The cryoballoon was invented to achieve circumferential pulmonary vein isolation more efficiently to compliment the shortcomings of point-by-point ablation by radiofrequency ablation (RFA). Its efficacy and safety were shown to be comparable to those of RFA, and the clinical outcomes have improved with the second-generation cryoballoon. The basic biophysics, implemental techniques, procedural recommendations, clinical outcomes, and complications of the cryoballoon are presented in this practical and systematic review.
Atrial Fibrillation ; Biophysics ; Catheter Ablation ; Cryosurgery ; Pulmonary Veins

The cryoballoon was invented to achieve circumferential pulmonary vein isolation more efficiently to compliment the shortcomings of point-by-point ablation by radiofrequency ablation (RFA). Its efficacy and safety were shown to be comparable to those of RFA, and the clinical outcomes have improved with the second-generation cryoballoon. The basic biophysics, implemental techniques, procedural recommendations, clinical outcomes, and complications of the cryoballoon are presented in this practical and systematic review.

BACKGROUND: Incisional hernia is a common complication following visceral organ transplantation. Transplant patients are at increased risk of primary and recurrent hernias due to chronic immune suppression and large incisions. We conducted a retrospective review of patients with a history of liver or kidney transplantation who underwent hernia repair to analyze outcomes and hernia recurrence. METHODS: This is a single center, retrospective review of 19 patients who received kidney and/or liver transplantation prior to presenting with an incisional hernia from 2011 to 2017. All hernias were repaired with open component separation technique (CST) with biologic mesh underlay. RESULTS: The mean age of patients was 61.0±8.3 years old, with a mean body mass index of 28.4±4.8 kg/m², 15 males (78.9%), and four females (21.1%). There were seven kidney, 11 liver, and one combined liver and kidney transplant patients. The most common comorbidities were hypertension (16 patients, 84.2%), diabetes (9 patients, 47.4%), and tobacco use (8 patients, 42.1%). Complications occurred in six patients (31.6%) including hematoma (1/19), abscess (1/19), seroma (2/19), and hernia recurrence (3/19) at mean follow-up of 28.7±22.8 months. With the exception of two patients with incomplete follow-up, all patients healed at a median time of 27 days. CONCLUSIONS: This small, retrospective series of complex open CST in transplant patients shows acceptable rates of long-term hernia recurrence and healing. By using a multidisciplinary approach for abdominal wall reconstruction, we believe that modified open CST with biologic mesh is a safe and effective technique in the transplant population with complex abdominal hernias.
Abdominal Wall ; Abscess ; Body Mass Index ; Comorbidity ; Female ; Follow-Up Studies ; Hematoma ; Hernia ; Hernia, Abdominal ; Herniorrhaphy ; Humans ; Hypertension ; Immunosuppression ; Incisional Hernia ; Kidney Transplantation ; Kidney ; Liver Transplantation ; Liver ; Male ; Organ Transplantation ; Recurrence ; Retrospective Studies ; Seroma ; Surgical Mesh ; Tobacco Use ; Transplants

Objective: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. Materials and Methods: Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists. Results: Among 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively. Conclusion CT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.

Objective: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. Materials and Methods: Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists. Results: Among 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively. Conclusion CT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.

Polycystic ovary syndrome (PCOS) is the most common metabolic and endocrine disorder in women. However, there is no agreement concerning how to diagnose and treat PCOS worldwide. Three practice guidelines or consensuses, including consensus from the European Society of Human Reproduction and Embryology (ESHRE)/the American Society for Reproductive Medicine (ASRM) in Rotterdam, diagnosis criteria and consensus in China, and clinical practice guideline from the Endocrine Society (ES) in the United States are widely recognized. The present paper may provide some guidance for clinical practice based on a comparative analysis of the above three practice guidelines or consensuses.
Adolescent ; Adult ; Consensus ; Female ; Humans ; Hyperandrogenism ; etiology ; Infertility, Female ; etiology ; Insulin Resistance ; Menstrual Cycle ; Obesity ; etiology ; Polycystic Ovary Syndrome ; complications ; diagnosis ; psychology ; therapy ; Practice Guidelines as Topic

Oligodontia is the congenital absence of six or more teeth and comprises the more severe forms of tooth agenesis. Many genes have been implicated in the etiology of tooth agenesis, which is highly variable in its clinical presentation. The purpose of this study was to identify associations between genetic mutations and clinical features of oligodontia patients. An online systematic search of papers published from January 1992 to June 2021 identified 381 oligodontia cases meeting the eligibility criteria of causative gene mutation, phenotype description, and radiographic records. Additionally, ten families with oligodontia were recruited and their genetic etiologies were determined by whole-exome sequence analyses. We identified a novel mutation in WNT10A (c.99_105dup) and eight previously reported mutations in WNT10A (c.433 G > A; c.682 T > A; c.318 C > G; c.511.C > T; c.321 C > A), EDAR (c.581 C > T), and LRP6 (c.1003 C > T, c.2747 G > T). Collectively, 20 different causative genes were implicated among those 393 cases with oligodontia. For each causative gene, the mean number of missing teeth per case and the frequency of teeth missing at each position were calculated. Genotype-phenotype correlation analysis indicated that molars agenesis is more likely linked to PAX9 mutations, mandibular first premolar agenesis is least associated with PAX9 mutations. Mandibular incisors and maxillary lateral incisor agenesis are most closely linked to EDA mutations.
Humans ; Phenotype ; Wnt Proteins

Although genome-wide association studies have identified more than eighty genetic variants associated with non-small cell lung cancer (NSCLC) risk, biological mechanisms of these variants remain largely unknown. By integrating a large-scale genotype data of 15 581 lung adenocarcinoma (AD) cases, 8350 squamous cell carcinoma (SqCC) cases, and 27 355 controls, as well as multiple transcriptome and epigenomic databases, we conducted histology-specific meta-analyses and functional annotations of both reported and novel susceptibility variants. We identified 3064 credible risk variants for NSCLC, which were overrepresented in enhancer-like and promoter-like histone modification peaks as well as DNase I hypersensitive sites. Transcription factor enrichment analysis revealed that USF1 was AD-specific while CREB1 was SqCC-specific. Functional annotation and gene-based analysis implicated 894 target genes, including 274 specifics for AD and 123 for SqCC, which were overrepresented in somatic driver genes (ER = 1.95, P = 0.005). Pathway enrichment analysis and Gene-Set Enrichment Analysis revealed that AD genes were primarily involved in immune-related pathways, while SqCC genes were homologous recombination deficiency related. Our results illustrate the molecular basis of both well-studied and new susceptibility loci of NSCLC, providing not only novel insights into the genetic heterogeneity between AD and SqCC but also a set of plausible gene targets for post-GWAS functional experiments.
Adenocarcinoma of Lung/genetics* ; Carcinoma, Non-Small-Cell Lung/genetics* ; Carcinoma, Squamous Cell/genetics* ; Genetic Heterogeneity ; Genetic Predisposition to Disease ; Genome-Wide Association Study ; Humans ; Lung Neoplasms/genetics* ; Polymorphism, Single Nucleotide