A Machine Learning Model Based on MRI Radiomics to Predict Response to Chemoradiation Among Patients with Rectal Cancer.

Background: With rectum-sparing protocols becoming more common for rectal cancer treatment, this study aimed to predict the pathological complete response (pCR) to preoperative chemoradiotherapy (pCRT) in rectal cancer patients using pre-treatment MRI and a radiomics-based machine learning approach.

Methods: We divided MRI-data from 102 patients into a training cohort ( = 72) and a validation cohort ( = 30). In the training cohort, 52 patients were classified as non-responders and 20 as pCR based on histological results from total mesorectal excision.

Machine learning predicts pulmonary Long Covid sequelae using clinical data.

Long COVID is a multi-systemic disease characterized by the persistence or occurrence of many symptoms that in many cases affect the pulmonary system. These, in turn, may deteriorate the patient's quality of life making it easier to develop severe complications. Being able to predict this syndrome is therefore important as this enables early treatment.

MiR203a-3p as a potential biomarker for synovial pathology associated with osteoarthritis: a pilot study.

Background: Osteoarthritis (OA) is a degenerative musculoskeletal disease that significantly impacts the quality of life. Currently, no validated biomarkers for early detection of OA are defined. The possibility of discovering OA biomarkers is the focus of this study.

Transition to Enteral Triazole Antifungal Therapy for Pediatric Invasive Candidiasis: Secondary Analysis of a Multicenter Cohort Study Conducted by the Pediatric Fungal Network.

Of 319 children with invasive candidiasis, 67 (21%) transitioned from intravenous to enteral antifungal therapy. Eight (12%) transitioned back to intravenous antifungal therapy, one due to perceived treatment failure defined by clinical progression or worsening. Global treatment response at study completion was successful in 66 participants who transitioned to enteral therapy.

Invention of novel 3-aminopiperidin-2-ones as calcitonin gene-related peptide receptor antagonists.

A novel series of 3-amino-piperidin-2-one-based calcitonin gene-related peptide (CGRP) receptor antagonists was invented based upon the discovery of unexpected structure-activity observations. Initial exploration of the structure-activity relationships enabled the generation of a moderately potent lead structure (4). A series of modifications, including ring contraction and inversion of stereocenters, led to surprising improvements in CGRP receptor affinity.

Environmental productivity growth, regulation and types of pollutants: Evidence from European firms.

European manufacturing firms have to cope with the new regulations that advocate a greener and more sustainable future with less emissions and at the same time enhance or at least maintain their productivity levels. A unique dataset is constructed by combining information on different firms' pollutants with their financial information during the 2011-2017 period. A non-radial directional distance function analysis is adopted with desirable and undesirable outputs to estimate environmental productivity growth and its components, which addresses the problem of heterogeneity.

Congenital Cytomegalovirus Testing Outcomes From the ValEAR Trial.

Objective: To determine the positivity rate of congenital cytomegalovirus (cCMV) testing among universal, hearing-targeted CMV testing (HT-cCMV) and delayed targeted dried blood spot (DBS) testing newborn screening programs, and to examine the characteristics of successful HT-cCMV testing programs.

Study Design: Prospective survey of birth hospitals performing early CMV testing.

Setting: Multiple institutions.

Unveiling the Diagnostic Potential of Linguistic Markers in Identifying Individuals with Parkinson's Disease through Artificial Intelligence: A Systematic Review.

While extensive research has documented the cognitive changes associated with Parkinson's disease (PD), a relatively small portion of the empirical literature investigated the language abilities of individuals with PD. Recently, artificial intelligence applied to linguistic data has shown promising results in predicting the clinical diagnosis of neurodegenerative disorders, but a deeper investigation of the current literature available on PD is lacking. This systematic review investigates the nature of language disorders in PD by assessing the contribution of machine learning (ML) to the classification of patients with PD.

X-rays radiomics-based machine learning classification of atypical cartilaginous tumour and high-grade chondrosarcoma of long bones.

Background: Atypical cartilaginous tumour (ACT) and high-grade chondrosarcoma (CS) of long bones are respectively managed with active surveillance or curettage and wide resection. Our aim was to determine diagnostic performance of X-rays radiomics-based machine learning for classification of ACT and high-grade CS of long bones.

Methods: This retrospective, IRB-approved study included 150 patients with surgically treated and histology-proven lesions at two tertiary bone sarcoma centres.

Pharmacologic characterization of atogepant: A potent and selective calcitonin gene-related peptide receptor antagonist.

Background: The trigeminal sensory neuropeptide calcitonin gene-related peptide (CGRP) is identified as an essential element in migraine pathogenesis.

Methods: and studies evaluated pharmacologic properties of the CGRP receptor antagonist atogepant. Radioligand binding using I-CGRP and cyclic adenosine monophosphate (cAMP) accumulation assays were conducted in human embryonic kidney 293 cells to assess affinity, functional potency and selectivity.

Using Radiomics and Machine Learning Applied to MRI to Predict Response to Neoadjuvant Chemotherapy in Locally Advanced Cervical Cancer.

Neoadjuvant chemotherapy plus radical surgery could be a safe alternative to chemo-radiation in cervical cancer patients who are not willing to receive radiotherapy. The response to neoadjuvant chemotherapy is the main factor influencing the need for adjunctive treatments and survival. In the present paper we aim to develop a machine learning model based on cervix magnetic resonance imaging (MRI) images to stratify the single-subject risk of cervical cancer.

A Radiomic-Based Machine Learning System to Diagnose Age-Related Macular Degeneration from Ultra-Widefield Fundus Retinography.

