The p110δ subunit of phosphoinositide 3-kinase (PI(3)K) is selectively expressed in leukocytes and is critical for lymphocyte biology. Here we report three different germline, heterozygous, gain-of-function mutations in the PIK3CD gene encoding p110δ in fourteen patients from seven families. These patients presented with sinopulmonary infections, lymphadenopathy, nodular lymphoid hyperplasia and CMV and/or EBV viremia. Strikingly, naïve and central memory T cells were severely deficient, while senescent effector T cells were over-represented. In vitro, patient T cells exhibited increased phosphorylation of Akt and hyperactivation of mTOR, enhanced glucose uptake and terminal effector differentiation. Importantly, treatment with rapamycin to inhibit mTOR activity in vivo partially restored naïve T cells, largely rescued the in vitro T cell defects, and improved clinical course.

Cytotoxic T lymphocyte antigen–4 (CTLA-4) is an inhibitory receptor found on immune cells. The consequences of mutations in CTLA4 in humans are unknown. We identified germline heterozygous mutations in CTLA4 in subjects with severe immune dysregulation from four unrelated families. Whereas Ctla4 heterozygous mice have no obvious phenotype, human CTLA4 haploinsufficiency caused dysregulation of FoxP3+ regulatory T (Treg) cells, hyperactivation of effector T cells, and lymphocytic infiltration of target organs. Patients also exhibited progressive loss of circulating B cells, associated with an increase of predominantly autoreactive CD21lo B cells and accumulation of B cells in nonlymphoid organs. Inherited human CTLA4 haploinsufficiency demonstrates a critical quantitative role for CTLA-4 in governing T and B lymphocyte homeostasis.

BACKGROUND. Monogenic IFN–mediated autoinflammatory diseases present in infancy with systemic inflammation, an IFN response gene signature, inflammatory organ damage, and high mortality. We used the JAK inhibitor baricitinib, with IFN-blocking activity in vitro, to ameliorate disease.METHODS. Between October 2011 and February 2017, 10 patients with CANDLE (chronic atypical neutrophilic dermatosis with lipodystrophy and elevated temperatures), 4 patients with SAVI (stimulator of IFN genes–associated [STING-associated] vasculopathy with onset in infancy), and 4 patients with other interferonopathies were enrolled in an expanded access program. The patients underwent dose escalation, and the benefit was assessed by reductions in daily disease symptoms and corticosteroid requirement. Quality of life, organ inflammation, changes in IFN-induced biomarkers, and safety were longitudinally assessed.RESULTS. Eighteen patients were treated for a mean duration of 3.0 years (1.5–4.9 years). The median daily symptom score decreased from 1.3 (interquartile range [IQR], 0.93–1.78) to 0.25 (IQR, 0.1–0.63) (P < 0.0001). In 14 patients receiving corticosteroids at baseline, daily prednisone doses decreased from 0.44 mg/kg/day (IQR, 0.31–1.09) to 0.11 mg/kg/day (IQR, 0.02–0.24) (P < 0.01), and 5 of 10 patients with CANDLE achieved lasting clinical remission. The patients’ quality of life and height and bone mineral density Z-scores significantly improved, and their IFN biomarkers decreased. Three patients, two of whom had genetically undefined conditions, discontinued treatment because of lack of efficacy, and one CANDLE patient discontinued treatment because of BK viremia and azotemia. The most common adverse events were upper respiratory infections, gastroenteritis, and BK viruria and viremia.CONCLUSION. Upon baricitinib treatment, clinical manifestations and inflammatory and IFN biomarkers improved in patients with the monogenic interferonopathies CANDLE, SAVI, and other interferonopathies. Monitoring safety and efficacy is important in benefit-risk assessment.TRIAL REGISTRATION. ClinicalTrials.gov NCT01724580 and NCT02974595.FUNDING. This research was supported by the Intramural Research Program of the NIH, NIAID, and NIAMS. Baricitinib was provided by Eli Lilly and Company, which is the sponsor of the expanded access program for this drug.

Artificial intelligence (AI) continues to garner substantial interest in medical imaging. The potential applications are vast and include the entirety of the medical imaging life cycle from image creation to diagnosis to outcome prediction. The chief obstacles to development and clinical implementation of AI algorithms include availability of sufficiently large, curated, and representative training data that includes expert labeling (eg, annotations). Current supervised AI methods require a curation process for data to optimally train, validate, and test algorithms. Currently, most research groups and industry have limited data access based on small sample sizes from small geographic areas. In addition, the preparation of data is a costly and time-intensive process, the results of which are algorithms with limited utility and poor generalization. In this article, the authors describe fundamental steps for preparing medical imaging data in AI algorithm development, explain current limitations to data curation, and explore new approaches to address the problem of data availability.

