA, the first framework that unites discriminative, restorative, and adversarial learning in a unified manner to collaboratively glean complementary visual information from unlabeled medical images for fine-grained semantic representation learning.

A cross-sectional study of correlations among blood-based biomarkers for intestinal permeability: A pilot study of United States veterans with posttraumatic stress disorder symptoms.

While many studies of intestinal permeability (IP) are focused on those with gastrointestinal (GI) disorders, there is a rising trend to analyze IP among individuals with mental health conditions including posttraumatic stress disorder (PTSD) with and without diagnosed GI conditions. This interest stems from the association between gut dysbiosis and chronic inflammation, which are mechanisms linked to stress-related somatic and mental health conditions. Efforts to date have resulted in the exploration of non-invasive and feasible measures to identify an IP biomarker that could also serve as a treatment target.

Assessing sleep health dimensions in frontline registered nurses during the COVID-19 pandemic: implications for psychological health and wellbeing.

The COVID-19 pandemic altered work environments of nurses, yielding high rates of stress and burnout. Potential protective factors, including effective sleep, may influence psychological health and wellbeing. Evidence about sleep in nurses may help develop interventions that mitigate burnout and poor psychological outcomes.

CAiD: Context-Aware Instance Discrimination for Self-supervised Learning in Medical Imaging.

Recently, self-supervised instance discrimination methods have achieved significant success in learning visual representations from unlabeled photographic images. However, given the differences between photographic and medical images, the efficacy of instance-based objectives, focusing on learning the most discriminative global features in the image (i.e.

POPAR: Patch Order Prediction and Appearance Recovery for Self-supervised Medical Image Analysis.

Vision transformer-based self-supervised learning (SSL) approaches have recently shown substantial success in learning visual representations from unannotated photographic images. However, their acceptance in medical imaging is still lukewarm, due to the significant discrepancy between medical and photographic images. Consequently, we propose POPAR (patch order prediction and appearance recovery), a novel vision transformer-based self-supervised learning framework for chest X-ray images.

Benchmarking and Boosting Transformers for Medical Image Classification.

Visual transformers have recently gained popularity in the computer vision community as they began to outrank convolutional neural networks (CNNs) in one representative visual benchmark after another. However, the competition between visual transformers and CNNs in medical imaging is rarely studied, leaving many important questions unanswered. As the first step, we benchmark how well existing transformer variants that use various (supervised and self-supervised) pre-training methods perform against CNNs on a variety of medical classification tasks.

DiRA: Discriminative, Restorative, and Adversarial Learning for Self-supervised Medical Image Analysis.

Discriminative learning, restorative learning, and adversarial learning have proven beneficial for self-supervised learning schemes in computer vision and medical imaging. Existing efforts, however, omit their synergistic effects on each other in a ternary setup, which, we envision, can significantly benefit deep semantic representation learning. To realize this vision, we have developed , the first framework that unites discriminative, restorative, and adversarial learning in a unified manner to collaboratively glean complementary visual information from unlabeled medical images for fine-grained semantic representation learning.

Correction to "Does the repeat dose of gonadotropin-releasing hormone agonist trigger in polycystic ovarian syndrome improve in vitro fertilization cycles outcome? A clinical trial study" [Int J Reprod BioMed 2020; 18: 485-490].

[This corrects the article on p. 485 in vol. 18 PMCPMC7385917.

Diagnostic Value of Immunoglobulin G Anti-Deamidated Gliadin Peptide Antibody for Diagnosis of Pediatric Celiac Disease: A Study from Shiraz, Iran.

Purpose: Screening serologic tests are important tools for the diagnosis of celiac disease (CD). Immunoglobulin (Ig)G anti-deamidated gliadin peptide (anti-DGP) is a relatively new autoantibody thought to have good diagnostic accuracy, comparable to that of anti-tissue transglutaminase (anti-tTG) antibody.

Methods: Pediatric patients (n=86) with a clinical suspicion of CD were included.

A Systematic Benchmarking Analysis of Transfer Learning for Medical Image Analysis.

Transfer learning from supervised ImageNet models has been frequently used in medical image analysis. Yet, no large-scale evaluation has been conducted to benchmark the efficacy of newly-developed pre-training techniques for medical image analysis, leaving several important questions unanswered. As the first step in this direction, we conduct a systematic study on the transferability of models pre-trained on iNat2021, the most recent large-scale fine-grained dataset, and 14 top self-supervised ImageNet models on 7 diverse medical tasks in comparison with the supervised ImageNet model.

A review on targeting tumor microenvironment: The main paradigm shift in the mAb-based immunotherapy of solid tumors.

Monoclonal antibodies (mAbs) as biological macromolecules have been remarked the large and growing pipline of the pharmaceutical market and also the most promising tool in modern medicine for cancer therapy. These therapeutic entities, which consist of whole mAbs, armed mAbs (i.e.

Contribution of Age, Brain Region, Mood Disorder Pathology, and Interindividual Factors on the Methylome of Human Microglia.

Background: Transcriptome studies have revealed age-, disease-, and region-associated microglial phenotypes reflecting changes in microglial function during development, aging, central nervous system homeostasis, and pathology. The molecular mechanisms that contribute to these transcriptomic changes are largely unknown. The aim of this study was to characterize the DNA methylation landscape of human microglia and the factors that contribute to variations in the microglia methylome.

Penetrating Ballistic Brain Injury Produces Acute Alterations in Sleep and Circadian-Related Genes in the Rodent Cortex: A Preliminary Study.

Traumatic brain injury (TBI) affects millions of Americans each year, with extremely high prevalence in the Veteran community, and sleep disturbance is one of the most commonly reported symptoms. Reduction in the quality and amount of sleep can negatively impact recovery and result in a wide range of behavioral and physiological symptoms, such as impaired cognition, mood and anxiety disorders, and cardiovascular effects. Thus, to improve long-term patient outcomes and develop novel treatments, it is essential to understand the molecular mechanisms involved in sleep disturbance following TBI.

Bisulfite Amplicon Sequencing Can Detect Glia and Neuron Cell-Free DNA in Blood Plasma.

Sampling the live brain is difficult and dangerous, and withdrawing cerebrospinal fluid is uncomfortable and frightening to the subject, so new sources of real-time analysis are constantly sought. Cell-free DNA (cfDNA) derived from glia and neurons offers the potential for wide-ranging neurological disease diagnosis and monitoring. However, new laboratory and bioinformatic strategies are needed.