The present study was conducted to investigate the potential of radiomics to develop an explainable AI-based system to be applied to ultra-widefield fundus retinographies (UWF-FRTs) with the objective of predicting the presence of the early signs of Age-related Macular Degeneration (AMD) and stratifying subjects with low- versus high-risk of AMD. The ultimate aim was to provide clinicians with an automatic classifier and a signature of objective quantitative image biomarkers of AMD. The use of Machine Learning (ML) and radiomics was based on intensity and texture analysis in the macular region, detected by a Deep Learning (DL)-based macular detector.

Adjunctive Diagnostic Studies Completed Following Detection of Candidemia in Children: Secondary Analysis of Observed Practice From a Multicenter Cohort Study Conducted by the Pediatric Fungal Network.

Background: Adjunctive diagnostic studies (aDS) are recommended to identify occult dissemination in patients with candidemia. Patterns of evaluation with aDS across pediatric settings are unknown.

Methods: Candidemia episodes were included in a secondary analysis of a multicenter comparative effectiveness study that prospectively enrolled participants age 120 days to 17 years with invasive candidiasis (predominantly candidemia) from 2014 to 2017.

MRI radiomics-based machine learning for classification of deep-seated lipoma and atypical lipomatous tumor of the extremities.

Purpose: To determine diagnostic performance of MRI radiomics-based machine learning for classification of deep-seated lipoma and atypical lipomatous tumor (ALT) of the extremities.

Material And Methods: This retrospective study was performed at three tertiary sarcoma centers and included 150 patients with surgically treated and histology-proven lesions. The training-validation cohort consisted of 114 patients from centers 1 and 2 (n = 64 lipoma, n = 50 ALT).

Inference with non-probability samples and survey data integration: a science mapping study.

In recent years, survey data integration and inference based on non-probability samples have gained considerable attention. Because large probability-based samples can be cost-prohibitive in many instances, combining a probabilistic survey with auxiliary data is appealing to enhance inferences while reducing the survey costs. Also, as new data sources emerge, such as big data, inference and statistical data integration will face new challenges.

MRI-based artificial intelligence to predict infection following total hip arthroplasty failure.

Purpose: To investigate whether artificial intelligence (AI) can differentiate septic from non-septic total hip arthroplasty (THA) failure based on preoperative MRI features.

Materials And Methods: We included 173 patients (98 females, age: 67 ± 12 years) subjected to first-time THA revision surgery after preoperative pelvis MRI. We divided the patients into a training/validation/internal testing cohort (n = 117) and a temporally independent external-testing cohort (n = 56).

Outcome after vaginal delivery of women with a previous medical history of surgically corrected anorectal malformations: a systematic review.

Objective: Discussion remains on how to advise women with a past medical history of surgically corrected anorectal malformations (ARMs) regarding vaginal delivery. The aim of this review is to evaluate and review the reported obstetrical complications and outcomes after vaginal delivery for these women.

Data Sources: A systematic search was performed from inception up to 25 July 2022 in PubMed, Embase.

Radiomics and Molecular Classification in Endometrial Cancer (The ROME Study): A Step Forward to a Simplified Precision Medicine.

Molecular/genomic profiling is the most accurate method to assess prognosis of endometrial cancer patients. Radiomic profiling allows for the extraction of mineable high-dimensional data from clinical radiological images, thus providing noteworthy information regarding tumor tissues. Interestingly, the adoption of radiomics shows important results for screening, diagnosis and prognosis, across various radiological systems and oncologic specialties.

Placental pathology, neonatal birth weight, and Apgar score in acute and distant SARS-CoV-2 infection.

Background: Most research on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection during pregnancy has been on acute infections with limited data on the effect of distant infection.

Aim: We examined placental pathology and neonatal outcomes in distant SARS-CoV-2 infection earlier in pregnancy compared to acute infections late in pregnancy/at birth and to non-SARS-CoV-2 infected patients with other placental pathologies/clinical presentations.

Methods: Placentas birthed to unvaccinated patients with SARS-CoV-2 reverse transcription-polymerase chain reaction (RT-PCR) testing and serology testing results from time of delivery were included in this study.

Corporate Social Responsibility Activities Through Twitter: From Topic Model Analysis to Indexes Measuring Communication Characteristics.

The communication of corporate social responsibility (CSR) highlights the behavior of the business toward CSR and their framework of sustainable development (SD), thus helping policymakers understand the role businesses play with respect to the 2030 Agenda. Despite its importance, this is still a relatively underexamined and emerging topic. In our paper, we focus on what businesses communicate about CSR through social media and how this relates to the Sustainable Development Goals (SDGs).

Successful diagnostic stewardship for testing in pediatrics.

Objective: To reduce both inappropriate testing for and diagnosis of healthcare-onset (HO) infections (CDIs).

Design: We performed a retrospective analysis of testing from hospitalized children before (October 2017-October 2018) and after (November 2018-October 2020) implementing restrictive computerized provider order entry (CPOE).

Setting: Study sites included hospital A (a ∼250-bed freestanding children's hospital) and hospital B (a ∼100-bed children's hospital within a larger hospital) that are part of the same multicampus institution.

Optimized Collaborative Brain-Computer Interfaces for Enhancing Face Recognition.

The aim of this study is to maximize group decision performance by optimally adapting EEG confidence decoders to the group composition. We train linear support vector machines to estimate the decision confidence of human participants from their EEG activity. We then simulate groups of different size and membership by combining individual decisions using a weighted majority rule.