We demonstrate use of iteratively pruned deep learning model ensembles for detecting pulmonary manifestation of COVID-19 with chest X-rays. This disease is caused by the novel Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) virus, also known as the novel Coronavirus (2019-nCoV). A custom convolutional neural network and a selection of ImageNet pretrained models are trained and evaluated at patient-level on publicly available CXR collections to learn modality-specific feature representations. The learned knowledge is transferred and fine-tuned to improve performance and generalization in the related task of classifying CXRs as normal, showing bacterial pneumonia, or COVID-19-viral abnormalities. The best performing models are iteratively pruned to reduce complexity and improve memory efficiency. The predictions of the best-performing pruned models are combined through different ensemble strategies to improve classification performance. Empirical evaluations demonstrate that the weighted average of the best-performing pruned models significantly improves performance resulting in an accuracy of 99.01% and area under the curve of 0.9972 in detecting COVID-19 findings on CXRs. The combined use of modality-specific knowledge transfer, iterative model pruning, and ensemble learning resulted in improved predictions. We expect that this model can be quickly adopted for COVID-19 screening using chest radiographs.

• Less than 60% of individuals who inherit a FAS mutation have a clinical manifestation of ALPS, implying a high carrier rate.• Major causes of morbidity and mortality in ALPS patients are sepsis following splenectomy and development of lymphoma.Autoimmune lymphoproliferative syndrome (ALPS) presents in childhood with nonmalignant lymphadenopathy and splenomegaly associated with a characteristic expansion of mature CD4 and CD8 negative or double negative T-cell receptor ab 1 T lymphocytes.Patients often present with chronic multilineage cytopenias due to autoimmune peripheral destruction and/or splenic sequestration of blood cells and have an increased risk of B-cell lymphoma. Deleterious heterozygous mutations in the FAS gene are the most common cause of this condition, which is termed ALPS-FAS. We report the natural history and pathophysiology of 150 ALPS-FAS patients and 63 healthy mutation-positive relatives evaluated in our institution over the last 2 decades. Our principal findings are that FAS mutations have a clinical penetrance of <60%, elevated serum vitamin B 12 is a reliable and accurate biomarker of ALPS-FAS, and the major causes of morbidity and mortality in these patients are the overwhelming postsplenectomy sepsis and development of lymphoma.With longer follow-up, we observed a significantly greater relative risk of lymphoma than previously reported. Avoiding splenectomy while controlling hypersplenism by using corticosteroid-sparing treatments improves the outcome in ALPS-FAS patients. This trial was registered at www.clinicaltrials.gov as

The computer-based process of identifying the boundaries of lung from surrounding thoracic tissue on computed tomographic (CT) images, which is called segmentation, is a vital first step in radiologic pulmonary image analysis. Many algorithms and software platforms provide image segmentation routines for quantification of lung abnormalities; however, nearly all of the current image segmentation approaches apply well only if the lungs exhibit minimal or no pathologic conditions. When moderate to high amounts of disease or abnormalities with a challenging shape or appearance exist in the lungs, computer-aided detection systems may be highly likely to fail to depict those abnormal regions because of inaccurate segmentation methods. In particular, abnormalities such as pleural effusions, consolidations, and masses often cause inaccurate lung segmentation, which greatly limits the use of image processing methods in clinical and research contexts. In this review, a critical summary of the current methods for lung segmentation on CT images is provided, with special emphasis on the accuracy and performance of the methods in cases with abnormalities and cases with exemplary pathologic findings. The currently available segmentation methods can be divided into five major classes: (a) thresholding-based, (b) region-based, (c) shape-based, (d) neighboring anatomy-guided, and (e) machine learning-based methods. The feasibility of each class and its shortcomings are explained and illustrated with the most common lung abnormalities observed on CT images. In an overview, practical applications and evolving technologies combining the presented approaches for the practicing radiologist are detailed.

CD55 deficiency with hyperactivation of complement, angiopathic thrombosis, and protein-losing enteropathy (the CHAPLE syndrome) is caused by abnormal complement activation due to biallelic loss-of-function mutations in CD55. (Funded by the National Institute of Allergy and Infectious Diseases and others.).